Development and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer

Background: Lymphovascular invasion (LVI) is an independent prognostic factor for patients with rectal cancer (RC). Recent studies have shown that deep learning (DL)-based magnetic resonance imaging (MRI) has potential in evaluating the treatment response of RC patients, but the role of MRI-based DL in assessing RC LVI remains unclear. This study sought to develop and validate a DL model to evaluate the LVI status of RC patients preoperatively based on MRI, and to test its performance at an external center.

Methods: The data of 489 patients with surgically confirmed RC were retrospectively collected from two centers. The training set and the internal validation set comprised 320 patients and 80 patients, respectively, from The Second Affiliated Hospital of Harbin Medical University; while the external testing set comprised 89 patients from Xinjiang Production and Construction Corps Tenth Division Beitun Hospital. All the patients underwent MRI examinations before surgery. Two separate image models were constructed based on the three-dimensional (3D) residual network (ResNet)-18 architecture, using only T2-weighted image (T2WI) data and diffusion-weighted image (DWI) data, respectively, to assess LVI, and a combined model was developed that integrated T2WI, DWI, and clinical factors to assess LVI. The performance of the T2WI- and DWI-based models, and the combination model was evaluated using the area under the curve (AUC) and the DeLong test. The clinical utility of these models was assessed by calibration curve analysis and decision curve analysis (DCA).

Results: The T2WI- and DWI-based DL models demonstrated robust capabilities in evaluating LVI in RC in both the internal validation set and the external test set. For the T2WI-based model, the AUC values reached 0.795 and 0.764 in the internal validation set and the external test set, respectively. For the DWI-based model, the AUC values reached 0.822 and 0.825 in the internal validation set and the external test set, respectively. The combined model exhibited superior performance, achieving AUC values of 0.899 and 0.848 in the internal validation set and the external test set, respectively. In the external test set, all three DL models exhibited robust calibration. The DCA also showed that the DWI-based model and the combined model offered a significantly greater overall net benefit in evaluating LVI than the T2WI-based model.

Conclusions: The multi-parametric MRI DL model demonstrated excellent performance in evaluating the LVI status of patients with RC. This model could serve as a complementary method for the non-invasive assessment of LVI in RC.