Frederick M. Howard

2.7k total citations · 3 hit papers
52 papers, 1.3k citations indexed

About

Frederick M. Howard is a scholar working on Cancer Research, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Frederick M. Howard has authored 52 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cancer Research, 20 papers in Oncology and 15 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Frederick M. Howard's work include Breast Cancer Treatment Studies (20 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Cancer Genomics and Diagnostics (12 papers). Frederick M. Howard is often cited by papers focused on Breast Cancer Treatment Studies (20 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Cancer Genomics and Diagnostics (12 papers). Frederick M. Howard collaborates with scholars based in United States, United Kingdom and Germany. Frederick M. Howard's co-authors include Olufunmilayo I. Olopade, Alexander T. Pearson, Catherine A. Gao, Emma Dyer, Siddhi Ramesh, Yuan Luo, Nikolay S. Markov, Rita Nanda, Sara Kochanny and Dezheng Huo and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and PLoS ONE.

In The Last Decade

Frederick M. Howard

48 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

2023 373 citations Frederick M. Howard, Emma Dyer et al. profile →
Epidemiology of Triple-Negative Breast Cancer

2021 188 citations Frederick M. Howard, Olufunmilayo I. Olopade profile →
Clinicopathologic Characteristics and Prognosis of ERBB2-Low Breast Cancer Among Patients in the National Cancer Database

2023 86 citations Daniel S. Peiffer, Fangyuan Zhao et al. profile →

Peers

Frederick M. Howard

ONCOL

RNMI

Narmin Ghaffari Laleh Germany

Shigao Huang China

Andreas Kleppe Norway

Nisha Sharma United Kingdom

Matahi Moarii France

Robert MacDonald Australia

Richard Colling United Kingdom

Eva Krieghoff‐Henning Germany

Balázs Ács Sweden

Elodie Pronier United States

Narmin Ghaffari Laleh Germany

Frederick M. Howard

260 ×0.7

ONCOL

603 ×1.6

542 ×1.5

RNMI

400 ×1.1

179 ×0.6

Citations per year, relative to Frederick M. Howard Frederick M. Howard (= 1×) peers Narmin Ghaffari Laleh

Countries citing papers authored by Frederick M. Howard

Since Specialization

Citations

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

Fields of papers citing papers by Frederick M. Howard

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frederick M. Howard. 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 Frederick M. Howard. The network helps show where Frederick M. Howard may publish in the future.

Co-authorship network of co-authors of Frederick M. Howard

This figure shows the co-authorship network connecting the top 25 collaborators of Frederick M. Howard. A scholar is included among the top collaborators of Frederick M. Howard 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 Frederick M. Howard. Frederick M. Howard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Peiffer, Daniel S., et al.. (2025). Adjuvant Chemotherapy Use for Hormone Receptor–Positive, ERBB2 -Negative Breast Cancer After RxPONDER Trial. JAMA Network Open. 8(12). e2549109–e2549109.

Howard, Frederick M., Peter A. Fasching, Cesar A. Santa‐Maria, et al.. (2025). Machine Learning–Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2− Early Breast Cancer Using Real-World and NATALEE Data. Clinical Cancer Research. 32(2). 428–437. 1 indexed citations

Chen, Nan, Margarite D. Matossian, Murtuza Rampurwala, et al.. (2025). A randomized phase II trial of nab-paclitaxel with or without mifepristone for advanced triple-negative breast cancer. Breast Cancer Research and Treatment. 211(1). 111–119. 1 indexed citations

Shubeck, Sarah P., et al.. (2025). Trends and Disparities in the Use of Immunotherapy for Triple-Negative Breast Cancer in the US. JAMA Network Open. 8(2). e2460243–e2460243. 3 indexed citations

Dolezal, James M., Frederick M. Howard, Sara Kochanny, et al.. (2025). Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine. Genome Medicine. 17(1). 87–87.

Zhao, Fangyuan, et al.. (2024). Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach. Breast Cancer Research. 26(1). 148–148. 4 indexed citations

Howard, Frederick M., Nan Chen, Olwen Hahn, et al.. (2024). Evaluation of cyclin-dependent kinase 4/6 inhibitor-induced serum creatinine elevations in patients with hormone receptor positive breast cancer. Journal of Oncology Pharmacy Practice. 3212339017–3212339017. 1 indexed citations

Howard, Frederick M., Siddhi Ramesh, James M. Dolezal, et al.. (2024). Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Science Advances. 10(46). eadq0856–eadq0856. 8 indexed citations

Howard, Frederick M., et al.. (2024). ASO Author Reflections: Breast-Conserving Surgery After Neoadjuvant Systemic Therapy for Early-Stage Breast Cancer: Quantitative Biomarkers and Disparities in the Precision-Medicine Era. Annals of Surgical Oncology. 31(13). 8904–8905. 1 indexed citations

10.

Dolezal, James M., Sara Kochanny, Emma Dyer, et al.. (2024). Slideflow: deep learning for digital histopathology with real-time whole-slide visualization. BMC Bioinformatics. 25(1). 134–134. 20 indexed citations

11.

Howard, Frederick M., et al.. (2024). Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023. JCO Clinical Cancer Informatics. 8(8). e2400077–e2400077. 7 indexed citations

12.

Dolezal, James M., Emma Dyer, Sara Kochanny, et al.. (2024). Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning. EBioMedicine. 107. 105276–105276. 5 indexed citations

13.

Miyashita, Minoru, Joshua S. K. Bell, Stéphane Wenric, et al.. (2023). Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations. Breast Cancer Research. 25(1). 12 indexed citations

14.

Polley, Eric C., Peter H. O’Donnell, Frederick M. Howard, et al.. (2023). Retrospective evaluation of adjuvant capecitabine dosing patterns in triple negative breast cancer.. Journal of Clinical Oncology. 41(16_suppl). e12515–e12515. 1 indexed citations

15.

Zhao, Fangyuan, Minoru Miyashita, Masaya Hattori, et al.. (2023). Racial Disparities in Pathological Complete Response Among Patients Receiving Neoadjuvant Chemotherapy for Early-Stage Breast Cancer. JAMA Network Open. 6(3). e233329–e233329. 19 indexed citations

16.

Pineda, Federico, Frederick M. Howard, Xiaobing Fan, et al.. (2023). Bilateral asymmetry of quantitative parenchymal kinetics at ultrafast DCE-MRI predict response to neoadjuvant chemotherapy in patients with HER2+ breast cancer. Magnetic Resonance Imaging. 104. 9–15. 4 indexed citations

17.

Vannier, Augustin, Fangyuan Zhao, Nan Chen, et al.. (2023). Validation of the RSClin risk calculator in the National Cancer Data Base. Cancer. 130(8). 1210–1220. 5 indexed citations

18.

Chen, Nan, Rita Nanda, Frederick M. Howard, et al.. (2023). Abstract P5-03-18: Co-occurring alterations in PALB2 germline carriers identified by liquid biopsy in patients with advanced breast cancer. Cancer Research. 83(5_Supplement). P5–3.

19.

Chen, Nan, Murtuza Rampurwala, Sailaja Kamaraju, et al.. (2023). A randomized phase II trial of nab-paclitaxel with or without mifepristone for advanced triple-negative breast cancer.. Journal of Clinical Oncology. 41(16_suppl). e13103–e13103. 3 indexed citations

20.

Pineda, Federico, Frederick M. Howard, Milica Medved, et al.. (2022). Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy. Academic Radiology. 29(10). 1469–1479. 6 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.

Explore authors with similar magnitude of impact