Literature DB >> 23971455

Consistency and efficiency of CT analysis of metastatic disease: semiautomated lesion management application within a PACS.

Les R Folio1, Aline Sandouk, Jiaxin Huang, Jeffrey M Solomon, Andrea B Apolo.   

Abstract

OBJECTIVE: The purpose of this study was to evaluate the success, consistency, and efficiency of a semiautomated lesion management application within a PACS in the analysis of metastatic lesions in serial CT examinations of cancer patients.
MATERIALS AND METHODS: Two observers using baseline and follow-up CT data independently reviewed 93 target lesions (17 lung, five liver, 71 lymph node) in 50 patients with either metastatic bladder or prostate cancer. The observers measured the longest axis (or short axis for lymph nodes) of each lesion and made Response Evaluation Criteria in Solid Tumors (RECIST) determinations using manual and lesion management application methods. The times required for examination review, RECIST calculations, and data input were recorded. The Wilcoxon signed rank test was used to assess time differences, and Bland-Altman analysis was used to assess interobserver agreement within the manual and lesion management application methods. Percentage success rates were also reported.
RESULTS: With the lesion management application, most lung and liver lesions were semiautomatically segmented. Comparison of the lesion management application and manual methods for all lesions showed a median time saving of 45% for observer 1 (p<0.05) and 28% for observer 2 (p=0.05) on follow-up scans versus 28% for observer 1 (p<0.05) and 9% for observer 2 (p=0.087) on baseline scans. Variability of measurements showed mean percentage change differences of only 8.9% for the lesion management application versus 26.4% for manual measurements.
CONCLUSION: With the lesion management application method, most lung and liver lesions were successfully segmented semiautomatically; the results were more consistent between observers; and assessment of tumor size was faster than with the manual method.

Entities:  

Mesh:

Year:  2013        PMID: 23971455      PMCID: PMC6771287          DOI: 10.2214/AJR.12.10136

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  11 in total

Review 1.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

Authors:  Awais Mansoor; Ulas Bagci; Brent Foster; Ziyue Xu; Georgios Z Papadakis; Les R Folio; Jayaram K Udupa; Daniel J Mollura
Journal:  Radiographics       Date:  2015 Jul-Aug       Impact factor: 5.333

Review 2.  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

3.  Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.

Authors:  Huy M Do; Lillian G Spear; Moozhan Nikpanah; S Mojdeh Mirmomen; Laura B Machado; Alexandra P Toscano; Baris Turkbey; Mohammad Hadi Bagheri; James L Gulley; Les R Folio
Journal:  Acad Radiol       Date:  2020-01       Impact factor: 3.173

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

Authors:  Laura B Machado; Andrea B Apolo; Seth M Steinberg; Les R Folio
Journal:  AJR Am J Roentgenol       Date:  2017-02       Impact factor: 3.959

5.  Resources Required for Semi-Automatic Volumetric Measurements in Metastatic Chordoma: Is Potentially Improved Tumor Burden Assessment Worth the Time Burden?

Authors:  Kathleen E Fenerty; Nicholas J Patronas; Christopher R Heery; James L Gulley; Les R Folio
Journal:  J Digit Imaging       Date:  2016-06       Impact factor: 4.056

6.  A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration.

Authors:  Maxine Tan; Zheng Li; Yuchen Qiu; Scott D McMeekin; Theresa C Thai; Kai Ding; Kathleen N Moore; Hong Liu; Bin Zheng
Journal:  IEEE Trans Med Imaging       Date:  2015-08-27       Impact factor: 10.048

7.  Rapid and Accurate MRI Segmentation of Peritumoral Brain Edema in Meningiomas.

Authors:  F Latini; E-M Larsson; M Ryttlefors
Journal:  Clin Neuroradiol       Date:  2015-11-24       Impact factor: 3.649

8.  Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST.

Authors:  Kathleen E Fenerty; Les R Folio; Nicholas J Patronas; Jennifer L Marté; James L Gulley; Christopher R Heery
Journal:  BMC Cancer       Date:  2016-08-23       Impact factor: 4.430

9.  Preparing Medical Imaging Data for Machine Learning.

Authors:  Martin J Willemink; Wojciech A Koszek; Cailin Hardell; Jie Wu; Dominik Fleischmann; Hugh Harvey; Les R Folio; Ronald M Summers; Daniel L Rubin; Matthew P Lungren
Journal:  Radiology       Date:  2020-02-18       Impact factor: 11.105

10.  18F-FDG PET Assessment of Malignant Pleural Mesothelioma: Total Lesion Volume and Total Lesion Glycolysis-The Central Role of Volume.

Authors:  James C Reynolds; Roberto Maass-Moreno; Anish Thomas; Alexander Ling; Emerson B Padiernos; Seth M Steinberg; Raffit Hassan
Journal:  J Nucl Med       Date:  2020-04-13       Impact factor: 11.082

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