Radiological characteristics and diagnostic clues for persistent descending mesocolon in patients with rectal cancer.

PURPOSE: Persistent descending mesocolon (PDM) increases the difficulty and colonic ischemia in the surgery of colorectal cancer, but the preoperative diagnostic criteria have not yet been clearly demonstrated. This study explored the MR imaging features and diagnostic criteria of PDM to improve the preoperative diagnostic rate. METHODS: The clinical data of 54 patients with PDM and 270 patients without PDM who underwent rectal surgery at the Department of Colorectal Surgery, Fujian Medical University Union Hospital, from March 2018 to December 2022 were analyzed, retrospectively. The radiological parameters of PDM from MRI were analyzed. RESULTS: On MRI T2WI axial image, the left edge of the abdominal aorta was defined as the reference line. The shortest vertical distance between the right edge of the descending colon and this line (dN) and the maximum transverse diameter of the peritoneal cavity (dA) at the same level and the maximum vertical distance between the right edge of the descending colon and this line (dW) were measured. There were significant statistical differences in dN, dW, dN/dW, and dN/dA between the PDM group and the non-PDM group. dN, dN/dW, and dN/dA have high diagnostic performance for the PDM. dN < 4.16 cm, dN/dW < 0.52, and dN/dA < 0.15 can all be used as clues to diagnose PDM. CONCLUSIONS: We propose a feasible set of diagnostic criteria for PDM based on abdominal MRI, which can quickly and accurately diagnose PDM, and provide some reference for preoperative planning and surgical decision-making.

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer.

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.