Changes in CT radiomic features predict survival and early response to CDK 4/6 inhibitors in hormone receptor positive metastatic breast cancer

Abstract

Abstract Background The combination of Cyclin-dependent kinase 4/6 inhibitors (CDKi) and endocrine therapy (ET) is the standard of care for hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC). Currently, there are no robust biomarkers that can predict response to CDKi, and so far, it is not clear which patients benefit from this therapy. Because MBC patients with liver metastases have a poorer prognosis and are consequently often treated with chemotherapy rather than endocrine therapy, developing predictive biomarkers that could identify patients likely to respond to CDKi is clinically important. We hypothesized that changes (“delta”) in the radiomic texture patterns on CT scans both within and outside metastatic liver lesions before and after CDKi therapy are associated with overall survival (OS) and can accurately assess early response to CDKi therapy. Methods From a retrospective patient registry, 73 HR + MBC patients with known liver metastases who received palbociclib (palbo) plus ET were identified from two different academic sites. One site was used as a training set (n = 32), while the other as a validation set (n = 41). Patients with objective response/stable disease per RECIST v1.1 were defined as ‘responders’, and those with progressive disease within 6 months were labelled ‘non-responders’. Radiomic texture and shape features measuring subtle differences in lesion heterogeneity and size on a pixel level were extracted from pre-treatment and post-treatment CT scans within the lesions measured for RECIST assessment, and the difference (delta) radiomic features were computed. Delta radiomic features were selected by least absolute shrinkage and selection operator with the Cox regression model within the training set and top selected features along with their corresponding coefficients were used for radiomic risk score (RRS) construction. The RRS was further evaluated for association with OS within the validation set. Patients were stratified into low and high-risk groups based on an ideal threshold of RRS identified in the training set and the association of RRS with OS was assessed with a log-rank test, Hazard ratio (HR (95% CI), and Harrell’s concordance index (C-index). In addition, a linear discriminant analysis (LDA) classifier was trained with identified features to predict RECIST-derived response in the validation set. Results RRS was found to be significantly associated with OS in training (HR: 2.9; 95% CI, 1.6–5.5; P = 0.0006; C-index = 0.82) and validation sets (HR: 2.4; 95% CI, 1.06–5.6; P = 0.035; C-index = 0.77). Median OS times in high and low-risk groups were 12.58 and 23.17 months, respectively (P = 5.7e-04). Compared to RECIST response in the training and validation set, delta radiomic features were able to assess early response with a ROC curve AUC of 0.74 and 0.72, respectively. Conclusions Delta radiomics analysis can reasonably predict response and survival in HR+/HER2- MBC patients treated with CDKi in combination with endocrine therapy.