A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

Radiol Med. 2024 Jun;129(6):864-878. doi: 10.1007/s11547-024-01817-8. Epub 2024 May 17.

Abstract

Objective: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.

Methods: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered.

Results: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set.

Conclusions: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Keywords: Breast cancer classification and prediction; Contrast-enhanced mammography; Machine learning; Radiomics.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Aged
  • Artificial Intelligence*
  • Breast Neoplasms* / diagnostic imaging
  • Breast Neoplasms* / pathology
  • Contrast Media*
  • Female
  • Humans
  • Italy
  • Mammography* / methods
  • Middle Aged
  • Neoplasm Grading
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Radiomics
  • Receptor, ErbB-2
  • Sensitivity and Specificity

Substances

  • Contrast Media
  • Receptor, ErbB-2