Skip to main content

Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer



The aim of this study was to establish a nomogram graph model to accurately predict the venous thromboembolism (VTE) risk probability in the general population with lung cancer.


Based on data from patients with lung cancer in Chongqing University Cancer Hospital of China, the independent risk factors of VTE were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated internally. The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve.


A total of 3398 lung cancer patients were included for analysis. The nomogram incorporated eleven independent VTE risk factors including karnofsky performance scale (KPS), stage of cancer, varicosity, chronic obstructive pulmonary disease (COPD), central venous catheter (CVC), albumin, prothrombin time (PT), leukocyte counts, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), dexamethasone, and bevacizumab. The C-index of the nomogram model was 0.843 and 0.791 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities.


We established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.


Venous thromboembolism (VTE) mainly comprises deep vein thrombosis (DVT) and pulmonary embolism (PE), which is one of the most common complications of malignant tumors [1]. Numerous previous studies have found that VTE in cancer patients was associated with increased mortality and worse quality of life [2]. Lung cancer is the second most prevalent cancer and the leading cause of tumor-related death globally [3]. Several studies have revealed that lung cancer patients are more prone to developing VTE [4, 5]. Zhang et al. [5] reported that VTE events occurred in up to 13.2% of patients with newly diagnosed lung cancer. In recent years, target therapy and immunotherapy dramatically improved the survival of lung cancer patients [6, 7]. However, these advancements in systematic treatment and prolonged survival increased the VTE incidence in lung cancer patients [8,9,10].

Several models have been developed to predict the VTE risk in patients, including Caprini score [11, 12], Padua score [13], Rogers score [14], and Khorana score [15]. These models were widely applied in clinical practice and accurately identified patients with a high risk of VTE. However, these models were designed for different subgroups of populations. Caprini model was mainly developed to evaluate the VTE risk of patients undergoing lung cancer resections [12]. In contrast, the Padua risk model was used for predicting VTE risk in hospitalized medical patients [13]. Lung cancer patients are a particular subgroup with unique characteristics, such as the driver gene status and treatment regimens. The application of these models to predict the VTE risk of lung cancer patients has significant limitations, for they can’t include all VTE risk factors in lung cancer patients. Thus, there is an urgent need to develop a VTE prediction model specifying lung cancer patients to guide clinical practice.

The Nomogram model is a visual graphic tool for accurately predicting each patient's risk probability of clinical events [16]. By producing a user-friendly graph, this prediction approach converts the conventional regression model into a visual risk assessment for each patient, which is undeniably practical and accurate. This model has been proven to be an accurate method for predicting cancer prognosis. Nomograms for VTE risk assessment have been studied in various cancers, including breast cancer [17], ovarian cancer [18, 19], lymphoma [20], and spinal metastasis tumors [21]. Recently, two studies [22, 23] developed a nomogram model to predict the risk probability of postoperative VTE in patients with early-stage lung cancer. However, in the era of precision medicine, there is still a lack of a nomogram model predicting VTE risk for the general population with lung cancer.

Therefore, in the current study, we construct a nomogram graph model to accurately predict the VTE risk probability in the general population with lung cancer. With the use of this model, physicians may precisely identify patients who are at high risk for developing VTE and can implement early preventive and treatment strategies to lower the chance of developing thrombosis.

Materials and methods

Patient population

Data were collected from the patients’ medical records in Chongqing University Cancer Hospital of China. As our previous reports [24], the inclusion criteria include: (1) age ≥ 18 years; (2) at least once hospitalization; (3) with newly histologically confirmed lung carcinoma; (4) with hospitalization date from January 2013 to December 2019. The exclusive criteria were as follows: (1) VTE occurred before the diagnosis of lung cancer and (2) died within 48 h after admission; (3) patients with incomplete information. All available data on the database were used to maximize the power and generalizability of the results. Patients who underwent several hospital stays were only counted once in the analysis.

