The Attenuation Distribution Across the Long Axis (ADLA): Preliminary Findings for Assessing Response to Cancer Treatment

Abstract

Rationale and objectives: Novel image analysis methods may be useful adjuncts to standard cancer treatment response assessment techniques. The attenuation distribution across the long axis (ADLA) is a simple measure of lesion heterogeneity that can be obtained while measuring the long axis diameter of a target lesion. The purpose of this study was to obtain preliminary validation of the ADLA method for predicting treatment response in a small clinical trial.

Materials and methods: Under an Institutional Review Board waiver, we obtained de-identified imaging and clinical data from a phase 2 trial of an investigational anticancer therapy at our institution. We retrospectively analyzed all patients with at least one liver metastasis measuring ≥15 mm on baseline contrast-enhanced computed tomography. For each patient at every imaging time point, up to two target liver lesions were evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and ADLA measurements. The ADLA was obtained as the standard deviation of the post-contrast computed tomography attenuation values in the portal venous phase across a linear function spanning the long-axis diameter. Using Kaplan-Meier survival analysis, the log-rank test was used to evaluate the ability of RECIST 1.1 and ADLA measurements to discriminate patients with longer overall survival (OS).

Results: Fifteen patients met inclusion criteria. Median survival was 149 days (range 57-487). Best overall response by the ADLA method successfully separated patients with longer OS (p = .04). Best overall response by RECIST 1.1 did not discriminate patients with longer survival (P > .05).

Conclusion: In retrospective data analysis from a phase 2 clinical trial, the ADLA method was more predictive of OS than RECIST 1.1. Further studies are needed to explore the utility of this measurement in predicting response to cancer treatment.

Keywords: Imaging biomarker; bevacizumab; cancer imaging; overall survival; quantitative imaging; response assessment.