A 3D deep learning model based on MRI for predicting lymphovascular invasion in rectal cancer

Abstract Background The assessment of lymphovascular invasion (LVI) is crucial in the management of rectal cancer; However, accurately evaluating LVI preoperatively using imaging remains challenging. Recent advances in radiomics have created opportunities for developing more accurate diagnostic tools. Purpose This study aimed to develop and validate a deep learning model for predicting LVI in rectal cancer patients using preoperative MR imaging. Methods These cases were randomly divided into a training cohort ( n = 233) and an validation cohort ( n = 101) at a ratio of 7:3. Based on the pathological reports, the patients were classified into positive and negative groups according to their LVI status. Based on the preoperative MRI T2WI axial images, the regions of interest (ROI) were defined from the tumor itself and the edges of the tumor extending outward by 5 pixels, 10 pixels, 15 pixels, and 20 pixels. The 2D and 3D deep learning features were extracted using the DenseNet121 architecture, and the deep learning models were constructed, including a total of ten models: GTV (the tumor itself), GPTV5 (the tumor itself and the tumor extending outward by 5 pixels), GPTV10, GPTV15, and GPTV20. To assess model performance, we utilized the area under the curve (AUC) and conducted DeLong test to compare different models, aiming to identify the optimal model for predicting LVI in rectal cancer. Results In the 2D deep learning model group, the 2D GPTV10 model demonstrated superior performance with an AUC of 0.891 (95% confidence interval [CI] 0.850–0.933) in the training cohort and an AUC of 0.841 (95% CI 0.767–0.915) in the validation cohort. The difference in AUC between this model and other 2D models was not statistically significant based on DeLong test ( p > 0.05); In the group of 3D deep learning models, the 3D GPTV10 model had the highest AUC, with a training cohort AUC of 0.961 (95% CI 0.940–0.982) and a validation cohort AUC of 0.928 (95% CI 0.881–0.976). DeLong test demonstrated that the performance of the 3D GPTV10 model surpassed other 3D models as well as the 2D GPTV10 model ( p < 0.05). Conclusion The study developed a deep learning model, namely 3D GPTV10, utilizing preoperative MRI data to accurately predict the presence of LVI in rectal cancer patients. By training on the tumor itself and its surrounding margin 10 pixels as the region of interest, this model achieved superior performance compared to other deep learning models. These findings have significant implications for clinicians in formulating personalized treatment plans for rectal cancer patients.