Testing machine learning algorithms' performance for non-invasively identifying breast cancer molecular subtypes using BI-RADS evaluations

Özcan İ. A., Aksoy Özcan Ü., Ekemen S., Ulus S., Oyan Uluç B.

San Antonio Breast Cancer Symposium, Texas, United States Of America, 4 - 10 December 2018, vol.79, no.4, pp.5

  • Publication Type: Conference Paper / Summary Text
  • Volume: 79
  • City: Texas
  • Country: United States Of America
  • Page Numbers: pp.5
  • Acibadem Mehmet Ali Aydinlar University Affiliated: Yes


Purpose: Inter- and intra-observer variability adversely affects interpretation of highly advantageous MRI data in breast cancer diagnosis, prognosis and therapeutic decisions. Machine learning (ML) might potentially provide support to the physicians for objective decision strategies. In this work, ML methods using BI-RADS features were tested for non-invasively determining molecular subtypes of breast cancer.

Methods: In this IRB approved study, Out of 126 consecutive patients' retrospective data with written consent, 69 patients (mean±std age 48.24±11.62 and range [27-82]) with full histopathological and MRI data were selected. In surgical histopathological data ER+PR+HER2- was classified as luminal HER2(-), ER+PR+HER+ was classified as luminal HER2(+), ER-PR-HER2+ was classified as HER2(enriched) and ER-PR-HER2- was classified as triple negative. The cohort revealed 51 luminal HER2(-), 11 luminal HER2(+), 7 triple negative cases without any HER2(enriched) occurrence. DCE, DW, T2W MRI data were obtained on a 1.5T Magnetom Espree (Siemens, Erlangen, Germany) scanner which were subsequently interpreted in consensus by 2 radiologists of 15 and 10 years of experience.

Age, mass and non-mass properties, non-enhancing BI-RADS findings, ADC values obtained from radiologist drawn ROIS and kinetic curve properties were fed to 22 standard ML algorithms provided in Matlab® (Mathworks, Natick, MA, USA) as predictors for 3 categories. The algorithms were run by cross validating on 50 folds whereby reported classifier accuracy was obtained from each of the observations when in held-out fold.

Results: Out of 22 ML algorithms tested Support Vector Machine's Fine Gaussian variant, Ensemble Boosted Trees and Fine K-Nearest Neighbor resulted in 76.8%,71.0% and 53.6% accuracy with (100%, 18%, 0%), (92%, 9%, 14%) and (67%, 18%, 14%) true positive rates for predicting each histopathological category respectively. Furthermore, PCA based dimension reduction worsened the outcomes indicating high sensitivity to the feature set.

Conclusion: ML algorithms demonstrated potential as a decision support system increasing assessment objectivity with an added non-invasiveness advantage. However, overall algorithm performance indicates further studies with larger cohorts and broader feature are necessary for refinements and improving ML methodology in breast cancer imaging.