https://doi.org/10.4081/btvb.2024.123
Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot
Submitted: 29 January 2024
Accepted: 22 March 2024
Published: 16 May 2024
Accepted: 22 March 2024
Abstract Views: 924
PDF: 180
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.
Blom JW, Doggen CJ, Osanto S, Rosendaal FR. Malignancies, prothrombotic mutations, and the risk of venous thrombosis. Jama 2005;293:715-22.
DOI: https://doi.org/10.1001/jama.293.6.715
Elting LS, Escalante CP, Cooksley C, et al. Outcomes and cost of deep venous thrombosis among patients with cancer. Arch Intern Med 2004;164:1653-61.
DOI: https://doi.org/10.1001/archinte.164.15.1653
Lyman GH, Eckert L, Wang Y, et al. Venous thromboembolism risk in patients with cancer receiving chemotherapy: a real-world analysis. Oncologist 2013;18:1321-9.
DOI: https://doi.org/10.1634/theoncologist.2013-0226
Mahajan A, Brunson A, Adesina O, et al. The incidence of cancer-associated thrombosis is increasing over time. Blood Adv 2022;6:307-20.
DOI: https://doi.org/10.1182/bloodadvances.2021005590
Mulder FI, Horváth-Puhó E, van Es N, et al. Venous thromboembolism in cancer patients: a population-based cohort study. Blood 2021;137:1959-69.
DOI: https://doi.org/10.1182/blood.2020007338
Sørensen HT, Pedersen L, van Es N, et al. Impact of venous thromboembolism on the mortality in patients with cancer: a population-based cohort study. Lancet Reg Health Eur 2023;34:100739.
DOI: https://doi.org/10.1016/j.lanepe.2023.100739
Crobach MJT, Anijs RJS, Brækkan SK, et al. Survival after cancer-related venous thrombosis: the Scandinavian Thrombosis and Cancer Study. Blood Adv 2023;7: 4072-9.
DOI: https://doi.org/10.1182/bloodadvances.2022009577
Bernard M. 15 Amazing Real-World Applications Of AI Everyone Should Know About. Forbes 2023. Available from: https://www.forbes.com/sites/bernardmarr/2023/05/10/15-amazing-real-world-applications-of-ai-everyone-should-know-about/?sh=3187455385e8
Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195.
DOI: https://doi.org/10.1186/s12916-019-1426-2
Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021;139:4-15.
DOI: https://doi.org/10.1093/bmb/ldab016
Gresele P. Artificial intelligence and machine learning in hemostasis and thrombosis. Bleeding Thromb Vasc Biol 2024;2.
DOI: https://doi.org/10.4081/btvb.2023.105
Jiang P, Sinha S, Aldape K, et al. Big data in basic and translational cancer research. Nat Rev Cancer 2022;22:625-39.
DOI: https://doi.org/10.1038/s41568-022-00502-0
Sweeney SM, Hamadeh HK, Abrams N, et al. Challenges to Using Big Data in Cancer. Cancer Res 2023;83:1175-82.
DOI: https://doi.org/10.1158/0008-5472.CAN-22-1274
Khorana AA, Mackman N, Falanga A, et al. Cancer-associated venous thromboembolism. Nat Rev Dis Primers 2022;8:11.
DOI: https://doi.org/10.1038/s41572-022-00336-y
Abdulla A, Davis WM, Ratnaweera N, et al. A meta-analysis of case fatality rates of recurrent venous thromboembolism and major bleeding in patients with cancer. Thromb Haemost 2020;120:702-13.
DOI: https://doi.org/10.1055/s-0040-1708481
Lee AYY. Venous thromboembolism treatment in patients with cancer: reflections on an evolving landscape. Bleeding Thromb Vasc Biol 2024;3.
DOI: https://doi.org/10.4081/btvb.2024.111
Douce DR, Holmes CE, Cushman M, et al. Risk factors for cancer-associated venous thromboembolism: The venous thromboembolism prevention in the ambulatory cancer clinic (VTE-PACC) study. J Thromb Haemost 2019;17: 2152-9.
DOI: https://doi.org/10.1111/jth.14614
Ross G. The Power of Natural Language Processing: Harvard Business Review 2022. Available from: https://hbr.org/2022/04/the-power-of-natural-language-processing
Casey A, Davidson E, Poon M, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inform Decis Mak 2021;21:179.
