Radiology Reports With Hyperlinks Improve Target Lesion Selection and Measurement Concordance in Cancer Trials.

OBJECTIVE Radiology reports often lack the measurements of target lesions that are needed for oncology clinical trials. When available, the measurements in the radiology reports often do not match those in the records used to calculate therapeutic response. This study assessed the clinical value of hyperlinked tumor measurements in multimedia-enhanced radiology reports in the PACS and the inclusion of a radiologist assistant in the process of assessing tumor burden. MATERIALS AND METHODS We assessed 489 target lesions in 232 CT examinations of 71 patients with metastatic genitourinary cancer enrolled in two therapeutic trials. We analyzed target lesion selection and measurement concordance between oncology records (used to calculate therapeutic response) and two types of radiology reports in the PACS: multimedia-enhanced radiology reports and text-only reports. For statistical tests, we used the Wilcoxon signed rank, Wilcoxon rank sum test, and Fisher method to combine p values from the paired and unpaired results. The Fisher exact test was used to compare overall measurement concordance. RESULTS Concordance on target lesion selection was greater for multimedia-enhanced radiology reports (78%) than the text-only reports (52%) (p = 0.0050). There was also improved overall measurement concordance with the multimedia-enhanced radiology reports (68%) compared with the text-only reports (38%) (p < 0.0001). CONCLUSION Compared with text-only reports, hyperlinked multimedia-enhanced radiology reports improved concordance of target lesion selection and measurement with the measurements used to calculate therapeutic response.

[1]  Les R Folio,et al.  Consistency and efficiency of CT analysis of metastatic disease: semiautomated lesion management application within a PACS. , 2013, AJR. American journal of roentgenology.

[2]  S Saini,et al.  Tumour size measurement in an oncology clinical trial: comparison between off-site and on-site measurements. , 2003, Clinical radiology.

[3]  Vahid Yaghmai,et al.  Radiologic assessment of response to therapy: comparison of RECIST Versions 1.1 and 1.0. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[4]  Tracy A Jaffe,et al.  Quantitative imaging in oncology patients: Part 2, oncologists' opinions and expectations at major U.S. cancer centers. , 2010, AJR. American journal of roentgenology.

[5]  Daniel L. Rubin,et al.  The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model , 2014, Journal of Digital Imaging.

[6]  K. Hopper,et al.  Analysis of interobserver and intraobserver variability in CT tumor measurements. , 1996, AJR. American journal of roentgenology.

[7]  C. Latulipe,et al.  Primary Care Providers’ Views of Patient Portals: Interview Study of Perceived Benefits and Consequences , 2016, Journal of medical Internet research.

[8]  D J Hawkes,et al.  Algorithms for radiological image registration and their clinical application , 1998, Journal of anatomy.

[9]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[10]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[11]  Menashe Benjamin,et al.  Quantitative Radiology Reporting in Oncology: Survey of Oncologists and Radiologists. , 2015, AJR. American journal of roentgenology.

[12]  Cristine Kao,et al.  Traditional text-only versus multimedia-enhanced radiology reporting: referring physicians' perceptions of value. , 2015, Journal of the American College of Radiology : JACR.

[13]  H. Hricak,et al.  Intra- and interobserver variability in CT measurements in oncology. , 2013, Radiology.

[14]  Merlijn Sevenster,et al.  Improved efficiency in clinical workflow of reporting measured oncology lesions via PACS-integrated lesion tracking tool. , 2015, AJR. American journal of roentgenology.

[15]  J. Peters,et al.  Preferences for structured reporting of measurement data: an institutional survey of medical oncologists, oncology registrars, and radiologists. , 2014, Academic radiology.

[16]  Les R Folio,et al.  Automated registration, segmentation, and measurement of metastatic melanoma tumors in serial CT scans. , 2013, Academic radiology.

[17]  E. Burnside,et al.  Toward best practices in radiology reporting. , 2009, Radiology.

[18]  Tracy A Jaffe,et al.  Quantitative imaging in oncology patients: Part 1, radiology practice patterns at major U.S. cancer centers. , 2010, AJR. American journal of roentgenology.