Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach
Random forests modeling based on combined radiomics and clinical characteristics of the pretreatment tumor images has the ability to predict the response or non-response to neoadjuvant treatment in LARC to an acceptable extent.
Authors
- Reza Paydar (ORCID: https://orcid.org/0000-0003-4296-1638)
- Dariush Moslemi (ORCID: https://orcid.org/0000-0002-4390-5606)
- Hamid Fallah Tafti (ORCID: https://orcid.org/0000-0002-8515-410X)
- Reza Reiazi (ORCID: https://orcid.org/0000-0003-1323-8942)
- Seyyed Hossein Mousavie Anijdan (ORCID: https://orcid.org/0000-0003-3862-8613)
- Ali Akbar Moghadamnia (ORCID: https://orcid.org/0000-0003-0100-978X)
Institutions
- Iran University of Medical Sciences (IR)
- Babol University of Medical Sciences (IR)
Publication Details
- Journal
- Journal of Medical Signals & Sensors
- Published
- 2024-12-01
- DOI
- https://doi.org/10.4103/jmss.jmss_46_23
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Iran University of Medical Sciences
- Babol University of Medical Sciences