Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

RATIONALE AND OBJECTIVES: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. MATERIALS AND METHODS: A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS: Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. CONCLUSION: Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Nomogram for predicting skip metastasis in cN0 papillary thyroid cancer patients at increased risk of lymph node metastasis.

BACKGROUND: Skip lymph node metastasis (SLNM) refers to lateral lymph node metastasis (LLNM) without involving central lymph node (CLN). Some microscopic nodal positivity may be difficult to detect before surgery due to atypical imaging characteristics. These patients are misdiagnosed as having clinically node-negative (cN0) papillary thyroid cancer (PTC) even after central lymph node dissection, leading to a high risk of developing LNM after surgery. Current prediction models have limited clinical utility, as they are only applicable to predict SLNM from clinically node-positive (cN+) PTC, not cN0 PTC, and this has little impact on treatment strategies. OBJECTIVES: This study aimed to establish a nomogram for preoperatively assessing the likelihood of SLNM in cN0 PTC patients with increased risk of LNM, thus optimizing their therapeutic options. MATERIAL AND METHODS: The records of 780 PTC patients undergoing thyroidectomy along with bilateral central lymph node dissection were retrospectively reviewed. The cN0 patients with postoperative LLNM (occult SLNM) and cN+ patients without central lymph node metastasis (CLNM) (typical SLNM) were included in the SLNM group (n = 82). The CLNM-negative cN0 patients without postoperative LLNM were assigned to the non-SLNM group (n = 698). The independent correlates of SLNM constituted the nomogram for determining the likelihood of SLNM in high-risk cN0 PTC patients. RESULTS: The independent correlates of SLNM were age (hazard ratio (HR) = 1.016), tumor location (HR = 1.801), tumor size (HR = 1.528), and capsular invasion (HR = 2.941). They served as components in the development of the nomogram. This model was verified to present acceptable discrimination. It showed good calibration and a decent net benefit when the predicted probability was <60%. CONCLUSIONS: We developed a nomogram incorporating preoperative clinical data to predict the probability of SLNM development in high-risk cN0 PTC patients, which contributed to their optimized treatment options.

Evaluation of the Efficacy of Ultrasound-Guided Local Lauromacrogol Injection Combined with Aspiration for Cesarean Scar Pregnancy: A Novel Treatment.

OBJECTIVES: To evaluate the efficacy of ultrasound-guided local lauromacrogol injection combined with aspiration for treating cesarean scar pregnancy (CSP). METHODS: From July 2016 to December 2016, 18 patients diagnosed with CSP were treated with ultrasound-guided local lauromacrogol injection combined with aspiration. Clinical data and outcome were analysed. RESULTS: All patients were treated successfully. The amount of bleeding ranged between 10 and 50 mL. The duration of hospitalization ranged between 2 and 11 days. Serum ß-human chorionic gonadotropin (ß-hCG) decreased to the nondetectable level within 19-41 days. Menstruation recovery occurred after 10-24 days of normalization of serum ß-hCG level. Reproductive functions were preserved, and there were no untoward effects or complications. CONCLUSIONS: Ultrasound-guided local lauromacrogol injection combined with aspiration is an effective CSP therapy, as it was associated with a high success rate, short hospitalization and fast recovery. However, its wider application and popularization have to be validated on a larger patient population affected by CSP.