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Background: Tumor-stroma interactions, as indicated by tumor-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. We aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). Materials and Methods: In this retrospective study including 2268 patients with resected CRC recruited from four centers, we developed an MDL model using preoperative CT images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort (n=956) and internal validation cohort (IVC, n=240) were randomly selected from center I. Patients in the external validation cohort1(EVC1, n=509), EVC2 (n=203), and EVC3 (n=360) were recruited from other three centers. Model performance was evaluated with respect to discrimination and calibration. Furthermore, we evaluated whether the model could predict the benefit from adjuvant chemotherapy. Results: The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95%CI, 0.800-0.910), 0.838(95% CI, 0.802-0.874), and 0.857(95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy (hazard ratio HR 0.391 95%CI, 0.230–0.666, P =0.0003; HR=0.46795%CI, 0.331-0.659, P <0.0001, respectively), whereas those with a low MDLS did not. Conclusion: The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.
Cui et al. (Mon,) studied this question.