Construction of the nomogram and statistical analysis

For nomogram construction and validation, we randomly divided all the patients into training (n = 2379) and validation (n = 1019) cohorts in a ratio of 7:3. In the training cohort, the association between clinical variables and VTE was analyzed using univariate logistic regression analysis. Variables that achieved significance at P < 0.05 in univariate analysis and variables with obvious clinical significance were entered into the multivariable analyses via Logistic regression model. Statistical analyses to identify independent predictive factors for VTE were performed by multivariable analysis. Based on the results of the multivariable analysis, a nomogram was constructed.

The nomogram was validated internally in the training cohort and externally in the validation cohort. The Harrell’s C-index (the concordance statistic) and the area under the receiver operating characteristic curve (AUC) were used to evaluate the discriminative ability of the nomogram. Bootstrapping method with 1000 resamples was utilized to generate the calibration curves for validation of the nomogram in the training cohort and in the validation cohort. The scores of each variable were calculated using the “nomogramEx” package in R. Based on the scores of each variable, the total scores for each patient could be calculated. The decision curve analysis (DCA) of the nomogram was plotted using the "rmda" package.

All analyses were performed with R 4.1.2 ( Statistical significance was defined as a two-sided p value of < 0.05.


Clinicopathologic characteristics of patients

A total of 3985 lung cancer patients from 2013 to 2019 were screened for this study (Fig. 1). Finally, 3398 patients were enrolled in the analysis. The eligible patients were divided into training cohort (n = 2379) and validation cohorts (n = 1019) according to the random split-sample method (split ratio: 7:3). In the training cohort, 89 (3.7%) patients experienced VTE during their hospital stay. While in the validation cohort, 36 (3.5%) patients experienced VTE. The baseline clinicopathologic characteristics of the patients and the association between VTE and clinicopathological features are shown in Table 1.

Fig. 1
figure 1

Flowchart detailing the inclusion and exclusion criteria that resulted in the final study cohorts

Table 1 Demographics and clinicopathologic characteristics of the training and validation sets

Independent predictive factors in the training set

The results of the logistic univariable analysis are shown in Table 2. Male gender (male vs. female; P = 0.004) was an unfavorable factor for VTE. Stage IV (I–II vs. III vs. IV; P = 0.004) and NSCLC type (NSCLC vs. SCLC; P = 0.027) were significantly associated with a higher rate of VTE. Patients with comorbidity of varicosity (P < 0.001) and COPD (P = 0.006) experienced a higher risk of VTE. In accordance with our expectation, patients with CVC showed a higher risk of VTE compared with those without CVC (P < 0.001). Lower albumin level was associated with a higher risk of VTE (P = 0.006). Patients with shorter PT have a higher risk for VTE than those with longer PT (P = 0.002). While we found that higher Leukocyte levels showed a higher risk of VTE (P = 0.01). With respect to the treatment factors which may impact the occurrence of VTE, the results revealed that EGFR-TKI (P < 0.001), Dexamethasone (P < 0.001), Platinum (P = 0.002), and Bevacizumab (P < 0.001) were the significantly favorable factors for VTE. All significant variables in the univariable analysis and variables with obvious clinical significance were entered into the multivariate logistic regression analysis using a stepwise method. All KPS (P = 0.048), stage of cancer (P = 0.015), varicosity (P < 0.001), COPD (P = 0.002), albumin (P = 0.006), PT (P = 0.015), Leukocyte (P = 0.006), EGFR-TKI (P < 0.001), Dexamethasone (P = 0.005), and Bevacizumab (P = 0.003) remained independent predictive factors in the multivariate logistic regression model.

Table 2 Logistic regression analysis of the risk factors for VTE in the training set

Predictive nomogram for VTE

A nomogram that incorporated the significant predictive factors and factors with clinical significance was constructed (Fig. 2). The nomogram demonstrated that PT was the most considerable contribution to the prediction for VTE, followed by varicosity and leukocyte. KPS, stage of cancer, and albumin showed a moderate impact on VTE. Each of these variables was assigned a score on the point scale. Then, by adding up the total score and locating it on the total point scale, we were able to draw a straight line down to determine the estimated probability of VTE.

