ASO Author Reflections: Enhancing Surgical Decision-Making for Breast Reconstruction—Machine Learning-Driven Prediction of Postoperative Quality of Life
Author:
Funder
National Cancer Institute
National Cancer Institute Support Grant
Publisher
Springer Science and Business Media LLC
Subject
Oncology,Surgery
Link
https://link.springer.com/content/pdf/10.1245/s10434-023-14008-y.pdf
Reference5 articles.
1. Pusic AL, Matros E, Fine N, et al. Patient-reported outcomes 1 year after immediate breast reconstruction: results of the mastectomy reconstruction outcomes consortium study. J Clin Oncol. 2017;35(22):2499–506. https://doi.org/10.1200/JCO.2016.69.9561.
2. Xu C, Subbiah IM, Lu SC, Pfob A, Sidey-Gibbons C. Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data. Qual Life Res. 2023;32(3):713–27. https://doi.org/10.1007/s11136-022-03284-y.
3. Pfob A, Sidey-Gibbons C, Lee HB, et al. Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy. Eur J Cancer. 2021;143(565):134–46. https://doi.org/10.1016/j.ejca.2020.11.006.
4. Xu C, Pfob A, Mehrara BJ, et al. Enhanced surgical decision-making tools in breast cancer: predicting 2-year postoperative physical, sexual, and psychosocial well-being following mastectomy and breast reconstruction (INSPiRED 004). Ann Surg Oncol. 2023. https://doi.org/10.1245/s10434-023-13971-w.
5. Pfob A, Sidey-Gibbons C. Systematic bias in medical algorithms: to include or not include discriminatory demographic information? JCO Clin Cancer Informatics. 2022;6:1–2. https://doi.org/10.1200/cci.21.00146.
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