Balázs Ács

3.9k total citations
50 papers, 1.2k citations indexed

About

Balázs Ács is a scholar working on Oncology, Cancer Research and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Balázs Ács has authored 50 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Oncology, 20 papers in Cancer Research and 16 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Balázs Ács's work include Breast Cancer Treatment Studies (15 papers), Cancer Immunotherapy and Biomarkers (14 papers) and AI in cancer detection (13 papers). Balázs Ács is often cited by papers focused on Breast Cancer Treatment Studies (15 papers), Cancer Immunotherapy and Biomarkers (14 papers) and AI in cancer detection (13 papers). Balázs Ács collaborates with scholars based in Sweden, Hungary and United States. Balázs Ács's co-authors include Johan Hartman, Mattias Rantalainen, David L. Rimm, Stephanie Robertson, Maria Toki, Yinxi Wang, Janina Kulka, Yalai Bai, Sandra Martínez-Morilla and Pok Fai Wong and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Cancer Research.

In The Last Decade

Balázs Ács

45 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Balázs Ács Sweden 17 563 461 436 333 230 50 1.2k
Hanne A. Askautrud Norway 12 563 1.0× 346 0.8× 246 0.6× 227 0.7× 317 1.4× 22 1.3k
Inka Zörnig Germany 15 618 1.1× 602 1.3× 469 1.1× 158 0.5× 352 1.5× 37 1.4k
Andreas Kleppe Norway 11 368 0.7× 460 1.0× 356 0.8× 219 0.7× 189 0.8× 22 1.0k
Robin Edwards United States 12 492 0.9× 205 0.4× 195 0.4× 219 0.7× 493 2.1× 25 1.1k
Maeve Mullooly Ireland 17 614 1.1× 291 0.6× 213 0.5× 264 0.8× 343 1.5× 49 1.2k
Dyke Ferber Germany 11 386 0.7× 507 1.1× 579 1.3× 116 0.3× 129 0.6× 23 1.1k
António Polónia Portugal 18 366 0.7× 809 1.8× 977 2.2× 161 0.5× 397 1.7× 49 1.7k
Stephanie Robertson Sweden 14 408 0.7× 296 0.6× 298 0.7× 195 0.6× 430 1.9× 28 1.1k
Michael Bockmayr Germany 21 649 1.2× 170 0.4× 172 0.4× 278 0.8× 394 1.7× 35 1.4k
Panu E. Kovanen Finland 26 691 1.2× 415 0.9× 422 1.0× 237 0.7× 627 2.7× 44 2.3k

Countries citing papers authored by Balázs Ács

Since Specialization
Citations

This map shows the geographic impact of Balázs Ács'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 Balázs Ács with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Balázs Ács more than expected).

Fields of papers citing papers by Balázs Ács

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Balázs Ács. 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 Balázs Ács. The network helps show where Balázs Ács may publish in the future.

Co-authorship network of co-authors of Balázs Ács

This figure shows the co-authorship network connecting the top 25 collaborators of Balázs Ács. A scholar is included among the top collaborators of Balázs Ács 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 Balázs Ács. Balázs Ács 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.
Li, Tianyi, Balázs Ács, Emmanouil G. Sifakis, et al.. (2025). Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data. npj Precision Oncology. 9(1). 310–310.
2.
Zerdes, Ioannis, Alexios Matikas, Artur Mezheyeuski, et al.. (2025). Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial. npj Breast Cancer. 11(1). 23–23. 3 indexed citations
3.
Liu, Xingrong, Balázs Ács, Sibylle Loibl, et al.. (2025). Prevalence and prognosis of patients with breast cancer eligible for adjuvant abemaciclib or ribociclib: a nationwide population-based study. The Lancet Regional Health - Europe. 59. 101471–101471.
4.
Ács, Balázs, Johan Hartman, Irma Fredriksson, et al.. (2024). Immune cell infiltrate in ductal carcinoma in situ and the risk of dying from breast cancer: case–control study. British journal of surgery. 111(2). 2 indexed citations
5.
Tsiknakis, Nikos, Georgios C. Manikis, Kang Wang, et al.. (2024). Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images. IEEE Journal of Biomedical and Health Informatics. 28(9). 5312–5322. 3 indexed citations
6.
Tsiknakis, Nikos, Johan Staaf, Ana Bosch, et al.. (2024). The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?. EClinicalMedicine. 78. 102928–102928. 6 indexed citations
7.
Wang, Yinxi, Wenwen Sun, Emelié Karlsson, et al.. (2024). Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay. Breast Cancer Research and Treatment. 206(1). 163–175. 4 indexed citations
8.
Looijen-Salamon, Monika, Shoko Vos, Enrico Munari, et al.. (2024). Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images. Scientific Reports. 14(1). 7136–7136. 12 indexed citations
9.
Tsiknakis, Nikos, Ioannis Zerdes, Georgios C. Manikis, et al.. (2023). Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis. PubMed. 34. 1–4. 2 indexed citations
10.
Bai, Yalai, Kimberly Cole, Sandra Martínez-Morilla, et al.. (2021). An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer. Clinical Cancer Research. 27(20). 5557–5565. 38 indexed citations
11.
Wang, Yinxi, Kimmo Kartasalo, Balázs Ács, et al.. (2021). Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer. Cancer Research. 81(19). 5115–5126. 41 indexed citations
12.
Kraft, Christian, Balázs Ács, Attila Marcell Szász, et al.. (2021). In Situ Hybridization of PRRSV-1 Combined with Digital Image Analysis in Lung Tissues of Pigs Challenged with PRRSV-1. Veterinary Sciences. 8(10). 235–235. 4 indexed citations
13.
Wang, Yinxi, Balázs Ács, Stephanie Robertson, et al.. (2021). Improved breast cancer histological grading using deep learning. Annals of Oncology. 33(1). 89–98. 125 indexed citations
14.
Zerdes, Ioannis, Alexios Matikas, Balázs Ács, et al.. (2021). Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study. npj Breast Cancer. 7(1). 144–144. 8 indexed citations
15.
Wong, Pok Fai, Wei Wei, James W. Smithy, et al.. (2019). Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma. Clinical Cancer Research. 25(8). 2442–2449. 103 indexed citations
16.
Ács, Balázs, Vasiliki Pelekanou, Yalai Bai, et al.. (2018). Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study. Laboratory Investigation. 99(1). 107–117. 83 indexed citations
17.
Ács, Balázs, Veronika Zámbó, Laura Vízkeleti, et al.. (2017). Ki-67 as a controversial predictive and prognostic marker in breast cancer patients treated with neoadjuvant chemotherapy. Diagnostic Pathology. 12(1). 20–20. 63 indexed citations
18.
Ács, Balázs, Lilla Madaras, Kristóf Kovács, et al.. (2017). Reproducibility and Prognostic Potential of Ki-67 Proliferation Index when Comparing Digital-Image Analysis with Standard Semi-Quantitative Evaluation in Breast Cancer. Pathology & Oncology Research. 24(1). 115–127. 16 indexed citations
19.
Ács, Balázs, Janina Kulka, Kristóf Kovács, et al.. (2017). Comparison of 5 Ki-67 antibodies regarding reproducibility and capacity to predict prognosis in breast cancer: does the antibody matter?. Human Pathology. 65. 31–40. 14 indexed citations
20.
Ács, Balázs, et al.. (2015). Current State of ERG as Biomarker in Prostatic Adenocarcinoma. Current Cancer Drug Targets. 15(8). 643–651. 5 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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