Nicholas Petrick

11.8k total citations
225 papers, 6.3k citations indexed

About

Nicholas Petrick is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Nicholas Petrick has authored 225 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 139 papers in Radiology, Nuclear Medicine and Imaging, 117 papers in Artificial Intelligence and 59 papers in Oncology. Recurrent topics in Nicholas Petrick's work include Radiomics and Machine Learning in Medical Imaging (117 papers), AI in cancer detection (94 papers) and Colorectal Cancer Screening and Detection (50 papers). Nicholas Petrick is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (117 papers), AI in cancer detection (94 papers) and Colorectal Cancer Screening and Detection (50 papers). Nicholas Petrick collaborates with scholars based in United States, Thailand and Japan. Nicholas Petrick's co-authors include Berkman Sahiner, Heang‐Ping Chan, Mark A. Helvie, Lubomir M. Hadjiiski, Mitchell M. Goodsitt, Dorit D. Adler, Marios A. Gavrielides, Weijie Chen, Brandon D. Gallas and Kyle J. Myers and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and Annals of Oncology.

In The Last Decade

Nicholas Petrick

217 papers receiving 6.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nicholas Petrick United States	43	3.7k	3.3k	1.7k	1.5k	981	225	6.3k
Bin Zheng United States	45	3.9k 1.1×	3.6k 1.1×	2.1k 1.2×	1.5k 1.0×	818 0.8×	392	7.8k
Berkman Sahiner United States	50	5.0k 1.4×	4.8k 1.4×	2.6k 1.5×	2.1k 1.4×	969 1.0×	294	8.0k
Robert M. Nishikawa United States	40	3.1k 0.8×	3.7k 1.1×	1.8k 1.1×	1.7k 1.1×	724 0.7×	230	5.9k
Nico Karssemeijer Netherlands	53	4.9k 1.3×	5.6k 1.7×	2.7k 1.6×	1.9k 1.2×	1.7k 1.7×	200	8.6k
Lubomir M. Hadjiiski United States	49	5.4k 1.5×	4.2k 1.2×	3.1k 1.8×	1.5k 1.0×	985 1.0×	290	8.6k
Mark A. Helvie United States	58	4.8k 1.3×	4.9k 1.5×	2.5k 1.5×	1.6k 1.0×	2.4k 2.4×	246	9.8k
Babak Ehteshami Bejnordi Netherlands	15	4.7k 1.3×	4.8k 1.4×	928 0.5×	3.0k 1.9×	736 0.8×	23	9.6k
Arnaud A. A. Setio Netherlands	15	5.4k 1.5×	4.4k 1.3×	1.7k 1.0×	2.9k 1.9×	611 0.6×	20	10.3k
Thijs Kooi Netherlands	5	4.6k 1.3×	4.4k 1.3×	992 0.6×	2.6k 1.7×	602 0.6×	10	9.2k
Justin Kirby United States	20	3.8k 1.0×	1.6k 0.5×	1.3k 0.7×	1.8k 1.1×	310 0.3×	32	6.0k

Countries citing papers authored by Nicholas Petrick

Since Specialization

Citations

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

Fields of papers citing papers by Nicholas Petrick

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicholas Petrick

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas Petrick. A scholar is included among the top collaborators of Nicholas Petrick 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 Nicholas Petrick. Nicholas Petrick 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

Subbaswamy, Adarsh, et al.. (2024). A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform. npj Digital Medicine. 7(1). 334–334. 6 indexed citations

Monod, Mélodie, Peter Krusche, Qian Cao, et al.. (2024). TorchSurv: A Lightweight Package for Deep Survival Analysis. The Journal of Open Source Software. 9(104). 7341–7341. 3 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

Sahiner, Berkman, Weijie Chen, Ravi K. Samala, & Nicholas Petrick. (2023). Data drift in medical machine learning: implications and potential remedies. British Journal of Radiology. 96(1150). 20220878–20220878. 77 indexed citations

