Fauzi, Mohammad Faizal Ahmad and Ahmad, Wan Siti Halimatul Munirah Wan and Jamaluddin, Mohammad Fareed and Lee, Jenny Tung Hiong and Khor, See Yee and Looi, Lai Meng and Abas, Fazly Salleh and Aldahoul, Nouar (2022) Allred scoring of ER-IHC stained whole-slide images for hormone receptor status in breast carcinoma. Diagnostics, 12 (12). ISSN 2075-4418, DOI https://doi.org/10.3390/diagnostics12123093.
Full text not available from this repository.Abstract
Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific regions and classifies them into negatively, weakly, moderately, and strongly stained, followed by Allred scoring for ER status evaluation. The generated Allred score relies heavily on accurate cell detection and classification and is compared against pathologists' manual estimation. Experiments on 40 whole-slide images show 82.5% agreement on hormonal treatment recommendation, which we believe could be further improved with an advanced learning model and enhancement to address the cases with 0% ER status. This promising system can automate the exhaustive exercise to provide fast and reliable assistance to pathologists and medical personnel. The system has the potential to improve the overall standards of prognostic reporting for cancer patients, benefiting pathologists, patients, and also the public at large.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Allred scoring; Estrogen receptor; Hormone receptor; Tumor biomarker; Breast carcinoma; Digital pathology |
| Subjects: | R Medicine > RB Pathology R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Medicine > Pathology Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Nov 2023 04:00 |
| Last Modified: | 24 Nov 2023 04:00 |
| URI: | http://eprints.um.edu.my/id/eprint/40283 |
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