Academic Journal
Uncovering Global Trends in Surgical Oncology Trials: Applying Natural Language Processing to ClinicalTrials.gov.
| Title: | Uncovering Global Trends in Surgical Oncology Trials: Applying Natural Language Processing to ClinicalTrials.gov. |
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| Authors: | Ahmed H; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Czarnecka Z; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Jatana S; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Rouhi A; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Brunet C; Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada., Khan A; Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada., Bashir S; Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada., McCall M; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Saleh A; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.; Office of Global Surgery, College of Health Sciences, University of Alberta, Edmonton, Canada., Schiller D; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada., Skubleny D; Department of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada. skubleny@ualberta.ca. |
| Source: | Annals of surgical oncology [Ann Surg Oncol] 2026 Aug; Vol. 33 (8), pp. 7648-7659. Date of Electronic Publication: 2026 Apr 05. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Springer Country of Publication: United States NLM ID: 9420840 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1534-4681 (Electronic) Linking ISSN: 10689265 NLM ISO Abbreviation: Ann Surg Oncol Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2005- : New York, NY : Springer Original Publication: New York, NY : Raven Press, c1994- |
| MeSH Terms: | Surgical Oncology*/trends , Clinical Trials as Topic*/statistics & numerical data , Neoplasms*/surgery , Natural Language Processing* , Machine Learning* , Databases, Factual*, Humans ; Neural Networks, Computer ; Classification Algorithms |
| Abstract: | Background: Identifying surgical oncology trials within the National Clinical Trial (NCT) database is challenging owing to the absence of medical specialty labels. We developed a machine learning classifier to systematically identify and analyze surgical oncology trials registered in the NCT database. Materials and Methods: We analyzed 425,736 trials. A training dataset of 4863 trials were labeled as surgical oncology according to Complex General Surgical Oncology (CGSO) neoplasms. Labels were applied by two independent reviewers, and disagreements resolved by a third, using study descriptions and Medical Subject Headings (MeSH). Neural networks were assessed using stratified 5-fold cross-validation. Models were evaluated using accuracy, balanced accuracy, specificity, sensitivity, and Cohen's kappa. Neural embedding representations were analyzed with principal component analysis (PCA). Trial characteristics, geospatial mapping and social network analyses were evaluated. Results: The selected model achieved excellent accuracy (94.1% [95%CI 93.3-94.9]). PCA of neural embeddings demonstrated separation of CGSO-relevant terms. We identified 24,345 surgical oncology trials. Trial volume was strongly associated with national GDP (r = 0.96) and global disparities were identified. Network analysis revealed strong collaboration amongst USA and European institutions. Chinese institutions demonstrated high trial leadership but relatively limited external collaboration. Academic centers including Memorial Sloan Kettering and MD Anderson remained prominent, while industry participation, was consistently evident. Conclusions: This study provides a machine learning approach to identify surgical oncology trials within the NCT database. Our research highlights collaboration networks amongst institutions and global disparities in surgical oncology research. Future work should incorporate funding information to better inform surgical research policy and development. (© 2026. Society of Surgical Oncology.) |