Cao, Qian, et al.. (2023). Characterization of mechanical stiffness using additive manufacturing and finite element analysis: potential tool for bone health assessment. SHILAP Revista de lepidopterología. 9(1). 32–32. 1 indexed citations

Gallas, Brandon D., Aldo Badano, Sarah Dudgeon, et al.. (2022). FDA fosters innovative approaches in research, resources and collaboration. Nature Machine Intelligence. 4(2). 97–98. 5 indexed citations

Gavrielides, Marios A., Rongping Zeng, Benjamin P. Berman, et al.. (2018). Discrimination of Pulmonary Nodule Volume Change for Low- and High-contrast Tasks in a Phantom CT Study with Low-dose Protocols. Academic Radiology. 26(7). 937–948. 4 indexed citations

Obuchowski, Nancy A., Andrew J. Buckler, Paul E. Kinahan, et al.. (2016). Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims. Academic Radiology. 23(4). 496–506. 20 indexed citations

Li, Qin, Qiao Huang, Min Zong, et al.. (2016). Volumetry of low‐contrast liver lesions with CT: Investigation of estimation uncertainties in a phantom study. Medical Physics. 43(12). 6608–6620. 7 indexed citations

10.

Kang, Le, Weijie Chen, Nicholas Petrick, & Brandon D. Gallas. (2014). Comparing two correlated C indices with right‐censored survival outcome: a one‐shot nonparametric approach. Statistics in Medicine. 34(4). 685–703. 274 indexed citations

11.

Gavrielides, Marios A., Qin Li, Rongping Zeng, et al.. (2013). Minimum Detectable Change in Lung Nodule Volume in a Phantom CT Study. Academic Radiology. 20(11). 1364–1370. 16 indexed citations

12.

Zeng, Rongping, Nicholas Petrick, Marios A. Gavrielides, & Kyle J. Myers. (2011). Approximations of noise covariance in multi-slice helical CT scans: impact on lung nodule size estimation. Physics in Medicine and Biology. 56(19). 6223–6242. 15 indexed citations

13.

Gavrielides, Marios A., Lisa M. Kinnard, Kyle J. Myers, et al.. (2010). A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom. Optics Express. 18(14). 15244–15244. 51 indexed citations

14.

Petrick, Nicholas, Berkman Sahiner, Heang‐Ping Chan, et al.. (2002). Breast Cancer Detection: Evaluation of a Mass-Detection Algorithm for Computer-aided Diagnosis—Experience in 263 Patients. Radiology. 224(1). 217–224. 58 indexed citations

15.

Sahiner, Berkman, Heang‐Ping Chan, Nicholas Petrick, Robert F. Wagner, & Lubomir M. Hadjiiski. (2000). Feature selection and classifier performance in computer‐aided diagnosis: The effect of finite sample size. Medical Physics. 27(7). 1509–1522. 105 indexed citations

16.

Chan, Heang‐Ping, Berkman Sahiner, Robert F. Wagner, & Nicholas Petrick. (1999). Classifier design for computer‐aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers. Medical Physics. 26(12). 2654–2668. 120 indexed citations

17.

Chan, Heang‐Ping, Berkman Sahiner, Kwok L. Lam, et al.. (1998). Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Medical Physics. 25(10). 2007–2019. 154 indexed citations

18.

Sahiner, Berkman, Heang‐Ping Chan, Nicholas Petrick, et al.. (1996). Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Transactions on Medical Imaging. 15(5). 598–610. 308 indexed citations

19.

Petrick, Nicholas, Heang‐Ping Chan, Datong Wei, et al.. (1996). Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. Medical Physics. 23(10). 1685–1696. 84 indexed citations

20.

Chan, Heang‐Ping, Mark A. Helvie, Berkman Sahiner, et al.. (1995). Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis. Medical Physics. 22(9). 1501–1513. 84 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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