Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5–30 mm pulmonary nodules: a retrospective analysis

Introduction

With the increasing popularity of CT screening and people’s increased attention to physical examinations, the detection rate of pulmonary nodules has increased significantly (Herder et al., 2005; Horeweg et al., 2014). In the United States, incidental pulmonary nodules observed on chest CT account for 31% of cases (Gould et al., 2015). Lung cancer is the leading cause of cancer-related death worldwide and is one of the most common cancers (Sung et al., 2021). The five-year survival rate for lung cancer decreases with increasing stage, with a five-year survival rate as high as 92% for stage IA1 (Goldstraw et al., 2016). Therefore, the early diagnosis of malignant pulmonary nodules is particularly important for follow-up treatment and improving patient survival.

The nomogram is built based on a multifactorial regression analysis that integrates multiple predictive factors. It transforms the complex regression equation into a visual graph by converting the relationship between the variables by the calibrated line segment (She et al., 2017). Due to its readability and convenience, the nomogram has gained more attention and applications in clinical research and medical practice.

Foreign guidelines recommend the use of risk prediction models to assess the risk of lung cancer (Gould et al., 2013), but these models may not be applicable to all populations (Bai et al., 2016). Although some prediction models have been proposed in China in recent years (Li et al., 2011; Liu et al., 2022; Zhou et al., 2022), there is still no unified and clear prediction model that can be used clinically. Therefore, the purpose of this study is to establish and validate an appropriate nomogram for predicting the probability of malignant pulmonary nodules by combining clinical and CT imaging characteristics, with the aim of providing evidence-based support for the clinical management of pulmonary nodules.

Materials & Methods

Patients

The study retrospectively analyzed patients with pulmonary nodules at the Affiliated Hospital of North Sichuan Medical College and Guang’an People’s Hospital between January 2019 and November 2021. Inclusion criteria required pulmonary nodules with a diameter of 5 mm to 30 mm, clear pathological diagnosis (benign nodules required surgical specimens, while malignant nodules required surgical or small biopsy specimens), and CT scan before pathological diagnosis. Exclusion criteria included completely calcified nodules, incomplete clinical information, and previous history of lung cancer. The study was approved by the Medical Ethics Committee of Affiliated Hospital of North Sichuan Medical College with file number 2022ER234-1. Informed consent was not obtained because this was a retrospective analysis where patient identity and privacy were protected. The process of patient selection is shown in Fig. 1.

Results

Feature selection

The study investigated the potential factors that influence the transformation of solitary pulmonary nodules into malignant tumors. Demographic data, including age, gender, smoking history, annual smoking volume, dust exposure history, family history of malignancy, and family history of lung cancer, and CT characteristics, including density of the nodule, nodule diameter, spiculation, edge, lobulation, shape, calcification, cavity, vacuole, vascular convergence, bronchiole, and pleural traction, were evaluated and included in the LASSO regression analysis. Thirteen features with non-zero coefficients were found after screening (Figs. 2A and 2B). These features included age, gender, family history of lung cancer, density of the nodule, nodule diameter, spiculation, lobulation, calcification, cavity, vacuole, vascular convergence, bronchiole, and pleural traction.

Discussion

In this retrospective study, we developed and validated a nomogram based on clinical and imaging features to differentiate early-stage lung cancer characterized by pulmonary nodules using logistic regression analysis. Gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction were identified as the most valuable predictors for identifying malignant lung nodules. The nomogram showed high accuracy and robustness in both the training and validation sets. In the training set, the AUC of the nomogram was 0.842, indicating an accuracy rate of approximately 84% in predicting the probability of malignancy in clinical pulmonary nodules. Robustness was confirmed in the validation set with an AUC of 0.844. The calibration curve showed good agreement between the predicted and actual probabilities in both the training and validation sets, suggesting that the model has a high degree of calibration. Decision curve analysis revealed our nomogram would be beneficial in guiding decisions about scenarios where all patients receive no intervention or all patients receive intervention when the threshold probability is within a reasonable range.

Physicians rely primarily on visual assessment of two-dimensional CT images to evaluate pulmonary nodules, which inevitably leads to individual errors (He et al., 2021). Consequently, differences in personal experience among different physicians may lead to controversies in clinical practice. Therefore, it is crucial to establish quantitative relationships between clinical features, radiographic findings, and pathologies to support accurate clinical decision making. Our nomogram accurately quantifies clinical and imaging features and can effectively guide clinicians in evaluating and making treatment decisions for pulmonary nodules.

