Artificial intelligence in digital breast pathology: Techniques and applications
Open accessAbstract
Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
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AI algorithms can analyze medical imaging and electronic health records to detect health changes that indicate presence of cancer.
“AI algorithms skillfully analyze medical imaging and electronic health records to detect health changes that indicate the presence of cancer.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [48]
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BackgroundEvaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA
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“In 1966, Prewitt [9 e 11] and Mendelsohn [12] fi rst proposed a method to scan images from a microscopic fi eld of a blood smear and use these scanned images to discern the presence of different cell types.”
BackgroundMethodInfluentialGAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image AnalysisPreprint
Xu, Z., Huang, X., Moro, C. F., Bozóky, B., & Zhang, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04059 · Cited by 74 · cited 1× · see citing works
“H&E stained WSIs that potentially obviates the need for IHC-based tissue testing [52,76]”
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“[10].”
Enterprise Implementation of Digital Pathology: Feasibility, Challenges, and Opportunities
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“Skaland et al. [49] used open source digital image analysis software to analyse HER2 IHC stains from 60 breast cancer cases with scores of 2 þ or 3 þ and found 100% concordance between the algorithm ’ s prediction and the consensus clinical status assessment.”
MethodReview of the national external quality assessment (EQA) scheme for breast pathology in the UK
Rakha, E. A., Bennett, R. L., Coleman, D., Pinder, S. E., & Ellis, I. O. (2016) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2016-203800 · Cited by 61 · see citing works
Computer-aided gleason grading of prostate cancer histopathological images using texton forests
Khurd, P., Bahlmann, C., Maday, P., Kamen, A., Gibbs‐Strauss, S. L., Genega, E. M., & Frangioni, J. V. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490096 · Cited by 60 · cited 1× · see citing works
“In this regard, interpretability of the model becomes an important topic [64,69,70].”
BackgroundKlimov, S., Miligy, I. M., Gertych, A., Jiang, Y., Toss, M. S., Rida, P. C. G., Ellis, I. O., Green, A. R., Krishnamurti, U., Rakha, E. A., et al. (2019) · Breast Cancer Research · doi:10.1186/s13058-019-1165-5 · Cited by 60 · cited 2× · see citing works
“Furthermore, it signi fi cantly identi fi ed patients that might bene fi t from additional therapy (validation cohort p ¼ 0.0006) [59].”
BackgroundMethodInfluentialBreast cancer histologic grading using digital microscopy: concordance and outcome association
Rakha, E. A., Aleskandarani, M., Toss, M. S., Green, A. R., Ball, G., Ellis, I. O., & Dalton, L. W. (2018) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2017-204979 · Cited by 51 · cited 1× · see citing works
“However, this is often not straightforward and signi fi cant research has gone into improving reliability and reducing variability of the assessment [6 e 8].”
BackgroundDiagnostic Efficiency in Digital Pathology
Mills, A. M., Gradecki, S. E., Horton, B. J., Blackwell, R., Moskaluk, C. A., Mandell, J. W., Mills, S. E., & Cathro, H. P. (2017) · The American Journal of Surgical Pathology · doi:10.1097/pas.0000000000000930 · Cited by 47 · cited 1× · see citing works
“A similar study conducted by Mills et al. assessed diagnostic ef fi ciency on 510 surgical pathology cases and found a median increase in diagnostic time of 4 s per case for digital reads compared to glass [16].”
MethodRakha, E. A., Ahmed, M., Aleskandarany, M. A., Hodi, Z., Lee, A. H. S., Pinder, S. E., & Ellis, I. O. (2016) · Histopathology · doi:10.1111/his.13117 · Cited by 36 · see citing works
Microarray data analysis for cancer classification
Osareh, A., & Shadgar, B. (2010) · International Symposium on Health Informatics and Bioinformatics (HIBIT) · doi:10.1109/hibit.2010.5478893 · Cited by 33 · cited 1× · see citing works
“using digitalized images of fine-needle aspiration biopsy samples [73].”
