Ayat Lashen

478 total citations
24 papers, 234 citations indexed

About

Ayat Lashen is a scholar working on Oncology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Ayat Lashen has authored 24 papers receiving a total of 234 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 15 papers in Cancer Research and 7 papers in Artificial Intelligence. Recurrent topics in Ayat Lashen's work include Breast Cancer Treatment Studies (12 papers), Cancer Genomics and Diagnostics (9 papers) and AI in cancer detection (7 papers). Ayat Lashen is often cited by papers focused on Breast Cancer Treatment Studies (12 papers), Cancer Genomics and Diagnostics (9 papers) and AI in cancer detection (7 papers). Ayat Lashen collaborates with scholars based in United Kingdom, Egypt and United States. Ayat Lashen's co-authors include Emad A. Rakha, Michael S. Toss, Nigel P. Mongan, Andrew R. Green, Asmaa Ibrahim, Ayaka Katayama, Srinivasan Madhusudan, Graham Ball, Mohsin Bilal and Shan E Ahmed Raza and has published in prestigious journals such as Cancer, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Ayat Lashen

21 papers receiving 230 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ayat Lashen United Kingdom 11 101 87 74 72 70 24 234
Gustaf Rosin Sweden 8 132 1.3× 154 1.8× 76 1.0× 120 1.7× 57 0.8× 10 340
Chiara Maria Lavinia Loeffler Germany 8 74 0.7× 73 0.8× 108 1.5× 43 0.6× 112 1.6× 12 240
Junjie Kuang China 6 77 0.8× 163 1.9× 65 0.9× 74 1.0× 118 1.7× 11 344
Yangshan Chen China 11 146 1.4× 167 1.9× 66 0.9× 200 2.8× 85 1.2× 17 454
Tiebao Meng China 11 73 0.7× 56 0.6× 75 1.0× 67 0.9× 221 3.2× 20 360
Guolin Ye China 10 153 1.5× 114 1.3× 29 0.4× 141 2.0× 35 0.5× 21 296
Jiarui Jiang China 11 185 1.8× 77 0.9× 70 0.9× 208 2.9× 96 1.4× 11 399
Gul S. Dalgin United States 9 122 1.2× 151 1.7× 45 0.6× 190 2.6× 27 0.4× 12 400
Liantao Guo China 7 76 0.8× 121 1.4× 22 0.3× 92 1.3× 42 0.6× 14 266
Paula Toro United States 8 37 0.4× 58 0.7× 92 1.2× 23 0.3× 125 1.8× 27 239

Countries citing papers authored by Ayat Lashen

Since Specialization
Citations

This map shows the geographic impact of Ayat Lashen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ayat Lashen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ayat Lashen more than expected).

Fields of papers citing papers by Ayat Lashen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ayat Lashen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ayat Lashen. The network helps show where Ayat Lashen may publish in the future.

Co-authorship network of co-authors of Ayat Lashen

This figure shows the co-authorship network connecting the top 25 collaborators of Ayat Lashen. A scholar is included among the top collaborators of Ayat Lashen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ayat Lashen. Ayat Lashen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
3.
Lashen, Ayat, Noorul Wahab, Michael S. Toss, et al.. (2024). Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence. Cancers. 16(22). 3849–3849. 1 indexed citations
4.
Lashen, Ayat, Michael S. Toss, Sameer Mirza, et al.. (2024). The characteristics and prognostic significance of histone H1 expression in breast cancer. Pathology. 56(6). 826–833. 2 indexed citations
5.
Lashen, Ayat, et al.. (2024). Clinicopathological Significance of Cyclin-Dependent Kinase 2 (CDK2) in Ductal Carcinoma In Situ and Early-Stage Invasive Breast Cancers. International Journal of Molecular Sciences. 25(9). 5053–5053. 8 indexed citations
6.
Lashen, Ayat, et al.. (2024). The Clinicopathological Significance of the Cyclin D1/E1–Cyclin-Dependent Kinase (CDK2/4/6)–Retinoblastoma (RB1/pRB1) Pathway in Epithelial Ovarian Cancers. International Journal of Molecular Sciences. 25(7). 4060–4060. 6 indexed citations
7.
Lashen, Ayat, Michael S. Toss, Catrin S. Rutland, et al.. (2024). Prognostic and Clinical Significance of the Proliferation Marker MCM7 in Breast Cancer. Pathobiology. 92(1). 1–10.
8.
Lashen, Ayat, Michael S. Toss, Islam M. Miligy, et al.. (2024). Nottingham prognostic x (NPx): a risk stratification tool in ER‐positive HER2‐negative breast cancer: a validation study. Histopathology. 85(3). 468–477. 1 indexed citations
9.
Wahab, Noorul, Michael S. Toss, Asmaa Ibrahim, et al.. (2023). Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. British Journal of Cancer. 129(11). 1747–1758. 22 indexed citations
10.
Lashen, Ayat, Jennie N. Jeyapalan, Michael S. Toss, et al.. (2023). Immune infiltration, aggressive pathology, and poor survival outcomes in RECQL helicase deficient breast cancers. Neoplasia. 47. 100957–100957. 3 indexed citations
11.
Lashen, Ayat, Michael S. Toss, Nigel P. Mongan, Andrew R. Green, & Emad A. Rakha. (2023). The clinical value of progesterone receptor expression in luminal breast cancer: A study of a large cohort with long‐term follow‐up. Cancer. 129(8). 1183–1194. 10 indexed citations
12.
Lashen, Ayat, et al.. (2023). Expression, assessment and significance of Ki67 expression in breast cancer: an update. Journal of Clinical Pathology. 76(6). 357–364. 20 indexed citations
13.
Ibrahim, Asmaa, Mostafa Jahanifar, Noorul Wahab, et al.. (2023). Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Modern Pathology. 37(3). 100416–100416. 11 indexed citations
14.
Wahab, Noorul, Michael S. Toss, Asmaa Ibrahim, et al.. (2023). Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence. Modern Pathology. 36(10). 100254–100254. 5 indexed citations
15.
Wahab, Noorul, Michael S. Toss, Islam M. Miligy, et al.. (2023). AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer. npj Precision Oncology. 7(1). 122–122. 8 indexed citations
16.
Lu, Wenqi, Ayat Lashen, Noorul Wahab, et al.. (2023). AI‐based intra‐tumor heterogeneity score of Ki67 expression as a prognostic marker for early‐stage ER+/HER2− breast cancer. The Journal of Pathology Clinical Research. 10(1). e346–e346. 4 indexed citations
17.
Lashen, Ayat, Michael S. Toss, Andrew R. Green, et al.. (2023). Characteristics and prognostic significance of polo‐like kinase‐1 (PLK1) expression in breast cancer. Histopathology. 83(3). 414–425. 14 indexed citations
18.
Lashen, Ayat, Michael S. Toss, Ayaka Katayama, et al.. (2021). Assessment of proliferation in breast cancer: cell cycle or mitosis? An observational study. Histopathology. 79(6). 1087–1098. 18 indexed citations
19.
Lashen, Ayat, Asmaa Ibrahim, Ayaka Katayama, et al.. (2021). Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images. Histopathology. 79(6). 913–925. 11 indexed citations
20.
Ibrahim, Asmaa, Ayat Lashen, Ayaka Katayama, et al.. (2021). Defining the area of mitoses counting in invasive breast cancer using whole slide image. Modern Pathology. 35(6). 739–748. 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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