Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma
This study demonstrates the superiority of AI-driven preprocessing techniques, such as SMORE, in improving MRI radiomic models, paving the way for enhanced clinical decision-making in PTC management.
Authors
- Robert Michael Tuttle (ORCID: https://orcid.org/0000-0001-9899-9702)
- Ramesh Paudyal (ORCID: https://orcid.org/0000-0003-0302-211X)
- Nora Katabi (ORCID: https://orcid.org/0000-0003-2974-3046)
- Vaios Hatzoglou (ORCID: https://orcid.org/0000-0003-0252-0694)
- Ashok R. Shaha (ORCID: https://orcid.org/0000-0001-7478-9436)
- Amita Shukla‐Dave (ORCID: https://orcid.org/0000-0001-7456-3197)
- Lawrence H. Schwartz
- B. Zhao (ORCID: https://orcid.org/0000-0001-9212-0293)
- Akash Shah (ORCID: https://orcid.org/0000-0001-9211-8628)
- Aditya Apte (ORCID: https://orcid.org/0000-0001-8120-0669)
- Richard J. Wong (ORCID: https://orcid.org/0000-0001-6259-7314)
- Abdalla Ibrahim (ORCID: https://orcid.org/0000-0003-4138-5755)
Institutions
- Memorial Sloan Kettering Cancer Center (US)
Publication Details
- Journal
- BJR|Artificial Intelligence
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/bjrai/ubaf006
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Institutes of Health
- National Cancer Institute