Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs). (March 2022)

• Record Type: Journal Article
• Authors: Sanli, I.; Osong, B.; Dekker, A.; TerHaag, K.; van Kuijk, S.M.J.; van Soest, J.; Wee, L.; Willems, P.C.
• Is Part Of: Clinical and translational radiation oncology. Volume 33(2022)
• Page Start: 57
• Page End: 65
• Publication Date: 2022-03
• Subjects: Spinal bone metastases -- SBM -- Radiomics -- Predictive model; Cancer -- Radiotherapy -- Periodicals; Oncology -- Periodicals; Cancer -- Radiotherapy; Oncology; Radiation Oncology; Neoplasms -- radiotherapy; Translational Medical Research; Periodicals; Electronic journals; Periodicals; 616.9940642
• DOI: 10.1016/j.ctro.2021.12.011
• Languages: English
• ISSNs: 2405-6308

Abstract

Highlights: Prediction of survival is crucial for guiding patient-tailored treatment. Radiomics can be described as the next era of possibilities in precision medicine. Radiomics model had an inferior performance with no added predictive power to the clinical predictive model. Abstract: Study design: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. Objective: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). Background data: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. Methods: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model. A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. Results: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability.