2025
Patterns in Symptoms Preceding Acute Care in Patients With Cancer. JAMA network open
Chang C, Chen JJ, Feng J, Friesner I, Mohindra S, Boreta L, Rabow MW, Braunstein SE, Benson R, Hong JC (2025).
Abstract
Importance: Patients with cancer frequently experience unplanned acute care with emergency department visits and hospitalization due to disease or treatment complications, which impacts outcomes, quality of life, and health care costs. There remains a knowledge gap in understanding patterns of symptoms that precede acute care events. Natural language processing (NLP) may enable greater understanding of the symptoms and identify differences across patient and cancer characteristics.
Objective: To characterize symptoms preceding acute care in patients with cancer and quantify differences in symptom documentation across sociodemographic and cancer histologic subgroups.
Design, setting, and participants: A cohort study in a single tertiary-care institution, including all acute care (emergency department and hospitalization) encounters for patients aged 18 years or older with a primary cancer diagnosis identified between January 1, 2013, and December 31, 2023.
Main outcomes and measures: Natural language processing was used to identify routine clinical documentation to characterize symptoms documented in the 30 days preceding acute care. Logistic regression analyses was used to examine the possible association between sex, age, race and ethnicity, insurance coverage, cancer histologic characteristics, and reported symptoms.
Results: Overall, 28 708 patients with cancer had 70 606 acute care visits with 854 830 associated preceding documented symptoms. Median age was 61 (IQR, 48-70) years. Men (37 861 encounters [53.62%]) and patients of White race (39 989 encounters [56.64%]) accounted for most acute care encounters. Pain (7.54% of documented symptoms), nausea (6.74%), and vomiting (5.79%) were the most frequently documented symptoms. Acute care encounters with patients who were female (adjusted odds ratio [AOR], 1.14; 95% CI, 1.10-1.18; P < .001), Asian (AOR, 1.22; 1.17-1.28; P < .001), Black (AOR, 1.17; 95% CI, 1.10-1.25; P < .001), American Indian or Alaska Native (AOR, 1.21; 95% CI, 1.01-1.44; P = .04), or Medicaid-insured (AOR, 1.10; 95% CI, 1.05-1.14; P < .001) were associated with a high documented symptom burden (>10 unique symptoms) preceding acute care visits. Patients aged 65 years or older (AOR, 0.96; 95% CI, 0.92-1.00; P = .04) or uninsured (AOR, 0.58; 95% CI, 0.45-0.76; P < .001) were less likely to have a high symptom burden documented before acute care events.
Conclusions and relevance: The findings of this study highlight common symptoms preceding acute care as well as the need for further research on interventions to reduce patient burden, improve quality of life, and reduce the use of acute care in patients with cancer.
"Who experiences large large model decay and why?" A Hierarchial Framework for Diagnosising Heterogeneous Performance Drift. International Conference on Machine Learning (ML)
Singh H, Xia F, Gossmann A, Chuang A, Hong JC, Feng J (2025).
Considerations in Translating AI to Improve Care. JAMA network open
Mandair D, Elia MV, Hong JC (2025).
Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study. JMIR Cancer
Spiegel DY, Friesner ID, Zhang W, Zack T, Yan G, Willcox J, Prionas N, Singer L, Park C, Hong JC (2025).
Abstract
Background: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling. Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP).
Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP).
Methods: Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from -1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0-1) corresponding to the intensity of 27 different emotions and neutrality.
Results: Of the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time.
Conclusions: Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations.
2024
Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts. JAMA Oncology
Friesner ID, Feng J, Kalnicki S, Garg M, Ohri N, Hong JC (2024).
Abstract
Importance: Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of patients at high risk for hospitalization who may benefit from proactive intervention. Objective: To develop and validate machine learning (ML) approaches based on daily step counts collected by wearable devices on prospective trials to predict hospitalizations during CRT.
Objective: To develop and validate machine learning (ML) approaches based on daily step counts collected by wearable devices on prospective trials to predict hospitalizations during CRT.
Design, setting, and participants: This study included patients with a variety of cancers enrolled from June 2015 to August 2018 on 3 prospective, single-institution trials of activity monitoring using wearable devices during CRT. Patients were followed up during and 1 month following CRT. Training and validation cohorts were generated temporally, stratifying for cancer diagnosis (70:30). Random forest, neural network, and elastic net-regularized logistic regression (EN) were trained to predict short-term hospitalization risk based on a combination of clinical characteristics and the preceding 2 weeks of activity data. To predict outcomes of activity data, models based only on activity-monitoring features and only on clinical features were trained and evaluated. Data analysis was completed from January 2022 to March 2023.
Main outcomes and measures: Model performance was evaluated in terms of the receiver operating characteristic area under curve (ROC AUC) in the stratified temporal validation cohort.