MethodMulti-Pass Adaptive Voting for Nuclei Detection in Histopathological Images
Lu, C. Y., Xu, H., Xu, J., Gilmore, H. L., Mandal, M. K., & Madabhushi, A. (2016) · Scientific Reports · doi:10.1038/srep33985 · Cited by 31 · cited 2× · see citing works
“tumour microenvironment AI-based assays to measure tumour intra-tumour and inter-tumour heterogeneity [26,56], identify and quantify nonepithelial cells such as fibroblast, neutrophils, lymphocytes and macrophages [77] and computerized image-based detection and grading of tumour infiltrating lymphocytic (TILs) in HER2…”
BackgroundMethodHossain, M. S., Hanna, M. G., Uraoka, N., Nakamura, T., Edelweiss, M., Brogi, E., Hameed, M., Yamaguchi, M., Ross, D. S., & Yagi, Y. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.4.047501 · Cited by 29 · cited 1× · see citing works
“[52], ER [50], PR and Her2neu images [75].”
BackgroundAutomatic Lymphocyte Detection in H&E Images with Deep Neural NetworksPreprint
Chen, J., & Srinivas, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03217 · Cited by 14 · see citing works
Sahiner, B., Tozbikian, G. H., Lozanski, G., Gürcan, M. N., & Şenaras, Ç. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2294999 · Cited by 14 · cited 2× · see citing works
“…of tumour in fi ltrating lymphocytic (TILs) in HER2 þ breast cancer [78] have been developed Receptor status and intrinsic subtype assessment AI algorithms have been developed to provide quantitative measurements of immunohistochemically stained Ki-67 [52], ER [50], PR and Her2neu images [75].”
MethodInfluentialTőkés, T., Tőkés, A.-M., Szentmártoni, G., Kiszner, G., Madaras, L., Kulka, J., Krenács, T., & Dank, M. (2016) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-016-1925-x · Cited by 11 · cited 1× · see citing works
“[80] or the presence of certain morphological features in the tumour to the response of specific therapy.”
BackgroundAndré, F., Ismaila, N., & Stearns, V. (2019) · Journal of Oncology Practice · doi:10.1200/jop.19.00264 · Cited by 9 · cited 1× · see citing works
“Current clinical practice relies on immunohistochemical (IHC) staining and manual comparison of staining intensity against a set of biomarker-speci fi c scoring systems [43,44].”
Method
Flagged first, then by citations
Research works printed but not linked: 3
- [77]Website Automatic lymphocyte detection in H&E images with deep neural networks 2016 https://arxiv.org/abs/1612.03217 · arXiv
- [78]Basavanhally A.N. Ganesan S. Agner S. Computerized image-based detection and grading of lymphocytic infiltration in HER2 breast cancer histopathology IEEE (Inst Electr Electron Eng) Trans Biomed Eng 57 3 2010 642 653 10.1109/tbme.2009.2035305 19884074 · DOI
- [79]Website Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer J R Soc Interface 2015 10.1098/rsif.2014.1153 PMC4305416 25505134 · DOI
Cited by
Sorted by citations
RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
Quazi, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01711-1 · Cited by 455 · see citing works
“In particular, ML and DL centered methodologies have gained popularity and developed as critical components of biomedical data analysis, owing to the abundance of medical data and the rapid advancement of analytics tools [9–13].”
RetractedArtificial intelligence (AI) and big data in cancer and precision oncology
Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.019 · Cited by 412 · see citing works
“Whole slide imaging (WSI) in the latest innovation in digital pathology [55].”
BackgroundArtificial intelligence in diagnostic pathology
Shafi, S., & Parwani, A. V. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01375-z · Cited by 350 · see citing works
“WSIs can be used to extract several features using computer vision algorithms, thereby enabling diagnostic predictions [64–69].”
