Daniel M. Labow

6.4k total citations · 1 hit paper
102 papers, 3.1k citations indexed

About

Daniel M. Labow is a scholar working on Surgery, Emergency Medicine and Oncology. According to data from OpenAlex, Daniel M. Labow has authored 102 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Surgery, 34 papers in Emergency Medicine and 33 papers in Oncology. Recurrent topics in Daniel M. Labow's work include Intraperitoneal and Appendiceal Malignancies (44 papers), Appendicitis Diagnosis and Management (34 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (23 papers). Daniel M. Labow is often cited by papers focused on Intraperitoneal and Appendiceal Malignancies (44 papers), Appendicitis Diagnosis and Management (34 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (23 papers). Daniel M. Labow collaborates with scholars based in United States, Spain and Italy. Daniel M. Labow's co-authors include Myron Schwartz, Sasan Roayaie, Umut Sarpel, Spiros Hiotis, Josep M. Llovet, Swan N. Thung, Maria Guido, Iris Blume, M. Isabel Fiel and Kevin C. Conlon and has published in prestigious journals such as Journal of Clinical Oncology, Gastroenterology and Hepatology.

In The Last Decade

Daniel M. Labow

96 papers receiving 3.1k citations

Hit Papers

A System of Classifying Microvascular Invasion to Predict... 2009 2026 2014 2020 2009 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel M. Labow United States 27 1.8k 1.2k 1.2k 878 686 102 3.1k
Piotr Czauderna Poland 30 1.8k 1.0× 2.4k 1.9× 1.4k 1.2× 536 0.6× 407 0.6× 108 4.1k
Masatoshi Makuuchi Japan 28 1.7k 1.0× 2.2k 1.8× 641 0.6× 633 0.7× 1.1k 1.6× 104 3.3k
Hiroshi Takamori Japan 28 1.1k 0.6× 686 0.6× 1.3k 1.1× 632 0.7× 585 0.9× 155 2.6k
Claus Ferdinand Eisenberger Germany 28 1.5k 0.8× 377 0.3× 749 0.6× 1.1k 1.3× 367 0.5× 91 3.1k
Masami Sakurai Japan 26 704 0.4× 982 0.8× 458 0.4× 530 0.6× 770 1.1× 136 2.5k
Ho-Kyung Chun South Korea 27 1.0k 0.6× 288 0.2× 1.8k 1.5× 599 0.7× 151 0.2× 96 2.4k
Henri Porte France 26 787 0.4× 302 0.2× 684 0.6× 1.5k 1.7× 187 0.3× 77 2.8k
Kiyoshi Kajiyama Japan 28 1.3k 0.7× 1.4k 1.2× 1.0k 0.9× 580 0.7× 664 1.0× 107 2.8k
Jun Hanaoka Japan 27 680 0.4× 321 0.3× 757 0.7× 659 0.8× 234 0.3× 202 2.3k
Francesco Leone Italy 26 906 0.5× 364 0.3× 1.4k 1.2× 612 0.7× 266 0.4× 134 2.5k

Countries citing papers authored by Daniel M. Labow

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Labow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Labow. 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 Daniel M. Labow. The network helps show where Daniel M. Labow may publish in the future.

