Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients

Overview

Journal J Imaging

Publisher MDPI

Specialty Radiology

Date 2024 Dec 27

PMID 39728194

Authors

Maryem Rhanoui

Mounia Mikram

Kamelia Amazian

Abderrahim Ait-Abderrahim

Siham Yousfi

Imane Toughrai

Affiliations

Soon will be listed here.

Abstract

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients' quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications.

Citing Articles

Constructing a Prognostic Model for Subtypes of Colorectal Cancer Based on Machine Learning and Immune Infiltration-Related Genes.

Wen Y, Liao J, Lu C, Huang L, Ma Y J Cell Mol Med. 2025; 29(4):e70437.

PMID: 40008534 PMC: 11862891. DOI: 10.1111/jcmm.70437.

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