Clinical Parameters Associated With Intracranial Progression Burden Following an Initial Stereotactic Radiosurgery Course in a Multi-institutional Brain Metastases Cohort.

Purpose: Following initial stereotactic radiosurgery (SRS), risk factors for high-burden intracranial progression (ICP) necessitating whole brain radiation remain poorly characterized. We hypothesize that specific clinical parameters at initial SRS are associated with high-burden ICP-defined as either ≥5 brain metastases (BMs) (ICP5) or ≥11 BMs (ICP11).

Materials And Methods: Across 2 institutions, we retrospectively identified all patients completing an initial SRS course from January 2015 to December 2020.

Multi-institutional Outcomes after Stereotactic Radiosurgery for Gastrointestinal Brain Metastases.

Purpose: To compare outcomes between gastrointestinal and nongastrointestinal patients with brain metastases after radiosurgery.

Methods And Materials: Retrospective cohort study identifying patients completing an initial course of radiosurgery between January 2015 and December 2020, with follow-up data collected through November 2022. Multi-institutional, academic referral centers.

Patterns in Symptoms Preceding Acute Care in Patients With Cancer.

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.

A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research.

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.

Racial variation in the advanced prostate cancer genome.

Background: Racial differences in metastatic castration-resistant prostate cancer (mCRPC) genomes have not yet been fully studied. We aimed to investigate transcriptomic, mutational, and clinical differences by race in a large multi-institutional cohort of men with mCRPC.

Methods: Genomic and clinicopathologic data from four mCRPC tumor biopsy cohorts were obtained and aggregated.

Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.

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).

Assessing Large Language Models for Oncology Data Inference From Radiology Reports.

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.

Delayed definitive management of localized prostate cancer: what do we know?

Delays in the work-up and definitive management of patients with prostate cancer are common, with logistics of additional work-up after initial prostate biopsy, specialist referrals, and psychological reasons being the most common causes of delays. During the COVID-19 pandemic and the subsequent surges, timing of definitive care delivery with surgery or radiotherapy has become a topic of significant concern for patients with prostate cancer and their providers alike. In response, recommendations for the timing of definitive management of prostate cancer with radiotherapy and radical prostatectomy were published but without a detailed rationale for these recommendations.

AI Use in Prostate Cancer: Potential Improvements in Treatments and Patient Care.

Artificial intelligence use in prostate cancer encompasses 4 main areas including diagnostic imaging, prediction of outcomes, histopathology, and treatment planning.

Determinants of Symptomatic Intracranial Progression After an Initial Stereotactic Radiosurgery Course.

Purpose: Clinical and imaging surveillance of patients with brain metastases is important after stereotactic radiosurgery (SRS) because many will experience intracranial progression (ITCP) requiring multidisciplinary management. The prognostic significance of neurologic symptoms at the time of ITCP is poorly understood.

Methods And Materials: This was a multi-institutional, retrospective cohort study from 2015 to 2020, including all patients with brain metastases completing an initial course of SRS.

Health Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study.

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.

Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts.

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.

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.

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.

Ctrl-C: a cross-sectional study of the electronic health record usage patterns of US oncology clinicians.

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.

Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists.

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.

Multi-institutional Development and External Validation of a Machine Learning Model for the Prediction of Distant Metastasis in Patients Treated by Salvage Radiotherapy for Biochemical Failure After Radical Prostatectomy.

Background: Up to 40% of patients with prostate cancer may develop biochemical recurrence after surgery, with salvage radiation therapy (SRT) being the only curative option. In 2016, Tendulkar et al. (Contemporary update of a multi-institutional predictive nomogram for salvage radiotherapy after radical prostatectomy.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

Purpose: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships.

Methods And Materials: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs.

Intracranial and Extracranial Progression and Their Correlation With Overall Survival After Stereotactic Radiosurgery in a Multi-institutional Cohort With Brain Metastases.

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.

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

Background: Pelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomogram are elegant and simple traditional tools used to estimate the risk of LNI and select patients for PLND.

Objective: To determine whether machine learning (ML) can improve patient selection and outperform currently available tools for predicting LNI using similar readily available clinicopathologic variables.

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.

Objectives: Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy.

Serial stereotactic body radiation therapy for oligometastatic prostate cancer detected by novel PET-based radiotracers.

Background: Radiopharmaceuticals, including Ga-68-prostate specific membrane antigen (PSMA)-11 and F-18-Fluciclovine, are increasingly used to inform therapies for prostate cancer (CaP). Stereotactic body radiation therapy (SBRT) to PET-detected oligometastatic CaP has been shown to improve progression free survival (PFS) and delay androgen deprivation therapy (ADT) compared to observation. For men who subsequently develop oligorecurrent CaP, outcomes following second SBRT are unknown.