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

• Published in: Cancers 2022-12, Vol.14 (24), p.6261
• Main Authors: Tsai, Huei-Yi, Tsai, Tsung-Yu, Wu, Chia-Hui, Chung, Wei-Shiuan, Wang, Jo-Ching, Hsu, Jui-Sheng, Hou, Ming-Feng, Chou, Ming-Chung
• Format: Article
• Language: English
• Subjects: Breast cancer; Care and treatment; Cell surface; CT imaging; Datasets; Integration; Learning algorithms; Machine learning; Methods; Neoadjuvant therapy; Patient outcomes; Predictions; Radiomics; Reproducibility; Statistical analysis; Support vector machines; Tomography; Tumors
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14246261

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.