Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence

Overview

Journal Cancer Res Commun

Specialty Oncology

Date 2024 May 6

PMID 38709069

Authors

Frank A Sinicrope

Garth D Nelson

Bahar Saberzadeh-Ardestani

Diana I Segovia

Rondell P Graham

Christina Wu

Catherine E Hagen

Sameer Shivji

Paul Savage

Dan D Buchanan

Mark A Jenkins

Amanda I Phipps

Carol Swallow

Loic LeMarchand

Steven Gallinger

Robert C Grant

Reetesh K Pai

Stephen N Sinicrope

Dongyao Yan

Kandavel Shanmugam

James Conner

David P Cyr

Richard Kirsch

Imon Banerjee

Steve R Alberts

Qian Shi

Rish K Pai

Affiliations

Soon will be listed here.

Abstract

Significance: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

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