| References: | Copeland EM III. Surgical oncology: a specialty in evolution: PubMed. Ann Surg Oncol. 1999. https://doi.org/10.1007/s10434-999-0424-6 . (PMID: 10.1007/s10434-999-0424-610493617) Bilimoria KY, Bentrem DJ, Lillemoe KD, Talamonti MS, Ko CY. Assessment of pancreatic cancer care in the United States based on formally developed quality indicators: PubMed. J Natl Cancer Inst. 2009;101(12):848–59. https://doi.org/10.1093/jnci/djp107 . (PMID: 10.1093/jnci/djp107195093662697207) Umscheid CA, Margolis DJ, Grossman CE. Key concepts of clinical trials: a narrative review. Postgrad Med. 2011;123(5):194–204. https://doi.org/10.3810/pgm.2011.09.2475 . (PMID: 10.3810/pgm.2011.09.2475219041023272827) Menezes AS, Barnes A, Scheer AS, et al. Clinical research in surgical oncology: an analysis of ClinicalTrials.gov. Ann Surg Oncol. 2013;20(12):3725–31. https://doi.org/10.1245/s10434-013-3054-y . (PMID: 10.1245/s10434-013-3054-y23800894) Purushotham AD, Lewison G, Sullivan R. The state of research and development in global cancer surgery: PubMed. Ann Surg. 2012;255(3):427–32. https://doi.org/10.1097/SLA.0b013e318246591f . (PMID: 10.1097/SLA.0b013e318246591f22281701) Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov: the final rule. N Engl J Med. 2016;375(20):1998–2004. https://doi.org/10.1056/NEJMsr1611785 . (PMID: 10.1056/NEJMsr1611785276354715225905) Sullivan R, Alatise OI, Anderson BO, et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 2015;16(11):1193–224. https://doi.org/10.1016/S1470-2045(15)00223-5 . (PMID: 10.1016/S1470-2045(15)00223-526427363) Unger JM, Cook E, Tai E, Bleyer A. The role of clinical trial participation in cancer research: barriers, evidence, and strategies. Am Soc Clin Oncol Educ Book. 2016. https://doi.org/10.1200/EDBK_156686 . (PMID: 10.1200/EDBK_156686272496995495113) Batanova E, Birmpa I, Meisser G. Use of machine learning to classify clinical research to identify applicable compliance requirements. Inform Med Unlocked. 2023;39:101255. https://doi.org/10.1016/j.imu.2023.101255 . (PMID: 10.1016/j.imu.2023.101255) Benoit K, Watanabe K, Wang H, et al. quanteda: An R package for the quantitative analysis of textual data. J Open Source Softw. 2018;3(30):774–774. https://doi.org/10.21105/joss.00774 . (PMID: 10.21105/joss.00774) R: A Language and Environment for Statistical Computing (Version 4.2.0) [Computer software]. 2022. Tensorflow: R Interface to 'TensorFlow'. R package version 2.16.0. 2024. https://cran.r-project.org/web/packages/tensorflow/index.html. Keras3: R Interface to 'Keras'. R package version 1.3.0. 2025. https://cran.r-project.org/web/packages/keras3/index.html. Rnaturalearth: World Map Data from Natural Earth. 2023. https://cran.r-project.org/web/packages/rnaturalearth/index.html. South A. rworldmap : a new R package for mapping global data. The R Journal. 2011;3(1):35–43. (PMID: 10.32614/RJ-2011-006) Group WB. World Bank. Current health expenditure (% of GDP). Accessed May 27, 2025. https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS. Group WB. World Bank. GDP (current US$). Accessed May 27, 2025. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD. Organization WH. World Health Organization. WHO Global health expenditure database (GHED). Accessed April 8, 2025. https://apps.who.int/nha/database. Organization. WH. World Health Organization. World health statistics 2024: monitoring health for the SDGs, sustainable development goals. Accessed April 8, 2025. https://www.who.int/publications/i/item/9789240094703. Gabani J, Mazumdar S, Suhrcke M. The effect of health financing systems on health system outcomes: a cross-country panel analysis. Health Econ. 2023;32(3):574–619. https://doi.org/10.1002/hec.4635 . (PMID: 10.1002/hec.463536480236) Igraph: Network Analysis and Visualization in R. Version R package version 2.1.4. 