Results: Step counts from 214 patients (median [range] age, 61 [53-68] years; 113 [52.8%] male) were included. EN based on step counts and clinical features had high predictive ability (ROC AUC, 0.83; 95% CI, 0.66-0.92), outperforming random forest (ROC AUC, 0.76; 95% CI, 0.56-0.87; P = .02) and neural network (ROC AUC, 0.80; 95% CI, 0.71-0.88; P = .36). In an ablation study, the EN model based on only step counts demonstrated greater predictive ability than the EN model with step counts and clinical features (ROC AUC, 0.85; 95% CI, 0.70-0.93; P = .09). Both models outperformed the EN model trained on only clinical features (ROC AUC, 0.53; 95% CI, 0.31-0.66; P < .001).
Conclusions and relevance: This study developed and validated a ML model based on activity-monitoring data collected during prospective clinical trials. Patient-generated health data have the potential to advance predictive ability of ML approaches. The resulting model from this study will be evaluated in an upcoming multi-institutional, cooperative group randomized trial.
Health Care Cost Reductions with Machine Learning–Directed Evaluations during Radiation Therapy — An Economic Analysis of a Randomized Controlled Study. NEJM AI
Natesan N, Eisenstein E, Thomas SM, Eclov NCW, Dalal NH, Stephens SJ, Malicki M, Shields S, Cobb A, Mowery YM, Niedzwiecki D, Tenenbaum JD, Palta M, Hong JC (2024).
Abstract
Background: Machine learning (ML) may cost-effectively direct health care by identifying patients most likely to benefit from preventative interventions to avoid negative and expensive outcomes. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT; NCT04277650) was a single-institution, randomized controlled study in which electronic health record-based ML accurately identified patients at high risk for acute care (emergency visit or hospitalization) during radiotherapy (RT) and targeted them for supplemental clinical evaluations. This ML-directed intervention resulted in decreased acute care utilization. Given the limited prospective data showing the ability of ML to direct interventions cost-efficiently, an economic analysis was performed.
Methods: A post hoc economic analysis was conducted of SHIELD-RT that included RT courses from January 7, 2019, to June 30, 2019. ML-identified high-risk courses (≥10% risk of acute care during RT) were randomized to receive standard of care weekly clinical evaluations with ad hoc supplemental evaluations per clinician discretion versus mandatory twice-weekly evaluations. The primary outcome was difference in mean total medical costs during and 15 days after RT. Acute care costs were obtained via institutional cost accounting. Physician and intervention costs were estimated via Medicare and Medicaid data. Negative binomial regression was used to estimate cost outcomes after adjustment for patient and disease factors.
Results: A total of 311 high-risk RT courses among 305 patients were randomized to the standard (n=157) or the intervention (n=154) group. Unadjusted mean intervention group supplemental visit costs were $155 per course (95% confidence interval, $142 to $168). The intervention group had fewer acute care visits per course (standard, 0.47; intervention, 0.31; P=0.04). Total mean adjusted costs were $3110 per course for the standard group and $1494 for the intervention group (difference in means, $1616 [95% confidence interval, $1450 to $1783]; P=0.03).
Conclusions: In this economic analysis of a randomized controlled, health care ML study, mandatory supplemental evaluations for ML-identified high-risk patients were associated with both reduced total medical costs and improved clinical outcomes. Further study is needed to determine whether economic results are generalizable. (Funded in part by The Duke Endowment, The Conquer Cancer Foundation, the Duke Department of Radiation Oncology, and the National Cancer Institute of the National Institutes of Health [R01CA277782]; ClinicalTrials.gov number, NCT04277650.).
Assessing Large Language Models for Oncology Data Inference From Radiology Reports. JCO clinical cancer informatics
Chen LC, Zack T, Demirci A, Sushil M, Miao B, Kasap C, Butte A, Collisson EA, Hong JC (2024).
Abstract
Purpose: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.
Methods: We analyzed 203 deidentified radiology reports, manually annotated for disease status, location, and indeterminate nodules needing follow-up. Using generative pre-trained transformer (GPT)-4, GPT-3.5-turbo, and open models such as Gemma-7B and Llama3-8B, we employed strategies such as ablation and prompt engineering to boost accuracy. Discrepancies between human and model interpretations were reviewed by a secondary oncologist.
Results: Among 164 patients with pancreatic tumor, GPT-4 showed the highest accuracy in inferring disease status, achieving a 75.5% correctness (F1-micro). Open models Mistral-7B and Llama3-8B performed comparably, with accuracies of 68.6% and 61.4%, respectively. Mistral-7B excelled in deriving correct inferences from objective findings directly. Most tested models demonstrated proficiency in identifying disease containing anatomic locations from a list of choices, with GPT-4 and Llama3-8B showing near-parity in precision and recall for disease site identification. However, open models struggled with differentiating benign from malignant postsurgical changes, affecting their precision in identifying findings indeterminate for cancer. A secondary review occasionally favored GPT-3.5's interpretations, indicating the variability in human judgment.
