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

Abstract

Background: 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).

Methods: 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.

Results: 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).

Conclusion: 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.

Keywords: Magnetic resonance angiography; Magnetic resonance imaging; Neoadjuvant therapy; Peritumoral vessels; Triple negative breast neoplasms.

Fig. 1

Flowchart of the study population

Fig. 2

Flowchart of the tumor and peritumoral vessel segmentation procedure. Tumor and peritumoral vessel segmentations were performed on the axial maximum intensity projection (MIP) of the first postcontrast phase. After breast segmentation, the lateral breast index tumor was segmented according to the tumor location. Peritumoral vessel on the MIP image was segmented using a multiscale Hessian-based filter. Additionally, the peritumoral vasculature by algorithm segmentation was generated via the intersection of the lateral tumor breast mask and the binary vessel segmentation region after reducing small gaps and filling holes. Finally, the vessel mask was identified via manual editing

Fig. 3

An illusion of vessel segmentation. (a) The axial maximum intensity projection (MIP) image in one patient. After the anatomic breast segmentation was performed (b), the tumor laterality was segmented according to the tumor location (c). Peritumoral vessel in the MIP image were enhanced and segmented with a multiscale Hessian-based filter (d). After hole filling and intersection steps were performed, peritumoral vasculature by algorithm segmentation was identified (e). The final vessel mask was identified via manual editing (f)

Fig. 4

Examples of vessel segmentation in two representative patients are shown. The upper row shows a patient with triple-negative breast cancer (TNBC) who did not achieve a pathologic complete response (pCR); the lower row shows a patient with TNBC who achieved a pCR. (a) and (d) are maximum intensity projection (MIP) images. (b) and (e) are peritumoral vessel segmented by algorithm and intratumoral segmentation. (c) and (f) are peritumoral vessel edited by the radiologist and intratumoral segmentation

Fig. 5

Receiver operating characteristic (ROC) curves generated using the three different radiomics models in the primary (a), internal validation (b), and TCIA (c) cohorts. The models included one that only used peritumoral vessel features (green), one that only used tumor features (blue), and the best fusion model (red)