2025. https://cran.r-project.org/web/packages/igraph/index.html. Tidygraph: A Tidy API for Graph Manipulation. Version R package version 1.3.1. 2024. R package version 1.3.1. Ggraph: An Implementation of Grammar of Graphics for Graphs and Networks. 2024. https://cran.r-project.org/package=ggraph. Blondel VD, Guillaume J-L, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech Theor Exp. 2008;2008(10):P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 . (PMID: 10.1088/1742-5468/2008/10/P10008) Freeman L. Centrality in social networks conceptual clarification. Soc Netw. 1978. https://doi.org/10.1016/0378-8733 . (PMID: 10.1016/0378-8733) Peng S, Zhou Y, Cao L, Yu S, Niu J, Jia W. Influence analysis in social networks: a survey. J Netw Comput Appl. 2018;106:17–32. https://doi.org/10.1016/j.jnca.2018.01.005 . (PMID: 10.1016/j.jnca.2018.01.005) Cox MAA, Cox TF. Multidimensional scaling. Handb Data Vis. 2008. https://doi.org/10.1007/978-3-540-33037-0_14 . (PMID: 10.1007/978-3-540-33037-0_14) Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Exp. 1991;21(11):1129–64. https://doi.org/10.1002/spe.4380211102 . (PMID: 10.1002/spe.4380211102) Shah R, Boffa D, Khan S, Judson B. The representation of surgery in oncology clinical trials: 2001 to 2022: PubMed. Ann Surg Oncol. 2023;30(12):7275–80. https://doi.org/10.1245/s10434-023-14064-4 . (PMID: 10.1245/s10434-023-14064-437556010) Perera ND, Turner BE, Shen JZ, et al. The surgical oncology clinical trial landscape: a cross-sectional analysis of ClinicalTrials.gov from 2008–2020. J Clin Oncol. 2021;39(15). https://doi.org/10.1200/JCO.2021.39.15_suppl.1561. Hirsch BR, Califf RM, Cheng SK, et al. Characteristics of oncology clinical trials: insights from a systematic analysis of ClinicalTrials.gov. JAMA Int Med. 2013;2013173(11):972–9. https://doi.org/10.1001/jamainternmed.2013.627 . (PMID: 10.1001/jamainternmed.2013.627) Zhang S, Liang F, Li W, Tannock I. Comparison of eligibility criteria between protocols, registries, and publications of cancer clinical trials: PubMed. J Natl Cancer Inst. 2016;108(11):djw129. https://doi.org/10.1093/jnci/djw129 . (PMID: 10.1093/jnci/djw129) Ramchurn TP, Nundy S. Randomised clinical trials in surgery: are we at a crossroads? Ann Med Surg. 2023;86(1):3–6. https://doi.org/10.1097/MS9.0000000000001457 . (PMID: 10.1097/MS9.0000000000001457) Sattari R, Bae J, Berkes E, Weinberg BA. The ripple effects of funding on researchers and output: PubMed. Sci Adv. 2022;8(16):eabb7348. https://doi.org/10.1126/sciadv.abb7348 . (PMID: 10.1126/sciadv.abb7348354522879032967) Choudhry NK. Randomized, controlled trials in health insurance systems. N Engl J Med. 2017;377(10):957–64. https://doi.org/10.1056/NEJMra1510058 . (PMID: 10.1056/NEJMra151005828877014) Mohammadi E, Olejniczak AJ, Walker GE, Nagarkatti P. The impact of geographic inequality in federal research funding: a comparative longitudinal study of research and scholarly outputs in EPSCoR versus non-EPSCoR states: PubMed. PloS one. 2023;18(6):e0286991. https://doi.org/10.1371/journal.pone.0286991 . (PMID: 10.1371/journal.pone.02869913732724310275440) |
| Contributed Indexing: | Keywords: Clinical trial; Global surgery; Machine learning; Natural language processing; Oncology; Randomized control trial; Surgery; Surgical oncology |
| Entry Date(s): | Date Created: 20260405 Date Completed: 20260707 Latest Revision: 20260707 |
| Update Code: | 20260708 |
| DOI: | 10.1245/s10434-026-19555-8 |
| PMID: | 41936683 |
| Database: | MEDLINE |
| ISSN: | 1534-4681 |
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| DOI: | 10.1245/s10434-026-19555-8 |