Artificial intelligence-integrated multimodal data-assisted magnetic resonance imaging for neoadjuvant chemoradiotherapy decision-making in cT1-2N0 rectal cancer

Vollständiger Abstract

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BACKGROUND Colorectal cancer represents a major global health burden, with neoadjuvant chemoradiotherapy as standard treatment for locally advanced rectal cancer, although preoperative magnetic resonance imaging (MRI) staging shows only moderate accuracy. A substantial proportion of patients staged as cT1-2N0 on MRI are subsequently upstaged after surgery, thereby missing neoadjuvant treatment and risking worse oncologic outcomes. AIM To develop a machine learning model that integrates multiomics profiles to improve the accuracy of neoadjuvant chemoradiotherapy decision-making in rectal cancer patients whose baseline MRI indicates cT1-2N0 disease. METHODS This multicenter cohort study consecutively enrolled patients who underwent pre-operative MRI and curative rectal cancer surgery at three institutions between January 2013 and December 2024. Participants were randomly allocated to training, internal validation and external validation sets. A pre-defined set of 2260 radiomic features was extracted from T2-weighted and diffusion-weighted images and fused with baseline clinical, hematological and pathological variables. Ten independent machine learning classifiers were constructed and compared with both physician decisions and imaging-only radiomic models. Model performance was evaluated with receiver operating characteristic analysis, calibration plots, decision curve analysis and confusion matrices. RESULTS A total of 1320 consecutive patients with clinically staged cT1-2N0 rectal cancer who underwent curative intent surgery were retrospectively enrolled from three tertiary centers: (1) Center 1 (n = 1009); (2) Center 2 (n = 246); and (3) Center 3 (n = 65). We developed a Clinical, Hematologic, Oncopathologic, and Radiomic Decision (CHORD) model, an XGBoost-based machine learning classifier that integrates seven preoperative MRI radiomic features with 16 clinical, hematologic, and pathologic variables via a CART regression tree algorithm in patients with cT1-2N0 rectal cancer. Externally validated, the CHORD model delivered an area under the curve of 0.927 and an F1-score of 0.825, attesting to its consistent and robust performance across independent cohorts. CONCLUSION Our study demonstrated that the CHORD model exhibits satisfactory performance in identifying cT1-2N0 rectal cancer patients who require neoadjuvant chemoradiotherapy by integrating preoperative multi-omics data, offering critical insights into reducing the omission rate of neoadjuvant therapy and enhancing the accuracy of preoperative radiological staging.