Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

Abstract

There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.

Applying oversampling before cross-validation will lead to high bias in radiomics

“…Although data leakage should always be avoided, this type of misapplication of resampling methods has seemingly been performed in a few radiomics studies [12][13][14] . Consequently, the question of the degree of bias arises, resulting in uncertainty regarding whether the results of such studies can be trusted.…”