Molly Griffin

1.1k total citations
19 papers, 703 citations indexed

About

Molly Griffin is a scholar working on Oncology, Pathology and Forensic Medicine and Cancer Research. According to data from OpenAlex, Molly Griffin has authored 19 papers receiving a total of 703 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 6 papers in Pathology and Forensic Medicine and 6 papers in Cancer Research. Recurrent topics in Molly Griffin's work include BRCA gene mutations in cancer (5 papers), AI in cancer detection (4 papers) and Global Cancer Incidence and Screening (3 papers). Molly Griffin is often cited by papers focused on BRCA gene mutations in cancer (5 papers), AI in cancer detection (4 papers) and Global Cancer Incidence and Screening (3 papers). Molly Griffin collaborates with scholars based in United States, China and Türkiye. Molly Griffin's co-authors include Nicholas J. Lowe, Gary Lask, Kevin S. Hughes, Danielle Braun, Christopher C.K. Ho, Jennifer K. Plichta, Emanuele Mazzola, Jessica Cintolo-Gonzalez, Ahmet Acar and Amanda L. Blackford and has published in prestigious journals such as Journal of Clinical Oncology, JNCI Journal of the National Cancer Institute and Breast Cancer Research and Treatment.

In The Last Decade

Molly Griffin

19 papers receiving 658 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Molly Griffin United States	13	220	177	161	151	130	19	703
Yiming Gao United States	17	100 0.5×	424 2.4×	152 0.9×	104 0.7×	134 1.0×	63	899
Brandi T. Nicholson United States	15	84 0.4×	376 2.1×	233 1.4×	46 0.3×	220 1.7×	25	945
Hans Jörg Altermatt Switzerland	14	61 0.3×	89 0.5×	96 0.6×	29 0.2×	35 0.3×	37	688
F. M. Solivetti Italy	18	156 0.7×	103 0.6×	224 1.4×	29 0.2×	18 0.1×	57	727
Robert T. Fazzio United States	17	106 0.5×	334 1.9×	54 0.3×	18 0.1×	50 0.4×	42	762
Fang‐I Lu Canada	16	33 0.1×	244 1.4×	309 1.9×	22 0.1×	189 1.5×	41	794
Markus Zutt Germany	16	249 1.1×	29 0.2×	298 1.9×	213 1.4×	23 0.2×	50	908
Eun Suk South Korea	14	94 0.4×	502 2.8×	165 1.0×	19 0.1×	60 0.5×	46	816
Wen‐Chi Foo United States	17	27 0.1×	169 1.0×	155 1.0×	56 0.4×	20 0.2×	42	693
Francesca Arezzo Italy	12	42 0.2×	129 0.7×	145 0.9×	21 0.1×	81 0.6×	65	514

Countries citing papers authored by Molly Griffin

Since Specialization

Citations

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

Fields of papers citing papers by Molly Griffin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Molly Griffin

This figure shows the co-authorship network connecting the top 25 collaborators of Molly Griffin. A scholar is included among the top collaborators of Molly Griffin 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 Molly Griffin. Molly Griffin 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:

19 of 19 papers shown

Guan, Zoe, Rong Tang, Molly Griffin, et al.. (2021). Familial pancreatic cancer: who should be considered for genetic testing?. Irish Journal of Medical Science (1971 -). 191(2). 641–650. 1 indexed citations

Tang, Rong, Molly Griffin, Pinky A. Bautista, et al.. (2020). The role of Micro-CT in imaging breast cancer specimens. Breast Cancer Research and Treatment. 180(2). 343–357. 32 indexed citations

Varlotto, John M., Isabel Cristina Martins Emmerick, M Decamp, et al.. (2020). The Incidence of Node-Positive Non-small-Cell Lung Cancer Undergoing Sublobar Resection and the Role of Radiation in Its Management. Frontiers in Oncology. 10. 417–417. 1 indexed citations

Emmerick, Isabel Cristina Martins, John M. Varlotto, Debra Maddox, et al.. (2020). The variance of lymphatic vascular invasion (LVI) diagnosis and its impact on overall survival(OS) independent of node stage in patients undergoing surgical resection on NSCLC.. Journal of Clinical Oncology. 38(15_suppl). e21060–e21060. 3 indexed citations

McCarthy, Anne Marie, Zoe Guan, Michaela Welch, et al.. (2019). Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort. JNCI Journal of the National Cancer Institute. 112(5). 489–497. 70 indexed citations

Hosny, Ahmed, Steven J. Keating, Beth Ripley, et al.. (2018). From Improved Diagnostics to Presurgical Planning: High-Resolution Functionally Graded Multimaterial 3D Printing of Biomedical Tomographic Data Sets. 3D Printing and Additive Manufacturing. 5(2). 103–113. 33 indexed citations

Tang, Rong, Lizhi Ouyang, Yue He, et al.. (2018). Machine learning to parse breast pathology reports in Chinese. Breast Cancer Research and Treatment. 169(2). 243–250. 22 indexed citations

Braun, Danielle, Jiabei Yang, Molly Griffin, Giovanni Parmigiani, & Kevin S. Hughes. (2018). A Clinical Decision Support Tool to Predict Cancer Risk for Commonly Tested Cancer‐Related Germline Mutations. Journal of Genetic Counseling. 27(5). 1187–1199. 32 indexed citations

Coopey, Suzanne B., et al.. (2018). The impact of patient age on breast cancer risk prediction models. The Breast Journal. 24(4). 592–598. 8 indexed citations

10.

Cintolo-Gonzalez, Jessica, Danielle Braun, Amanda L. Blackford, et al.. (2017). Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Research and Treatment. 164(2). 263–284. 119 indexed citations

11.

Mazzola, Emanuele, Suzanne B. Coopey, Molly Griffin, et al.. (2017). Reassessing risk models for atypical hyperplasia: age may not matter. Breast Cancer Research and Treatment. 165(2). 285–291. 12 indexed citations

12.

Patel, Kunal, et al.. (2016). The Role of Micro-CT in 3D Histology Imaging. Pathobiology. 83(2-3). 140–147. 38 indexed citations

13.

Yala, Adam, Regina Barzilay, Laura Salama, et al.. (2016). Using machine learning to parse breast pathology reports. Breast Cancer Research and Treatment. 161(2). 203–211. 72 indexed citations

14.

Plichta, Jennifer K., Molly Griffin, Joseph V. Thakuria, & Kevin S. Hughes. (2016). What's New in Genetic Testing for Cancer Susceptibility?. PubMed. 30(9). 787–99. 19 indexed citations

15.

Tang, Rong, Molly Griffin, Anthony H. Bui, et al.. (2015). Prediction of primary breast cancer size and T-stage using micro-computed tomography in lumpectomy specimens. Journal of Pathology Informatics. 6(1). 60–60. 12 indexed citations

16.

Persyn, Russell Alan, et al.. (2008). The Watershed Management Approach. OakTrust (Texas A&M University Libraries). 2 indexed citations

17.

Ho, Christopher C.K., et al.. (1995). Laser Resurfacing in Pigmented Skin. Dermatologic Surgery. 21(12). 1035–1037. 112 indexed citations

18.

Lowe, Nicholas J., Gary Lask, & Molly Griffin. (1995). Laser Skin Resurfacing. Dermatologic Surgery. 21(12). 1017–1019. 106 indexed citations

19.

Reiff, D.B., Julie Cooke, Molly Griffin, & Rosalind Given-Wilson. (1994). Ductal carcinoma in situ presenting as a stellate lesion on mammography. Clinical Radiology. 49(6). 396–399. 9 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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