DOI: https://doi.org/10.1186/s12911-021-01533-7
Ford E, Carroll JA, Smith HE, et al. Extracting information from the text of electronic medical records to improve case detection: a systematic review. J Am Med Inform Assoc 2016;23:1007-15.
DOI: https://doi.org/10.1093/jamia/ocv180
Korngiebel DM, Mooney SD. Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery. NPJ Digit Med 2021;4:93.
DOI: https://doi.org/10.1038/s41746-021-00464-x
Rösler W, Altenbuchinger M, Baeßler B, et al. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023;149:7997-8006.
DOI: https://doi.org/10.1007/s00432-023-04667-5
Evans RS, Lloyd JF, Aston VT, et al. Computer surveillance of patients at high risk for and with venous thromboembolism. AMIA Annu Symp Proc 2010;2010:217-21.
Reichley RM, Henderson KE, Currie AM, et al. Natural language processing to identify venous thromboembolic events. AMIA Annu Symp Proc 2007:1089.
Pham AD, Névéol A, Lavergne T, et al. Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinform 2014;15:266.
DOI: https://doi.org/10.1186/1471-2105-15-266
Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. Jama 2011;306:848-55.
DOI: https://doi.org/10.1001/jama.2011.1204
Verma AA, Masoom H, Pou-Prom C, et al. Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patients. Thromb Res 2022;209:51-8.
DOI: https://doi.org/10.1016/j.thromres.2021.11.020
Wendelboe A, Saber I, Dvorak J, et al. Exploring the applicability of using natural language processing to support nationwide venous thromboembolism surveillance: model evaluation study. JMIR Bioinform Biotech 2022;3.
DOI: https://doi.org/10.2196/preprints.36877
Shi J, Hurdle JF, Johnson SA, et al. Natural language processing for the surveillance of postoperative venous thromboembolism. Surgery 2021;170:1175-82.
DOI: https://doi.org/10.1016/j.surg.2021.04.027
Gálvez JA, Pappas JM, Ahumada L, et al. The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children. J Thromb Thrombolysis 2017;44: 281-90.
DOI: https://doi.org/10.1007/s11239-017-1532-y
Pasha AK, McBane RD, Chaudhary R, et al. Timing of venous thromboembolism diagnosis in hospitalized and non-hospitalized patients with COVID-19. Thromb Res 2021;207:150-7.
DOI: https://doi.org/10.1016/j.thromres.2021.09.021
Maghsoudi A, Zhou E, Guffey D, et al. A Transformer Natural Language Processing Algorithm for Cancer Associated Thrombosis Phenotype. Blood 2023;142:1267.
DOI: https://doi.org/10.1182/blood-2023-184756
Dunbar A, Bolton KL, Devlin SM, et al. Genomic profiling identifies somatic mutations predicting thromboembolic risk in patients with solid tumors. Blood 2021;137:2103-13.
DOI: https://doi.org/10.1182/blood.2020007488
Li A, da Costa WLJr, Guffey D, et al. Developing and optimizing a computable phenotype for incident venous thromboembolism in a longitudinal cohort of patients with cancer. Res Pract Thromb Haemost 2022;6:e12733.
DOI: https://doi.org/10.1002/rth2.12733
Subramanian NG, Pleitez HG, Nguyen D, et al. Diagnostic performance of natural language processing in detection of acute cancer VTE. J Clin Oncol 2023;41:e19062-e.
DOI: https://doi.org/10.1200/JCO.2023.41.16_suppl.e19062
Lim W, Le Gal G, Bates SM, et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: diagnosis of venous thromboembolism. Blood Adv 2018;2:3226-56.
DOI: https://doi.org/10.1182/bloodadvances.2018024828
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.
DOI: https://doi.org/10.1038/s41568-018-0016-5
Chen MM, Terzic A, Becker AS, et al. Artificial intelligence in oncologic imaging. Eur J Radiol Open 2022;9:100441.
DOI: https://doi.org/10.1016/j.ejro.2022.100441
Kwok CS, Wong CW, Lovatt S, et al. Misdiagnosis of pulmonary embolism and missed pulmonary embolism: A systematic review of the literature. Health Sci Rev 2022;3: 100022.
DOI: https://doi.org/10.1016/j.hsr.2022.100022
Xu H, Li H, Xu Q, et al. Automatic detection of pulmonary embolism in computed tomography pulmonary angiography using Scaled-YOLOv4. Med Phys 2023;50:4340-50.
