Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning.
| Autor: | Moghadas-Dastjerdi H; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada., Rahman SH; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada., Sannachi L; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada., Wright FC; Surgical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Surgery, University of Toronto, Toronto, ON, Canada., Gandhi S; Division of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Medicine, University of Toronto, Toronto, ON, Canada., Trudeau ME; Division of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Medicine, University of Toronto, Toronto, ON, Canada., Sadeghi-Naini A; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada. Electronic address: asn@yorku.ca., Czarnota GJ; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada. Electronic address: gregory.czarnota@sunnybrook.ca. |
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| Jazyk: | angličtina |
| Zdroj: | Translational oncology [Transl Oncol] 2021 Oct; Vol. 14 (10), pp. 101183. Date of Electronic Publication: 2021 Jul 19. |
| DOI: | 10.1016/j.tranon.2021.101183 |
| Abstrakt: | Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC (Copyright © 2021. Published by Elsevier Inc.) |
| Databáze: | MEDLINE |
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