Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer

Abstract

BACKGROUND

Lymph node metastasis (LNM) is closely linked to poor prognosis in patients with colorectal cancer (CRC). An accurate preoperative evaluation of lymph node status is crucial for tailoring individualized treatment plans. However, dependable methods for predicting lymph node involvement and assessing prognosis prior to surgery are still inadequate.

AIM

To develop a nomogram combining clinical features, radiomics and deep learning (DL) features for predicting LNM and prognosis in patients with resectable CRC.

METHODS

Two hundred and seventy-eight patients with pathologically confirmed CRC from two hospitals were retrospectively enrolled. Radiomics and DL features were extracted from preoperative three-phase contrast-enhanced computed tomography images within the intratumoral and peritumoral-3 mm regions. Using logistic regression combined with three feature selection methods, a clinical-DL-radiomics nomogram (DLRN) was developed. SHapley Additive exPlanations were employed to interpret the model. Additionally, Cox regression analysis was performed to identify risk factors for 3-year recurrence-free survival and to construct a prognostic model.

RESULTS

The DLRN demonstrated significantly superior performance in differentiating LNM compared to both the clinical model and the radiomics model. The area under the receiver operating characteristic curve for the DLRN was 0.944, 0.878, and 0.855 in the training, internal validation, and external validation cohorts, respectively. Calibration curves and decision curve analysis confirmed that the DLRN exhibited excellent calibration and strong clinical utility. Furthermore, the prognostic model based on the DLRN score showed robust performance in predicting 3-year recurrence-free survival, achieving an area under the receiver operating characteristic curves of 0.826, 0.788, and 0.755 across the three cohorts, respectively.

CONCLUSION

The DLRN demonstrated excellent performance in predicting LNM in CRC. Additionally, the prognostic model derived from the DLRN score effectively stratified patients according to their risk of recurrence.

Keywords: Colorectal cancer; Radiomics; Deep learning; Lymph node metastasis; Prognosis; Contrast-enhanced computed tomography; Nomogram; SHapley Additive exPlanation

Core Tip: Accurate preoperative prediction of lymph node metastasis is crucial for optimizing treatment strategies in colorectal cancer. In this study, we developed an interpretable clinical-deep learning-radiomics nomogram (DLRN) by integrating clinical features with multi-regional radiomics and deep learning features. Moreover, the DLRN-based prognostic model effectively predicted 3-year recurrence-free survival. As a noninvasive preoperative tool, the DLRN demonstrated strong predictive accuracy for lymph node metastasis in colorectal cancer and offers a practical means for individualized risk stratification and informed treatment decision-making.