Conclusion: LLMs, especially GPT-4, are proficient in deriving oncologic insights from radiology reports. Their performance is enhanced by effective summarization strategies, demonstrating their potential in clinical support and health care analytics. This study also underscores the possibility of zero-shot open model utility in environments where proprietary models are restricted. Finally, by providing a set of annotated radiology reports, this paper presents a valuable data set for further LLM research in oncology.
Narrative Review on the Application of Large Language Models to Support Cancer Care and Research. Yearbook of medical informatics
Benson R, Elia M, Hyams B, Chang JH, Hong JC (2024).
Abstract
Objectives: The emergence of large language models has resulted in a significant shift in informatics research and carries promise in clinical cancer care. Here we provide a narrative review of the recent use of large language models (LLMs) to support cancer care, prevention, and research.
Methods: We performed a search of the Scopus database for studies on the application of bidirectional encoder representations from transformers (BERT) and generative-pretrained transformer (GPT) LLMs in cancer care published between the start of 2021 and the end of 2023. We present salient and impactful papers related to each of these themes.
Results: Studies identified focused on aspects of clinical decision support (CDS), cancer education, and support for research activities. The use of LLMs for CDS primarily focused on aspects of treatment and screening planning, treatment response, and the management of adverse events. Studies using LLMs for cancer education typically focused on question-answering, assessing cancer myths and misconceptions, and text summarization and simplification. Finally, studies using LLMs to support research activities focused on scientific writing and idea generation, cohort identification and extraction, clinical data processing, and NLP-centric tasks.
Conclusions: The application of LLMs in cancer care has shown promise across a variety of diverse use cases. Future research should utilize quantitative metrics, qualitative insights, and user insights in the development and evaluation of LLM-based cancer care tools. The development of open-source LLMs for use in cancer care research and activities should also be a priority.
AI Use in Prostate Cancer: Potential Improvements in Treatments and Patient Care. Oncology (Williston Park, N.Y.)
James B Yu Md Mhs Fastro, Hong JC (2024).
Abstract: Artificial intelligence use in prostate cancer encompasses 4 main areas including diagnostic imaging, prediction of outcomes, histopathology, and treatment planning.
Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways. Pharmacogenomics and personalized medicine
Lin H, Ni L, Phuong C, Hong JC (2024).
Abstract: Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.
2023
Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study. Pharmacogenomics and personalized medicine
Hong JC, Patel P, Eclov NCW, Stephens SJ, Mowery YM, Tenenbaum JD, Palta M (2023).
Abstract: Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.
Ctrl-C: A Cross-Sectional Study of the EHR Usage Patterns of US Oncology Clinicians. JNCI cancer spectrum
Sinha S, Holmgren AJ, Hong JC, Rotenstein LS (2023).
Abstract: Despite some positive impact, the use of electronic health records (EHRs) has been associated with negative effects, such as emotional exhaustion. We sought to compare EHR use patterns for oncology vs nononcology medical specialists. In this cross-sectional study, we employed EHR usage data for 349 ambulatory health-care systems nationwide collected from the vendor Epic from January to August 2019. We compared note composition, message volume, and time in the EHR system for oncology vs nononcology clinicians. Compared with nononcology medical specialists, oncologists had a statistically significantly greater percentage of notes derived from Copy and Paste functions but less SmartPhrase use. They received more total EHR messages per day than other medical specialists, with a higher proportion of results and system-generated messages. Our results point to priorities for enhancing EHR systems to meet the needs of oncology clinicians, particularly as related to facilitating the complex documentation, results, and therapy involved in oncology care.
Intracranial and Extracranial Progression and Their Correlation With Overall Survival After Stereotactic Radiosurgery in a Multi-institutional Cohort With Brain Metastases. JAMA network open
Carpenter DJ, Leng J, Arshad M, Giles W, Kirkpatrick JP, Floyd SR, Chmura SJ., Salama JK, Hong JC (2023).
Abstract
Importance: Clinical trials for metastatic malignant neoplasms are increasingly being extended to patients with brain metastases. Despite the preeminence of progression-free survival (PFS) as a primary oncologic end point, the correlation of intracranial progression (ICP) and extracranial progression (ECP) events with overall survival (OS) is poorly understood for patients with brain metastases following stereotactic radiosurgery (SRS).
Objective: To determine the correlation of ICP and ECP with OS among patients with brain metastases completing an initial SRS course.
Design, setting, and participants: This multi-institutional retrospective cohort study was conducted from January 1, 2015, to December 31, 2020. We included patients who completed an initial course of SRS for brain metastases during the study period, including receipt of single and/or multifraction SRS, prior whole-brain radiotherapy, and brain metastasis resection. Data analysis was performed on November 15, 2022. Exposures: Non-OS end points included intracranial PFS, extracranial PFS, PFS, time to ICP, time to ECP, and any time to progression. Progression events were radiologically defined, incorporating multidisciplinary clinical consensus.
