Response to “Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma”-Reference-Cited by-同舟云学术

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Response to “Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma”

Published:2023 Issue:12 Volume:24 Page:1306
ISSN:1229-6929
Container-title:Korean Journal of Radiology
language:en
Short-container-title:Korean J Radiol

Author:

Park Yae Won¹^ORCID,Han Kyunghwa¹^ORCID,Kim Se Hoon²^ORCID

Affiliation:

1. Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.

2. Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.

Publisher

XMLink

Subject

Radiology, Nuclear Medicine and imaging

Link

https://kjronline.org/pdf/10.3348/kjr.2023.0940

Reference15 articles.

1. Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma

2. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence

3. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

4. Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results

5. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

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