Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma

Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.

METHODS

This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.

RESULTS

Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying "ADC_wavelet-HLH_glszm_ZoneEntropy" and "DCE_wavelet-HLL_gldm_DependenceVariance" as the most significant features for the model's predictions.

CONCLUSION

The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.

CRITICAL RELEVANCE STATEMENT

An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.

Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study

Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients' survival. Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis. Thus, radiomics provides a new approach to noninvasive assessment of breast cancer prognosis. Ultrasound is one of the commonest clinical means of examining breast cancer. In recent years, some results of research into ultrasound radiomics for diagnosing breast cancer, predicting lymph node status, treatment response, recurrence and survival times, and other aspects, have been published. In this article, we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis. We aim to provide a reference for radiomics researchers, promote the development of ultrasound radiomics, and advance its clinical application.

Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

Background

Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC.

Objectives

To investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC.

Methods

A total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling.

Results

359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively.

Conclusion

Ultrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.