A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.

BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas. METHODS: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: identifying infiltrated brain areas segmentation of gliomas. The multi-task model leverages shaped location and boundary information to enhance the performance of both tasks. Our retrospective study involved 354 glioma patients (grades II-IV) with single or multiple brain area infiltrations, which were divided into training (N = 270), validation (N = 30), and independent test (N = 54) sets. We evaluated the predictive performance using the area under the receiver operating characteristic curve (AUC) and Dice scores. RESULTS: Our multi-task model achieved impressive results in the independent test set, with an AUC of 94.95% (95% CI, 91.78-97.58), a sensitivity of 87.67%, a specificity of 87.31%, and accuracy of 87.41%. Specifically, for grade II-IV glioma, the model achieved AUCs of 95.25% (95% CI, 91.09-98.23, 84.38% sensitivity, 89.04% specificity, 87.62% accuracy), 98.26% (95% CI, 95.22-100, 93.75% sensitivity, 98.15% specificity, 97.14% accuracy), and 93.83% (95%CI, 86.57-99.12, 92.00% sensitivity, 85.71% specificity, 87.37% accuracy) respectively for the identification of infiltrated brain areas. Moreover, our model achieved a mean Dice score of 87.60% for the whole tumor segmentation. CONCLUSIONS: Experimental results show that our multi-task model achieved superior performance and outperformed the state-of-the-art methods. The impressive performance demonstrates the potential of our work as an innovative solution for identifying tumor-infiltrated brain areas and suggests that it can be a practical tool for supporting clinical decision making.

A spaced retraining schedule with 2-day interval improves the acquisition and retention of simulation-based basic arthroscopic skills.

PURPOSE: To compare the effect of three differently spaced retraining schedules (1-day, 2-day, and 1-week intervals) on the acquisition of basic arthroscopic skills and skill retention after 3 months. METHODS: Thirty orthopaedic residents without arthroscopic experience were enrolled in a double-blind, randomised, parallel-controlled trial. Spaced retaining schedules were divided into massed training and retraining phases. Participants were required to obtain perfect scores in all tasks on the simulator in the massed training phase, followed by a pretest to evaluate the training effect. During the retraining phase, participants were randomly assigned to Groups A (1-day interval), B (2-day interval) or C (1-week interval). A posttest was used to evaluate the effect of different retraining patterns. Follow-up evaluations were conducted at 1 week, 1 month and 3 months after the completion of spaced retraining schedules to measure skill retention. One-way ANOVA and paired-sample t tests were used for statistical analysis. RESULTS: Significant between-group differences in diagnostic arthroscopy (137.0 ± 24.8 vs. 140.1 ± 21.3 vs. 175.3 ± 27.4 s, P(A-C) = 0.005, P(B-C) = 0.010) and loose body removal (193.1 ± 33.9 vs. 182.0 ± 32.1 vs. 228.7 ± 42.9 s, P(B-C) = 0.025) completion times were observed. No significant differences were found in other posttest metrics. An assessment of skill retention after the 3-month follow-up (Evaluation 3) showed significant differences in diagnostic arthroscopy completion time (202.5 ± 53.3 vs. 172.0 ± 27.2 vs. 225.5 ± 42.1 s, P(B-C) = 0.026). No significant differences were found in other Evaluation 3 metrics. CONCLUSION: The 2-day retraining schedule was the most effective for the acquisition and retention of basic arthroscopic skills and could be integrated into arthroscopic skills curricula. After a 3-month follow-up, residents who followed this schedule showed better skill retention than those who followed the 1-week interval schedule. LEVEL OF EVIDENCE: Level I.