Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images
Accurate assessment of Human Epidermal growth factor receptor 2 (HER2) status in breast cancer is crucial for determining treatment eligibility for targeted therapies. Although machine learning approaches have shown promise in automating HER2 status classification from immunohistochemistry (IHC) ima...
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IEEE
2025-01-01
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| author | Surayuth Pintawong Shanop Shuangshoti Tikamporn Jitpasutham Somruethai Shuangshoti Kulachet Wiwatwarayos Thananop Kobchaisawat Thanarat H. Chalidabhongse |
| author_facet | Surayuth Pintawong Shanop Shuangshoti Tikamporn Jitpasutham Somruethai Shuangshoti Kulachet Wiwatwarayos Thananop Kobchaisawat Thanarat H. Chalidabhongse |
| author_sort | Surayuth Pintawong |
| collection | DOAJ |
| description | Accurate assessment of Human Epidermal growth factor receptor 2 (HER2) status in breast cancer is crucial for determining treatment eligibility for targeted therapies. Although machine learning approaches have shown promise in automating HER2 status classification from immunohistochemistry (IHC) images, they struggle to maintain reliable accuracy in equivocal cases. This study introduces a novel application of Conformal Prediction (CP) framework that, instead of making potentially incorrect predictions for uncertain cases, explicitly identifies them for further examination. The CP framework achieves this by generating prediction sets whose size reflects uncertainty, inherently aligning with the clinical practice of identifying cases that require confirmatory testing. Using dual in situ hybridization (DISH) results as the gold standard for HER2 status, we developed our system using handcrafted features—including color intensity, local binary patterns, and Haralick features—combined with tree-based classifiers. In experiments on 2,570 IHC images (94 cases) from the Institute of Pathology, Bangkok, Thailand, our framework demonstrated the ability to make highly accurate predictions on unambiguous cases while automatically filtering out uncertain ones. At a significance level of 0.05, the XGBoost-based conformal predictor achieved 90.1% (95% CI: 87.4-92.2%) accuracy on the equivocal cases, while identifying 69.8% (95% CI: 67.7-71.9%) of cases as requiring additional testing. This selective prediction approach represents a practical step toward reliable automated HER2 assessment by focusing on high-confidence predictions. The source code of our implementation is available at: <uri>https://github.com/Surayuth/her2cp.git</uri>. |
| format | Article |
| id | doaj-art-77b5e87b570d42569a3ce1bc0c4b8521 |
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| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-77b5e87b570d42569a3ce1bc0c4b85212025-08-20T02:10:34ZengIEEEIEEE Access2169-35362025-01-0113516455165910.1109/ACCESS.2025.355293410933960Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC ImagesSurayuth Pintawong0https://orcid.org/0009-0006-7507-9827Shanop Shuangshoti1Tikamporn Jitpasutham2Somruethai Shuangshoti3Kulachet Wiwatwarayos4Thananop Kobchaisawat5Thanarat H. Chalidabhongse6https://orcid.org/0000-0002-7249-6135Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Medical Services, Ministry of Public Health, Institute of Pathology, Bangkok, ThailandDepartment of Medical Services, Ministry of Public Health, Institute of Pathology, Bangkok, ThailandEikonnex AI Company Ltd., Bangkok, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandAccurate assessment of Human Epidermal growth factor receptor 2 (HER2) status in breast cancer is crucial for determining treatment eligibility for targeted therapies. Although machine learning approaches have shown promise in automating HER2 status classification from immunohistochemistry (IHC) images, they struggle to maintain reliable accuracy in equivocal cases. This study introduces a novel application of Conformal Prediction (CP) framework that, instead of making potentially incorrect predictions for uncertain cases, explicitly identifies them for further examination. The CP framework achieves this by generating prediction sets whose size reflects uncertainty, inherently aligning with the clinical practice of identifying cases that require confirmatory testing. Using dual in situ hybridization (DISH) results as the gold standard for HER2 status, we developed our system using handcrafted features—including color intensity, local binary patterns, and Haralick features—combined with tree-based classifiers. In experiments on 2,570 IHC images (94 cases) from the Institute of Pathology, Bangkok, Thailand, our framework demonstrated the ability to make highly accurate predictions on unambiguous cases while automatically filtering out uncertain ones. At a significance level of 0.05, the XGBoost-based conformal predictor achieved 90.1% (95% CI: 87.4-92.2%) accuracy on the equivocal cases, while identifying 69.8% (95% CI: 67.7-71.9%) of cases as requiring additional testing. This selective prediction approach represents a practical step toward reliable automated HER2 assessment by focusing on high-confidence predictions. The source code of our implementation is available at: <uri>https://github.com/Surayuth/her2cp.git</uri>.https://ieeexplore.ieee.org/document/10933960/Breast cancerconformal predictiondigital pathologyhandcrafted featuresHER2 statusimmunohistochemistry |
| spellingShingle | Surayuth Pintawong Shanop Shuangshoti Tikamporn Jitpasutham Somruethai Shuangshoti Kulachet Wiwatwarayos Thananop Kobchaisawat Thanarat H. Chalidabhongse Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images IEEE Access Breast cancer conformal prediction digital pathology handcrafted features HER2 status immunohistochemistry |
| title | Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images |
| title_full | Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images |
| title_fullStr | Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images |
| title_full_unstemmed | Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images |
| title_short | Conformal Prediction for Uncertainty Quantification and Reliable HER2 Status Classification in Breast Cancer IHC Images |
| title_sort | conformal prediction for uncertainty quantification and reliable her2 status classification in breast cancer ihc images |
| topic | Breast cancer conformal prediction digital pathology handcrafted features HER2 status immunohistochemistry |
| url | https://ieeexplore.ieee.org/document/10933960/ |
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