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Erschienen in:
05.09.2019 | Clinical Investigation
CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept
Erschienen in: CardioVascular and Interventional Radiology | Ausgabe 12/2019
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Introduction
To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.
Materials and Methods
This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features.
Results
Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024).
Discussion
Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.