Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats - Belfatto, Antonella; White, Derek A.; Mason, Ralph P.; Zhang, Zhang; Stojadinovic, Strahinja; Baroni, Guido; Cerveri, Pietro (Medical physics (Lancaster), 2016)
Keywords: Animals; Artificial neural networks; Biological material, e.g. blood, urine; Haemocytometers; BIOLOGICAL PHYSICS AND RESPONSE PREDICTION; Biomedical modeling; biomedical MRI; blood; BOLD; cancer; Clinical applications; Computer modeling; Digital computing or data processing equipment or methods, specially adapted for specific applications; Dose Fractionation, Radiation; Dose‐volume analysis; dosimetry; Dosimetry/exposure assessment; feedforward neural nets; Fractional distillation; fractionation; Image data processing or generation, in general; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines; Inference methods or devices; inverse problems; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; learning (artificial intelligence); LQ model; Magnetic Resonance Imaging; Male; mathematical model; medical image processing; Medical radiation safety; Models, Biological; MRI: anatomic, functional, spectral, diffusion; neural network; Neural Networks, Computer; Oxygen - metabolism; Pneumodyamics, respiration; pneumodynamics; prostate cancer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - metabolism; Prostatic Neoplasms - pathology; Prostatic Neoplasms - radiotherapy; radiation therapy; Radiation Tolerance; Radiation treatment; radio‐sensitivity; Rats; Scintigraphy; TOLD; Tumor Burden; tumours; prostate cancer; TOLD; radio-sensitivity; mathematical model; BOLD; neural network; LQ model