BACKGROUND Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging. AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients. METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort ( n = 242) and validation cohort ( n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection ( n = 6) and machine-learning ( n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved ( P < 0.001 in the training cohort, and P = 0.045 in the validation cohort). CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

ObjectiveTo develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC).MethodsThis retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).ResultsOne hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P &lt; 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039).ConclusionsThe combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.

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BACKGROUND Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task. AIM To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC. METHODS This study retrospectively enrolled 219 patients with RC [TDs + LNM - ( n = 89); LNM + TDs - ( n = 115); TDs + LNM + ( n = 15)] from a single center between September 2016 and September 2021. Single-positive patients ( i.e. , TDs + LNM - and LNM + TDs - ) were classified into the training ( n = 163) and validation ( n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients ( i.e. , TDs + LNM + ) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm. RESULTS Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 ( P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs + LNM + ( n = 15); single-positive ( n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%). ...