BackgroundMethodA framework for breast cancer classification using Multi-DCNNs
Ragab, D. A., Attallah, O., Sharkas, M., Ren, J., & Marshall, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104245 · Cited by 205 · see citing works
Hierarchical graph representations in digital pathology
Pati, P., Jaume, G., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102264 · Cited by 168 · see citing works
“Further, they enabled computed aided diagnostics to leverage artificial intelligence [7], [8], especially deep learning, to address various digital pathology tasks in breast diagnosis [9], such as nuclei segmentation [10], [11], nuclei classification [12], [13], gland segmentation [14], [15], tumor detection [16]–[18]…”
BackgroundWaqas, A., Bui, M. M., Glassy, E. F., Naqa, I. E., Borkowski, P., Borkowski, A., & Rasool, G. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100255 · Cited by 149 · see citing works
Deep learning of rock images for intelligent lithology identification
Xu, Z., Ma, W., Lin, P., Shi, H., Pan, D., & Liu, T. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104799 · Cited by 136 · see citing works
Current and future applications of artificial intelligence in pathology: a clinical perspective
Rakha, E. A., Toss, M. S., Shiino, S., Gamble, P. R., Jaroensri, R., Mermel, C. H., & Chen, P.-H. C. (2020) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2020-206908 · Cited by 127 · see citing works
“23 25 27 Accurate assessment of quantitative features such as immunohistochemical biomarker assessment, counting cells and evaluating the degree of various tissue features such as spatial arrangement of cells, density of structures, pattern of distribution and architecture of the tissue are other…”
BackgroundBadidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
The state of the art for artificial intelligence in lung digital pathology
Viswanathan, V. S., Toro, P., Corredor, G., Mukhopadhyay, S., & Madabhushi, A. (2022) · The Journal of Pathology · doi:10.1002/path.5966 · Cited by 105 · see citing works
Patel, S. K., George, B., & Rai, V. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.01177 · Cited by 100 · see citing works
Closing the translation gap: AI applications in digital pathology
Steiner, D. F., Chen, P.-H. C., & Mermel, C. H. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188452 · Cited by 89 · see citing works
Hameed, Z., García-Zapirain, B., Aguirre, J. J., & Isaza-Ruget, M. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19278-2 · Cited by 89 · see citing works
Deep learning models for histologic grading of breast cancer and association with disease prognosis
Jaroensri, R., Wulczyn, E., Hegde, N., Brown, T., Flament-Auvigne, I., Tan, F. E., Cai, Y., Nagpal, K., Rakha, E. A., Dabbs, D. J., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00478-y · Cited by 84 · see citing works
Digital Pathology: Transforming Diagnosis in the Digital Age
Kiran, N. K., Sapna, F., Kiran, F., Kumar, D., Raja, F., Shiwlani, S., Paladini, A., Sonam, F., Bendari, A., Perkash, R. S., et al. (2023) · Cureus · doi:10.7759/cureus.44620 · Cited by 83 · see citing works
“The constraints above significantly impact efficiency, collaboration, and accessibility, thereby driving the transition toward digital pathology [3].”
BackgroundSenousy, Z., Abdelsamea, M. M., Gaber, M. M., Abdar, M., Acharya, U. R., Khosravi, A., & Nahavandi, S. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3107446 · Cited by 83 · see citing works
“Thanks to the advancement of computer aid diagnosis (CAD) frameworks that have made the difference in reducing the workload and improved the detection accuracy [2].”
BackgroundA survey on artificial intelligence in histopathology image analysis
Abdelsamea, M. M., Zidan, U., Senousy, Z., Gaber, M. M., Rakha, E. A., & Ilyas, M. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1474 · Cited by 80 · see citing works
“With the recent trend of deep learning, more publications are using learning-based methods due to their superior performance (Ibrahim et al., 2020).”
BackgroundKeelan, S., Flanagan, M. P., & Hill, A. D. K. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.622621 · Cited by 75 · see citing works
“These advances in computer aided diagnostics have the potential to replace some of the expensive multi-gene assays (105, 106).”
MethodThakur, N., Alam, M. R., Abdul‐Ghafar, J., & Chong, Y. (2022) · Cancers · doi:10.3390/cancers14143529 · Cited by 63 · see citing works
“If images are scanned at low resolution, then the model will be unable to differentiate the detailed information required to evaluate tissues [66].”
BackgroundC-Net: A reliable convolutional neural network for biomedical image classification
Barzekar, H., & Yu, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116003 · Cited by 58 · see citing works
“The accuracy of the models is growing continuously as the digital pathology is expanding and more datasets are becoming publicly available [13].”
BackgroundMcCaffrey, C., Jahangir, C. A., Murphy, C. L., Burke, C., Gallagher, W. M., & Rahman, A. (2024) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2024.2346545 · Cited by 60 · see citing works
“The utility of AI in detecting image-based prognostic/predictive biomarkers has been demonstrated to be particularly promising for breast cancer [4–6].”
BackgroundWahab, N., Miligy, I. M., Dodd, K., Sahota, H., Toss, M. S., Lu, W., Jahanifar, M., Bilal, M., Graham, S., Park, Y., et al. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.256 · Cited by 52 · see citing works
“However, the verification may be time-consuming and the AI annotations may bias the pathologist to agree with the AI annotations on regions or cells that are hard for humans to differentiate [35].”
