Amber L. Simpson

6.9k total citations
123 papers, 2.2k citations indexed

About

Amber L. Simpson is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Biomedical Engineering. According to data from OpenAlex, Amber L. Simpson has authored 123 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Radiology, Nuclear Medicine and Imaging, 36 papers in Surgery and 35 papers in Biomedical Engineering. Recurrent topics in Amber L. Simpson's work include Radiomics and Machine Learning in Medical Imaging (48 papers), Pancreatic and Hepatic Oncology Research (24 papers) and Hepatocellular Carcinoma Treatment and Prognosis (24 papers). Amber L. Simpson is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (48 papers), Pancreatic and Hepatic Oncology Research (24 papers) and Hepatocellular Carcinoma Treatment and Prognosis (24 papers). Amber L. Simpson collaborates with scholars based in United States, Canada and France. Amber L. Simpson's co-authors include Richard Kinh Gian, William R. Jarnagin, Michael I. Miga, Mithat Gönen, Jayasree Chakraborty, Peter J. Allen, T. Peter Kingham, Thomas S. Pheiffer, Abhishek Midya and Ronald P. DeMatteo and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Amber L. Simpson

119 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Amber L. Simpson United States	29	1.1k	693	599	551	425	123	2.2k
Jacob Sosna Israel	30	1.3k 1.1×	630 0.9×	567 0.9×	1.0k 1.9×	201 0.5×	138	3.3k
Andrea Schenk Germany	24	479 0.4×	152 0.2×	576 1.0×	431 0.8×	501 1.2×	101	1.9k
Marc L. Kessler United States	42	2.6k 2.4×	282 0.4×	459 0.8×	675 1.2×	348 0.8×	105	4.5k
Pretesh Patel United States	33	1.4k 1.2×	458 0.7×	305 0.5×	636 1.2×	596 1.4×	180	3.1k
Mischa S. Hoogeman Netherlands	48	3.3k 3.0×	414 0.6×	714 1.2×	908 1.6×	252 0.6×	225	6.4k
Prateek Prasanna United States	25	2.8k 2.5×	533 0.8×	199 0.3×	577 1.0×	197 0.5×	111	3.8k
Dan Ruan United States	32	1.8k 1.7×	296 0.4×	217 0.4×	576 1.0×	322 0.8×	193	3.2k
Arjan Bel Netherlands	37	2.5k 2.3×	356 0.5×	626 1.0×	1.2k 2.3×	131 0.3×	201	4.6k
Sheng Xu United States	32	1.6k 1.5×	118 0.2×	455 0.8×	943 1.7×	489 1.2×	133	3.6k
Wenjun Liao China	26	306 0.3×	478 0.7×	239 0.4×	362 0.7×	236 0.6×	156	2.3k

Countries citing papers authored by Amber L. Simpson

Since Specialization

Citations

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

Fields of papers citing papers by Amber L. Simpson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amber L. Simpson. 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 Amber L. Simpson. The network helps show where Amber L. Simpson may publish in the future.

Co-authorship network of co-authors of Amber L. Simpson

This figure shows the co-authorship network connecting the top 25 collaborators of Amber L. Simpson. A scholar is included among the top collaborators of Amber L. Simpson 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 Amber L. Simpson. Amber L. Simpson 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

Zhu, Xiaodan, et al.. (2025). Targeted generative data augmentation for automatic metastases detection from free-text radiology reports. Frontiers in Artificial Intelligence. 8. 1513674–1513674. 2 indexed citations

Peoples, Jacob, Mohammad Hamghalam, Natalie Gangai, et al.. (2025). Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases. PubMed. 2(UNSURE2023). 2326–2357. 1 indexed citations

Gagnière, Johan, Abhishek Midya, Rikiya Yamashita, et al.. (2025). Postoperative Pancreatic Fistula After Pancreatoduodenectomy. Annals of Surgery.

Sussman, Jonathan, Samantha B. Kemp, Daniel Traum, et al.. (2024). Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer. Cancer Research. 84(14). 2364–2376. 8 indexed citations

Hamghalam, Mohammad, Robert B. Moreland, David Gómez, et al.. (2024). Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography. Canadian Association of Radiologists Journal. 75(3). 534–541. 6 indexed citations

Simpson, Amber L., Jacob Peoples, John M. Creasy, et al.. (2024). Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases. Scientific Data. 11(1). 172–172. 14 indexed citations

Nakajima, Erica C., Amber L. Simpson, Jan Bogaerts, et al.. (2024). Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group. JCO Precision Oncology. 8(8). e2300687–e2300687. 3 indexed citations

Gian, Richard Kinh, et al.. (2023). Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon. Cancers. 15(20). 4909–4909. 3 indexed citations

Andrieu, Pamela Causa, Jennifer S. Golia Pernicka, Rona Yaeger, et al.. (2022). Natural Language Processing of Computed Tomography Reports to Label Metastatic Phenotypes With Prognostic Significance in Patients With Colorectal Cancer. JCO Clinical Cancer Informatics. 6(6). e2200014–e2200014. 9 indexed citations

10.

Phillips, Susan P., Sheryl Spithoff, & Amber L. Simpson. (2022). Artificial intelligence and predictive algorithms in medicine. Canadian Family Physician. 68(8). 570–572. 9 indexed citations

11.

Hassan, Muhammad, Carlie Sigel, Michael Doukas, et al.. (2021). EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, with Prognostic Stratification Boosting. 520–531. 3 indexed citations

12.

Yamashita, Rikiya, Jayasree Chakraborty, Joanne F. Chou, et al.. (2019). Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. European Radiology. 30(1). 195–205. 55 indexed citations

13.

Wang, Tao, Esther Drill, Efsevia Vakiani, et al.. (2019). Distinct histomorphological features are associated with IDH1 mutation in intrahepatic cholangiocarcinoma. Human Pathology. 91. 19–25. 12 indexed citations

14.

Attiyeh, Marc A., Jayasree Chakraborty, Lior Gazit, et al.. (2018). Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. HPB. 21(2). 212–218. 39 indexed citations

15.

Pak, Linda M., Jayasree Chakraborty, Mithat Gönen, et al.. (2018). Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort. Journal of the American College of Surgeons. 226(5). 835–843. 8 indexed citations

16.

Aherne, Emily A., Linda M. Pak, Debra A. Goldman, et al.. (2018). Intrahepatic cholangiocarcinoma: can imaging phenotypes predict survival and tumor genetics?. Abdominal Radiology. 43(10). 2665–2672. 32 indexed citations

17.

Creasy, John M., Abhishek Midya, Jayasree Chakraborty, et al.. (2018). Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases. European Radiology. 29(1). 458–467. 12 indexed citations

18.

Midya, Abhishek, Rikiya Yamashita, Jayasree Chakraborty, et al.. (2018). Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging. Abdominal Radiology. 43(12). 3271–3278. 43 indexed citations

19.

Leung, Universe, Amber L. Simpson, Raphael L. C. Araújo, et al.. (2014). Remnant Growth Rate after Portal Vein Embolization Is a Good Early Predictor of Post-Hepatectomy Liver Failure. Journal of the American College of Surgeons. 219(4). 620–630. 70 indexed citations

20.

Simpson, Amber L., et al.. (1993). SUBCUTANEOUS ALVEOLAR SOFT‐PART SARCOMA MIMICKING METASTATIC HYPERNEPHROMA. International Journal of Clinical Practice. 47(6). 342–343. 2 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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