Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients. MATERIALS AND METHODS: A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI). RESULTS: The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI. CONCLUSION: The nomogram demonstrated a reliable ability to predict LVI in IBC patients.

Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma.

PURPOSE: This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. METHODS: The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. RESULTS: The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. CONCLUSION: The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.

Ex vivo near-infrared targeted imaging of human bladder carcinoma by ICG-anti-CD47.

Objective: The low detection rate of early-stage and small tumors remains a clinical challenge. A solution to this unmet need is urgently warranted for the accurate diagnosis and treatment of bladder cancer (BC). This study aimed to evaluate the feasibility of CD47 as a target for optical molecular imaging of human BC and conduct preliminary ex vivo imaging experiments. Method: Using indocyanine green (ICG) and a CD47 antibody (anti-CD47), we synthesized a new targeted fluorescent probe ICG-anti-CD47. A total of 25 patients undergoing radical cystectomy were prospectively included in ex vivo imaging experiments. Following surgery, the freshly isolated bladder specimens were incubated with ICG-anti-CD47, and images were captured under white light and near-infrared (NIR) light. Standard histopathologic evaluation was performed, and findings were correlated with those of CD47-targeted NIR molecular imaging. Results: Based on the ex vivo imaging experiments, 23 and 2 patients were pathologically diagnosed with bladder urothelial carcinoma and bladder squamous cell carcinoma, respectively. There were no adverse effects of ICG-anti-CD47 on the histological structure of the tumor and normal uroepithelium. In the NIR grayscale images, the mean fluorescence intensity of the tumor tissue was significantly higher than that of the adjacent normal background tissue, which markedly improved tumor visualization. Conclusion: Anti-CD47-targeted NIR molecular imaging may be a feasible and powerful strategy for the accurate diagnosis of BC. Nevertheless, larger-scale randomized trials are warranted to verify the present findings.