title: Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.
abstract: Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, "spam" filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the "barrier to entry" for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods--logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)--and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.
authors.affiliation: Medical Scientist Training Program, University of Pittsburgh-Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: John
authors.initials: J
authors.lastname: Kang
authors.affiliation: Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: Russell
authors.initials: R
authors.lastname: Schwartz
authors.affiliation: Departments of Radiation Oncology and Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
authors.forename: John
authors.initials: J
authors.lastname: Flickinger
authors.affiliation: Department of Radiation Oncology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. Electronic address: beriwals@upmc.edu.
authors.forename: Sushil
authors.initials: S
authors.lastname: Beriwal
mesh_terms: D001185:Artificial Intelligence; D004941:Esophagitis / Q000209:etiology; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000532:radiotherapy; D000069550:Machine Learning / Q000706:statistics & numerical data / Q000639:trends; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011379:Prognosis; D018787:Radiation Oncology; D017564:Radiation Pneumonitis / Q000209:etiology; D011878:Radiotherapy / Q000706:statistics & numerical data; D011879:Radiotherapy Dosage; D015203:Reproducibility of Results; D060388:Support Vector Machine; D016896:Treatment Outcome; D014987:Xerostomia / Q000209:etiology