Main outcomes and measures: The primary outcome was correlation of surrogate end points to OS. Clinical end points were estimated from time of SRS completion via the Kaplan-Meier method, while end-point correlation to OS was measured using normal scores rank correlation with the iterative multiple imputation approach.
Results: This study included 1383 patients, with a mean age of 63.1 years (range, 20.9-92.8 years) and a median follow-up of 8.72 months (IQR, 3.25-19.68 months). The majority of participants were White (1032 [75%]), and more than half (758 [55%]) were women. Common primary tumor sites included the lung (757 [55%]), breast (203 [15%]), and skin (melanoma; 100 [7%]). Intracranial progression was observed in 698 patients (50%), preceding 492 of 1000 observed deaths (49%). Extracranial progression was observed in 800 patients (58%), preceding 627 of 1000 observed deaths (63%). Irrespective of deaths, 482 patients (35%) experienced both ICP and ECP, 534 (39%) experienced ICP (216 [16%]) or ECP (318 [23%]), and 367 (27%) experienced neither. The median OS was 9.93 months (95% CI, 9.08-11.05 months). Intracranial PFS had the highest correlation with OS (ρ = 0.84 [95% CI, 0.82-0.85]; median, 4.39 months [95% CI, 4.02-4.92 months]). Time to ICP had the lowest correlation with OS (ρ = 0.42 [95% CI, 0.34-0.50]) and the longest median time to event (median, 8.76 months [95% CI, 7.70-9.48 months]). Across specific primary tumor types, correlations of intracranial PFS and extracranial PFS with OS were consistently high despite corresponding differences in median outcome durations.
Conclusions and relevance: The results of this cohort study of patients with brain metastases completing SRS suggest that intracranial PFS, extracranial PFS, and PFS had the highest correlations with OS and time to ICP had the lowest correlation with OS. These data may inform future patient inclusion and end-point selection for clinical trials.
Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists. Practical radiation oncology
Waters MR, Aneja S, Hong JC (2023).
Abstract: Recent advances in artificial intelligence (AI), such as generative AI and large language models (LLMs), have generated significant excitement about the potential of AI to revolutionize our lives, work, and interaction with technology. This article explores the practical applications of LLMs, particularly ChatGPT, in the field of radiation oncology. We offer a guide on how radiation oncologists can interact with LLMs like ChatGPT in their routine clinical and administrative tasks, highlighting potential use cases of the present and future. We also highlight limitations and ethical considerations, including the current state of LLMs in decision making, protection of sensitive data, and the important role of human review of AI-generated content.
2022
A Clinical Reasoning-Encoded Case Library Developed through Natural Language Processing. Journal of general internal medicine
Zack T, Dhaliwal G, Geha R, Margaretten M, Murray S, Hong JC (2022)
Abstract
Importance: Case reports that externalize expert diagnostic reasoning are utilized for clinical reasoning instruction but are difficult to search based on symptoms, final diagnosis, or differential diagnosis construction. Computational approaches that uncover how experienced diagnosticians analyze the medical information in a case as they formulate a differential diagnosis can guide educational uses of case reports.
Objective: To develop a "reasoning-encoded" case database for advanced clinical reasoning instruction by applying natural language processing (NLP), a sub-field of artificial intelligence, to a large case report library.
Design: We collected 2525 cases from the New England Journal of Medicine (NEJM) Clinical Pathological Conference (CPC) from 1965 to 2020 and used NLP to analyze the medical terminology in each case to derive unbiased (not prespecified) categories of analysis used by the clinical discussant. We then analyzed and mapped the degree of category overlap between cases.
Results: Our NLP algorithms identified clinically relevant categories that reflected the relationships between medical terms (which included symptoms, signs, test results, pathophysiology, and diagnoses). NLP extracted 43,291 symptoms across 2525 cases and physician-annotated 6532 diagnoses (both primary and related diagnoses). Our unsupervised learning computational approach identified 12 categories of medical terms that characterized the differential diagnosis discussions within individual cases. We used these categories to derive a measure of differential diagnosis similarity between cases and developed a website ( universeofcpc.com ) to allow visualization and exploration of 55 years of NEJM CPC case series.
Conclusions: Applying NLP to curated instances of diagnostic reasoning can provide insight into how expert clinicians correlate and coordinate disease categories and processes when creating a differential diagnosis. Our reasoning-encoded CPC case database can be used by clinician-educators to design a case-based curriculum and by physicians to direct their lifelong learning efforts.
Opportunities to use electronic health record audit logs to improve cancer care. Cancer medicine
Huilgol YS., Adler-Milstein J, Ivey SL, Hong JC (2022).