DOI: https://doi.org/10.1002/mp.16218
Ajmera P, Kharat A, Seth J, et al. A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography. BMC Med Imaging 2022;22:195.
DOI: https://doi.org/10.1186/s12880-022-00916-0
Huang SC, Kothari T, Banerjee I, et al. PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ Digit Med 2020;3:61.
DOI: https://doi.org/10.1038/s41746-020-0266-y
Zhang H, Cheng Y, Chen Z, et al. Clot burden of acute pulmonary thromboembolism: comparison of two deep learning algorithms, Qanadli score, and Mastora score. Quant Imaging Med Surg 2022;12:66-79.
DOI: https://doi.org/10.21037/qims-21-140
Xi L, Xu F, Kang H, et al. Clot ratio, new clot burden score with deep learning, correlates with the risk stratification of patients with acute pulmonary embolism. Quant Imaging Med Surg 2024;14:86-97.
DOI: https://doi.org/10.21037/qims-23-322
Huang C, Tian J, Yuan C, et al. Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network. Biomed Res Int 2019;2019: 3401683.
DOI: https://doi.org/10.1155/2019/3401683
Sun C, Xiong X, Zhang T, et al. Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study. Biomed Res Int 2021;2021:4989297.
DOI: https://doi.org/10.1155/2021/4989297
Hwang JH, Seo JW, Kim JH, et al. Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography. Diagnostics (Basel) 2022;12.
DOI: https://doi.org/10.3390/diagnostics12020274
Seo JW, Park S, Kim YJ, et al. Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach. Sci Rep 2023;13:967.
DOI: https://doi.org/10.1038/s41598-022-25849-0
Kainz B, Heinrich MP, Makropoulos A, et al. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med 2021;4:137.
DOI: https://doi.org/10.1038/s41746-021-00503-7
Koh DM, Papanikolaou N, Bick U, et al. Artificial intelligence and machine learning in cancer imaging. Commun Med (Lond) 2022;2:133.
DOI: https://doi.org/10.1038/s43856-022-00199-0
Dumitriu LaGrange D, Hofmeister J, Rosi A, et al. Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies. Neurosci Inform 2023;3:100114.
DOI: https://doi.org/10.1016/j.neuri.2022.100114
Karande GY, Hedgire SS, Sanchez Y, et al. Advanced imaging in acute and chronic deep vein thrombosis. Cardiovasc Diagn Ther 2016;6:493-507.
DOI: https://doi.org/10.21037/cdt.2016.12.06
Quencer KB, Friedman T, Sheth R, Oklu R. Tumor thrombus: incidence, imaging, prognosis and treatment. Cardiovasc Diagn Ther 2017;7:S165-s77.
DOI: https://doi.org/10.21037/cdt.2017.09.16
Timp JF, Braekkan SK, Versteeg HH, Cannegieter SC. Epidemiology of cancer-associated venous thrombosis. Blood 2013;122:1712-23.
DOI: https://doi.org/10.1182/blood-2013-04-460121
Bosch FTM, Mulder FI, Kamphuisen PW, et al. Primary thromboprophylaxis in ambulatory cancer patients with a high Khorana score: a systematic review and meta-analysis. Blood Adv 2020;4:5215-25.
DOI: https://doi.org/10.1182/bloodadvances.2020003115
Khorana AA, Kuderer NM, Culakova E, et al. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood 2008;111:4902-7.
DOI: https://doi.org/10.1182/blood-2007-10-116327
Mulder FI, Candeloro M, Kamphuisen PW, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis. Haematologica 2019;104:1277-87.
DOI: https://doi.org/10.3324/haematol.2018.209114
Li A, Kuderer NM, Garcia DA, et al. Direct oral anticoagulant for the prevention of thrombosis in ambulatory patients with cancer: A systematic review and meta-analysis. J Thromb Haemost 2019;17:2141-51.
DOI: https://doi.org/10.1111/jth.14613
Ferroni P, Zanzotto FM, Scarpato N, et al. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Dis Markers 2017;2017: 8781379.
DOI: https://doi.org/10.1155/2017/8781379
Liu S, Zhang F, Xie L, et al. Machine learning approaches for risk assessment of peripherally inserted Central catheter-related vein thrombosis in hospitalized patients with cancer. Int J Med Inform 2019;129:175-83.
