Samuel G. Armato

11.7k total citations · 1 hit paper
176 papers, 5.2k citations indexed

About

Samuel G. Armato is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Samuel G. Armato has authored 176 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 145 papers in Radiology, Nuclear Medicine and Imaging, 116 papers in Pulmonary and Respiratory Medicine and 34 papers in Artificial Intelligence. Recurrent topics in Samuel G. Armato's work include Radiomics and Machine Learning in Medical Imaging (97 papers), Lung Cancer Diagnosis and Treatment (56 papers) and Occupational and environmental lung diseases (49 papers). Samuel G. Armato is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (97 papers), Lung Cancer Diagnosis and Treatment (56 papers) and Occupational and environmental lung diseases (49 papers). Samuel G. Armato collaborates with scholars based in United States, Australia and United Kingdom. Samuel G. Armato's co-authors include Heber MacMahon, Maryellen L. Giger, Kunio Doi, Feng Li, William F. Sensakovic, Shusuke Sone, Hedy L. Kindler, Heber MacMahon, Anna K. Nowak and Hania Al‐Hallaq and has published in prestigious journals such as Journal of Clinical Oncology, Radiology and CHEST Journal.

In The Last Decade

Samuel G. Armato

171 papers receiving 5.0k 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.

Treatment of Malignant Pleural Mesothelioma: American Society of Clinical Oncology Clinical Practice Guideline

2018 260 citations Hedy L. Kindler, Nofisat Ismaila et al. Journal of Clinical Oncology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Samuel G. Armato United States	37	3.6k	3.5k	943	697	572	176	5.2k
Shigehiko Katsuragawa Japan	36	2.9k 0.8×	1.8k 0.5×	1.4k 1.5×	502 0.7×	768 1.3×	129	4.1k
Henkjan Huisman Netherlands	41	4.4k 1.2×	4.6k 1.3×	785 0.8×	693 1.0×	715 1.3×	124	6.5k
Michael Pringle United Kingdom	9	2.0k 0.6×	989 0.3×	919 1.0×	567 0.8×	727 1.3×	9	3.8k
Justin Kirby United States	20	3.8k 1.1×	1.3k 0.4×	1.6k 1.7×	931 1.3×	1.8k 3.1×	32	6.0k
Lawrence Tarbox United States	9	2.2k 0.6×	834 0.2×	1.0k 1.1×	617 0.9×	787 1.4×	16	3.3k
John Freymann United States	18	3.6k 1.0×	1.1k 0.3×	1.5k 1.6×	851 1.2×	1.7k 3.0×	28	5.6k
Bruce A. Vendt United States	8	2.1k 0.6×	802 0.2×	959 1.0×	584 0.8×	739 1.3×	9	3.3k
Paul Koppel Netherlands	11	2.1k 0.6×	788 0.2×	932 1.0×	592 0.8×	730 1.3×	13	3.2k
Mathieu Hatt France	46	6.4k 1.8×	2.1k 0.6×	678 0.7×	1.6k 2.3×	316 0.6×	163	7.0k

Countries citing papers authored by Samuel G. Armato

Since Specialization

Citations

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

Fields of papers citing papers by Samuel G. Armato

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel G. Armato. 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 Samuel G. Armato. The network helps show where Samuel G. Armato may publish in the future.

Co-authorship network of co-authors of Samuel G. Armato

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel G. Armato. A scholar is included among the top collaborators of Samuel G. Armato 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 Samuel G. Armato. Samuel G. Armato 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

Kindler, Hedy L., et al.. (2024). Assessing radiomic feature robustness using agreement over image perturbation. 11. 110–110. 1 indexed citations

Kindler, Hedy L., et al.. (2024). The use of radiomics on computed tomography scans for differentiation of somatic BAP1 mutation status for patients with pleural mesothelioma. 16. 113–113. 1 indexed citations

Kindler, Hedy L., et al.. (2024). Radiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma. Journal of Medical Imaging. 11(6). 64501–64501. 1 indexed citations