Fig. 2
figure 2

A nomogram model was constructed based on the independent risk factors identified by multivariate logistic regression analysis. The nomogram is used to find the position of each variable on the corresponding axis. Firstly, you draw a vertical line for each of the variables of your patient to the Points axis for the score of each variable; secondly, you sum up the scores of all valuables you read on the Points scale to obtain Total Points; finally, you draw a vertical line from the Total Points axis to determine the risk of VTE at the lower line of the nomogram

Furthermore, for clinically convenient prediction of VTE risk in lung cancer patients, an online application was developed by utilizing the R package "DynNom" ( It is available at After determining the parameters, the probability of VTE can be generated by clicking the “predict” button.

Validation and calibration of the nomogram

In the training cohort, the C-index for the established nomogram to predict VTE was 0.843 with 95% CI 0.798–0.889, while in the validation cohort, the C-index was 0.791 with 95% CI 0.725–0.858 (Fig. 3). The calibration plots of the nomogram revealed that the agreement between the predicted and observed VTE risk was optimal both in training (Fig. 4A) and validation cohorts (Fig. 4B).

Fig. 3
figure 3

ROC curves of the nomogram for VTE risk prediction in the training and validation cohorts

Fig. 4
figure 4

Calibration plot of the nomogram for VTE risk in the training cohort (A) and validation cohort (B)

Performance of the nomogram in stratifying risk of patients

DCA was performed to evaluate the clinical utility of the nomogram model by calculating its net benefit in various risk thresholds for screening. The DCA visually illustrated the model's clinical utility based on a continuum of VTE risk thresholds (X axis) and the net benefit of employing the model to stratify the risk of the patients (Y axis) relative to the hypothesis that no patient will have a VTE. The decision curve in Fig. 5 indicated that when the threshold probability for a patient or a doctor is within a range from 8 to 60%, there will be more net benefit than either treating all patients or treating none by using the nomogram to decide whether or not to conduct treatment. The decision curve revealed that the nomogram performed well and was feasible to make beneficial clinical decisions.

Fig. 5
figure 5

Decision-curve analysis (DCA) of VTE


VTE is a common complication in lung cancer patients, and its occurrence significantly increases patient mortality [4]. Although assessment models, such as Caprini RAM and Padua Prediction Score, were developed to evaluate the risk of VTE [25]. However, there is few VTE risk model specifying lung cancer, the second most common cancer worldwide [3]. This study identified the dependent VTE risk factors and established a reliable nomogram to accurately predict the VTE risk for lung cancer patients. To our best knowledge, this is the first study with a large sample size that develop a nomogram for predicting the risk of VTE in the general population with stage I-IV lung cancer.

Nomogram is a method to accurately predict the probability of occurrence of each individual outcome event, which can convert a complex regression equation into a simple and visual graph [26], making the prediction model more readable and practical. This outstanding feature has led to increased interest in medical research and clinical practice for the diagram, which is frequently used to predict the prognosis of patients with cancers [16]. In this study, we constructed a nomogram prediction model for VTE risk including KPS, stage of cancer, varicosity, COPD, CVC, albumin, PT, leukocyte counts, EGFR-TKI, dexamethasone, and bevacizumab. The C-index of 0.843 demonstrates the accurate prediction power of this nomogram prediction model, which has been verified internally. Recently, the nomogram model has been established and proven to accurately predict the risk of VTE in multiple kinds of cancers, including lung cancer [22, 23], breast cancer [17], ovarian cancer [18, 19], lymphoma [20], and spinal metastasis tumor [21]. Cai et al. [22] studied a group of patients with stage IA NSCLC and established a nomogram for predicting the probability of postoperative VTE risk. Their prediction model contained variables including age, preoperative D-dimer, and intermuscular vein dilation, which is different from the predictive factors included in the nomogram model in our study. This result may be explained by the heterogeneity of the study population. Our study enrolled lung cancer patients with stage I-IV, including NSCLC and SCLC. Furthermore, another possible explanation for this is that some patients in this cohort treated by EGFR-TKIs, bevacizumab, and dexamethasone, which is not usually used in the stage IA NSCLC patients. Beside those factors, previous studies [22, 23] mainly focused on the postoperative VTE risk in lung cancer. A recently reported study [27] established a nomogram-based risk assessment model for venous thromboembolism, but the number of enrolled patients was relatively limited. Compared with previous models, our study developed a nomogram model based on a large number of patients with a more general representation for lung cancer VTE risk prediction.

