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Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Related Experiment Video

Updated: Jan 13, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Leveraging Centralized Health System Data Management and Large Language Model-Based Data Preprocessing to Identify

Fekede Asefa Kumsa¹, Christopher L Brett², Soheil Hashtarkhani¹

¹Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN.

JCO Clinical Cancer Informatics

|October 28, 2025

Summary

This summary is machine-generated.

Identifying radiation therapy interruptions (RTI) is crucial for cancer care quality. Factors like cancer type, Medicaid coverage, and social vulnerability predict RTI, enabling targeted interventions.

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Last Updated: Jan 13, 2026

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Published on: April 11, 2018

Area of Science:

Oncology
Health Informatics
Public Health

Background:

Unplanned radiation therapy interruptions (RTI) compromise cancer treatment quality.
Identifying risk factors for RTI is essential for improving patient outcomes.

Purpose of the Study:

To evaluate the use of a centralized electronic health record warehouse and large language model (LLM) for data preprocessing.
To facilitate the identification of risk factors associated with radiation therapy interruptions (RTI).

Main Methods:

Analysis of demographic, behavioral, clinical, and neighborhood data for 2,130 radiotherapy patients.
Measurement of treatment interruptions as missed days, adjusted for weekends/holidays.
Multinomial logistic regression to identify factors associated with moderate (2-4 days) and severe (≥5 days) RTI.

Main Results:

Moderate RTI (15.8%) linked to genitourinary/prostate cancer and Medicaid coverage.
Severe RTI (7.7%) associated with marital status, head/neck/gynecologic cancers, Medicaid, radiation dose, and neighborhood social vulnerability.
LLM-based preprocessing enabled efficient identification of these associations.

Conclusions:

Automated data preprocessing effectively identified key factors associated with RTI.
Marital status, disease site, Medicaid coverage, and social vulnerability are significant predictors of RTI.
Data-driven risk assessment and intervention strategies are needed to maintain cancer treatment quality.