Whole tumour- and subregion-based radiomics of contrast-enhanced mammography in differentiating HER2 expression status of invasive breast cancers: A double-centre pilot study
Menée auprès de 352 patientes ayant bénéficié d'une mammographie préopératoire avec rehaussement de contraste (âge moyen : 51,1 ans), cette étude met en évidence l'intérêt des caractéristiques radiomiques de l'hétérogénéité intratumorale de l'ensemble de la tumeur et de ses sous-régions pour déterminer le statut HER2 des tumeurs invasives
Objectives : To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers.
Methods : 352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models.
Results : The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73–0.86), 0.79 ± 0.06 (95% CI: 0.67–0.90), and 0.79 ± 0.05 (95% CI: 0.69–0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts.
Conclusions : Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model’s performance.
British Journal of Cancer , résumé, 2024