The association between the use of corticosteroids and VTE has been investigated widely. Previous studies demonstrated that inhaled or oral corticosteroids significantly increased the risk of PE in patients with chronic inflammatory diseases [28, 29]. Dexamethasone, a kind of corticosteroids, is widely used in various clinical scenarios of patients with lung cancer, such as brain metastasis and inflammatory pain caused by bone metastasis. Wolpert et al. [30] studied the thrombosis risk in patients with brain metastasis. They found that the use of dexamethasone was confirmed to be independently associated with VTE (OR 2.27, 95% CI 1.5–4.5, P = 0.011). Consistent with previous findings, our result revealed that dexamethasone was a significant risk factor for VTE (OR 3.88, 95% CI 1.52–9.95, P = 0.005). A possible biological mechanism could explain the association between dexamethasone and the development of VTE. Brotman et al.’s study [31] has provided evidence that oral dexamethasone leads to a procoagulant state in healthy volunteers. In their study, dexamethasone 3 mg twice daily or placebo was given for five days to healthy male volunteers. They found that dexamethasone increased clotting factor levels VII, VIII, and XI and fibrinogen. Further investigations are needed to explore the mechanisms behind this finding.

Our results revealed that lung cancer patients who received EGFR-TKIs treatment have an odds ratio of VTE incidence of 4.71 (2.90–7.67, P < 0.01) compared with patients without EGFR-TKI treatment. A number of recent studies have reported similar results. Yang et al. [8] found that the odds ratio of VTE occurrence was significantly increased in patients with EGFR-TKI treatment relative to patients without the treatment. Lee et al.’s study [32] also demonstrated that the treatment with EGFR-TKI was associated with a 60% increased risk of VTE, which is consistent with our findings. There are several possible explanations for this result. Prior studies have verified the cross-talk between EGFR and VEGFR pathways [33, 34]. Activation of the EGFR pathway increases the production of tumor-derived VEGF that acts on endothelial cells in a paracrine manner to promote angiogenesis [34]. Inhibition of angiogenesis by both EGFR-TKIs and macromolecule anti-EGFR antibodies could lead to thrombosis occurrence [8, 35, 36]. Secondly, previous studies revealed that platelet activation could be triggered by EGFR-TKI treatment and low-dose aspirin treatment is effective on EGFR-TKI-induced skin rashes [37, 38]. The increase in platelet activation will lead to formation of platelet plugs and clots. Another possible explanation for this is that majority of patients received EGFR-TKI treatment in this cohort were staged III and IV with high PS score, all of which are dependent risk factor for VTE.

Bevacizumab is a recombinant humanized monoclonal antibody against VEGF and is widely used in the treatment of lung cancer, either in monotherapy or in combination with chemotherapy or immunotherapy [39, 40]. The impact of bevacizumab on VTE in cancer patients was widely investigated. Nalluri and colleagues’ meta-analysis [9] included a total of 7956 patients with a variety of advanced solid tumors to evaluate the association of bevacizumab with VTE. Their findings revealed that the incidence of all-grade VTE was 11.9% among patients receiving bevacizumab treatment. Bevacizumab significantly increased the risk of VTE with an RR of 1.33 (95% CI 1.13–1.56; P < 0.001) compared with controls. Consequently, several recent studies concluded similar results in various cancer types [41,42,43,44,45]. Consistent with previous reports, our results demonstrated that bevacizumab was also identified as an independent risk factor for VTE with an OR of 3.14 (95% CI 1.46–6.77; P = 0.003). Several mechanisms could explain this observation. Firstly, by inhibiting VEGF-induced endothelium regeneration, bevacizumab may expose subendothelial procoagulant phospholipids, which could result in thrombosis [46]. Secondly, the reduction of nitric oxide and prostacyclin resulting by bevacizumab was significantly predisposed to thromboembolic events [47]. Thirdly, the increased expression of proinflammatory cytokines may promote the inflammation of the vessel wall and initiate thrombosis in an intact vein [48, 49]. Additionally, the prolonged survival by bevacizumab treatment may be one of the causes of the increased risk of VTE.

