Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

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.

Detection of residual metastatic tumor in the brain following Gamma Knife radiosurgery using a single or a series of magnetic resonance imaging scans: An autopsy study.

The aim of the present study was to investigate the usefulness of magnetic resonance image (MRI) for the detection of residual tumors following Gamma Knife radiosurgery (GKR) for brain metastases based on autopsy cases. The study investigated two hypotheses: i) Whether a single MRI may detect the existence of a tumor; and ii) whether a series of MRIs may detect the existence of a tumor. The study is a retrospective case series in a single institution. A total of 11 brain metastases in 6 patients were treated with GKR between 2002 and 2011. Histopathological specimens from autopsy were compared with reconstructed follow-up MRIs. The maximum diameters of the lesions on MRI series were measured, and the size changes classified. The primary sites in the patients were the kidneys (n=2), lung (n=1), breast (n=1) and colon (n=1), as well as 1 adenocarcinoma of unknown origin. The median prescribed dose for radiosurgery was 20 Gy (range, 18-20 Gy), and median time interval between GKR and autopsy was 10 months (range, 1.6-20 months). The pathological outcomes included 7 remissions and 4 failures. Enhanced areas on gadolinium-enhanced MRI contained various components: Viable tumor cells, tumor necrosis, hemorrhage, inflammation and vessels. Regarding the first hypothesis, it was impossible to distinguish pathological failure from remission with a single MRI scan due to the presence of various components. Conversely, in treatment response (remission or failure), on time-volume curves of MRI scans were in agreement with pathological findings, with the exception of progressive disease in the acute phase (0-3 months). Thus, regarding the second hypothesis, time-volume curves were useful for predicting treatment responses. In conclusion, it was difficult to predict treatment response using a single MRI, and a series of MRI scans were required to detect the existence of a tumor.

Views on life and death of physicians, nurses, cancer patients and general population in Japan.

This study aimed to investigate views on life and death among physicians, nurses, cancer patients, and the general population in Japan and examine factors affecting these views. We targeted 3,140 physicians, 470 nurses, 450 cancer patients, and 3,000 individuals from the general population. We used the Death Attitudes Inventory (DAI) to measure attitudes toward life and death. The collection rates were 35% (1,093/3,140), 78% (366/470), 69% (310/450), and 39% (1,180/3,000) for physicians, nurses, patients, and the general population, respectively. We found that age, sex, social role (i.e., physician, nurse, cancer patient, and general population) were significantly correlated with DAI subscales. Compared with general population, attitudes toward death of physicians, nurses and cancer patients differed significantly even after adjusted their age and sex. Our study is the first to analyze differences in views on life and death among physicians, nurses, cancer patients, and the general population in Japan.