2022
DOI: 10.1007/978-3-030-98253-9_25
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A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers

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Cited by 7 publications
(6 citation statements)
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References 17 publications
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“…ComBat strategy of AAC reduced batch-effect for all four features, which is provided in Fig. S2 superior performance compared to the ML approaches similar to ours (0.683 [28] and 0.659 [29]), whereas it was found close to the best result (C-index, 0.720 [30]) and better than the other DL approaches (0.660 [31] and 0.698 [32]).…”
Section: Comparison With Other Challengers' Resultssupporting
confidence: 51%
“…ComBat strategy of AAC reduced batch-effect for all four features, which is provided in Fig. S2 superior performance compared to the ML approaches similar to ours (0.683 [28] and 0.659 [29]), whereas it was found close to the best result (C-index, 0.720 [30]) and better than the other DL approaches (0.660 [31] and 0.698 [32]).…”
Section: Comparison With Other Challengers' Resultssupporting
confidence: 51%
“…tumoral environment, nodal metastases). Fully automatic algorithms are expected to provide optimal results, which was already highlighted by several papers in the context of the HECKTOR challenge [19,56,2,49,46]. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.…”
Section: Discussion: Putting the Results Into Contextmentioning
confidence: 95%
“…In [56], Starke et al (team "ia-h-ai") built a strategy based on standard radiomics modeling, addressing both Tasks 2 and 3. They first strategically split the training data into 3 folds, ensuring that for each split, one of the centers is present in the validation set but not the training.…”
Section: Results: Reporting Of Challenge Outcomementioning
confidence: 99%
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