Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Abstract

Purpose: This study aimed to create a nomogram model (NM) that combines clinical-radiological factors with radiomics features of both intra- and peritumoral regions extracted from pretherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, in order to establish a reliable method for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Methods: A total of 214 patients were randomly divided into a training set (n=149) and a test set (n=65) in a ratio of 7:3. Radiomics features were extracted from intratumoral region and 2-mm, 4-mm, 6-mm, 8-mm peritumoral regions on DCE-MRI images, and selected the optimal peritumoral region. The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. Independent risk factors for clinical-radiological features were obtained by univariate and multivariate logistic regression analysis, and clinical model (CM) was constructed. Finally, the CIPRM Rad-score combined with clinical-radiological factors was used to construct a NM. The performance of different models were evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).

Results: In our study, the 6-mm peritumoral size was considered to be the optimal peritumoral region. The CM is constructed based on three independent risk factors: estrogen receptor (ER), Ki-67, and breast edema score (BES). Incorporating ER, Ki-67, BES, and CIPRM Rad-score (combined intra- and 6-mm peritumoral) into the nomogram achieved a reliable predictive performance. And the area under the curve (AUC), sensitivity, specificity, and accuracy of the NM was 0.911, 0.848, 0.831, 0.826 for the training set and 0.897, 0.893, 0.784, 0.815 for the test set, respectively.

Conclusion: The NM has a good value for early prediction of pCR after NAC in breast cancer patients.

Keywords: breast cancer; intratumoral; neoadjuvant chemotherapy; nomogram; pathological complete response; peritumoral; radiomics.

Figure 1

Workflow chart of the patients included in this study.

Figure 2

Schematic illustration of intratumoral and peritumoral ROI segmentation. The subject, a 49-year-old female diagnosed with invasive carcinoma in the right breast, exhibited non-pCR following NAC. (A) Maximal cross-sectional of tumor as observed on the second phase DCE-MRI image. (B-E) The red area is the intratumoral ROI, and the corresponding peritumoral ROIs were obtained by intratumoral ROI (red) automatic expansions by 2-mm (yellow), 4-mm (blue), 6-mm (purple) and 8-mm (green). (F) The 3D visualization intratumoral ROI with layer-by-layer delineation.

Figure 3

The workflow for the radiomics analysis.

Figure 4

The nomogram was developed based on ER, Ki-67, BES and CIPRM Rad-score.

Figure 5

ROC curves of five models in the training set (A) and test sets (B). IRM, intratumoral radiomics model; PRM, 6-mm peritumoral radiomics model; CIPRM, combined intra- and 6-mm peritumoral radiomics model; CM, clinical-radiological model; NM, nomogram model.

Figure 6

Calibration curves of the nomogram model in the training (A) and test sets (B).

Figure 7

Decision curves of five models in the training set (A) and test sets (B).IRM, intratumoral radiomics model; PRM, 6-mm peritumoral radiomics model; CIPRM, combined intra- and 6-mm peritumoral radiomics model; CM, clinical-radiological model; NM, nomogram model.