Paul Knechtges

664 total citations
27 papers, 482 citations indexed

About

Paul Knechtges is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Surgery. According to data from OpenAlex, Paul Knechtges has authored 27 papers receiving a total of 482 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Oncology and 8 papers in Surgery. Recurrent topics in Paul Knechtges's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Pancreatic and Hepatic Oncology Research (8 papers) and MRI in cancer diagnosis (4 papers). Paul Knechtges is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Pancreatic and Hepatic Oncology Research (8 papers) and MRI in cancer diagnosis (4 papers). Paul Knechtges collaborates with scholars based in United States, China and Netherlands. Paul Knechtges's co-authors include Ruth C. Carlos, Bradley A. Erickson, William A. Hall, E.S. Paulson, D. Schött, X. Allen Li, Kiyoko Oshima, Matthew A. Barish, Ying Liang and Zhiwu Wang and has published in prestigious journals such as Journal of Clinical Oncology, Gastroenterology and PLoS ONE.

In The Last Decade

Paul Knechtges

26 papers receiving 468 citations

Peers

Paul Knechtges

RNMI

ONCOL

PRM

SURGE

Antonio Piras Italy

Matea Pavic Switzerland

Mario Jreige Switzerland

Jennifer S. Golia Pernicka United States

Derek Grose United Kingdom

Hendrik Dapper Germany

James Janopaul‐Naylor United States

Michael C. Tjong Canada

Simeng Zhu United States

O. Miranda France

Antonio Piras Italy

Paul Knechtges

186 ×0.6

RNMI

128 ×0.6

ONCOL

79 ×0.7

PRM

41 ×0.5

SURGE

57 ×1.0

Citations per year, relative to Paul Knechtges Paul Knechtges (= 1×) peers Antonio Piras

Countries citing papers authored by Paul Knechtges

Since Specialization

Citations

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

Fields of papers citing papers by Paul Knechtges

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Knechtges

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

Szabó, Anikó, Andrew S. Nencka, Paul Knechtges, et al.. (2022). Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models. Journal of Gastrointestinal Surgery. 27(1). 122–130. 6 indexed citations

Hall, William J., et al.. (2022). Predicting Treatment Response From Extracellular Volume Fraction for Chemoradiation Therapy of Pancreatic Cancer. International Journal of Radiation Oncology*Biology*Physics. 115(3). 803–808. 13 indexed citations

Liang, Ying, D. Schött, Zhiwu Wang, et al.. (2020). Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks. Radiotherapy and Oncology. 145. 193–200. 69 indexed citations

Blank, Jacqueline J., Nicholas G. Berger, Paul Knechtges, et al.. (2019). Initial Experience With Staging Rectal Adenocarcinoma Using 7T Magnetic Resonance Imaging. Journal of Surgical Research. 245. 434–440. 1 indexed citations

Schött, D., Taly Gilat Schmidt, William A. Hall, et al.. (2019). Estimation of changing gross tumor volume from longitudinal CTs during radiation therapy delivery based on a texture analysis with classifier algorithms: a proof-of-concept study. Quantitative Imaging in Medicine and Surgery. 9(7). 1189–1200. 2 indexed citations

Liang, Ying, D. Schött, William A. Hall, et al.. (2019). On the Development of MRI-Based Auto-Segmentation of Pancreatic Tumor Using Deep Neural Networks. International Journal of Radiation Oncology*Biology*Physics. 105(1). S201–S201. 1 indexed citations

Chen, Xiaojian, Kiyoko Oshima, D. Schött, et al.. (2017). Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study. PLoS ONE. 12(6). e0178961–e0178961. 68 indexed citations

Barnes, Chad A., Elizabeth A. Krzywda, Kathleen K. Christians, et al.. (2017). Development of a high risk pancreatic screening clinic using 3.0 T MRI. Familial Cancer. 17(1). 101–111. 21 indexed citations

Hall, William A., H.D. Heerkens, E.S. Paulson, et al.. (2017). Pancreatic gross tumor volume contouring on computed tomography (CT) compared with magnetic resonance imaging (MRI): Results of an international contouring conference. Practical Radiation Oncology. 8(2). 107–115. 14 indexed citations

10.

Kugler, Nathan W., Thomas Carver, Paul Knechtges, et al.. (2016). Thoracostomy tube function not trajectory dictates reintervention. Journal of Surgical Research. 206(2). 380–385. 6 indexed citations

11.

Heerkens, H.D., William A. Hall, Paul Knechtges, et al.. (2016). Recommendations for MRI-based contouring of gross tumor volume and organs at risk for radiation therapy of pancreatic cancer. Practical Radiation Oncology. 7(2). 126–136. 41 indexed citations

12.

Bock, Jonathan M., et al.. (2016). Clinical Conundrum: Killian-Jamieson Diverticulum with Paraesophageal Hernia. Dysphagia. 31(4). 587–591. 3 indexed citations

13.

Dalah, Entesar Z., A. Tai, Kiyoko Oshima, et al.. (2015). SU‐E‐J‐271: Correlation of CT Number Change with Radiation Treatment Response for Pancreatic Cancer. Medical Physics. 42(6Part11). 3329–3329. 1 indexed citations

14.

Knechtges, Paul, et al.. (2014). Application of Kaizen Methodology to Foster Departmental Engagement in Quality Improvement. Journal of the American College of Radiology. 11(12). 1126–1130. 24 indexed citations

15.

Knechtges, Paul, et al.. (2013). Utilizing the 5S Methodology for Radiology Workstation Design: Applying Lean Process Improvement Methods. Journal of the American College of Radiology. 10(8). 633–634. 11 indexed citations

16.

Oh, Young S. & Paul Knechtges. (2011). Fistula between the Jejunum and the Inferior Vena Cava after Esophagojejunal Anastomosis. Case Reports in Gastroenterology. 5(3). 546–552. 4 indexed citations

17.

McFarland, Elizabeth G., Joel G. Fletcher, Perry J. Pickhardt, et al.. (2009). ACR Colon Cancer Committee White Paper: Status of CT Colonography 2009. Journal of the American College of Radiology. 6(11). 756–772.e4. 63 indexed citations

18.

Rangnekar, Amol S., Daniel M. Morgan, Paul Knechtges, et al.. (2008). M1806 Complaints Suggestive of Irritable Bowel Syndrome Are Common in Patients with Puborectalis Dyssynergia: An Under-Recognized Overlap Syndrome. Gastroenterology. 134(4). A–423. 3 indexed citations

19.

Knechtges, Paul, et al.. (2007). National and Local Trends in CT Colonography Reimbursement: Past, Present, and Future. Journal of the American College of Radiology. 4(11). 776–799. 16 indexed citations

20.

Knechtges, Paul, et al.. (2007). Fistula-in-Ano: The Role of Imaging in Diagnosis and Presurgical Planning. Seminars in Colon and Rectal Surgery. 18(2). 111–121.

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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