Automated tracking of quantitative assessments of tumor burden in clinical trials.
THERE ARE TWO KEY CHALLENGES HINDERING EFFECTIVE USE OF QUANTITATIVE ASSESSMENT OF IMAGING IN CANCER RESPONSE ASSESSMENT: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.
Department of Radiology, Stanford University, Stanford, CA ; Department of Biomedical Informatics Research, Stanford University, Stanford, CA.
Daniel L
DL
Rubin
Department of Radiology, Stanford University, Stanford, CA.
Debra
D
Willrett
Department of Radiology, Stanford University, Stanford, CA.
Martin J
MJ
O'Connor
Department of Computer Science, University of São Paulo, São Paulo, Brazil.
Cleber
C
Hage
LIPADE Laboratory (EA 2517), Université Paris Descartes, Paris, France.
Camille
C
Kurtz
Department of Computer Science, University of São Paulo, São Paulo, Brazil.
Dilvan A
DA
Moreira