ArticleAbdominal radiology (New York)2026
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
Funding
Abstract
objectiveThe aim of this study was to investigate the diagnostic performance of the 2.5-dimensional (2.5D) ensemble deep learning (DL) model based on
methodsIn this retrospective study, 177 CRC patients who underwent preoperative
resultsThe ProximalPeritumoral-DenseNet201 model (training cohort: AUC = 0.840, accuracy = 0.772, F1-score = 0.714; internal test cohort: AUC = 0.738, accuracy = 0.796, F1-score = 0.645; parameter amount = 18.097 M, inference time = 47.500 ms) and PrimaryLesion-ResNet50 model (training cohort: AUC = 0.746, accuracy = 0.740, F1-score = 0.628; internal test cohort: AUC = 0.733, accuracy = 0.704, F1-score = 0.619; parameter amount = 23.512 M, inference time = 38.200 ms) achieved an optimal balance between performance and computational efficiency. The performance of the Fusion model combined with the ProximalPeritumoral-DenseNet201 model and PrimaryLesion-ResNet50 model was further improved, with an AUC of 0.874, an accuracy of 0.821, and an F1-score of 0.766 in the training cohort. In the internal test cohort, the AUC was 0.824, the accuracy was 0.815, and the F1-score was 0.722. The Fusion model outperformed the Radiomics and Clinical models. Moreover, it showed good clinical utility and calibration.
conclusionThe 2.5D ensemble DL model based on
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.