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

ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.

The Attenuation Distribution Across the Long Axis of Breast Cancer Liver Metastases at CT: A Quantitative Biomarker for Predicting Overall Survival

OBJECTIVE

The objective of our study was to compare attenuation distribution across the long-axis (ADLA) measurements, Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, and Choi criteria for predicting overall survival (OS) in patients with metastatic breast cancer treated with bevacizumab.

MATERIALS AND METHODS

We obtained HIPAA-compliant data from a prospective, multisite, phase 3 trial of bevacizumab for the treatment of metastatic breast cancer. For patients with one or more liver metastases measuring 15 mm or larger at baseline, we evaluated up to two target liver lesions using RECIST, Choi criteria, and ADLA measurements, with the latter defined as the SD of the CT attenuation values of each pixel along the tumor long-axis diameter. The optimal percentage change threshold for defining an ADLA response was computed by cross-validation analysis in a Cox model. The log-rank test was applied to evaluate RECIST, Choi criteria, and ADLA for discriminating patients with superior OS. The predictive accuracies of all three techniques were compared using Brier scores and areas under the ROC curve (AUC). All analyses were performed separately using best overall response (BOR) and response at the first follow-up time point (FU1).

RESULTS

One hundred sixty-four patients met the inclusion criteria. A 25% decrease in the ADLA measurement from baseline was the optimal ADLA response threshold for BOR and FU1. RECIST, Choi criteria, and ADLA successfully identified patients with superior OS when using BOR (RECIST, p = 0.02; Choi and ADLA, p < 0.001), but only Choi criteria and ADLA measurements were successful when using FU1 (RECIST, p = 0.43; Choi and ADLA, p < 0.001). In a direct comparison, ADLA measurements outperformed both RECIST and Choi criteria using BOR (95% CI for Brier score differences, ADLA-RECIST [-0.58 to -0.08] and ADLA-Choi [-0.55 to -0.06]; 95% CI for AUC differences, ADLA-RECIST [0.16-0.33] and ADLA-Choi [0.17-0.36]) as well as using FU1 (95% CI for Brier score differences, ADLA-RECIST [-0.77 to -0.08] and ADLA-Choi [-0.58 to -0.03]; 95% CI for AUC differences, ADLA-RECIST [0.22-0.39] and ADLA-Choi [0.01-0.22]).

CONCLUSION

ADLA measurements may be a useful noninvasive indicator of cancer treatment response. Because ADLA measurements may be extracted relatively easily using existing radiologist workflows, further investigation of the ADLA technique is warranted.