A number of limitations need to be noted regarding the present study. First, all case enrolled in the current study was from a single center, which inevitably introduced bias and weakened the statistical power. Second, the current study has significant inherent limitations caused by the retrospective study design. Third, previous studies implied that driver gene alterations might impact the VTE occurrence in lung cancer patients, but the detailed information on driver genes was not accessed in this study. The association of driver genes with VTE could not be analyzed in our study. Consequently, in order to address the critical issues mentioned above, there is a strong need to develop a study with a well-designed prospective design, multi-centers, and large-size samples with detailed gene information in the future.

In conclusion, we established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.

Availability of data and materials

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation, to any qualified researcher.


  1. Cohen O, Ageno W, Farjat AE, Turpie AGG, Weitz JI, Haas S, Goto S, Goldhaber SZ, Angchaisuksiri P, Gibbs H, et al. Management strategies and clinical outcomes in patients with inferior vena cava thrombosis: data from GARFIELD-VTE. J Thromb Haemost. 2022;20(2):366–74.

    Article  PubMed  Google Scholar 

  2. Wan Puteh SE, Ibrahim R, Yusak S, Nik Adnan NN, Ahmat ANMF. Cancer-associated thrombosis: implications toward health-related quality of life. Semin Thromb Hemost. 2022;48(4):490–6.

    Article  PubMed  Google Scholar 

  3. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  4. Vitale C, D’Amato M, Calabrò P, Stanziola AA, Mormile M, Molino A. Venous thromboembolism and lung cancer: a review. Multidiscip Respir Med. 2015;10(1):28.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhang Y, Yang Y, Chen W, Guo L, Liang L, Zhai Z, Wang C. Prevalence and associations of VTE in patients with newly diagnosed lung cancer. Chest. 2014;146(3):650–8.

    Article  PubMed  Google Scholar 

  6. Mok TS, Wu Y-L, Thongprasert S, Yang C-H, Chu D-T, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361(10):947–57.

    Article  CAS  PubMed  Google Scholar 

  7. Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, Gottfried M, Peled N, Tafreshi A, Cuffe S, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med. 2016;375(19):1823–33.

    Article  CAS  PubMed  Google Scholar 

  8. Yang Y, Zhou Z, Niu X-M, Li Z-M, Chen Z-W, Jian H, Ai X-H, Cheng B-J, Liao M-L, Lu S. Clinical analysis of postoperative venous thromboembolism risk factors in lung cancer patients. J Surg Oncol. 2012;106(6):736–41.

    Article  PubMed  Google Scholar 

  9. Nalluri SR, Chu D, Keresztes R, Zhu X, Wu S. Risk of venous thromboembolism with the angiogenesis inhibitor bevacizumab in cancer patients: a meta-analysis. JAMA. 2008;300(19):2277–85.

    Article  CAS  PubMed  Google Scholar 

  10. Kacimi SEO, Moeinafshar A, Haghighi SS, Saghazadeh A, Rezaei N. Venous thromboembolism in cancer and cancer immunotherapy. Crit Rev Oncol Hematol. 2022;178: 103782.

    Article  PubMed  Google Scholar 

  11. Pannucci CJ, Swistun L, MacDonald JK, Henke PK, Brooke BS. Individualized venous thromboembolism risk stratification using the 2005 Caprini score to identify the benefits and harms of chemoprophylaxis in surgical patients: a meta-analysis. Ann Surg. 2017;265(6):1094–103.

