Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer

• Published in: BMC medical imaging 2024-06, Vol.24 (1), p.136-11
• Main Authors: Xie, Tianwen, Gong, Jing, Zhao, Qiufeng, Wu, Chengyue, Wu, Siyu, Peng, Weijun, Gu, Yajia
• Format: Article
• Language: English
• Subjects: Adjuvant treatment; Adult; Aged; Algorithms; Breast cancer; Cancer; Cancer therapies; Chemoradiotherapy; Chemotherapy; Datasets; Female; Hessian matrices; Humans; Magnetic resonance angiography; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Middle Aged; Neoadjuvant Therapy; Open source software; Pathologic Complete Response; Performance evaluation; Performance prediction; Peritumoral vessels; Prediction models; Pretreatment; Python; Radiomics; Retrospective Studies; Surgery; Treatment Outcome; Triple negative breast neoplasms; Triple Negative Breast Neoplasms - diagnostic imaging; Triple Negative Breast Neoplasms - drug therapy; Triple Negative Breast Neoplasms - pathology; Triple Negative Breast Neoplasms - therapy; Tumors
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01311-7

To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.