ABSTRACT The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose–volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike’s information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.

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The nitrilase gene of Rhodococcus rhodochrous J1 was expressed in Escherichia coli using the expression vector, pKK223-3. The recombinant E. coli JM109 cells hydrolyzed enantioselectively 2-methyl-2-propylmalononitrile to form (S)-2-cyano-2-methylpentanoic acid (CMPA) with 96 % e.e. Under optimized conditions, 80 g (S)-CMPA l(-1) was produced with a molar yield of 97 % at 30 °C after a 24 h without any by-products.

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Purpose/Objective(s): In the volumetric modulated arc therapy (VMAT), the planning configurations such as arc or collimator angles are selected on experientially, then the dose distribution was optimized iteratively. However, some cases may reselect the planning configurations when the resultant dose distribution was undesirable. To produce the adequate planning efficiently, the configurations should be optimized before dose distribution optimization. One of the most difficult subjects of it is the huge number of choices, it is not realistic to do for each patient. Design of experiments (DoE) is the one of techniques for optimization issues, it will identify the few factors which have large impact to results from large number of them, then obtained the optimized result by best combination of inputs. To obtain the adequate results with small number trials, DoE method was employed. Materials/Methods: CT data of the head and lung phantom were acquired using 16-slice CT (Light Speed 16-slice; GE Healthcare, Waukesha WI, USA). The target delineations were transformed from clinical CT using deformable image registration in mimicry of clinical situation, then modified it appropriately. The planning configurations such as arc angles, couch angles, or collimator angles were selected from two candidate values, respectively. To reduce the number of trials, orthogonal table was used which is the one of DoE tools to identify the main factor efficiently. By using the table, the trial number could reduce from 32 to 8 for each case. The dose volume histogram was calculated, then analyzed the best combination. Finally, dose distribution was calculated, then compared with it of conventional planning. Results: In the case of nasal cavity cancer (planed dose was 20 Gy), 3 fields were used, the best arc angles, couch angles, collimator angles, beam energies and size of beam window for field 1/2/3 were 135 -225 /135 -225 /70 -330 , 0 /0 /90 , 350 /80 /80 , 10X/10X/10X, and wide/wide/wide, respectively. The maximum dose of volume (Dmax) of left eye, right eye, spinal cord were 17.7 Gy, 7.0 Gy, 15.3 Gy in the conventional, and 15.8 Gy, 6.1 Gy, 14.8 Gy in the DoE, respectively. In the case of head and neck cancer (planed dose was 70 Gy), DoE was performed independently, Dmax of brain stem, spinal cord were 45.8 Gy, 43.6 Gy in the conventional, and 44.5Gy, 42.5 Gy in the DoE, respectively. The DoE configurations would have geometrical advantage to reduce organs at risk dose in these case. In the mediastinal lymph node metastasis (planed dose was 60 Gy), Dmax of esophagus, spinal cord, trachea were 24.4 Gy, 10.7 Gy, 8.7 Gy in the conventional, and 26.8 Gy, 10.4 Gy, 8.6 Gy in the DoE, respectively. We confirmed that the conventional configuration had been chosen the reasonable selection in this case. Conclusion: The optimization of beam configurations with DoE is expected to found the reasonable solution efficiently. These results indicate the effectiveness of this strategy for the clinical usage in the VMAT. Abstract 3664 Clinical featur...