This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib.
In this institutional review board–approved, Health Insurance Portability and Accountability Act–compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest–based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods.
When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP.
Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.
From the Departments of *Imaging Physics,
§Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Received for publication September 7, 2018; accepted November 5, 2018.
Correspondence to: Khaled M. Elsayes, MD, Department of Radiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030 (e-mail: email@example.com).
This study was funded in part by the O'Donnell Foundation and NIH DP2OD007044-01S1 funding mechanisms.
This article does not contain any studies with human participants or animals performed by any of the authors.
The authors declare no conflict of interest.