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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).
Xu, Cai; Pfob, André; Mehrara, Babak J; Yin, Peimeng; Nelson, Jonas A; Pusic, Andrea L; Sidey-Gibbons, Chris.
Afiliação
  • Xu C; Section of Patient Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. cairxu@gmail.com.
  • Pfob A; MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX, USA. cairxu@gmail.com.
  • Mehrara BJ; MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Yin P; Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany.
  • Nelson JA; Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Pusic AL; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
  • Sidey-Gibbons C; Department of Plastic and Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ann Surg Oncol ; 30(12): 7046-7059, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37516723
BACKGROUND: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. PATIENTS AND METHODS: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site's data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. RESULTS: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69-0.70; improved: AUC range 0.81-0.82), sexual well-being (worsened: AUC range 0.76-0.77; improved: AUC range 0.74-0.76), and psychosocial well-being (worsened: AUC range 0.64-0.66; improved: AUC range 0.66-0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. CONCLUSIONS: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mamoplastia Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mamoplastia Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article