Abstracts Background Anastomotic leakage (AL) is a serious complication after anterior resection. The purpose of this study was to determine the role of microvascular density (MVD) in AL and to develop a nomogram to accurately predict AL. Methods This study retrospectively enrolled 477 consecutive patients who underwent anterior resection for rectal cancer from January 2011 to January 2019. Tissue samples of the resection margins were assessed for MVD. Univariate and multivariate regression analyses were used to identify the risk factors for AL. Results The incidence of clinical AL was 6.7%. MVD in the distal margin was associated with AL (P < .001). Univariate and multivariate regression analysis identified the following variables as independent risk factors for AL: preoperative albumin ≤35 g/L (odds ratio [OR] = 2.511), neoadjuvant treatment (OR = 3.560), location of tumor ≤7 cm (OR = 3.381), blood loss ≥100 mL (OR = 2.717), and MVD in the distal margin ≤20 (OR = 4.265). Then, a nomogram including these predictors was developed. The nomogram showed good discrimination (AUC = 0.816) and calibration (concordance index = 0.816). The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions MVD in the distal margin is closely associated with AL. The nomogram can be used for individualized prediction of AL after anterior resection for patients with rectal cancer.

ObjectivesMetachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images.MethodsSix-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value.ResultsPatients were divided into low- and high-LM risk score groups according to the cutoff value and significant differences were observed in the LM of the different risk score groups in the training and validation cohorts (P&lt;0.001). Multivariable analysis revealed that the LM risk score, VELIPI, pT stage and pN stage were independent predictors of LM. Then, the prediction model was developed and presented as a nomogram to predict the 1-, 2-, and 3-year probability of LM. The integrated nomogram achieved satisfactory discrimination, with C-indexes of 0.807 (95% CI: 0.787, 0.827) and 0.812 (95% CI: 0.773, 0.850) and AUCs of 0.840 (95% CI: 0.795, 0.885) and 0.848 (95% CI: 0.766, 0.931) in the training and validation cohorts, respectively. Favorable calibration of the nomogram was confirmed in the training and validation cohorts. Integrated discrimination improvement and net reclassification index indicated that the integrated nomogram was superior to the traditional clinicopathological model. Decision curve analysis confirmed that the nomogram has clinical application value.ConclusionsThe LM risk score based on ResNet-50 and digital HE images was significantly associated with LM. The integrated nomogram could identify stage I-III CRC patients at high risk of LM after primary colectomy, so it may serve as a potential tool to choose the appropriate treatment to improve the prognosis of stage I-III CRC patients.