Vol-PACT: A foundation for the NIH public-private partnership that supports sharing of clinical trial data for the development of improved imaging biomarkers in oncology Journal Article
| Authors: | Dercle, L.; Connors, D. E.; Tang, Y.; Adam, S. J.; Gönen, M.; Hilden, P.; Karovic, S.; Maitland, M.; Moskowitz, C. S.; Kelloff, G.; Zhao, B.; Oxnard, G. R.; Schwartz, L. H. |
| Article Title: | Vol-PACT: A foundation for the NIH public-private partnership that supports sharing of clinical trial data for the development of improved imaging biomarkers in oncology |
| Abstract: | Purpose To develop a public-private partnership to study the feasibility of a new approach in collecting and analyzing clinically annotated imaging data from landmark phase III trials in advanced solid tumors. Patients and Methods The collection of clinical trials fulfilled the following inclusion criteria: completed randomized trials of > 300 patients, highly measurable solid tumors (non-small-cell lung cancer, colorectal cancer, renal cell cancer, and melanoma), and required sponsor and institutional review board sign-offs. The new approach in analyzing computed tomography scans was to transfer to an academic image analysis laboratory, draw contours semi-automatically by using in-house-developed algorithms integrated into the open source imaging platform Weasis, and perform serial volumetric measurement. Results The median duration of contracting with five sponsors was 12 months. Ten trials in 7,085 patients that covered 12 treatment regimens across 20 trial arms were collected. To date, four trials in 3,954 patients were analyzed. Source imaging data were transferred to the academic core from 97% of trial patients (n = 3,837). Tumor imaging measurements were extracted from 82% of transferred computed tomography scans (n = 3,162). Causes of extraction failure were nonmeasurable disease (n = 392), single imaging time point (n = 224), and secondary captured images (n = 59). Overall, clinically annotated imaging data were extracted in 79% of patients (n = 3,055), and the primary trial end point analysis in each trial remained representative of each original trial end point. Conclusion The sharing and analysis of source imaging data from large randomized trials is feasible and offer a rich and reusable, but largely untapped, resource for future research on novel trial-level response and progression imaging metrics. © 2018 American Society of Clinical Oncology. |
| Keywords: | adult; aged; major clinical study; advanced cancer; disease marker; cancer patient; colorectal cancer; quality control; melanoma; image analysis; diagnostic imaging; automation; information processing; cancer research; renal cell carcinoma; feasibility study; algorithm; data analysis; image processing; non small cell lung cancer; volumetry; randomized controlled trial (topic); phase 3 clinical trial (topic); public-private partnership; human; male; female; priority journal; article; x-ray computed tomography; solid malignant neoplasm |
| Journal Title: | JCO Clinical Cancer Informatics |
| Volume: | 2 |
| ISSN: | 2473-4276 |
| Publisher: | American Society of Clinical Oncology |
| Date Published: | 2018-01-01 |
| Language: | English |
| DOI: | 10.1200/cci.17.00137 |
| PUBMED: | 30652552 |
| PROVIDER: | scopus |
| PMCID: | PMC6873999 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 3 February 2020 -- Source: Scopus |
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