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Towards quality control and harmonization of deep learning CT radiomics: An in-silico feasibility study with virtual colorectal liver metastases.

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Title: Towards quality control and harmonization of deep learning CT radiomics: An in-silico feasibility study with virtual colorectal liver metastases.
Authors: Venugopal M; Technology & Innovation Center, GE HealthCare, JFWTC, Bengaluru, Karnataka, India., Ramani S; Technology & Innovation Center, GE HealthCare, Niskayuna, New York, USA., Peoples JJ; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Do RKG; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Simpson AL; School of Computing/Department of Biomedical and Molecular Sciences, Queen's University, Ontario, Canada., Wang G; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA., De Man B; Technology & Innovation Center, GE HealthCare, Niskayuna, New York, USA.
Source: Medical physics [Med Phys] 2026 May; Vol. 53 (5), pp. e70500.
Publication Type: Journal Article
Journal Info: Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE
Database: MEDLINE Ultimate
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              Text: 2026 May
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