Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response

TL;DR

This study introduces a novel blended PET-CT registration method to accurately quantify local metabolic tumor volume changes, improving prediction of tumor response after chemo-radiotherapy in esophageal cancer.

Contribution

The paper presents a new blended PET-CT registration approach that outperforms traditional methods in measuring tumor volume changes and predicting treatment response.

Findings

Blended PET-CT registration correlates better with ground truth (R=0.88) than PET-PET or CT-CT methods.

The proposed method achieves 82.3% accuracy in predicting tumor response using a single radiomic feature.

The framework can replace conventional CT-based registration for longitudinal tumor response evaluation.

Abstract

Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (soft-tissue) cancer are measured by registering follow-up PET to baseline PET using the same transformation obtained by deformable registration of follow-up CT to baseline CT. Such approach is suboptimal because PET and CT capture fundamentally different properties (metabolic vs. anatomy) of a tumor. In this work we combined PET and CT images into a single blended PET-CT image and registered follow-up blended PET-CT image to baseline blended PET-CT image. B-spline regularized diffeomorphic registration was used to characterize the large MTV shrinkage. Jacobian of the resulting transformation was computed to measure the local MTV changes. Radiomic features (intensity and texture) were then extracted from the Jacobian map to predict pathologic tumor response. Local MTV changes calculated using blended PET-CT registration achieved the highest correlation with ground truth segmentation (R=0.88) compared to PET-PET (R=0.80) and CT-CT (R=0.67) registrations. Moreover, using blended PET-CT registration, the multivariate prediction model achieved the highest accuracy with only one Jacobian co-occurrence texture feature (accuracy=82.3%). This novel framework can replace the conventional approach that applies CT-CT transformation to the PET data for longitudinal evaluation of tumor response.