Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

Journal of the American Medical Informatics Association

Volumn 20, Issue 4, 2013, Pages 688-695

  Mani, Subramani ^a   Chen, Yukun ^b   Li, Xia ^c   Arlinghaus, Lori ^c   Chakravarthy, A Bapsi ^c,d   Abramson, Vandana ^c,d   Bhave, Sandeep R ^e   Levy, Mia A ^b,c,d   Xu, Hua ^b,f   Yankeelov, Thomas E ^c,d,g,h  

^a UNIVERSITY OF NEW MEXICO SCHOOL OF MEDICINE   (United States)

^b VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^c VANDERBILT UNIVERSITY   (United States)

^d VANDERBILT INGRAM CANCER CENTER   (United States)

^e WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f UNIVERSITY OF TEXAS   (United States)

^g VANDERBILT UNIVERSITY   (United States)

^h VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBOPLATIN; CISPLATIN; CYCLOPHOSPHAMIDE; DOCETAXEL; DOXORUBICIN; LAPATINIB; PACLITAXEL; TRASTUZUMAB;

AREA UNDER THE CURVE; ARTICLE; BREAST CARCINOMA; CANCER ADJUVANT THERAPY; CANCER PATIENT; CLINICAL ARTICLE; CONTRAST ENHANCEMENT; DIFFUSION WEIGHTED IMAGING; HUMAN; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; QUANTITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY; TREATMENT RESPONSE;

BREAST CANCER; DCE-MRI; DIFFUSION MRI; MACHINE LEARNING; NEOADJUVANT CHEMOTHERAPY; PREDICTIVE MODELING;

ADULT; AGED; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BREAST NEOPLASMS; FEMALE; HUMANS; LOGISTIC MODELS; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; NEOADJUVANT THERAPY; PROGNOSIS; TREATMENT OUTCOME;

EID: 84882999508     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-001332     Document Type: Article

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