    Article  PubMed  Google Scholar 

  12. Hachey KJ, Hewes PD, Porter LP, Ridyard DG, Rosenkranz P, McAneny D, Fernando HC, Litle VR. Caprini venous thromboembolism risk assessment permits selection for postdischarge prophylactic anticoagulation in patients with resectable lung cancer. J Thorac Cardiovasc Surg. 2016;151(1):37.

    Article  PubMed  Google Scholar 

  13. Barbar S, Noventa F, Rossetto V, Ferrari A, Brandolin B, Perlati M, De Bon E, Tormene D, Pagnan A, Prandoni P. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost. 2010;8(11):2450–7.

    Article  CAS  PubMed  Google Scholar 

  14. Rogers SO, Kilaru RK, Hosokawa P, Henderson WG, Zinner MJ, Khuri SF. Multivariable predictors of postoperative venous thromboembolic events after general and vascular surgery: results from the patient safety in surgery study. J Am Coll Surg. 2007;204(6):1211–21.

    Article  PubMed  Google Scholar 

  15. Khorana AA, Dalal M, Lin J, Connolly GC. Incidence and predictors of venous thromboembolism (VTE) among ambulatory high-risk cancer patients undergoing chemotherapy in the United States. Cancer. 2013;119(3):648–55.

    Article  CAS  PubMed  Google Scholar 

  16. Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70.

    Article  PubMed  Google Scholar 

  17. Li J, Qiang WM, Wang Y, Wang XY. Development and validation of a risk assessment nomogram for venous thromboembolism associated with hospitalized postoperative Chinese breast cancer patients. J Adv Nurs. 2021;77(1):473–83.

    Article  PubMed  Google Scholar 

  18. Wang L, Wei S, Zhou B, Wu S. A nomogram model to predict the venous thromboembolism risk after surgery in patients with gynecological tumors. Thromb Res. 2021;202:52–8.

    Article  CAS  PubMed  Google Scholar 

  19. Wang Y, Zhou H, Zhong G, Fu Z, Peng Y, Yao T. Development and validation of a nomogram to predict the probability of venous thromboembolism in patients with epithelial ovarian cancer. Clin Appl Thromb Hemost. 2022;28:107602962210955.

    Article  Google Scholar 

  20. Yang J, Zhang Y, Yang P, Zhang X, Li M, Zou L. A novel nomogram based on prognostic factors for predicting venous thrombosis risk in lymphoma patients. Leuk Lymphoma. 2021;62(10):2383–91.

    Article  CAS  PubMed  Google Scholar 

  21. Zhang H-R, Xu M-Y, Yang X-G, Wang F, Zhang H, Yang L, Qiao R-Q, Li J-K, Zhao Y-L, Zhang J-Y, et al. Nomogram for predicting the postoperative venous thromboembolism in spinal metastasis tumor: a multicenter retrospective study. Front Oncol. 2021;11: 629823.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Cai Y, Dong H, Li X, Liu Y, Hu B, Li H, Miao J, Chen Q. Development and validation of a nomogram to assess postoperative venous thromboembolism risk in patients with stage IA non-small cell lung cancer. Cancer Med. 2022;12(2):1217–27.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li Y, Shen L, Ding J, Xie D, Yang J, Zhao Y, Carretta A, Petersen RH, Gilbert S, Hida Y, et al. Derivation and validation of a nomogram model for pulmonary thromboembolism in patients undergoing lung cancer surgery. Transl Lung Cancer Research. 2021;10(4):1829–40.

    Article  Google Scholar 

  24. Lei H, Zhang M, Wu Z, Liu C, Li X, Zhou W, Long B, Ma J, Zhang H, Wang Y, 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhou H, Hu Y, Li X, Wang L, Wang M, Xiao J, Yi Q. Assessment of the risk of venous thromboembolism in medical inpatients using the Padua prediction score and Caprini risk assessment model. J Atheroscler Thromb. 2018;25(11):1091–104.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Park SY. Nomogram: an analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018;155(4):1793.