DOI: https://doi.org/10.1016/j.ijmedinf.2019.06.001
Jin S, Qin D, Liang BS, et al. Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables. Int J Med Inform 2022;161:104733.
DOI: https://doi.org/10.1016/j.ijmedinf.2022.104733
Lei H, Zhang M, Wu Z, et al. Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning. Front Cardiovasc Med 2022;9:845210.
DOI: https://doi.org/10.3389/fcvm.2022.845210
Meng L, Wei T, Fan R, et al. Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients. Asia Pac J Oncol Nurs 2022;9:100128.
DOI: https://doi.org/10.1016/j.apjon.2022.100128
Li A, La J, May SB, et al. Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems. J Clin Oncol 2023;41:2926-38.
DOI: https://doi.org/10.1200/JCO.22.01542
Min L, Bao H, Bu F, et al. Machine-Learning-Assisted Procoagulant Extracellular Vesicle Barcode Assay toward High-Performance Evaluation of Thrombosis-Induced Death Risk in Cancer Patients. ACS Nano 2023;17:19914-24.
DOI: https://doi.org/10.1021/acsnano.3c04615
Munoz AJ, Souto JC, Lecumberri R, et al. Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning. Thromb Res 2023;228:181-8.
DOI: https://doi.org/10.1016/j.thromres.2023.06.015
Qiao N, Zhang Q, Chen L, et al. Machine learning prediction of venous thromboembolism after surgeries of major sellar region tumors. Thromb Res 2023;226:1-8.
DOI: https://doi.org/10.1016/j.thromres.2023.04.007
Verstovsek S, Krecak I, Heidel FH, et al. Identifying Patients with Polycythemia Vera at Risk of Thrombosis after Hydroxyurea Initiation: The Polycythemia Vera-Advanced InBleeding tegrated Models (PV-AIM) Project. Biomedicines 2023;11.
DOI: https://doi.org/10.3390/biomedicines11071925
Xu Q, Lei H, Li X, et al. Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients. Heliyon 2023;9: e12681.
DOI: https://doi.org/10.1016/j.heliyon.2022.e12681
Finlayson SG, Subbaswamy A, Singh K, et al. The Clinician and Dataset Shift in Artificial Intelligence. N Engl J Med 2021;385:283-6.
DOI: https://doi.org/10.1056/NEJMc2104626
Yang J, Soltan AAS, Clifton DA. Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit Med. 2022;5:69.
DOI: https://doi.org/10.1038/s41746-022-00614-9
Charilaou P, Battat R. Machine learning models and over-fitting considerations. World J Gastroenterol 2022;28:605-7.
DOI: https://doi.org/10.3748/wjg.v28.i5.605
Pan SJ, Yang Q. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 2010;22: 1345-59.
DOI: https://doi.org/10.1109/TKDE.2009.191
Xu J, Glicksberg BS, Su C, et al. Federated Learning for Healthcare Informatics. J Healthc Inform Res 2021;5:1-19.
DOI: https://doi.org/10.1007/s41666-020-00082-4
Ferryman K, Mackintosh M, Ghassemi M. Considering Biased Data as Informative Artifacts in AI-Assisted Health Care. N Engl J Med 2023;389:833-8.
DOI: https://doi.org/10.1056/NEJMra2214964
Jabbour S, Fouhey D, Shepard S, et al. Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study. Jama 2023;330: 2275-84.
DOI: https://doi.org/10.1001/jama.2023.22295
Datta T, Brunson A, Mahajan A, et al. Racial disparities in cancer-associated thrombosis. Blood Adv 2022;6:3167-77.
DOI: https://doi.org/10.1182/bloodadvances.2021006209
Wiredu C, Haynes N, Guerra C, Ky B. Racial and Ethnic Disparities in Cancer-Associated Thrombosis. Thromb Haemost 2022;122:662-5.
DOI: https://doi.org/10.1055/a-1674-0259
Van Laere S, Muylle KM, Cornu P. Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper. Int J Health Policy Manag 2022;11:3159-63.
DOI: https://doi.org/10.34172/ijhpm.2021.144
Stark L. Medicine’s Lessons for AI Regulation. N Engl J Med 2023;389:2213-5.
DOI: https://doi.org/10.1056/NEJMp2309872
World Health Organization. Regulatory considerations on artificial intelligence for health. Geneva: World Health Organization, 2023.
