Literature DB >> 24772204

Automated tracking of quantitative assessments of tumor burden in clinical trials.

Daniel L Rubin1, Debra Willrett2, Martin J O'Connor2, Cleber Hage3, Camille Kurtz4, Dilvan A Moreira3.   

Abstract

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.

Entities:  

Year:  2014        PMID: 24772204      PMCID: PMC3998692          DOI: 10.1593/tlo.13796

Source DB:  PubMed          Journal:  Transl Oncol        ISSN: 1936-5233            Impact factor:   4.243


  49 in total

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2.  Informatics methods to enable sharing of quantitative imaging research data.

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Journal:  Magn Reson Imaging       Date:  2012-07-06       Impact factor: 2.546

3.  Re: New guidelines to evaluate the response to treatment in solid tumors (ovarian cancer).

Authors:  Peyton T Taylor; Doris Haverstick
Journal:  J Natl Cancer Inst       Date:  2005-01-19       Impact factor: 13.506

Review 4.  Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives.

Authors:  James P B O'Connor; Alan Jackson; Marie-Claude Asselin; David L Buckley; Geoff J M Parker; Gordon C Jayson
Journal:  Lancet Oncol       Date:  2008-08       Impact factor: 41.316

5.  End points and United States Food and Drug Administration approval of oncology drugs.

Authors:  John R Johnson; Grant Williams; Richard Pazdur
Journal:  J Clin Oncol       Date:  2003-04-01       Impact factor: 44.544

Review 6.  Critical issues in response evaluation on computed tomography: lessons from the gastrointestinal stromal tumor model.

Authors:  Haesun Choi
Journal:  Curr Oncol Rep       Date:  2005-07       Impact factor: 5.075

7.  Tool support to enable evaluation of the clinical response to treatment.

Authors:  Mia A Levy; Daniel L Rubin
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

8.  Radiological interpretation 2020: toward quantitative image assessment.

Authors:  John M Boone
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

9.  New staging and response criteria for non-Hodgkin lymphoma and Hodgkin lymphoma.

Authors:  Bruce D Cheson
Journal:  Radiol Clin North Am       Date:  2008-03       Impact factor: 2.303

10.  Imaging response assessment in oncology.

Authors:  S D Curran; A U Muellner; L H Schwartz
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

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  28 in total

1.  Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound.

Authors:  Alfiia Galimzianova; Sean M Siebert; Aya Kamaya; Terry S Desser; Daniel L Rubin
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

2.  Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.

Authors:  Nathaniel C Swinburne; David Mendelson; Daniel L Rubin
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

3.  Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

Authors:  Imon Banerjee; Sadhika Malladi; Daniela Lee; Adrien Depeursinge; Melinda Telli; Jafi Lipson; Daniel Golden; Daniel L Rubin
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-02

Review 4.  Multimedia-enhanced Radiology Reports: Concept, Components, and Challenges.

Authors:  Les R Folio; Laura B Machado; Andrew J Dwyer
Journal:  Radiographics       Date:  2018 Mar-Apr       Impact factor: 5.333

5.  Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study.

Authors:  Shaimaa Bakr; Sebastian Echegaray; Rajesh Shah; Aya Kamaya; John Louie; Sandy Napel; Nishita Kothary; Olivier Gevaert
Journal:  J Med Imaging (Bellingham)       Date:  2017-08-21

6.  [Mesenchymal abdominal tumors].

Authors:  T Helmberger
Journal:  Radiologe       Date:  2018-01       Impact factor: 0.635

7.  LesionTracker: Extensible Open-Source Zero-Footprint Web Viewer for Cancer Imaging Research and Clinical Trials.

Authors:  Trinity Urban; Erik Ziegler; Rob Lewis; Chris Hafey; Cheryl Sadow; Annick D Van den Abbeele; Gordon J Harris
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

8.  Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.

Authors:  E F Luque; N Miranda; D L Rubin; D A Moreira
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

Review 9.  Methods and challenges in quantitative imaging biomarker development.

Authors:  Richard G Abramson; Kirsteen R Burton; John-Paul J Yu; Ernest M Scalzetti; Thomas E Yankeelov; Andrew B Rosenkrantz; Mishal Mendiratta-Lala; Brian J Bartholmai; Dhakshinamoorthy Ganeshan; Leon Lenchik; Rathan M Subramaniam
Journal:  Acad Radiol       Date:  2015-01       Impact factor: 3.173

Review 10.  Quantitative Imaging in Cancer Clinical Trials.

Authors:  Thomas E Yankeelov; David A Mankoff; Lawrence H Schwartz; Frank S Lieberman; John M Buatti; James M Mountz; Bradley J Erickson; Fiona M M Fennessy; Wei Huang; Jayashree Kalpathy-Cramer; Richard L Wahl; Hannah M Linden; Paul E Kinahan; Binsheng Zhao; Nola M Hylton; Robert J Gillies; Laurence Clarke; Robert Nordstrom; Daniel L Rubin
Journal:  Clin Cancer Res       Date:  2016-01-15       Impact factor: 12.531

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