Opening book details…
Can I read Deep Learning Prediction of Pathological Complete Response, Residual Cancer Burden, and Progression-free Survival in Breast Cancer Patients on EtoBox?
Deep Learning Prediction of Pathological Complete Response, Residual Cancer Burden, and Progression-free Survival in Breast Cancer Patients by Dammu, Hongyi (author);Ren, Thomas (author);Duong, Tim Q. (author);Saha, Sriparna (editor) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Deep Learning Prediction of Pathological Complete Response, Residual Cancer Burden, and Progression-free Survival in Breast Cancer Patients about?
The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs. In the I-SPY-1 TRIAL, 155 patients with stage 2 or 3 breast cancer with breast tumors underwent neoadjuvant chemotherapy met the inclusion/exclusion criteria. The inputs were dynamic-contrast-enhanced (DCE) MRI, and T2- weighted MRI as three-dimensional whole-images without the tumor segmentation, as well as molecular subtypes and demographics. The outcomes were PCR, RCB, and PFS. Three (“Integrated”, “Stack” and “Concatenation”) CNN were evaluated using receiver-operating characteristics and mean absolute errors. The Integrated approach outperformed the “Stack” or “Concatenation” CNN. Inclusion of both MRI and non-MRI data outperformed either alone. The combined pre- and post-neoadjuvant chemotherapy data outperformed either alone. Using the best model and data combination, PCR prediction yielded an accuracy of 0.81±0.03 and AUC
Who reads Deep Learning Prediction of Pathological Complete Response, Residual Cancer Burden, and Progression-free Survival in Breast Cancer Patients?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Dammu, Hongyi (author);Ren, Thomas (author);Duong, Tim Q. (author);Saha, Sriparna (editor)
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)