Abstract: The rapid adoption of electronic health records (EHRs) has created extensive repositories of digitized data that can be used to inform improvements in care delivery, processes, and patient outcomes. While the clinical data captured in EHRs are widely used for such efforts, EHRs also capture audit log data that reflect how users interact with the EHR to deliver care. Automatically collected audit log data provide a unique opportunity for new insights into EHR user behavior and decision-making processes. Here, we provide an overview of audit log data and examples that could be used to improve oncology care and outcomes in four domains: diagnostic reasoning and consumption, care team collaboration and communication, patient outcomes and experience, and provider burnout/fatigue. This data source could identify gaps in performance and care, physician uptake of EHR features that enhance decision-making, and integration of data trends for oncology. Ensuring researchers and oncologists are familiar with the data's potential and developing the data engineering capacity to utilize this rich data source, will expand the breadth of research to improve cancer care.
Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study. BMC bioinformatics
Hong JC, Eclov NCW, Stephens SJ, Mowery YM, Palta M (2022).
Abstract: Background: Artificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study.
Analysis of Serious Adverse Event Reporting for Patients Enrolled in Cancer Clinical Trials During the COVID-19 Pandemic. JAMA oncology
Ragavan MV, Legaspi N, LaLanne A, Hong JC, Small EJ, Borno HT (2022).
COVID-19 Outcomes Among Patients With Cancer: Observations From the University of California Cancer Consortium COVID-19 Project Outcomes Registry. The oncologist
Borno HT, Kim MO, Hong JC, Yousefi S, Lin A, Tolstykh I, Zhang S, McKay RR, Harismendy O, Cinar P, Rugo H, Koshkin VS, Rabow M, Wang C, Bailey A, Small EJ (2022).
Abstract
Background: The risks associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated illness, coronavirus disease 2019 (COVID-19), among patients with a cancer diagnosis have not been fully characterized. This study leverages data from a multi-institutional cohort study, the University of California Cancer COVID Consortium, to evaluate outcomes associated with SARS-CoV-2 infection among patients with cancer.
Methods: Clinical data were collected from March to November 2020 and included patient demographics, cancer history and treatment, SARS-CoV-2 exposure and testing, and COVID-19 clinical management and outcomes. Multivariate ordinal logistic regression permitting unequal slopes was used to evaluate the impact of demographic, disease, and treatment factors on SARS-CoV-2 related hospitalization, intensive care unit (ICU) admission, and mortality.
Findings: Among all evaluated patients (n = 303), 147 (48%) were male, 118 (29%) were older adults (≥65 years old), and 104 (34%) were non-Hispanic white. A subset (n = 63, 21%) had hematologic malignancies and the remaining had solid tumors. Patients were hospitalized for acute care (n = 79, 26%), ICU-level care (n = 28, 9%), or died (n = 21, 7%) due to COVID-19. Patients with ≥2 comorbidities were more likely to require acute care (odds ratio [OR] 2.09 [95% confidence interval (CI), 1.23-3.55]). Cough was identified as a significant predictor of ICU hospitalization (OR 2.16 [95% CI, 1.03-4.57]). Importantly, mortality was associated with an active cancer diagnosis (OR 3.64 [95% CI, 1.40-9.5]) or advanced age (OR 3.86 [95% CI, 1.2-12.44]).
Interpretation: This study observed that patients with active cancer or advanced age are at an increased risk of death from COVID-19. These study observations can inform risk counseling related to COVID-19 for patients with a cancer diagnosis.
COVID-19 outcomes in patients with cancer: Findings from the University of California health system database. Cancer medicine
Kwon DH, Cadena J, Nguyen S, Chan KHR., Soper B, Gryshuk AL, Hong JC, Ray P, Huang FW (2022).
Abstract
Background: The interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes.
Methods: We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers. We identified adults tested for SARS-CoV-2 from 2/1/2020-12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS-CoV-2 test. Secondary outcomes were SARS-CoV-2 positivity and severe COVID-19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer-related factors associated with outcomes.
Results: We identified 409,462 patients undergoing SARS-CoV-2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67-0.74, p < 0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19.
Conclusions: In this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings.
2021
Disparities in Electronic Health Record Patient Portal Enrollment Among Oncology Patients. JAMA Oncology
Sinha S, Garriga M, Naik N, McSteen BW, Odisho AY, Lin A, Hong JC (2021).
Abstract: This cohort study examines disparities in patient portal use among oncology patients.
Association of mental health diagnosis with race and all-cause mortality after a cancer diagnosis: Large-scale analysis of electronic health record data. Cancer
Chen WC, Boreta L, Braunstein SE, Rabow MW, Kaplan LE, Tenenbaum JD, Morin O, Park CC, Hong JC (2021).
Abstract
Background: Disparity in mental health care among cancer patients remains understudied.
