A Lesion-Based Response Prediction Model Using Pretherapy PET/CT Image Features for Y90 Radioembolization to Hepatic Malignancies

• Published in: Technology in cancer research & treatment 2017-10, Vol.16 (5), p.620-629
• Main Authors: Mehta, Rahul, Cai, Kejia, Kumar, Nishant, Knuttinen, M. Grace, Anderson, Thomas M., Lu, Hui, Lu, Yang
• Format: Article
• Language: English
• Subjects: Adult; Aged; Bayesian analysis; Computed tomography; Disease Progression; Embolization, Therapeutic - methods; Female; Fluorine; Fluorodeoxyglucose F18; Glucose; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Lesions; Liver cancer; Liver Neoplasms - diagnosis; Liver Neoplasms - pathology; Liver Neoplasms - secondary; Liver Neoplasms - therapy; Male; Middle Aged; Original; Patients; Positron emission tomography; Positron Emission Tomography Computed Tomography - methods; Precision medicine; Prediction models; Prognosis; Sensitivity analysis; Tomography; Treatment Outcome; Yttrium; Yttrium Radioisotopes - administration & dosage
• ISSN: 1533-0346; 1533-0338
• DOI: 10.1177/1533034616666721

We present a probabilistic approach to identify patients with primary and secondary hepatic malignancies as responders or nonresponders to yttrium-90 radioembolization therapy. Recent advances in computer-aided detection have decreased false-negative and false-positive rates of perceived abnormalities; however, there is limited research in using similar concepts to predict treatment response. Our approach is driven by the goal of precision medicine to determine pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging parameters to facilitate the identification of patients who would benefit most from yttrium-90 radioembolization therapy, while avoiding complex and costly procedures for those who would not. Our algorithm seeks to predict a patient’s response by discovering common co-occurring image patterns in the lesions of baseline fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scans by extracting invariant shape and texture features. The extracted imaging features were represented as a distribution of each subject based on the bag-of-feature paradigm. The distribution was applied in a multinomial naive Bayes classifier to predict whether a patient would be a responder or nonresponder to yttrium-90 radioembolization therapy based on the imaging features of a pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scan. Comprehensive published criteria were used to determine lesion-based clinical treatment response based on fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging findings. Our results show that the model is able to predict a patient with liver cancer as a responder or nonresponder to yttrium-90 radioembolization therapy with a sensitivity of 0.791 using extracted invariant imaging features from the pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography test. The sensitivity increased to 0.821 when combining extracted invariant image features with variable features of tumor volume.