Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

• Published in: Medical physics (Lancaster) Vol. 43; no. 3; pp. 1275 - 1284
• Main Authors: Belfatto, Antonella, White, Derek A., Mason, Ralph P., Zhang, Zhang, Stojadinovic, Strahinja, Baroni, Guido, Cerveri, Pietro
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.03.2016
• Subjects: 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
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4941746

Purpose: Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle “one-fits-all,” with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization. Methods: The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity Δ SI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal Δ R 1 and transverse Δ R 2 * relaxation rates. Results: An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the Δ R 2 * (−0.65) for the oxy group. A further subdivision according to positive and negative values of Δ R 2 * showed a larger average radio-sensitivity for the oxy rats with Δ R 2 * < 0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks. Conclusions: While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.