    Article  PubMed  Google Scholar 

  27. Li H, Tian Y, Niu H, He L, Cao G, Zhang C, Kaiweisierkezi K, Luo Q. Derivation, validation and assessment of a novel nomogram-based risk assessment model for venous thromboembolism in hospitalized patients with lung cancer: a retrospective case control study. Front Oncol. 2022;12: 988287.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sneeboer MMS, Hutten BA, Majoor CJ, Bel EHD, Kamphuisen PW. Oral and inhaled corticosteroid use and risk of recurrent pulmonary embolism. Thromb Res. 2016;140:46–50.

    Article  CAS  PubMed  Google Scholar 

  29. Stuijver DJF, Majoor CJ, van Zaane B, Souverein PC, de Boer A, Dekkers OM, Büller HR, Gerdes VEA. Use of oral glucocorticoids and the risk of pulmonary embolism: a population-based case-control study. Chest. 2013;143(5):1337–42.

    Article  CAS  PubMed  Google Scholar 

  30. Wolpert F, Berghoff AS, Grossenbacher B, Lareida A, Löb R, Roth P, Neidert MC, Andratschke N, Le Rhun E, Preusser M, et al. Venous thromboembolic events in patients with brain metastases: the PICOS score. Eur J Cancer. 2020;134:75–85.

    Article  CAS  PubMed  Google Scholar 

  31. Brotman DJ, Girod JP, Posch A, Jani JT, Patel JV, Gupta M, Lip GYH, Reddy S, Kickler TS. Effects of short-term glucocorticoids on hemostatic factors in healthy volunteers. Thromb Res. 2006;118(2):247–52.

    Article  CAS  PubMed  Google Scholar 

  32. Lee Y-G, Kim I, Lee E, Bang S-M, Kang CH, Kim YT, Kim HJ, Wu H-G, Kim YW, Kim TM, et al. Risk factors and prognostic impact of venous thromboembolism in Asian patients with non-small cell lung cancer. Thromb Haemost. 2014;111(6):1112–20.

    Article  CAS  PubMed  Google Scholar 

  33. Goldman CK, Kim J, Wong WL, King V, Brock T, Gillespie GY. Epidermal growth factor stimulates vascular endothelial growth factor production by human malignant glioma cells: a model of glioblastoma multiforme pathophysiology. Mol Biol Cell. 1993;4(1):121–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Larsen AK, Ouaret D, El Ouadrani K, Petitprez A. Targeting EGFR and VEGF(R) pathway cross-talk in tumor survival and angiogenesis. Pharmacol Ther. 2011;131(1):80–90.

    Article  CAS  PubMed  Google Scholar 

  35. Magnus N, Garnier D, Rak J. Oncogenic epidermal growth factor receptor up-regulates multiple elements of the tissue factor signaling pathway in human glioma cells. Blood. 2010;116(5):815–8.

    Article  CAS  PubMed  Google Scholar 

  36. Hu H, Hao L, Yan B, Li X, Zhu Y, Yao J, Wang G, Jiang Q. Gefitinib inhibits retina angiogenesis by affecting VEGF signaling pathway. Biomed Pharmacother. 2018;102:115–9.

    Article  CAS  PubMed  Google Scholar 

  37. Kanazawa S, Yamaguchi K, Kinoshita Y, Muramatsu M, Komiyama Y, Nomura S. Aspirin reduces adverse effects of gefitinib. Anticancer Drugs. 2006;17(4):423–7.

    Article  CAS  PubMed  Google Scholar 

  38. Kanazawa S, Yamaguchi K, Kinoshita Y, Siomi K, Tujimoto M, Hanayama K, Sasada S, Hirashima T, Imamura F, Nomura S. Effect of low-dose aspirin for skin rash associated with erlotinib therapy in patients with lung cancer. Platelets. 2009;20(1):70–1.

    Article  CAS  PubMed  Google Scholar 

  39. Sandler A, Gray R, Perry MC, Brahmer J, Schiller JH, Dowlati A, Lilenbaum R, Johnson DH. Paclitaxel-carboplatin alone or with bevacizumab for non–small-cell lung cancer. N Engl J Med. 2006;355(24):2542–50.