Methods: A large, retrospective, single tertiary-care institution cohort study was conducted based on deidentified electronic health record data of 54,852 adult cancer patients without prior mental health diagnosis (MHD) diagnosed at the University of California, San Francisco between January 2012 and September 2019. The exposure of interest was early-onset MHD with or without psychotropic medication (PM) within 12 months of cancer diagnosis and primary outcome was all-cause mortality.
Results: There were 8.2% of patients who received a new MHD at a median of 197 days (interquartile range, 61-553) after incident cancer diagnosis; 31.0% received a PM prescription; and 3.7% a mental health-related visit (MHRV). There were 62.6% of patients who were non-Hispanic White (NHW), 10.8% were Asian, 9.8% were Hispanic, and 3.8% were Black. Compared with NHWs, minority cancer patients had reduced adjusted odds of MHDs, PM prescriptions, and MHRVs, particularly for generalized anxiety (Asian odds ratio [OR], 0.66, 95% CI, 0.55-0.78; Black OR, 0.60, 95% CI, 0.45-0.79; Hispanic OR, 0.72, 95% CI, 0.61-0.85) and selective serotonin-reuptake inhibitors (Asian OR, 0.43, 95% CI, 0.37-0.50; Black OR, 0.51, 95% CI, 0.40-0.61; Hispanic OR, 0.79, 95% CI, 0.70-0.89). New early MHD with PM was associated with elevated all-cause mortality (12-24 months: hazard ratio [HR], 1.43, 95% CI, 1.25-1.64) that waned by 24 to 36 months (HR, 1.18, 95% CI, 0.95-1.45).
Conclusions: New mental health diagnosis with PM was a marker of early mortality among cancer patients. Minority cancer patients were less likely to receive documentation of MHDs or treatment, which may represent missed opportunities to identify and treat cancer-related mental health conditions.
Strategies to Turn Real-world Data Into Real-world Knowledge. JAMA Network Open
Hong JC (2021).
Assessing Clinical Outcomes in a Data-Rich World-A Reality Check on Real-World Data. JAMA Network Open
Hong JC, Butte AJ (2021).
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nature Cancer
Morin O, Vallieres M, Braunstein S, Barrios Ginart J, Upadhaya T, Woodruff HC, Zwanenburg A, Chatterjee A, Villanueva-Meyer JE, Valdez G, Chen W, Hong JC, Yom SS, Solberg TD, Lock S, Seuntjens J, Park C, Lambin P (2021).
Abstract: Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual’s disease course unfolds.
Risk Stratification for Imminent Risk of Death at the Time of Palliative Radiotherapy Consultation. JAMA Network Open
Wu SY, Yee E, Vasudevan HN, Fogh SE, Boreta L, Braunstein SE, Hong JC (2021).
Abstract: This cohort study of patients with advanced cancer who received palliative radiotherapy within 30 days of death assesses models of prognostic criteria for providing radiotherapy at the end of life and compares outcomes with similar prognostic tools.
Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach. Scientific Reports
Hong JC, Hauser ER, Redding TS 4th, Sims KJ, Gellad ZF, O'Leary MC, Hyslop T, Madison AN, Qin X, Weiss D, Bullard AJ, Williams CD, Sullivan BA, Lieberman D, Provenzale D (2021).
Abstract: Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.
2020
System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning–Directed Clinical Evaluations During Radiation and Chemoradiation. Journal of Clinical Oncology
Hong JC, Eclov NCW, Dalal NH, Thomas SM, Stephens SJ, Malicki M, Shields S, Cobb A, Mowery YM, Niedzwiecki D, Tenenbaum JD, Palta M (2020).
Abstract
Purpose: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.
Patients and methods: During this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.
Results: Twice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851).
Conclusion: In this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs.
Closing the Gap Between Machine Learning and Clinical Cancer Care-First Steps Into a Larger World. JAMA Oncology
Kang J, Morin O, Hong JC (2020).
Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts. JAMIA Open
Hong JC, Fairchild AT, Tanksley JP, Palta M, Tenenbaum JD (2020).
Abstract
Objectives: Expert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians.
Materials and methods: Two independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1.
Results: The NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms.
Conclusion: NLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.
Inter-rater reliability in toxicity identification: Limitations of current standards. International Journal of Radiation Oncology Biology Physics
Fairchild AT, Tanksley JP, Tenenbaum JD, Palta M, Hong JC (2020).
Abstract
Purpose: The National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 is the standard for oncology toxicity encoding and grading, despite limited validation. We assessed interrater reliability (IRR) in multireviewer toxicity identification.
Methods and materials: Two reviewers independently reviewed 100 randomly selected notes for weekly on-treatment visits during radiation therapy from the electronic health record. Discrepancies were adjudicated by a third reviewer for consensus. Term harmonization was performed to account for overlapping symptoms in CTCAE. IRR was assessed based on unweighted and weighted Cohen's kappa coefficients.
