A Lesion-Based Response Prediction Model Using Pretherapy PET/CT Image Features for Y90 Radioembolization to Hepatic Malignancies.
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.
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.|2 Department of Bioengineering, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Rahul
R
Mehta
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.|2 Department of Bioengineering, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.|3 The Center for MR Research, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Kejia
K
Cai
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Nishant
N
Kumar
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
M Grace
MG
Knuttinen
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Thomas M
TM
Anderson
2 Department of Bioengineering, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Hui
H
Lu
1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Yang
Y
Lu
D000328:Adult; D000368:Aged; D018450:Disease Progression; D004621:Embolization, Therapeutic / Q000379:methods; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000556:secondary / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods; D011379:Prognosis; D016896:Treatment Outcome; D015021:Yttrium Radioisotopes / Q000008:administration & dosage*
D015021:Yttrium Radioisotopes; D019788:Fluorodeoxyglucose F18; C000615496:Yttrium-90
FDG PET/CT; Y90 radioembolization; liver cancer; machine vision; pretherapy; therapy response prediction