Abstract

Contrary to national guidelines, women with ovarian cancer often receive treatment at the end of life, potentially due to the difficulty in accurately estimating prognosis. We trained machine learning algorithms to guide prognosis by predicting 180-day mortality for women with ovarian cancer using patient-reported outcomes (PRO) data. We collected data from a single academic cancer institution in the United States. Women completed biopsychosocial PRO measures every 90 days. We randomly partitioned our dataset into training and testing samples. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into an unweighted voting ensemble. We assessed each algorithm's accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) using testing data. We recruited 245 patients who completed 1319 PRO assessments. The final voting ensemble produced state-of-the-art results on the task of predicting 180-day mortality for ovarian cancer paitents (Accuracy = 0.79, Sensitivity = 0.71, Specificity = 0.80, AUROC = 0.76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment. Machine learning algorithms trained using PRO data offer encouraging performance in predicting whether a woman with ovarian cancer will die within 180 days. This model could be used to drive data-driven end-of-life care and address current shortcomings in care delivery. Our model demonstrates the potential of biopsychosocial PROM information to make substantial contributions to oncology prediction modeling. This model could inform clinical decision-making Future research is needed to validate these findings in a larger, more diverse sample.

Details

Title
Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data
Author
Sidey-Gibbons, Chris J. 1 ; Sun, Charlotte 2 ; Schneider, Amy 2 ; Lu, Sheng-Chieh 1 ; Lu, Karen 2 ; Wright, Alexi 3 ; Meyer, Larissa 2 

 University of Texas MD Anderson Cancer Center, Section of Patient-Centered Analytics, Department of Symptom Research, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 Dana Farber Cancer Institute, Department of Medical Oncology, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910); Harvard Medical School, Department of Medicine, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2748053539
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.