James M. Dolezal

1.7k total citations
29 papers, 679 citations indexed

About

James M. Dolezal is a scholar working on Artificial Intelligence, Molecular Biology and Oncology. According to data from OpenAlex, James M. Dolezal has authored 29 papers receiving a total of 679 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Molecular Biology and 7 papers in Oncology. Recurrent topics in James M. Dolezal's work include AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cancer Genomics and Diagnostics (5 papers). James M. Dolezal is often cited by papers focused on AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cancer Genomics and Diagnostics (5 papers). James M. Dolezal collaborates with scholars based in United States, Italy and United Kingdom. James M. Dolezal's co-authors include Edward V. Prochownik, Alexander T. Pearson, Sara Kochanny, Huabo Wang, Frederick M. Howard, Jefree J. Schulte, Nicole A. Cipriani, Sucheta Kulkarni, Jie Lu and Rita Nanda and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

James M. Dolezal

26 papers receiving 675 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James M. Dolezal United States	15	271	229	192	172	112	29	679
Andreas Heindl United Kingdom	12	290 1.1×	171 0.7×	173 0.9×	202 1.2×	246 2.2×	19	776
Santiago González Spain	8	250 0.9×	226 1.0×	190 1.0×	186 1.1×	101 0.9×	12	615
Clinton J.V. Campbell Canada	14	265 1.0×	184 0.8×	85 0.4×	99 0.6×	126 1.1×	31	809
Stephanie Robertson Sweden	14	430 1.6×	298 1.3×	195 1.0×	296 1.7×	408 3.6×	28	1.1k
Ken Takasawa Japan	15	264 1.0×	142 0.6×	123 0.6×	188 1.1×	50 0.4×	27	652
Liangqun Lu United States	5	428 1.6×	170 0.7×	255 1.3×	200 1.2×	82 0.7×	7	819
Tarjei S. Hveem Norway	12	161 0.6×	265 1.2×	211 1.1×	345 2.0×	332 3.0×	21	841
Lana X. Garmire United States	10	713 2.6×	163 0.7×	348 1.8×	184 1.1×	101 0.9×	22	1.1k
Daniel Heim Germany	10	125 0.5×	253 1.1×	79 0.4×	142 0.8×	84 0.8×	17	578
Margaret Guo United States	9	507 1.9×	72 0.3×	118 0.6×	202 1.2×	103 0.9×	15	993

Countries citing papers authored by James M. Dolezal

Since Specialization

Citations

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

Fields of papers citing papers by James M. Dolezal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James M. Dolezal

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

All Works

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20 of 20 papers shown

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.

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

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

Cameron, Robert B., A. Esposito, Luca Porcu, et al.. (2024). Evaluation of Major Pathologic Response and Pathologic Complete Response as Surrogate End Points for Survival in Randomized Controlled Trials of Neoadjuvant Immune Checkpoint Blockade in Resectable in NSCLC. Journal of Thoracic Oncology. 19(7). 1108–1116. 23 indexed citations

Sacco, Matteo Antonio, Aliya N. Husain, A. Esposito, et al.. (2024). AB041. Deep learning discriminates thymic epithelial tumors histological subtypes using digital pathology. Mediastinum. 8. AB041–AB041. 1 indexed citations

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

Souza, Lucas Lacerda de, Felipe Paiva Fonseca, Anna Luíza Damaceno Araújo, et al.. (2023). Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. Journal of Oral Pathology and Medicine. 52(3). 197–205. 16 indexed citations

Partin, Alexander, Thomas Brettin, Yitan Zhu, et al.. (2023). Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images. Frontiers in Medicine. 10. 1058919–1058919. 6 indexed citations

Howard, Frederick M., James M. Dolezal, Sara Kochanny, et al.. (2023). Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence. npj Breast Cancer. 9(1). 25–25. 30 indexed citations

10.

Dolezal, James M., Andrew Srisuwananukorn, Dmitry Karpeyev, et al.. (2022). Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology. Nature Communications. 13(1). 6572–6572. 75 indexed citations

11.

Dolezal, James M. & Ari J. Rosenberg. (2022). Induction Chemotherapy in Low-Risk HPV+ Oropharyngeal Cancer. Current Treatment Options in Oncology. 23(1). 54–67. 4 indexed citations

12.

Dolezal, James M., Sara Kochanny, Sagar Rakshit, et al.. (2022). The use of artificial intelligence with uncertainty estimation to predict lung cancer relapse from histopathology.. Journal of Clinical Oncology. 40(16_suppl). 8549–8549.

13.

Dolezal, James M., Chih‐Yi Liao, Sara Kochanny, et al.. (2020). Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features. Modern Pathology. 34(5). 862–874. 43 indexed citations

14.

Rosenberg, Ari, Sara Kochanny, James M. Dolezal, et al.. (2020). Prediction of histologic and molecular subsets of soft tissue sarcoma using deep learning.. Journal of Clinical Oncology. 38(15_suppl). e23529–e23529. 1 indexed citations

15.

Wang, Huabo, James M. Dolezal, Sucheta Kulkarni, et al.. (2018). Myc and ChREBP transcription factors cooperatively regulate normal and neoplastic hepatocyte proliferation in mice. Journal of Biological Chemistry. 293(38). 14740–14757. 30 indexed citations

16.

Dolezal, James M., et al.. (2018). Diagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers. BMC Cancer. 18(1). 275–275. 70 indexed citations

17.

Jackson, L. Elizabeth, Sucheta Kulkarni, Huabo Wang, et al.. (2017). Genetic Dissociation of Glycolysis and the TCA Cycle Affects Neither Normal nor Neoplastic Proliferation. Cancer Research. 77(21). 5795–5807. 28 indexed citations

18.

Kulkarni, Sucheta, James M. Dolezal, Huabo Wang, et al.. (2017). Ribosomopathy-like properties of murine and human cancers. PLoS ONE. 12(8). e0182705–e0182705. 29 indexed citations

19.

Dolezal, James M., Huabo Wang, Sucheta Kulkarni, et al.. (2017). Sequential adaptive changes in a c-Myc-driven model of hepatocellular carcinoma. Journal of Biological Chemistry. 292(24). 10068–10086. 40 indexed citations

20.

Wang, Huabo, Jie Lu, Lia R. Edmunds, et al.. (2016). Coordinated Activities of Multiple Myc-dependent and Myc-independent Biosynthetic Pathways in Hepatoblastoma. Journal of Biological Chemistry. 291(51). 26241–26251. 43 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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