Comparative Effectiveness of Tumor Response Assessment Methods: Standard of Care Versus Computer-Assisted Response Evaluation.

Abstract

PURPOSE:

To compare the effectiveness of metastatic tumor response evaluation with computed tomography using computer-assisted versus manual methods.

MATERIALS AND METHODS:

In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, 11 readers from 10 different institutions independently categorized tumor response according to three different therapeutic response criteria by using paired baseline and initial post-therapy computed tomography studies from 20 randomly selected patients with metastatic renal cell carcinoma who were treated with sunitinib as part of a completed phase III multi-institutional study. Images were evaluated with a manual tumor response evaluation method (standard of care) and with computer-assisted response evaluation (CARE) that included stepwise guidance, interactive error identification and correction methods, automated tumor metric extraction, calculations, response categorization, and data and image archiving. A crossover design, patient randomization, and 2-week washout period were used to reduce recall bias. Comparative effectiveness metrics included error rate and mean patient evaluation time.

RESULTS:

The standard-of-care method, on average, was associated with one or more errors in 30.5% (6.1 of 20) of patients, whereas CARE had a 0.0% (0.0 of 20) error rate ( P < .001). The most common errors were related to data transfer and arithmetic calculation. In patients with errors, the median number of error types was 1 (range, 1 to 3). Mean patient evaluation time with CARE was twice as fast as the standard-of-care method (6.4 minutes v 13.1 minutes; P < .001).

CONCLUSION:

CARE reduced errors and time of evaluation, which indicated better overall effectiveness than manual tumor response evaluation methods that are the current standard of care.

• Authors: Brian C Allen, Edward Florez, Reza Sirous, Seth T Lirette, Michael Griswold, Erick M Remer, Zhen J Wang, Jacob E Bieszczad, Kelly L Cox, Ajit H Goenka, Candace M Howard-Claudio, Hyunseon C Kang, Sadhna B Nandwana, Rupan Sanyal, Atul B Shinagare, J Clark Henegan, Judd Storrs, Matthew S Davenport, Balaji Ganeshan, Amit Vasanji, Brian Rini, Andrew D Smith
• Journal: JCO clinical cancer informatics (JCO Clin Cancer Inform) Vol. 1 Pg. 1-16 (11 2017) ISSN: 2473-4276 [Electronic] United States
• PMID: 30657391 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Aged
• Clinical Trials, Phase III as Topic
• Female
• Humans
• Image Processing, Computer-Assisted
• Male
• Medical Informatics (methods)
• Medical Oncology (methods)
• Middle Aged
• Multicenter Studies as Topic
• Neoplasms (diagnosis, therapy)
• Observer Variation
• Quality Assurance, Health Care (methods)
• Standard of Care
• Surveys and Questionnaires
• Tomography, X-Ray Computed (methods)
• Treatment Outcome