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

Figure 5 Individual prediction and analysis of patient No. 112 using the Clinical, Hematologic, Oncopathologic, and Radiomic Decision Model. A: Axial section of the patient’s T2-weighted (T2W) magnetic resonance imaging (MRI) image; B: Coronal section of the patient’s T2W MRI image; C: Sagittal section of the patient’s T2W MRI image; D: The patient’s diffusion weighted imaging MRI image; E: Axial section of the patient’s T2W MRI image with radiologist’s segmentation; F: Coronal section of the patient’s T2W MRI image with radiologist’s segmentation; G: Sagittal section of the patients T2W MRI image with radiologist’s segmentation; H: The patient’s diffusion weighted imaging MRI image with radiologist’s segmentation; I: Web deployment of the Clinical, Hematologic, Oncopathologic, and Radiomic Decision model and individual prediction results; J: Bar chart of decisions made by Clinical, Hematologic, Oncopathologic, and Radiomic Decision and other machine-learning models; K: Bar chart of SHapley Additive exPlanations contributions of the patient’s features. CHORD: Clinical, Hematologic, Oncopathologic, and Radiomic Decision; ECOG: Eastern Cooperative Oncology Group; GBM: Gradient boosting machine; KNN: K-nearest neighbors; SHAP: SHapley Additive exPlanations; SVM: Support vector machine.