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Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

Matthias Dietzel, Rüdiger Schulz‐Wendtland, Stephan Ellmann, R Zoubi, Evelyn Wenkel, Matthias Hammon, Paola Clauser, Michael Uder, Ingo B. Runnebaum, Pascal Baltzer

2020Scientific Reports22 citationsDOIOpen Access PDF

Abstract

Abstract To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPI VAV . Prediction of DSD by NPI VAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell’s C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell’s C = 75.3%) was enhanced by VAV (NPI VAV : Harrell’s C = 81.0%). Most of all, the NPI VAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a “gate keeper” in the management of breast cancer patients. Such a “gate keeper” could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.

Topics & Concepts

Breast cancerMedicineHazard ratioOncologyInternal medicineConfidence intervalProportional hazards modelCancerBreast MRINottingham Prognostic IndexMammographyRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisBreast Lesions and Carcinomas
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