Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

Pendekatan Pembelajaran Mesin untuk Memprediksi Luaran Terapi Radiasi: Perspektif Klinisi

Clicks: 566
ID: 109940
2015
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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 tha …
Onkologi radiasi senantiasa berakar kuat dalam pemodelan, mulai dari era awal kurva isoefek hingga inisiatif kontemporer Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC). Dalam beberapa tahun terakhir, pemodelan medis untuk tujuan prognostik maupun terapeutik telah berkembang pesat kar …
Reference Key
j2015internationalmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kang J;Schwartz R;Flickinger J;Beriwal S;;
Journal international journal of radiation oncology, biology, physics
Year 2015
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/26581149
Keywords
support vector machine artificial intelligence neural networks National Center for Biotechnology Information NCBI NLM MEDLINE review humans pubmed abstract nih national institutes of health national library of medicine pmid:26581149 doi:10.1016/j.ijrobp.2015.07.2286 john kang russell schwartz sushil beriwal esophagitis / etiology logistic models lung neoplasms / radiotherapy machine learning* / statistics & numerical data machine learning* / trends models statistical computer* prognosis radiation oncology* radiation pneumonitis / etiology radiotherapy dosage radiotherapy* / statistics & numerical data reproducibility of results treatment outcome xerostomia / etiology

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