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المؤلفSaidul, Kabir
المؤلفVranic, Semir
المؤلفMahmood Al Saady, Rafif
المؤلفSalman Khan, Muhammad
المؤلفSarmun, Rusab
المؤلفAlqahtani, Abdulrahman
المؤلفAbbas, Tariq O.
المؤلفChowdhury, Muhammad E.H.
تاريخ الإتاحة2024-03-14T10:47:42Z
تاريخ النشر2023-10-07
اسم المنشورExpert Systems with Applications
المعرّفhttp://dx.doi.org/10.1016/j.eswa.2023.122051
الاقتباسKabir, S., Vranic, S., Al Saady, R. M., Khan, M. S., Sarmun, R., Alqahtani, A., ... & Chowdhury, M. E. (2024). The utility of a deep learning-based approach in Her-2/neu assessment in breast cancer. Expert Systems with Applications, 238, 122051.
الرقم المعياري الدولي للكتاب0957-4174
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S0957417423025538
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53087
الملخصIntroductionHER-2/neu is a protein present on the surface of specific cancer cells and has been linked to the development and progression of certain cancer types. It is present in 15 to 20% of breast cancers and is clinically significant due to the availability of multiple anti-Her2 treatment options. Immunohistochemistry (IHC) is the most commonly used method to evaluate and quantify the expression of Her-2/neu. Although IHC is well-standardized in clinical practice, it is still subjected to inter-observer variability. Automating Her-2/neu scoring can improve accuracy, efficiency, consistency, and cost-effectiveness while reducing pathologists' workload. Materials and MethodsA deep learning-based automatic framework was utilized for the automatic detection of Her-2/neu score from whole slide images (WSI). The framework consists of three phases: identification of tumor patches, scoring of tumor patches, and Her-2/neu score prediction for whole slide images (WSI) based on the distribution of each score. This work used the dataset from the University of Warwick HER2 challenge contest. Two expert pathologists evaluated all 86 WSIs and assigned Her-2/neu scores to them. In addition, patches were generated from 50 WSIs and annotated individually by the pathologists. A total of 6641 extracted patches were generated out of which, 947 were labeled as 0, 327 as 1+, 1401 as 2+, 2950 as 3+, and 1016 were marked for discarding. Four pre-trained image classification models, namely DenseNet201, GoogleNet, MobileNet_v2, and a Vision Transformer based model, were fine-tuned, and tested on the generated patches. In order to predict the Her-2/neu score of the entire WSI, a random forest classifier was trained to predict the Her-2/neu score from the percentages of patches of each score present in the whole slide image. ResultsIn patch classification performances, the vision transformer-based model outperformed the other models by achieving an accuracy of 92.6% on tumor patch classification and 91.15% on patch score classification. The random forest classifier achieved an accuracy of 88% on four score classification (0, 1+, 2 + and 3 + ) and 96% on three score classification (0/1+, 2 + and 3 + ). ConclusionThe proposed deep learning-based framework for the automatic detection and evaluation of Her-2/neu expression in breast cancer obtained encouraging results. This framework has the potential to be used as a prognostic tool, providing a cost-effective and time-efficient alternative for generating clinically relevant results. However, additional research is required to assess the applicability of this pipeline in different contexts.
راعي المشروعThis work was made possible by student grant # QUST-1-CENG-2023–941 from the Qatar University and is also supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1444). The open access publication cost is covered by Qatar National Library.
اللغةen
الناشرElsevier
الموضوعHer-2/neu
Breast cancer
Immunohistochemistry (IHC)
Hematoxylin-eosin (H&E)
Artificial intelligence
Deep learning
Classification
العنوانThe utility of a deep learning-based approach in Her-2/neu assessment in breast cancer
النوعArticle
رقم المجلد238
Open Access user License http://creativecommons.org/licenses/by/4.0/
ESSN1873-6793
dc.accessType Full Text


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