Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods
The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment.
Authors
- Belinda Curpen (ORCID: https://orcid.org/0000-0003-3271-8087)
- Nicole Look-Hong
- Michael C. Kolios (ORCID: https://orcid.org/0000-0002-9994-8293)
- Laurentius O. Osapoetra (ORCID: https://orcid.org/0000-0001-9278-0953)
- Sonal Gandhi (ORCID: https://orcid.org/0000-0001-8993-1327)
- Archya Dasgupta (ORCID: https://orcid.org/0000-0001-8341-2622)
- Frances C. Wright (ORCID: https://orcid.org/0000-0003-1904-4997)
- Maureen Trudeau (ORCID: https://orcid.org/0000-0002-0938-4499)
- Schontal Halstead
- Andrea Eisen (ORCID: https://orcid.org/0000-0001-8037-9556)
- Gregory J. Czarnota (ORCID: https://orcid.org/0000-0002-0519-2182)
- David Alberico
- Amir Moslemi (ORCID: https://orcid.org/0000-0003-0849-1943)
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Toronto (CA)
- Health Sciences Centre (CA)
- Sunnybrook Research Institute
- Toronto Metropolitan University (CA)
Publication Details
- Journal
- Tomography
- Published
- 2025-03-13
- DOI
- https://doi.org/10.3390/tomography11030033
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Natural Sciences and Engineering Research Council of Canada