BackgroundArtificial Intelligence in Pathology: From Prototype to Product
Homeyer, A., Lotz, J., Schwen, L. O., Weiss, N., Romberg, D., Höfener, H., Zerbe, N., & Hufnagl, P. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_84_20 · Cited by 45 · see citing works
“[6‐10] Recent reviews discuss AI applications in pathology and specifically in breast pathology.[11, 12] Another much‐studied application of AI is the extraction of novel immuno‐ oncology biomarkers from pathology images.”
BackgroundWerutsky, G., Barrios, C. H., Cardona, A. F., Albergaria, A., Valencia, A., Ferreira, C. G., Rolfo, C. D., de Azambuja, E., Rabinovich, G. A., Sposetti, G., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00523-4 · Cited by 45 · see citing works
Albusayli, R., Graham, J. D., Pathmanathan, N., Shaban, M., Raza, S. E. A., Minhas, F., Armes, J. E., & Rajpoot, N. (2023) · The Journal of Pathology · doi:10.1002/path.6061 · Cited by 42 · see citing works
“However, the digitisation of tissue slides into whole-slide images (WSIs) opens the possibility of studying the spatial tumour microenvironment (sTME) to mine for novel digital image-based markers using modern artificial intelligence (AI) and deep learning-based image analysis [21].”
BackgroundDodington, D. W., Lagree, A., Tabbarah, S., Mohebpour, M., Naini, A. S., Tran, W. T., & Lu, F. (2021) · Breast Cancer Research and Treatment · doi:10.1007/s10549-020-06093-4 · Cited by 40 · see citing works
Value assessment of artificial intelligence in medical imaging: a scoping review
Fasterholdt, I., Naghavi‐Behzad, M., Rasmussen, B. S., Kjølhede, T., Skjøth, M. M., Hildebrandt, M. G., & Kidholm, K. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00918-y · Cited by 38 · see citing works
Liu, Y., Bilodeau, E. A., Pollack, B., & Batmanghelich, K. N. (2022) · Oral Oncology · doi:10.1016/j.oraloncology.2022.106109 · Cited by 36 · see citing works
Agouri, H. E., Azizi, M. M., Attar, H. E., Khannoussi, M. E., Ibrahimi, A., Kabbaj, R., Kadiri, H., Bekarsabein, S., Echcharif, S., Mounjid, C., et al. (2022) · BMC Research Notes · doi:10.1186/s13104-022-05936-1 · Cited by 35 · see citing works
“Meanwhile, most medical image datasets are usually much smaller because of patient privacy issues and the need for expert annotation and labelling [4].”
BackgroundHacking, S. M., Yakirevich, E., & Wang, Y. (2022) · Cancers · doi:10.3390/cancers14143469 · Cited by 34 · see citing works
Innovations and Challenges in Breast Cancer Care: A Review
Chopra, S., Khosla, M., & Vidya, R. (2023) · Medicina · doi:10.3390/medicina59050957 · Cited by 34 · see citing works
“It can help speed up the diagnostic process with a limited workforce [45].”
BackgroundDuenweg, S. R., Bobholz, S. A., Lowman, A. K., Stebbins, M. A., Winiarz, A., Nath, B., Kyereme, F., Iczkowski, K. A., & LaViolette, P. S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100321 · Cited by 35 · see citing works
The Underlying Mechanisms and Emerging Strategies to Overcome Resistance in Breast Cancer
Kannan, K., Srinivasan, A., Kannan, A., & Ali, N. (2025) · Cancers · doi:10.3390/cancers17172938 · Cited by 34 · see citing works
“Recent ad-Cancers vances in AI medicine aid in early detection, whereas nanotechnology enables targeted drug delivery [62].”
Artificial intelligence in renal pathology: Current status and future
Feng, C., & Liu, F. (2022) · Biomolecules and Biomedicine · doi:10.17305/bjbms.2022.8318 · Cited by 35 · see citing works
“In recent years, DL algorithms have shown excellent application prospects in pathology image analysis, for example, tumor region identification and metastasis detection in oncology [9, 10, 15, 21, 22].”
BackgroundArtificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application
Ibrahim, A., Jahanifar, M., Wahab, N., Toss, M. S., Makhlouf, S., Atallah, N. M., Lashen, A., Katayama, A., Graham, S., Bilal, M., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100416 · Cited by 33 · see citing works
Zhang, X., Liu, S. S., Ma, J., & Qu, W. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154633 · Cited by 31 · see citing works
Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature
Wang, X., & Li, B. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.624820 · Cited by 30 · see citing works
A pyramid architecture-based deep learning framework for breast cancer detection
Sui, D., Liu, W., Chen, J., Zhao, C., Ma, X., Guo, M., & Tian, Z. (2021) · BioMed Research International · doi:10.1155/2021/2567202 · Cited by 29 · see citing works
Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology
Fatima, G., Al-Hmadi, H. B., Mahdi, A. A., Hadi, N. R., Fedačko, J., Magomedova, A. G., Parvez, S., & Raza, A. M. (2024) · Cureus · doi:10.7759/cureus.71890 · Cited by 29 · see citing works
“By augmenting the pathologist's expertise with AI, digital pathology has the potential to reduce diagnostic errors and ensure more consistent and accurate results [3].”
Anaya-Isaza, A., Mera-Jiménez, L., Cabrera-Chavarro, J. M., Guachi‐Guachi, L., Peluffo-Ordóñez, D. H., & Patiño, J. I. R. (2021) · IEEE Access · doi:10.1109/access.2021.3127862 · Cited by 29 · see citing works
Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice
Atallah, N. M., Toss, M. S., Verrill, C. L., Salto‐Tellez, M., Snead, D. R. J., & Rakha, E. A. (2021) · Modern Pathology · doi:10.1038/s41379-021-01000-8 · Cited by 29 · see citing works
Song, J., Zheng, Y., Ullah, M. Z., Wang, J., Jiang, Y., Xu, C., Zou, Z., & Ding, G. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02391-4 · Cited by 28 · see citing works
Greenberg, A., Aizic, A., Zubkov, A., Borsekofsky, S., Hagege, R. R., & Hershkovitz, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-82869-y · Cited by 26 · see citing works
Vidal, J. M., Tsiknakis, N., Staaf, J., Bosch, A., Ehinger, A., Niméus, E., Salgado, R., Bai, Y., Rimm, D. L., Hartman, J., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102928 · Cited by 26 · see citing works
Transformative laboratory medicine enabled by microfluidic automation and artificial intelligence
Huang, P., Lan, H., Liu, B., Mo, Y., Gao, Z., Ye, H., & Pan, T. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.117046 · Cited by 26 · see citing works
Chen, Y., Li, H., Janowczyk, A. R., Toro, P., Corredor, G., Whitney, J. R., Lu, C. Y., Koyuncu, C., Mokhtari, M., Buzzy, C. A., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00545-y · Cited by 26 · see citing works
Katayama, A., Aoki, Y., Watanabe, Y., Horiguchi, J., Rakha, E. A., & Oyama, T. (2024) · International Journal of Clinical Oncology · doi:10.1007/s10147-024-02513-3 · Cited by 25 · see citing works
“Addressing biases in training datasets, ensuring algorithm robustness, validating biomarkers across diverse populations, and ensuring robust data privacy are ongoing challenges [1, 4].”
InfluentialDeep learning assessment of metastatic relapse risk from digitized breast cancer histological slides
Garberis, I., Gaury, V., Saillard, C., Drubay, D., Elgui, K., Schmauch, B., Jaeger, A., Herpin, L., Linhart, J., Sapateiro, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60824-z · Cited by 24 · see citing works
Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence
Ivanova, M. D., Pescia, C., Trapani, D., Venetis, K., Frascarelli, C., Mane, E., Cursano, G., Sajjadi, E., Scatena, C., Cerbelli, B., et al. (2024) · Cancers · doi:10.3390/cancers16111981 · Cited by 25 · see citing works
“The widely employed visual “eyeballing” method is time-efficient, and numerous studies have underscored substantial interobserver variability associated with this approach [87,88].”
