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Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation

Crossref DOI link: https://doi.org/10.1007/s00431-024-05476-9

Published Online: 2024-02-28

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Conte, Luana https://orcid.org/0000-0002-8741-3478
Amodeo, Ilaria https://orcid.org/0000-0002-2650-0084
De Nunzio, Giorgio https://orcid.org/0000-0002-1998-0286
Raffaeli, Genny https://orcid.org/0000-0001-9175-9394
Borzani, Irene https://orcid.org/0000-0003-0386-4712
Persico, Nicola https://orcid.org/0000-0001-9028-9597
Griggio, Alice https://orcid.org/0000-0002-2947-4627
Como, Giuseppe https://orcid.org/0009-0009-2120-0427
Cascio, Donato https://orcid.org/0000-0001-6522-1259
Colnaghi, Mariarosa

Mosca, Fabio https://orcid.org/0000-0001-6477-0299
Cavallaro, Giacomo https://orcid.org/0000-0002-4921-1437
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Text and Data Mining valid from 2024-02-28

Version of Record valid from 2024-02-28
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Article History

Received: 16 December 2023

Revised: 2 February 2024

Accepted: 6 February 2024

First Online: 28 February 2024

Declarations

:

: The present study was done on the principles of good clinical practice and the Helsinki Declaration. This study was approved by the local ethics committee (Milan Area 2, Italy) with approval number/ID 800_2020bis. However, due to the study retrospective nature, informed consent was waived by the Ethics Committee. The study was registered at ClinicalTrials.gov with the identifier NCT04609163.

: Written informed consent was obtained from the parents.

: The authors declare no competing interests.

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