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March 1, 2026Open Access

Integrating Multiomics and Whole Slide Imaging for Predicting the Malignant Transformation of Precancerous Rectal Lesions

Key Points

This research focuses on predicting the malignant transformation of precancerous rectal lesions by integrating different data types.
Developed a multimodal framework using artificial intelligence.
Integrated whole slide imaging and multiomics data.
Analyzed biological signals associated with lesions.
The AI-based framework showed improved accuracy in predicting malignant changes.
Integration of molecular and morphological data provided deeper insights into lesion progression.

Abstract

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

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Integrating Multiomics and Whole Slide Imaging for Predicting the Malignant Transformation of Precancerous Rectal Lesions

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Abstract

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