Results: Between reviewers, the unweighted kappa was 0.68 (95% confidence interval, 0.65-0.71) and the weighted kappa was 0.59 (0.22-1.00). IRR was consistent between symptoms noted as present or absent with a kappa of 0.6 (0.66-0.71) and 0.6 (0.65-0.69), respectively.
Conclusions: Significant discordance suggests toxicity identification, particularly retrospectively, is a complex and error-prone task. Strategies to minimize IRR, including training and simplification of the CTCAE criteria, should be considered in trial design and future terminologies.
Electronic health record (EHR) data mining for AI healthcare. Artificial Intelligence in Medicine: Technical Basis and Clinical Applications
Lin AL, Chen WC, Hong JC (2020). In: Xing L, Giger ML, Min J, editors. Amsterdam: Elsevier.
2019
A Nomogram for Testosterone Recovery After Combined Androgen Deprivation and Radiation Therapy for Prostate Cancer. International Journal of Radiation Oncology Biology Physics
Spiegel DY, Hong JC, Oyekunle T, Waters L, Lee WR, Salama JK, Koontz BF (2019).
Abstract
Purpose: Testosterone recovery (TR) after androgen deprivation therapy (ADT) and radiation therapy (RT) is not well characterized. We studied TR in men who received RT and either short-term ADT (STADT) or long-term ADT (LTADT) and aimed to create a nomogram to predict TR.
Methods and materials: We identified consecutive localized prostate cancer patients treated with ADT-RT at 2 academic medical centers from January 2011 to October 2016 with documented baseline testosterone. TR was time from last ADT injection to testosterone normalization. The Kaplan-Meier method was used to estimate time to TR. Cox proportional hazards models identified TR predictors. A nomogram was trained with site 1 and externally validated with site 2.
Results: A total of 340 patients were included; 69.7% received STADT for a median duration of 6 months; 30.3% received LTADT for a median duration of 24.3 months. Median follow-up was 26.7 months. Median time for TR was 17.2 months for STADT and 24.0 months for LTADT patients (P = .004). The 2-year cumulative incidence of TR was 53.1% after LTADT versus 65.7% after STADT (P = .004). On multivariate analysis, shorter duration ADT (hazard ratio [HR], 0.96; P = .004), higher pretreatment testosterone (HR, 1.004; P < .001), and lower body mass index (HR, 0.95; P = .002) were associated with shorter time to TR. Older age (HR, 0.97; P = .09) and white race (HR, 0.67; P = .06) trended as longer TR predictors. A nomogram was generated to predict probability of TR at 1, 2, and 3 years. The concordance index was 0.71 (95% confidence interval, 0.64-0.78) for the validation cohort.
Conclusions: In this population of localized prostate cancer patients, TR after ADT-RT was variable. Using baseline testosterone, ADT duration, body mass index, age, and race, a predictive nomogram can estimate the likelihood of TR.
Increasing PET Use in Small Cell Lung Cancer: Survival Improvement and Stage Migration in the VA Central Cancer Registry. Journal of the National Comprehensive Cancer Network
Hong JC, Boyer MJ, Spiegel DY, Williams CD, Tong BC, Shofer SL, Moravan MJ, Kelley MJ, Salama JK (2019).
Abstract
Background: Accurate staging for small cell lung cancer (SCLC) is critical for determining appropriate therapy. The clinical impact of increasing PET adoption and stage migration is well described in non-small cell lung cancer but not in SCLC. The objective of this study was to evaluate temporal trends in PET staging and survival in the Veterans Affairs Central Cancer Registry and the impact of PET on outcomes.
Patients and Methods: Patients diagnosed with SCLC from 2001 to 2010 were identified. PET staging, overall survival (OS), and lung cancer-specific survival (LCSS) were assessed over time. The impact of PET staging on OS and LCSS was assessed for limited-stage (LS) and extensive-stage (ES) SCLC.
Results: From 2001 to 2010, PET use in a total of 10,135 patients with SCLC increased from 1.1% to 39.2%. Median OS improved for all patients (from 6.2 to 7.9 months), those with LS-SCLC (from 10.9 to 13.2 months), and those with ES-SCLC (from 5.0 to 7.0 months). Among staged patients, the proportion of ES-SCLC increased from 63.9% to 65.7%. Among 1,536 patients with LS-SCLC treated with concurrent chemoradiotherapy, 397 were staged by PET. In these patients, PET was associated with longer OS (median, 19.8 vs 14.3 months; hazard ratio [HR], 0.78; 95% CI, 0.68-0.90; P<.0001) and LCSS (median, 22.9 vs 16.7 months; HR, 0.74; 95% CI, 0.63-0.87; P<.0001) with multivariate adjustment and propensity-matching. In the 6,143 patients with ES-SCLC, PET was also associated with improved OS and LCSS.
