What question did this study set out to answer?

The aim is to develop an automated system for lesion classification to better assess tumor burden and address challenges in RECIST application.

April 5, 2026

Abstract 2788: Multi-site development of automated lesion classification for comprehensive tumor burden assessment: Addressing the RECIST trial-practice disconnect.

Key Points

The aim is to develop an automated system for lesion classification to better assess tumor burden and address challenges in RECIST application.
Developed a dual-architecture system with UNet and ResNet50 models.
Trained on 2,464 CT scans from diverse scanner platforms.
Applied data augmentation to imbalanced classes for improved classification performance.
Achieved an 86% accuracy and 91% sensitivity for malignancy detection.
Demonstrated a precision-recall area under curve of 0.89.
Stable performance across different scanner manufacturers without recalibration.

Abstract

Abstract Background: Response Evaluation Criteria in Solid Tumors (RECIST) mandate manual selection of 2-5 target lesions with unidimensional measurements, generating inter-reader discordance beyond 30% while folding 3D tumor dynamics into categorical outcomes with limited biological interpretability. RECIST protocols are rarely used in routine practice, creating a trial-practice disconnect that undermines endpoint validity. Automated volumetric quantification of total tumor burden represents an alternative contingent on reliable autonomous model performance across diverse imaging environments. Methods: We developed a modular dual-architecture system combining UNet segmentation with ResNet50 classification, trained on 2,464 CT scans from 1,324 patients across three continents (North/South America Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2788.

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Cite This Study

Pavlechko et al. (Fri,) studied this question.

synapsesocial.com/papers/69d1fdf7a79560c99a0a45bf https://doi.org/https://doi.org/10.1158/1538-7445.am2026-2788

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