A nomogram for predicting probability of low risk of MammaPrint results in women with clinically high-risk breast cancer

We aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2–) breast cancer and minimal axillary burden (N0-1) tumors using clinicopathological factors of patients who underwent an MMP test for decision making regarding adjuvant chemotherapy. A total of 409 T1-3 N0-1 M0 HR + and HER2– breast cancer patients whose MMP genomic risk results and clinicopathological factors were available from 2017 to 2020 were analyzed. With randomly selected 306 patients, we developed a nomogram for predicting a low-risk subgroup of MMP results and externally validated with remaining patients (n = 103). Multivariate analysis revealed that the age at diagnosis, progesterone receptor (PR) score, nuclear grade, and Ki-67 were significantly associated with MMP risk results. We developed an MMP low-risk predictive nomogram. With a cut off value at 5% and 95% probability of low-risk MMP, the nomogram accurately predicted the results with 100% positive predictive value (PPV) and negative predictive value respectively. When applied to cut-off value at 35%, the specificity and PPV was 95% and 86% respectively. The area under the receiver operating characteristic curve was 0.82 (95% confidence interval [CI] 0.77 to 0.87). When applied to the validation group, the nomogram was accurate with an area under the curve of 0.77 (95% CI 0.68 to 0.86). Our nomogram, which incorporates four traditional prognostic factors, i.e., age, PR, nuclear grade, and Ki-67, could predict the probability of obtaining a low MMP risk in a cohort of high clinical risk patients. This nomogram can aid the prompt selection of patients who does not need additional MMP testing.

Breast Imaging Findings of Microcalcifications in Ductal Carcinoma in Situ and Their Correlations with Pathological and Biological Features

Background: Mammography (MMG) is the primary screening tool for breast cancer, as microcalcifications are the most common MMG finding in ductal carcinoma in situ (DCIS). The use of high-frequency transducers facilitates the visualization of calcifications on ultrasonography (USG), especially in patients with dense breasts and cancer symptoms. Although a correlation has been reported between the imaging features of DCIS and pathological features, few studies have focused on multiple imaging modalities. Objectives: To evaluate the correlation of DCIS microcalcifications in breast imaging with pathological and biological features. Patients and Methods: The MMG and USG findings of 125 lesions detected in 123 patients, diagnosed with pure DCIS, were retrospectively reviewed according to the breast imaging-reporting and data system (BI-RADS). The USG and comparable MMG findings of microcalcifications were divided into three groups: group 1 (MMG negative, USG negative), group 2 (MMG positive, USG negative), and group 3 (MMG positive, USG positive). The pathological findings (nuclear grade and comedo necrosis) and biological features [estrogen (ER) positive group, human epidermal growth factor receptor 2 (HER2) positive group, triple negative group, and Ki-67 index] were compared with the MMG and USG features using Chi-square test. Results: Microcalcifications were observed on MMG in 83 (66.4%) DCIS lesions. Positive microcalcifications on MMG were significantly associated with a high nuclear grade (P = 0.001) and comedo necrosis (P = 0.001). Positive microcalcifications on MMG were significantly associated with ER negativity (P = 0.023), HER2 positivity (P = 0.002), and increased Ki-67 index (P = 0.001). There were 62 lesions (49.6%) without microcalcifications on USG (group 1 and group 2), while there were 63 (50.4%) lesions with microcalcifications on USG (group 3). Positive microcalcifications on MMG were significantly associated with ER-negative group (P = 0.023), HER2-positive group (P = 0.002), and increased Ki 67 index (P = 0.001). Conclusion: Based on the present results, DCIS microcalcifications detected via imaging were significantly associated with poor prognostic pathological factors, such as a high nuclear grade and comedo necrosis, as well as poor prognostic biological factors, including ER negativity, HER2 positive group, and a high Ki-67 index.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.