Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer.

Machine learning algorithms were used to analyze the odds and predictors of complications of thyroid damage after radiation therapy in patients with head and neck cancer. This study used decision tree (DT), random forest (RF), and support vector machine (SVM) algorithms to evaluate predictors for the data of 137 head and neck cancer patients. Candidate factors included gender, age, thyroid volume, minimum dose, average dose, maximum dose, number of treatments, and relative volume of the organ receiving X dose (X: 10, 20, 30, 40, 50, 60 Gy). The algorithm was optimized according to these factors and tenfold cross-validation to analyze the state of thyroid damage and select the predictors of thyroid dysfunction. The importance of the predictors identified by the three machine learning algorithms was ranked: the top five predictors were age, thyroid volume, average dose, V50 and V60. Of these, age and volume were negatively correlated with thyroid damage, indicating that the greater the age and thyroid volume, the lower the risk of thyroid damage; the average dose, V50 and V60 were positively correlated with thyroid damage, indicating that the larger the average dose, V50 and V60, the higher the risk of thyroid damage. The RF algorithm was most accurate in predicting the probability of thyroid damage among the three algorithms optimized using the above factors. The Area under the receiver operating characteristic curve (AUC) was 0.827 and the accuracy (ACC) was 0.824. This study found that five predictors (age, thyroid volume, mean dose, V50 and V60) are important factors affecting the chance that patients with head and neck cancer who received radiation therapy will develop hypothyroidism. Using these factors as the prediction basis of the algorithm and using RF to predict the occurrence of hypothyroidism had the highest ACC, which was 82.4%. This algorithm is quite helpful in predicting the probability of radiotherapy complications. It also provides references for assisting medical decision-making in the future.

Forecasting of oil-spill trajectories by using SCHISM and X-band radar.

In this study, we propose a two-step strategy for tracking oil-spill trajectories. First, an X-band radar is established to monitor oil spills. Accordingly, we propose a radar image-processing technique for identifying the oil slicks from the nautical radar images. Second, we apply the SCHISM to determine the water surface elevations and currents at the event site and obtain the trajectories of the oil slicks using a Lagrangian particle-tracking method incorporated in the SCHISM. An oil-spill event caused by the container ship T. S. Taipei is used as a case study for testing the capability of the proposed oil-tracking strategy. The SCHISM simulation results for the fouled coastline obtained using the wind data from a nearby data buoy agree quite well with those obtained from field observations. However, the predicted fouled coastline based on the forecasted wind data is unsatisfactory. The reasons for the unsatisfactory prediction are discussed and revealed.