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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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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Large Language Model and Natural Language Processing Approach to Identify Cancer Recurrence From Pathology Reports.

Diego Bayona¹, Daniel K Ebner¹, Lydia Ekama¹

¹Department of Radiation Oncology, Mayo Clinic, Rochester, MN.

JCO Clinical Cancer Informatics

|March 25, 2026

Summary

This summary is machine-generated.

Automated machine learning, including Google Gemini 1.5 Pro, can efficiently identify cancer recurrence from pathology reports. Gemini 1.5 Pro demonstrated superior accuracy, accelerating the analysis of recurrence data for clinical use.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Cancer recurrence is a critical outcome, but large databases lack recurrence data, necessitating manual review.
Automated analysis of medical records can accelerate the generation of recurrence data for research and intervention development.

Purpose of the Study:

To evaluate the efficacy of automated machine learning tools, specifically Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro, in identifying cancer recurrence from pathology reports.
To compare the performance of these AI models against a manually curated dataset.

Main Methods:

A cohort of 7,054 patients treated with radiation therapy between 2010-2018 with verified cancer status was analyzed.
Pathology reports were collected for patients with recurrent disease, and AI models (AutoNLP, Gemini 1.5 Pro) were trained for binary classification of recurrence.
Model performance was compared against a gold-standard manually developed dataset.

Main Results:

Google Gemini 1.5 Pro demonstrated superior performance over AutoNLP in classifying cancer recurrence.
Gemini 1.5 Pro achieved higher accuracy, precision, recall, negative predictive value, and specificity.
The AI models showed promise for rapid and accurate extraction of recurrence status from pathology reports.

Conclusions:

Both AutoNLP and Google Gemini 1.5 Pro are effective tools for identifying cancer recurrence from pathology reports.
Google Gemini 1.5 Pro exhibits superior performance, making it a highly suitable tool for clinical translation and large-scale data analysis.