    Article  CAS  PubMed  Google Scholar 

  40. Socinski MA, Jotte RM, Cappuzzo F, Orlandi F, Stroyakovskiy D, Nogami N, Rodríguez-Abreu D, Moro-Sibilot D, Thomas CA, Barlesi F, et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N Engl J Med. 2018;378(24):2288–301.

    Article  CAS  PubMed  Google Scholar 

  41. Patel JN, Jiang C, Hertz DL, Mulkey FA, Owzar K, Halabi S, Ratain MJ, Friedman PN, Small EJ, Carducci MA, et al. Bevacizumab and the risk of arterial and venous thromboembolism in patients with metastatic, castration-resistant prostate cancer treated on Cancer and Leukemia Group B (CALGB) 90401 (Alliance). Cancer. 2015;121(7):1025–31.

    Article  CAS  PubMed  Google Scholar 

  42. Kuk A, Magnowska M, Suchy W, Swierczynska J, Zaborowski MP, Gaca M, Nowak-Markwitz E. Retrospective evaluation of thromboembolism risk in ovarian cancer patients treated with bevacizumab. Target Oncol. 2017;12(4):495–503.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Matsumura C, Chisaki Y, Sakimoto S, Sakae H, Yano Y. Evaluation of thromboembolic events in cancer patients receiving bevacizumab according to the Japanese Adverse Drug Event Report database. J Oncol Pharm Pract. 2018;24(1):22–7.

    Article  PubMed  Google Scholar 

  44. Singh GK, Menon N, Jadhav MM, Walavalkar R, Dsouza H, Roy S, Das S, Srinivas S, Vallathol DH, Patil VM. Thromboembolic events in brain tumour patients on bevacizumab. Acta Oncol. 2020;59(12):1543–6.

    Article  CAS  PubMed  Google Scholar 

  45. Saerens M, De Jaeghere EA, Kanervo H, Vandemaele N, Denys H, Naert E. Risk of thrombo-embolic events in ovarian cancer: does bevacizumab tilt the scale? A systematic review and meta-analysis. Cancers. 2021;13(18):4603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kilickap S, Abali H, Celik I. Bevacizumab, bleeding, thrombosis, and warfarin. J Clin Oncol. 2003;21(18):3542 (author reply 3543).

    Article  PubMed  Google Scholar 

  47. Zachary I. Signaling mechanisms mediating vascular protective actions of vascular endothelial growth factor. Am J Physiol Cell Physiol. 2001;280(6):C1375–86.

    Article  CAS  PubMed  Google Scholar 

  48. Hesser BA, Liang XH, Camenisch G, Yang S, Lewin DA, Scheller R, Ferrara N, Gerber H-P. Down syndrome critical region protein 1 (DSCR1), a novel VEGF target gene that regulates expression of inflammatory markers on activated endothelial cells. Blood. 2004;104(1):149–58.

    Article  CAS  PubMed  Google Scholar 

  49. Branchford BR, Carpenter SL. The role of inflammation in venous thromboembolism. Front Pediatr. 2018;6:142.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


This study was supported by grants from the Chongqing Natural Science Foundation Project (cstc2018jcyjAX0806); National Natural Science Foundation Project (81972857).

Author information

Authors and Affiliations



Data curation, NZ and LS; Formal analysis, HL; Funding acquisition, YW; Investigation, WZ; Methodology, MS; Resources, YJ; Software, DY; Supervision, YX and YW; Writing—original draft, DT. All authors reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yue Xie or Ying Wang.

Ethics declarations

Ethics approval and consent to participate

The study was conducted according to the Declaration of Helsinki's ethical standards for medical research involving human people. The protocol was reviewed and approved by the Ethics Committee of Chongqing University Cancer Hospital (CZLS2023027-A).

Competing interests

No potential conflicts of interest were disclosed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lei, H., Tao, D., Zhang, N. et al. Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer. Cancer Cell Int 23, 40 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Venous thromboembolism
  • Nomogram
  • Lung cancer
  • Predictive model