title: Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods.
abstract: OBJECTIVE
  Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate.
  
  
  METHODS
  The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥ 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level.
  
  
  RESULTS
  Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6.
  
  
  CONCLUSIONS
  Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.
authors.affiliation: School of Physics, University of Western Australia, Western Australia 6009, Australia and School of Health Sciences, National University of Malaysia, Bangi 43600, Malaysia.
authors.forename: Noorazrul
authors.initials: N
authors.lastname: Yahya
authors.affiliation: School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Ebert
authors.affiliation: Institute for Health Research, University of Notre Dame, Fremantle, Western Australia 6959, Australia.
authors.forename: Max
authors.initials: M
authors.lastname: Bulsara
authors.affiliation: School of Physics, University of Western Australia, Western Australia 6009, Australia.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: House
authors.affiliation: Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia.
authors.forename: Angel
authors.initials: A
authors.lastname: Kennedy
authors.affiliation: Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia and School of Surgery, University of Western Australia, Western Australia 6009, Australia.
authors.forename: David J
authors.initials: DJ
authors.lastname: Joseph
authors.affiliation: School of Medicine and Public Health, University of Newcastle, New South Wales 2308, Australia.
authors.forename: James W
authors.initials: JW
authors.lastname: Denham
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015897:Comorbidity; D005500:Follow-Up Studies; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011379:Prognosis; D011467:Prostate / Q000528:radiation effects*; D011471:Prostatic Neoplasms / Q000150:complications / Q000532:radiotherapy*; D012372:ROC Curve; D011878:Radiotherapy / Q000009:adverse effects* / Q000379:methods; D012720:Severity of Illness Index; D014555:Urination Disorders / Q000175:diagnosis* / Q000209:etiology*