Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

Abstract

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7–0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical–radiomics models were trained for each algorithm. Radiomics and clinical–radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.

Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients

“…Tsai et al. ( 27 ) established a model based on the radiomic features and clinical features of CT, and all 6 clinical features were used in the construction of the clinical model. The final AUC was 0.69, showing moderate performance, while the AUCs of our clinical model were 0.864 and 0.781, showing better efficacy.…”

“…Considering that the number of selected predictors after univariate analysis and multifactor analysis is small, unable to reflect most clinical information of patients, and has little clinical adaptability, we intended to incorporate all eight clinical factors into the model construction. Tsai et al (27) established a model based on the radiomic features and clinical features of CT, and all 6 clinical features were used in the construction of the clinical model. The final AUC was 0.69, showing moderate performance, while the AUCs of our clinical model were 0.864 and 0.781, showing better efficacy.…”

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy

“…(2) The significant variables identified in the multivariate analysis were consistent with previous studies. Fourth, it is worth noting that manual tumor segmentation for radiomics analysis is time-consuming and labor-intensive (Tsai et al 2022 ), which may limit its clinical usefulness. Finally, since radiomics features are potentially dependent on imaging quality and incorporating clinical and radiomic signature into one nomogram might reduce the robustness of the model.…”

Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children

“…Six of the 10 features extracted in the present study belong to spatial gray level co-occurrence matrix (GLCM) features. Previous studies have shown that GLCM features are helpful in the pretreatment prediction of pathological complete response to no special type(NST) in breast cancer [ 27 ]. GLSZM texture features have been useful in differentiating between two different tumors in studies using CT imaging omics to distinguish between pelvic rhabdomyosarcoma and yolk cystoma in children [ 28 ].…”