Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth

PLoS Computational Biology

Volumn 10, Issue 8, 2014, Pages

  Benzekry, Sébastien ^a,b   Lamont, Clare ^b   Beheshti, Afshin ^b   Tracz, Amanda ^c   Ebos, John M L ^c   Hlatky, Lynn ^b   Hahnfeldt, Philip ^b  

^a UNIVERSITÉ BORDEAUX 1   (France)

^b TUFTS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c ROSWELL PARK CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ORGANS; FORECASTING; TUMORS;

DATAPOINTS; EXPERIMENTAL SYSTEM; EXPONENTIALS; HUMAN BREAST; IN-VIVO; INTERNAL COMPLEXITY; LEWIS LUNG CARCINOMATA; POWER LAW MODEL; SIMPLE++; TUMOR GROWTH;

GROWTH KINETICS;

ACCURACY; ARTICLE; BREAST CARCINOMA; CANCER PROGNOSIS; COMPUTER PREDICTION; LEWIS CARCINOMA; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEASUREMENT ERROR; POPULATION DISTRIBUTION; QUANTITATIVE ANALYSIS; STATISTICAL MODEL; TUMOR GROWTH; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; BREAST TUMOR; C57BL MOUSE; EXPERIMENTAL NEOPLASM; FEMALE; HUMAN; LUNG TUMOR; MALE; MOUSE; NEOPLASM; PATHOLOGY; TUMOR CELL LINE;

ANIMALS; BREAST NEOPLASMS; CELL LINE, TUMOR; COMPUTATIONAL BIOLOGY; FEMALE; HUMANS; LUNG NEOPLASMS; MALE; MICE; MICE, INBRED C57BL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NEOPLASMS; NEOPLASMS, EXPERIMENTAL;

EID: 84908005989     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003800     Document Type: Article

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