Co-authorship network of co-authors of Daniel M. Labow

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Labow. A scholar is included among the top collaborators of Daniel M. Labow 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 Daniel M. Labow. Daniel M. Labow is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Judy, Thomas M. Li, Camilo Correa‐Gallego, et al.. (2025). Assessing Clinical Factors and Communication Barriers Impacting Postoperative Regret in Patients with Pancreatic Cancer. Annals of Surgical Oncology. 33(4). 3553–3562.
2.
Frank, Richard C., Tammy Lo, Deep Pandya, et al.. (2025). Pancreatic Cancer Screening in New-onset and Deteriorating Diabetes: Preliminary Results From the PANDOME Study. The Journal of Clinical Endocrinology & Metabolism. 111(1). e148–e155. 3 indexed citations
4.
Yu, Allen T., Elizabeth Gleeson, Yael Berger, et al.. (2023). Perineural Invasion of Pancreatic Ductal Adenocarcinoma is Associated with Early Recurrence after Neoadjuvant Therapy Followed by Resection. World Journal of Surgery. 47(7). 1801–1808. 4 indexed citations
5.
Berger, Yael, Daniel Solomon, Umut Sarpel, et al.. (2023). Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy: Effects of postoperative fluids beyond the first 24 h. Journal of Surgical Oncology. 128(7). 1133–1140. 1 indexed citations
6.
Gleeson, Elizabeth, Allen T. Yu, Noah A. Cohen, et al.. (2022). Hyperthermic intraperitoneal chemotherapy does not increase risk of major complication or failure to rescue in cytoreductive surgery. Journal of Surgical Oncology. 126(4). 781–786. 1 indexed citations
7.
Violi, Naïk Vietti, Somali Gavane, Sherif Heiba, et al.. (2022). FDG-PET/MRI for the preoperative diagnosis and staging of peritoneal carcinomatosis: a prospective multireader pilot study. Abdominal Radiology. 48(12). 3634–3642. 5 indexed citations
8.
Gleeson, Elizabeth, Daniel M. Labow, Deepa Magge, et al.. (2021). Lymphocyte-To-Monocyte Ratio Predicts Survival After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy. Biomarkers in Medicine. 15(12). 965–975.
9.
Cui, Shengjie, et al.. (2021). Hidradenocarcinoma: a rare but challenging diagnosis. Clinical Imaging. 75. 138–142. 5 indexed citations
10.
Song, Won‐Min, Xiandong Lin, Xuehong Liao, et al.. (2019). Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer. International Journal of Cancer. 146(5). 1268–1280. 16 indexed citations
11.
Minkowitz, Harold S., Sonia Singla, Robin D. Kim, et al.. (2019). Prospective, Randomized, Phase II, Non-Inferiority Study to Evaluate the Safety and Efficacy of Topical Thrombin (Human) Grifols as Adjunct to Hemostasis During Vascular, Hepatic, Soft Tissue, and Spinal Open Surgery. Journal of the American College of Surgeons. 229(5). 497–507e1. 5 indexed citations
12.
Solomon, Daniel, Spiros Hiotis, Daniel M. Labow, et al.. (2019). Staging gallbladder cancer with lymphadenectomy: the practical application of new AHPBA and AJCC guidelines. HPB. 21(11). 1563–1569. 20 indexed citations
14.
Attalla, Kyrollis, Kenneth Haines, Daniel M. Labow, & Reza Mehrazin. (2017). Squamous Cell Carcinoma of the Renal Pelvis: Atypical Presentation of a Rare Malignancy. Urology Case Reports. 13. 137–139. 2 indexed citations
15.
Sarpel, Umut, John Spivack, Yaniv Berger, et al.. (2016). The effect of locoregional therapies in patients with advanced hepatocellular carcinoma treated with sorafenib. HPB. 18(5). 411–418. 6 indexed citations
16.
Tabrizian, Parissa, Ghalib Jibara, Jaclyn F. Hechtman, et al.. (2014). Outcomes following resection of intrahepatic cholangiocarcinoma. HPB. 17(4). 344–351. 51 indexed citations
18.
Fiel, Maria Isabel, Wei Luan, Amir Rosenblatt, et al.. (2013). Impact of liver fibrosis on prognosis following liver resection for hepatitis B-associated hepatocellular carcinoma. British Journal of Cancer. 109(3). 573–581. 61 indexed citations
19.
Conlon, Kevin C., Daniel M. Labow, Denis H. Y. Leung, et al.. (2001). Prospective Randomized Clinical Trial of the Value of Intraperitoneal Drainage After Pancreatic Resection. Annals of Surgery. 234(4). 487–494. 387 indexed citations
20.
Liu, David, Daniel M. Labow, Nael Martini, et al.. (1999). Pulmonary Metastasectomy for Head and Neck Cancers. Annals of Surgical Oncology. 6(6). 572–578. 103 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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