Conclusions: From 2001 to 2010, PET staging increased in this large cohort, with a corresponding relative increase in ES-SCLC. PET was associated with greater OS and LCSS for LS-SCLC and ES-SCLC, likely reflecting stage migration and stage-appropriate therapy. These findings emphasize the importance of PET in SCLC and support its routine use.
Radiotherapy Treatment Planning in the Age of AI: Are We Ready Yet? Technology in Cancer Research and Treatment
Zheng D, Hong JC, Wang C, Zhu X (2019).
Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future. Technology in Cancer Research and Treatment
Wang C, Zhu X, Hong JC, Zheng D (2019).
Abstract: Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works.
2018
Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm. JCO Clinical Cancer Informatics
Hong JC, Niedzwiecki D, Palta M, Tenenbaum JD (2018).
Abstract
Purpose: Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.
Methods: A total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.
Results: All methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).
Conclusion: ML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.
High-volume providers and brachytherapy practice: A Medicare provider utilization and payment analysis. Brachytherapy
Hong JC, Spiegel DY, Havrilesky LJ, Chino JP (2018).
Abstract
Purpose: Brachytherapy is an important component of the treatment of gynecologic and prostate cancers, with data supporting its impact on clinical outcomes. Prior data have suggested that brachytherapy tends to be focused at high-volume centers. Medicare reimbursement data can provide an understanding of the distribution of brachytherapy cases among billing providers. The objective of this study is to quantify the distribution of brachytherapy cases and high volume providers.
Methods and materials: The Medicare Physician and Other Supplier Public Use File was queried for individual physicians who had performed brachytherapy for more than 10 patients with gynecologic or prostate cancer in the years 2012-2015. Aggregate data were also queried. Trends were identified, and basic summary statistics were tabulated.
Results: During the study period, there was an increase in vaginal brachytherapy (3328 unique cases in 2012-4308 in 2015) but a decrease in intrauterine implants, such as tandem placements (1522 in 2012-1307 in 2015) and prostate brachytherapy (8860 in 2012-6527 in 2015). High-volume providers treating more than 10 patients represented a disproportionate number of patients treated, particularly with intra-uterine brachytherapy, representing no more than 1.2% of the active providers in a given year but up to 11.1% of intra-uterine brachytherapy cases.
Conclusions: Among Medicare claims, a small number of providers accounted for a significant proportion of gynecologic and prostate brachytherapy cases, particularly in the case of intrauterine implants. The vast majority of brachytherapy providers perform limited cases in this population. Efforts toward improving access to intrauterine implants in Medicare patients should be a national priority.
Classification for long-term survival in oligometastatic patients treated with ablative radiotherapy: A multi-institutional pooled analysis. PLoS One
Hong JC, Ayala-Peacock DN, Lee J, Blackstock AW, Okunieff P, Sung MW, Weichselbaum RR, Kao J, Urbanic JJ, Milano MT, Chmura SJ, Salama JK (2018).
Abstract
Background: Radiotherapy is increasingly used to treat oligometastatic patients. We sought to identify prognostic criteria in oligometastatic patients undergoing definitive hypofractionated image-guided radiotherapy (HIGRT).
Methods: Exclusively extracranial oligometastatic patients treated with HIGRT were pooled. Characteristics including age, sex, primary tumor type, interval to metastatic diagnosis, number of treated metastases and organs, metastatic site, prior systemic therapy for primary tumor treatment, prior definitive metastasis-directed therapy, and systemic therapy for metastasis associated with overall survival (OS), progression-free survival (PFS), and treated metastasis control (TMC) were assessed by the Cox proportional hazards method. Recursive partitioning analysis (RPA) identified prognostic risk strata for OS and PFS based on pretreatment factors.
Results: 361 patients were included. Primary tumors included non-small cell lung (17%), colorectal (19%), and breast cancer (16%). Three-year OS was 56%, PFS was 24%, and TMC was 72%. On multivariate analysis, primary tumor, interval to metastases, treated metastases number, and mediastinal/hilar lymph node, liver, or adrenal metastases were associated with OS. Primary tumor site, involved organ number, liver metastasis, and prior primary disease chemotherapy were associated with PFS. OS RPA identified five classes: class 1: all breast, kidney, or prostate cancer patients (BKP) (3-year OS 75%, 95% CI 66-85%); class 2: patients without BKP with disease-free interval of 75+ months (3-year OS 85%, 95% CI 67-100%); class 3: patients without BKP, shorter disease-free interval, ≤ two metastases, and age < 62 (3-year OS 55%, 95% CI 48-64%); class 4: patients without BKP, shorter disease-free interval, ≥ three metastases, and age < 62 (3-year OS 38%, 95% CI 24-60%); class 5: all others (3-year OS 13%, 95% CI 5-35%). Higher biologically effective dose (BED) (p < 0.01) was associated with OS.
Conclusions: We identified clinical factors defining oligometastatic patients with favorable outcomes, who we hypothesize are most likely to benefit from metastasis-directed therapy.