Li, Feng, Christopher M. Straus, Hedy L. Kindler, et al.. (2024). Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance). Journal of Imaging Informatics in Medicine. 38(2). 967–978. 1 indexed citations

Kalpathy-Cramer, Jayashree, et al.. (2023). Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. 8. 13–13. 1 indexed citations

Harmath, Carla, et al.. (2022). The role of imaging in diagnosis and management of malignant peritoneal mesothelioma: a systematic review. Abdominal Radiology. 47(5). 1725–1740. 5 indexed citations

Li, Feng, Samuel G. Armato, Roger Engelmann, et al.. (2021). Anatomic Point–Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. Journal of Digital Imaging. 34(4). 922–931. 1 indexed citations

Straus, Christopher M., et al.. (2019). P1.06-04 Deep Learning-Based Segmentation of Mesothelioma on CT Scans: Application to Patient Scans Exhibiting Pleural Effusion. Journal of Thoracic Oncology. 14(10). S478–S478. 1 indexed citations

Kindler, Hedy L., Nofisat Ismaila, Samuel G. Armato, et al.. (2018). Treatment of Malignant Pleural Mesothelioma: American Society of Clinical Oncology Clinical Practice Guideline. Journal of Clinical Oncology. 36(13). 1343–1373. 260 indexed citations breakdown →

10.

Sokol, Elizabeth, Roger Engelmann, Wenjun Kang, et al.. (2018). Computer‐assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma. Pediatric Blood & Cancer. 65(12). e27417–e27417. 3 indexed citations

11.

Giger, Maryellen L., et al.. (2018). Variations in algorithm implementation among quantitative texture analysis software packages. 767. 55–55. 1 indexed citations

12.

Rasin, Alexander, Eli T. Brown, Jacob Furst, et al.. (2017). An Integrated Database and Smart Search Tool for Medical Knowledge Extraction from Radiology Teaching Files. Knowledge Discovery and Data Mining. 10–18. 6 indexed citations

13.

Kindler, Hedy L., Mark Ferguson, Buerkley Rose, et al.. (2017). P2.06-029 Pilot Window-Of-Opportunity Study of Pembrolizumab in Patients with Resectable Malignant Pleural Mesothelioma (MPM). Journal of Thoracic Oncology. 12(1). S1089–S1089. 2 indexed citations

14.

Armato, Samuel G., Ping Li, Aliya N. Husain, et al.. (2015). Radiologic–pathologic correlation of mesothelioma tumor volume. Lung Cancer. 87(3). 278–282. 13 indexed citations

15.

Armato, Samuel G., Anna K. Nowak, Roslyn J. Francis, Masha Kocherginsky, & Michael Byrne. (2014). Observer Variability in Mesothelioma Tumor Thickness Measurements: Defining Minimally Measurable Lesions. Journal of Thoracic Oncology. 9(8). 1187–1194. 22 indexed citations

16.

Cunliffe, Alexandra, Hania Al‐Hallaq, Zacariah E. Labby, et al.. (2012). Lung texture in serial thoracic CT scans: Assessment of change introduced by image registrationa). Medical Physics. 39(8). 4679–4690. 26 indexed citations

17.

Armato, Samuel G. & Bram van Ginneken. (2008). Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics. Medical Physics. 35(10). 4488–4500. 7 indexed citations

18.

Oxnard, Geoffrey R., Samuel G. Armato, & Hedy L. Kindler. (2006). Modeling of mesothelioma growth demonstrates weaknesses of current response criteria. Lung Cancer. 52(2). 141–148. 55 indexed citations

19.

Armato, Samuel G., Geoffrey R. Oxnard, Heber MacMahon, et al.. (2004). Measurement of mesothelioma on thoracic CT scans: A comparison of manual and computer‐assisted techniques. Medical Physics. 31(5). 1105–1115. 69 indexed citations

20.

Armato, Samuel G., Feng Li, Maryellen L. Giger, et al.. (2002). Lung Cancer: Performance of Automated Lung Nodule Detection Applied to Cancers Missed in a CT Screening Program. Radiology. 225(3). 685–692. 212 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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