Khullar D, Casalino LP, Qian Y, et al. Perspectives of Patients About Artificial Intelligence in Health Care. JAMA Netw Open 2022;5:e2210309.
DOI: https://doi.org/10.1001/jamanetworkopen.2022.10309
Lam BD, Dodge L, Datta S, et al. HTRS2023.P4.12 Perceptions on the potential for artificial intelligence to improve venous thromboembolism prevention. Res Pract Thromb Haemost 2023;7:100260.
DOI: https://doi.org/10.1016/j.rpth.2023.100260
Liu W, Liu M, Guo X, et al. Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning. Eur Radiol 2020;30:3567-75.
DOI: https://doi.org/10.1007/s00330-020-06699-8
How to Cite
Patell, R., Zwicker, J. I., Singh, R., & Mantha, S. (2024). Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot. Bleeding, Thrombosis and Vascular Biology, 3(s1). https://doi.org/10.4081/btvb.2024.123
Copyright (c) 2024 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Most read articles by the same author(s)
- Maria J. Fernandez Turizo, Rushad Patell, Jeffrey I. Zwicker, Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. s1 (2024): 12th ICTHIC
Similar Articles
- Paolo Simioni, Vittorio Pengo, Paolo Prandoni, Thrombosis and hemostasis at the University of Padua: a reappraisal on the occasion of its 800th year of history , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
- Juan Eirís, Marina Suárez-Terrón, Pablo Granados, David Martínez-Campuzano, Ana Rosa Cid, Saturnino Haya, Santiago Bonanad, Allopurinol-induced acquired von Willebrand syndrome , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 4 (2023)
- Daniela Poli, Walter Ageno, Emilia Antonucci, Salvatore Bradamante, Eugenio Bucherini, Paolo Chiarugi, Antonio Chistolini, Benilde Cosmi, Anna Falanga, Antonio Insana, Domenico Lione, Rosa Maria Lombardi, Giuseppe Malcangi, Rossella Marcucci, Giuliana Martini, Lucilla Masciocco, Carmelo Paparo, Daniele Pastori, Simona Pedrini, Vittorio Pengo, Pasquale Pignatelli, Andrea Toma, Sophie Testa, Gualtiero Palareti, Management of anticoagulation in atrial fibrillation patients in Italy: insight from the Atrial Fibrillation-Survey on Anticoagulated Patients Register (AF-START) , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 2 (2023)
- Vittorio Pengo, Luca Sarti, Emilia Antonucci, Stefania Di Agostino, Elisa Bison, Gentian Denas, Daniela Poli, Pier Luigi Meroni, Roberto Gerli, Antonella Radice, Gualtiero Palareti, on behalf of FIRMA group and START collaborators, Descriptive analysis of patients positive for anti-phospholipid antibodies included in two Italian registries , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 3 (2023)
- Silvia Cardi, Filippo Catalani, Luca Valerio, Stefano Barco, From rare to aware: confronting Lemierre syndrome , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. 3 (2024)
- Guest Editors: Anna Falanga, Benjamin Brenner, Alok A. Khorana, Welcome to the 12th ICTHIC! , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. s1 (2024): 12th ICTHIC
- Daniela Tormene, Elena Campello, Chiara Simion, Anna Poretto, Paolo Prandoni, Paolo Simioni, Combined oral contraceptives and the risk of venous thromboembolism carriers of antithrombin, protein C or S deficiency: Sub-analysis of a prospective cohort study , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
- Kartik Akkihal, Thomas Varkey, James Kelbert, The use of creatine and the development of deep vein thrombosis. A scoping review , Bleeding, Thrombosis and Vascular Biology: Vol. 3 No. 3 (2024)
- Rosina Albisinni, Tommaso Marrazzo, Arta Karruli, Sabrina Manduca, Giuseppe Nobile, Nicola Galdieri, Marisa De Feo, Successful treatment of aortic arch mural thrombosis with low-dose, ultra-slow-flow thrombolysis: a case report and literature review , Bleeding, Thrombosis and Vascular Biology: Vol. 1 No. 3 (2022)
- Anna Falanga, Hugo Ten Cate, Bianca Rocca, How to manage antithrombotic treatments in thrombocytopenic patients with cancer. Comments on the European Haematology Association guidelines , Bleeding, Thrombosis and Vascular Biology: Vol. 2 No. 1 (2023)
You may also start an advanced similarity search for this article.