Ponraj, A., Nagaraj, P., Balakrishnan, D., Srinivasu, P. N., Shafi, J., Kim, W., & Ijaz, M. F. (2025) · Digital Health · doi:10.1177/20552076241313161 · Cited by 22 · see citing works
Artificial Intelligence: Singularity Approaches
TerKonda, S. P., TerKonda, A. A., Sacks, J. M., Kinney, B. M., Gurtner, G. C., Nachbar, J. M., Reddy, S., & Jeffers, L. L. C. (2023) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000010572 · Cited by 24 · see citing works
The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies
Jiang, V. S., Pavlovic, Z. J., & Hariton, E. (2023) · Obstetrics and Gynecology Clinics of North America · doi:10.1016/j.ogc.2023.09.003 · Cited by 22 · see citing works
Rakha, A., Hettiarachchi, H., Rady, D., Gaber, M. M., Rakha, E. A., & Abdelsamea, M. M. (2021) · Economies · doi:10.3390/economies9040137 · Cited by 22 · see citing works
Artificial Intelligence in Pharmaceutical Field - A Critical Review
Shanbhogue, M. H., Thirumaleshwar, S., Tegginamath, P. K., & Hemanth Kumar, S. (2021) · Current Drug Delivery · doi:10.2174/1567201818666210617100613 · Cited by 21 · see citing works
Brattoli, B., Mostafavi, M., Lee, T., Jung, W., Ryu, J., Park, S., Park, J., Pereira, S., Shin, S.-H., Choi, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00770-z · Cited by 19 · see citing works
Landscape of Artificial Intelligence in Breast Cancer (2000–2021): A Bibliometric Analysis
Zhang, Y., Yu, C., Zhao, F., Xu, H., Zhu, C., & Li, Y. (2022) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2708224 · Cited by 19 · see citing works
“Advances of AI also accelerate the improvement of digital histopathological diagnosis, with more accurate detection, classification and prediction [11].”
BackgroundFaur, A. C., Buzaș, R., Lăzărescu, A. E., & Ghenciu, L. A. (2024) · Life · doi:10.3390/life14060727 · Cited by 19 · see citing works
“The inputs for these ML can be data represented by age, weight, gender, features considered risk factors, gross aspects, anatomical landmarks or histopathological slides [66,67].”
BackgroundZhu, Q., Dai, H., Qiu, F., Lou, W., Wang, X., Deng, L., & Shi, C. (2024) · Translational Oncology · doi:10.1016/j.tranon.2023.101855 · Cited by 18 · see citing works
“It can be used as a convenient and ideal method in clinical auxiliary diagnosis [25,26].”
MethodSong, J., Zheng, Y., Xu, C., Zou, Z., Ding, G., & Huang, W. (2021) · Medical Physics · doi:10.1002/mp.15390 · Cited by 17 · see citing works
Nittas, V., Daniore, P., Landers, C., Gille, F., Amann, J., Hubbs, S., Puhan, M. A., Vayena, E., & Blasimme, A. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000189 · Cited by 19 · see citing works
Siddiqui, M. F., Mouna, A., Nicolas, G. N., Rahat, S. A. A., Nurmamatovna, M. A., Emmanuel, N., & Tashmatova, N. M. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9221-5_14 · Cited by 16 · see citing works
A transfer learning‐based system for grading breast invasive ductal carcinoma
Sujatha, R., Chatterjee, J. M., Angelopoulou, A., Kapetanios, E., Srinivasu, P. N., & Hemanth, D. J. (2022) · IET Image Processing · doi:10.1049/ipr2.12660 · Cited by 16 · see citing works
“Reference [14] illustrated various applications carried out for tumour detection, grading, and subtype the breast cancer, prediction, and medication for the same with the AI approach.”
Bujak, J. K., Kłęk, S., Balawejder, M., Kociniak, A., Wilkus, K., Szatanek, R., Orzeszko, Z., Welanyk, J., Torbicz, G., Jęckowski, M., et al. (2023) · JMIR Research Protocols · doi:10.2196/45872 · Cited by 16 · see citing works
“There have been successful attempts at applying AI in clinical histopathology [15,16], mammogram analysis [17], and immuno-oncological studies involving checkpoint inhibitors”
BackgroundA survey on cell nuclei instance segmentation and classification: Leveraging context and attention
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- Authors
- Ibrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C., & Rakha, E. A.
- Affiliations
- Nottingham University Hospitals NHS Trust (GB)
- Google (United States) (US)
- Birmingham City University (GB)
- Corresponding author
- Rakha, Emad A.
- Venue
- The Breast
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 49
- Pages
- 267-273
- ISSN
- 0960-9776 (ISSN-L)
- PMC
- PMC7375550
- OpenAlex
- W2995106101
- Cited by
- 202
- References
- 81
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 39 · 2023: 45 · 2022: 34 · 2021: 30 · 2020: 11 · 2019: 1
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Breast (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, digital pathology, breast cancer, histopathological diagnosis, diagnostic precision
- MeSH terms
- Artificial Intelligence, Breast, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted
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