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Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results
This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360533/ https://www.ncbi.nlm.nih.gov/pubmed/34383858 http://dx.doi.org/10.1371/journal.pone.0256152 |
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author | An, Chansik Park, Yae Won Ahn, Sung Soo Han, Kyunghwa Kim, Hwiyoung Lee, Seung-Koo |
author_facet | An, Chansik Park, Yae Won Ahn, Sung Soo Han, Kyunghwa Kim, Hwiyoung Lee, Seung-Koo |
author_sort | An, Chansik |
collection | PubMed |
description | This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) “Simple” task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) “difficult” task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes. |
format | Online Article Text |
id | pubmed-8360533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83605332021-08-13 Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results An, Chansik Park, Yae Won Ahn, Sung Soo Han, Kyunghwa Kim, Hwiyoung Lee, Seung-Koo PLoS One Research Article This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) “Simple” task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) “difficult” task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes. Public Library of Science 2021-08-12 /pmc/articles/PMC8360533/ /pubmed/34383858 http://dx.doi.org/10.1371/journal.pone.0256152 Text en © 2021 An et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article An, Chansik Park, Yae Won Ahn, Sung Soo Han, Kyunghwa Kim, Hwiyoung Lee, Seung-Koo Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title | Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title_full | Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title_fullStr | Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title_full_unstemmed | Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title_short | Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results |
title_sort | radiomics machine learning study with a small sample size: single random training-test set split may lead to unreliable results |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360533/ https://www.ncbi.nlm.nih.gov/pubmed/34383858 http://dx.doi.org/10.1371/journal.pone.0256152 |
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