Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters

Abstract

Purpose We developed a radiomics strategy that incorporating radiomics features extracted from dual-view mammograms and clinical parameters for identifying benign and malignant breast lesions, and validated whether the radiomics assessment can improve the accurate diagnosis of breast cancer.Methods A total of 380 patients with 621 breast lesions utilizing mammograms on craniocaudal (CC) and mediolateral oblique (MLO) views were randomly allocated into the training (n=486) and testing (n=135) sets in this retrospective study. A total of 1184 and 2368 radiomics features were extracted from singleposition ROI and position-paired ROI, respectively. Clinical parameters were then combined for better prediction. The recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) methods were used to select optimal predictive features. Random forest algorithm was used to conduct the predictive model, and the performance was evaluated with area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, speci city and accuracy.Results After preprocessing, 467 radiomics features and clinical parameters remained in the single-view and dual-view models. The random forest model using a combination of dual-view radiomics and clinical parameters achieved a favorable performance (area under curve [AUC]: 0.804, 95% con dence interval [CI]: 0.668-0.916) in the distinction of benign and malignant breast lesions, which outperformed single-view model and model without clinical parameters.Conclusions Incorporating with radiomics features of dual-view (CC&MLO) mammogram, age, breast density and type of suspicious lesions can provide a non-invasive approach to evaluate the malignancy prediction of breast lesions, which can facilitate clinical decision making.

Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study

“…An intriguing study [36] highlights the importance of integrating dual-view radiomics with clinical characteristics in the context of breast cancer, demonstrating superior performance in malignancy evaluation. This is consistent with our methodology, in which the combination of radiomic and clinical variables produced increased discriminatory power, highlighting the need of a thorough approach in the prediction modeling of breast cancer.…”

An MRI‐based machine learning radiomics can predict short‐term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study

“…It has been widely used in clinical research in tumors diagnosing, and curative effects predicting 24–27 . Radiomics aims to quantitatively extract features from diagnostic images and capture the nature of tissues and lesions, as well as to assess the tissue heterogeneity of a patient's tumor 28 . Biopsy captures heterogeneity in a small subset of tumors, while radiomics captures heterogeneity over the entire tumor volume.…”

“…[24][25][26][27] Radiomics aims to quantitatively extract features from diagnostic images and capture the nature of tissues and lesions, as well as to assess the tissue heterogeneity of a patient's tumor. 28 Biopsy captures heterogeneity in a small subset of tumors, while radiomics captures heterogeneity over the entire tumor volume. Radiomics profiles have been shown to predict response to NACT in patients with CSCC, allowing clinicians to identify patients who may benefit from NACT.…”

Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images

“…Radiomics has been used in various modalities in the field of breast cancer [28]. In recent years, a number of studies, particularly those on mammography [29], have reported its usefulness not only for predicting whether a tumor is benign or malignant [30,31], but also its molecular subtypes [32,33], risk of recurrence [34], and prognosis [35]. Thus, a lot of information can be obtained from the radiomics features of mammography images; therefore, the risk of developing breast cancer can also be calculated, leading to individually optimized breast cancer screening methods [36].…”