Numerous studies have demonstrated that lobulation is a reliable factor for differentiating benign from malignant pulmonary nodules (Qi et al., 2020; Chu et al., 2020; Liu et al., 2020). In our study, we also found that lobulation (OR =1.756, 95% CI [1.013–3.056]) was an independent risk factor for such differentiation. Furthermore, our research indicated that gender (OR =2.489, 95% CI [1.688–3.700]) is another predictor of benign and malignant pulmonary nodules, with females having a higher likelihood of developing malignant nodules. This is consistent with recent studies suggesting that lung adenocarcinoma, a more common type of lung cancer than squamous cell carcinoma (Austin et al., 2013), typically affects women more often than men due to differences in disease susceptibility and risk factors (Bai et al., 2016; Kinoshita et al., 2017). Our study also found that solid nodules (OR =0.414, 95% CI [0.220–0.761]) were a protective factor, whereas mixed ground-glass nodules (OR =3.269, 95% CI [1.573–6.883]) were a risk factor. This indicates that ground-glass nodules are more likely to be malignant than solid nodules. Research by Henschke et al. (2002) supports this finding, showing that mixed ground-glass nodules had a malignancy rate of 63%, compared to 18% for pure ground-glass nodules and 7% for solid nodules. In addition, vascular convergence sign, an indicator of malignant tumor growth and metastasis (Nishida et al., 2006; Raghu et al., 2019), was also identified as an independent risk factor for pulmonary nodules in our study, consistent with previous research (Huang et al., 2017; Wang et al., 2018).

As nodular diameter increases, so does the probability of malignancy (MacMahon et al., 2017). Nodules with a maximum diameter of less than five mm rarely have a malignancy rate above 0.4%, while nodules between 5 and 10 mm have a 1.3% probability of being cancerous. Nodules larger than 10 mm have a much higher malignancy rate of 15.2% (Horeweg et al., 2014). In our study, we found that as the diameter of nodules increased between 10 mm and 20 mm, the risk of malignancy increased by approximately 0.6 times for each 1 mm increase in diameter. When nodules exceeded 20 mm in diameter, the risk of malignancy increased by approximately 1.9 times per 1 mm increase. Calcification was identified as a protective factor in our model. This is because calcification is typically associated with the healing of old lesions and represents stable, benign lesions (Gorospe et al., 2020). Features such as diffuse, layered, and central calcifications are highly suggestive of benign lesions, such as inflammatory pseudotumors, whereas only a small percentage of malignant nodules exhibit eccentric or punctate calcifications (Zhou et al., 2021). Our study also included several characteristic signs of malignant pulmonary nodules. These include vacuole (OR =2.908, 95% CI [1.571–5.605]), bronchiole (OR =5.419, 95% CI [3.229–9.486]), and pleural traction (OR =1.658, 95% CI [1.079–2.555]), which are consistent with the findings of previous studies (Xia et al., 2021; Zhao et al., 2021; Hou et al., 2021).

Our study has several advantages. First, we developed a nomogram to build prediction models, which facilitates the quantification of risks and provides more intuitive results. Second, all nodules included in our study had clear pathologic diagnoses, ensuring that our results were based on accurate data. Third, by screening data from two research centers, our study was able to obtain a larger volume of data and broader coverage, resulting in a prediction model that is more universally applicable. Fourth, our model was developed based on CT and clinical features, which makes the scoring factors for pulmonary nodules simple and easy to obtain. In comparison, other models that incorporate radiomics, ctDNA, and serum tumor markers may be less accessible to clinicians. As such, our model may be easier to disseminate, particularly to outpatient physicians who need a quick and accurate assessment of benign and malignant pulmonary nodules to make informed decisions about diagnosis and treatment.

This study has several limitations. First, our study was retrospective, and further verification of its performance will be required in future prospective studies. Second, although the internal validation of the model showed excellent stability and calibration, the external validation of the model using separate data still needs to be performed in the future. Third, in order to make the model more concise, accessible, and user-friendly, we did not include radiomics, ctDNA, and serum tumor markers in our study. However, the inclusion of these factors may help to improve the accuracy of the model in the future.

Conclusions

A nomogram based on clinical and CT characteristics was developed and validated in our study to quantify the probability of pulmonary nodule presenting as early lung cancer. This nomogram has potential value in the clinical management of pulmonary nodules.

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