Modeling Plan-Related Clinical Complications Using Machine Learning Tools in a Multiplan IMRT Framework

International Journal of Radiation Oncology Biology Physics

Volumn 74, Issue 5, 2009, Pages 1617-1626

  Zhang, Hao H ^d   D'Souza, Warren D ^b   Shi, Leyuan ^a   Meyer, Robert R ^c  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

^c University of Wisconsin   (United States)

^d NONE

Author keywords

IMRT planning; Machine learning; OAR complications; Rectal bleeding; Saliva flow rate

Indexed keywords

IMRT PLANNING; MACHINE LEARNING; OAR COMPLICATIONS; RECTAL BLEEDING; SALIVA FLOW RATE;

BODY FLUIDS; EDUCATION; ERRORS; FLOW RATE; MACHINE TOOLS; PHYSICAL THERAPY; ROBOT LEARNING;

LEARNING ALGORITHMS;

ARTICLE; CONTROLLED STUDY; HEAD AND NECK CANCER; INTENSITY MODULATED RADIATION THERAPY; MACHINE LEARNING; PRIORITY JOURNAL; PROSTATE CANCER; RADIATION DOSE; RADIATION SICKNESS; RECTUM HEMORRHAGE; SALIVATION; TREATMENT PLANNING; VALIDATION STUDY; XEROSTOMIA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONFIDENCE INTERVALS; DECISION TREES; GASTROINTESTINAL HEMORRHAGE; HEAD AND NECK NEOPLASMS; HUMANS; MALE; PAROTID GLAND; PROSTATIC NEOPLASMS; RADIATION INJURIES; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED; RADIOTHERAPY, INTENSITY-MODULATED; RECTUM; SALIVATION; TUMOR BURDEN; XEROSTOMIA;

EID: 67651115903     PISSN: 03603016     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijrobp.2009.02.065     Document Type: Article

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