Deep learning approach for survival prediction for patients with synovial sarcoma.

Synovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its clinical usage. The study involved 242 patients who were diagnosed with synovial sarcoma in three institutions between March 2001 and February 2013. The patients were randomly divided into a training set (80%) and a testing set (20%). Fivefold cross validation was performed utilizing the training set. The test set was retained for the final testing. A Cox proportional hazard model, simple neural network, and the proposed survival neural network were all trained utilizing the same training set, and fivefold cross validation was performed. The final testing was performed utilizing the isolated test data to determine the best prediction model. The multivariate Cox proportional hazard regression analysis revealed that size, initial metastasis, and margin were independent prognostic factors. In fivefold cross validation, the median value of the receiver-operating characteristic curve (area under the curve) was 0.87 in the survival neural network, which is significantly higher compared to the area under the curve of 0.792 for the simple neural network (p = 0.043). In the final test, survival neural network model showed the better performance (area under the curve: 0.814) compared to the Cox proportional hazard model (area under the curve: 0.629; p = 0.0001). The survival neural network model predicted survival of synovial sarcoma patients more accurately compared to Cox proportional hazard model.

Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.

Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = - 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.