An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

Abstract

ObjectivesWe aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies.MethodsFrom January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model.ResultsThree predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p<0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness.ConclusionsThe model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

“…Additionally, a radiomics model utilizing ultrasound, as devised by Huang et al. ( 33 ), exhibits robust diagnostic performance for sclerosing adenosis and invasive ductal carcinoma, with AUCs of 0.886 and 0.779 in the validation and independent validation cohorts. A prior investigation demonstrates that employing radiomics data from both CC and MLO positions outperforms using CC or MLO positions alone in classification ( 34 ).…”

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

“…Wei et al [ 5 ] constructed a new radiomics model using imaging phenotype and clinical variables to predict the overall survival time (OS) of hepatocellular carcinoma (HCC) patients receiving stereotactic body radiation therapy (SBRT). Huang et al [ 6 ] developed an ultrasound-based radiomics model to distinguish between sclerosing adenopathy (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsy. Ramtohul et al [ 7 ] evaluated whether the radiological characteristics based on multi parameter dynamic enhanced MRI could help to distinguish the expression of HER2 in breast cancer.…”