{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:45:26Z","timestamp":1792601126562,"version":"4.5.0"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Predicting reduced health-related quality of life (HRQoL) after resection of a benign or low-grade brain tumour provides the opportunity for early intervention, and targeted expenditure of scarce supportive care resources. We aimed to develop, and evaluate the performance of, machine learning (ML) algorithms to predict HRQoL outcomes in this patient group.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Using a large prospective dataset of HRQoL outcomes in patients surgically treated for low grade glioma, acoustic neuroma and meningioma, we investigated the capability of ML to predict a) HRQoL-impacting symptoms persisting between 12 and 60 months from tumour resection and b) a decline in global HRQoL by more than the minimum clinically important difference below a normative population mean within 12 and 60 months after resection. Ten-fold cross-validation was used to measure the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (PR-AUC), sensitivity, and specificity of models. Six ML algorithms were explored per outcome: Random Forest Classifier, Decision Tree Classifier, Logistic Regression, K Neighbours Classifier, Support Vector Machine, and Gradient Boosting Machine.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The final cohort included 262 patients. Outcome measures for which AUC&gt;0.9 were Appetite loss, Constipation, Nausea and vomiting, Diarrhoea, Dyspnoea and Fatigue. AUC was between 0.8 and 0.9 for global HRQoL and Financial difficulty. Pain and Insomnia achieved AUCs below 0.8. PR-AUCs were similar overall to the AUC of each respective classifier.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>ML algorithms based on routine demographic and perioperative data show promise in their ability to predict HRQoL outcomes in patients with low grade and benign brain tumours between 12 and 60 months after surgery.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0267931","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T17:33:07Z","timestamp":1651685587000},"page":"e0267931","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":26,"title":["Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9603-5659","authenticated-orcid":true,"given":"Roshan","family":"Karri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4122-3767","authenticated-orcid":true,"given":"Yi-Ping Phoebe","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharine J.","family":"Drummond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"issue":"4","key":"pone.0267931.ref001","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1053\/j.seminoncol.2014.06.002","article-title":"Health-related quality of life or quantity of life: A difficult trade-off in primary brain tumors?","volume":"41","author":"L Dirven","year":"2014","journal-title":"Semin Oncol"},{"issue":"14","key":"pone.0267931.ref002","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1016\/S0959-8049(02)00173-9","article-title":"Health related quality of life assessment methodology and reported outcomes in randomised controlled trials of primary brain cancer patients","volume":"38","author":"F Efficace","year":"2002","journal-title":"Eur J Cancer"},{"key":"pone.0267931.ref003","unstructured":"EORTC | European Organisation for Research and Treatment of Cancer: EORTC [Internet]. 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Available from: https:\/\/www.expertsystem.com\/machine-learning-definition\/"},{"issue":"12","key":"pone.0267931.ref017","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1038\/nmeth.4526","article-title":"Machine learning: A primer","volume":"14","author":"D Bzdok","year":"2017","journal-title":"Nat Methods"},{"key":"pone.0267931.ref018","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104127","article-title":"Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy","volume":"129","author":"Z Yang","year":"2021","journal-title":"Comput Biol Med"},{"issue":"5","key":"pone.0267931.ref019","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1007\/s00520-012-1672-8","article-title":"Predicting quality of life after breast cancer surgery using ANN-based models: Performance comparison with MR","volume":"21","author":"JT Tsai","year":"2013","journal-title":"Support Care Cancer"},{"issue":"1","key":"pone.0267931.ref020","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10549-012-2174-6","article-title":"Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models","volume":"135","author":"HY Shi","year":"2012","journal-title":"Breast Cancer Res Treat"},{"issue":"2","key":"pone.0267931.ref021","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0089851","article-title":"PrediQt-Cx: Post treatment health related quality of life prediction model for cervical cancer patients","volume":"9","author":"S Kumar","year":"2014","journal-title":"PLoS One"},{"key":"pone.0267931.ref022","doi-asserted-by":"crossref","first-page":"I32","DOI":"10.1093\/neuonc\/noy152","article-title":"Life after surgical resection of a meningioma: A prospective cross-sectional study evaluating health-related quality of life","volume":"21","author":"F Nassiri","year":"2019","journal-title":"Neuro Oncol"},{"key":"pone.0267931.ref023","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.jocn.2021.03.038","article-title":"Life after surgical resection of a low-grade glioma: A prospective cross-sectional study evaluating health-related quality of life","volume":"88","author":"KX Teng","year":"2021","journal-title":"J Clin Neurosci [Internet]"},{"key":"pone.0267931.ref024","unstructured":"Quality of Life of Cancer Patients\u2014EORTC\u2014Quality of Life: EORTC\u2013Quality of Life [Internet]. [cited 2020 Jul 1]. Available from: https:\/\/qol.eortc.org\/questionnaire\/eortc-qlq-c30\/"},{"key":"pone.0267931.ref025","unstructured":"Manuals\u2014EORTC\u2014Quality of Life: EORTC\u2013Quality of Life [Internet]. [cited 2020 Jul 1]. Available from: https:\/\/qol.eortc.org\/manuals\/"},{"issue":"1","key":"pone.0267931.ref026","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1200\/JCO.1998.16.1.139","article-title":"Interpreting the significance of changes in health-related quality-of- life scores","volume":"16","author":"D Osoba","year":"1998","journal-title":"J Clin Oncol"},{"issue":"6","key":"pone.0267931.ref027","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/BF00439229","article-title":"The interpretation of scores from the EORTC quality of life questionnaire QLQ-C30","volume":"5","author":"MT King","year":"1996","journal-title":"Qual Life Res"},{"key":"pone.0267931.ref028","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","edition":"2"},{"key":"pone.0267931.ref029","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"V Chawla N","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pone.0267931.ref030","first-page":"1","article-title":"Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer","volume":"7","author":"Y Zhang","year":"2017","journal-title":"Sci Rep [Internet]"},{"issue":"3","key":"pone.0267931.ref031","doi-asserted-by":"crossref","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","article-title":"The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets","volume":"10","author":"T Saito","year":"2015","journal-title":"PLoS One [Internet]"},{"issue":"3","key":"pone.0267931.ref032","first-page":"e0174944","article-title":"Can machine-learning improve cardiovascular","volume":"51","author":"SF Weng","year":"2015","journal-title":"Med Care [Internet]"},{"key":"pone.0267931.ref033","volume-title":"Neural Networks: Tricks of the Trade Lecture Notes in Computer Science","author":"Y. 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