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)

被引:4
|
作者
Xu, Cai [1 ,2 ]
Pfob, Andre [2 ,3 ]
Mehrara, Babak J. [4 ]
Yin, Peimeng [5 ]
Nelson, Jonas A. [4 ]
Pusic, Andrea L. [6 ,7 ]
Sidey-Gibbons, Chris [1 ,2 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Div Internal Med, Sect Patient Ctr Analyt, Houston, TX 77030 USA
[2] Univ Texas MD Anderson Canc Ctr, INSPiRED Canc Care Integrated Syst Patient Reporte, Houston, TX 77030 USA
[3] Heidelberg Univ Hosp, Dept Obstet & Gynecol, Heidelberg, Germany
[4] Mem Sloan Kettering Canc Ctr, Dept Plast & Reconstruct Surg, New York, NY USA
[5] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN USA
[6] Harvard Med Sch, Patient Reported Outcome Value & Experience PROVE, Dept Surg, Boston, MA USA
[7] Brigham & Womens Hosp, Boston, MA USA
关键词
Machine learning; Postmastectomy breast reconstruction; PRO; QOL; PATIENT-REPORTED OUTCOMES; QUALITY; IMPACT;
D O I
10.1245/s10434-023-13971-w
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundWe 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 MethodsWe 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.ResultsOverall, 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.ConclusionsMachine 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.
引用
收藏
页码:7046 / 7059
页数:14
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