Tumor treating fields (TTF) therapy is a noninvasive method that uses alternating electric fields to treat various types of cancer. This study demonstrates the combined effect of TTF and radiothera...

A medical linear accelerator (LINAC) delivers high-energy X-rays or electrons to the patient’s tumor. In this study, we categorized failures and predicted downtime leading to discontinuous radiation treatment using a descriptive time series analysis of a 20-year maintenance dataset of a medical LINAC. A LINAC dataset of failure records for 359 instances was collected from 2001 to 2021. Next, we performed institution-specific seasonal autoregressive integrated moving average (ARIMA) modeling to analyze the causes of the failure categories and predict the downtime. Furthermore, we evaluated the performance of the predictive model using standard error metrics and statistical methods. Our results show that the downtime will increase by 95 h/year after 2022 and 100 h/year after 2023. The accumulated downtime in 2029 is predicted to be a maximum of 2820 h. The modeled seasonal ARIMA showed statistical significance (p < 0.001) with a residual error of ${\sigma }^2$ (328.33 ± 9.4). In addition, the forecasting performance of the model was assessed using the mean absolute percentage error (MAPE). The failure parts where the major downtime occurred were the multileaf collimator (25.2%), gantry and couch motion part (15.4%), dosimetric part (11.7%), and computer console (10.0%). Using the development of the ARIMA model specific to our institution, the downtime is predicted to reach up to 2820 h.

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

This study aimed to compare the dosimetric effect of traditional metallic ports and radio frequency identification (RFID) ports (Motiva Flora^®) on post-mastectomy volumetric modulated arc therapy (VMAT) planning for left-sided breast cancer. Computed tomography (CT) simulation was performed on an anthropomorphic torso phantom by attaching two types of tissue expander on the left chest wall. For the comparison of CT artifacts, five points of interest were selected and compared: point A = central chest wall, B = medial chest wall, point C = lateral chest wall, point D = axilla, and point E = left anterior descending artery. VMAT planning using two partial arcs with a single isocenter was generated, and dosimetric parameters were investigated. Compared to metallic ports, RFID ports tremendously decreased distortion on CT images, with the exception of point D. The V_5Gy, V_10Gy, V_15Gy, V_20Gy, V_30Gy, and D_mean values of the heart in RFID ports were lower than those in metallic ports. The V_5Gy, V_15Gy, V_20Gy, V_30Gy, and D_mean values of the ipsilateral lung in RFID ports were also lower than those in metallic ports. RFID ports showed superior dosimetric results for doses to the heart and lungs as compared to that in metallic ports.