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Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images

Scientific reports, 2023-11, Vol.13 (1), p.20518-20518, Article 20518 [Peer Reviewed Journal]

2023. The Author(s). ;The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-023-46619-6 ;PMID: 37993544

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  • Title:
    Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images
  • Author: Voon, Wingates ; Hum, Yan Chai ; Tee, Yee Kai ; Yap, Wun-She ; Nisar, Humaira ; Mokayed, Hamam ; Gupta, Neha ; Lai, Khin Wee
  • Subjects: Breast - pathology ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - pathology ; Carcinoma ; Carcinoma, Ductal ; Carcinoma, Ductal, Breast - pathology ; Classification ; Female ; Humans ; Hypotheses ; Invasiveness ; Machine Learning ; Maskininlärning ; Neural networks ; Neural Networks, Computer ; Staining and Labeling ; Stains ; Statistical analysis
  • Is Part Of: Scientific reports, 2023-11, Vol.13 (1), p.20518-20518, Article 20518
  • Description: Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-023-46619-6
    PMID: 37993544
  • Source: Open Access: PubMed Central
    AUTh Library subscriptions: ProQuest Central
    MEDLINE
    SWEPUB Freely available online
    DOAJ Directory of Open Access Journals
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