2020
DOI: 10.1016/j.ijrobp.2020.07.324
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Comparative Clinical Evaluation Of Deep-Learning-Based Algorithms In Auto-Segmentation Of Organs-At-Risk For Head And Neck Cancers

Abstract: achieved when Radiomics was used alone. The combination of Radiomics with addiction provided better AUC of 0.86 and accuracy of 88%. Accuracy for T staging was 60 % with AUC 0.81, while for recurrence combination of Radiomics, baseline ADC values and clinical addiction gave us best model with accuracy of 81%, AUC 0.76 and kappa of 0.45. (Details in table) Conclusion: The study is an effort to bridge the unmet need of translational predictive biomarkers in stratification of HPV positive OPSCC patients based on … Show more

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“…Although it is time-consuming and intra-observer and inter-observer differences usually occur, scholars worldwide have been trying to find a more rapid and more accurate method or evaluate the already existing processes. Scholars have recently published many DL studies on auto-segmentation ( 19 23 ), involving various algorithms and machine learning techniques, especially DL methods. Yang et al.…”
Section: Discussionmentioning
confidence: 99%
“…Although it is time-consuming and intra-observer and inter-observer differences usually occur, scholars worldwide have been trying to find a more rapid and more accurate method or evaluate the already existing processes. Scholars have recently published many DL studies on auto-segmentation ( 19 23 ), involving various algorithms and machine learning techniques, especially DL methods. Yang et al.…”
Section: Discussionmentioning
confidence: 99%