Development and validation of a risk model with variables related to non-small cell lung cancer in patients with pulmonary nodules: a retrospective study

Lung cancer is a form of malignancy arising due to the unrestrained growth of bronchial and lung cells [1, 2], and it is one of the leading causes of mortality in the world [3]. Rates of lung cancer have been rising rapidly in recent years, particularly in more heavily industrialized nations [4]. Currently, lung cancer patients exhibit 5-year survival rates of approximately 16.6% [5], and roughly 1 million individuals in China are predicted to be diagnosed with lung cancer by the year 2025 such that China exhibits the highest global lung cancer incidence rate. Accounting for the main port of lung cancer, the early treatment of NSCLC is the hot spot of the current research.

Key risk factors associated with lung cancer development include specific genetic mutations, smoking, and environmental exposures such as air pollution. There is also some evidence suggesting that factors such as a poor diet, alcohol intake, estrogen levels, the smoking of marijuana, and infection with human papillomavirus (HPV), human immunodeficiency virus (HIV), and Epstein-Barr virus may increase lung cancer risk, although such evidence remains somewhat inconclusive [6]. Analyses of patient computed tomography (CT) scans often reveal pulmonary nodules, and many models have been developed to gauge the link between such nodules and lung cancer risk, including the Brock model [7] and the Mayo model. These models, however, often do not take epidemiological variables, clinical findings, and CT scan results into consideration at the same time, limiting their value as predictors of the relative risk of a given pulmonary nodule being malignant. The development of more reliable and accurate predictive tools has the potential to enable early intervention and treatment for NSCLC patients, maximizing their odds of positive outcomes. Herein, we analyzed 28 variables that were considered potentially relevant to the diagnosis of a given pulmonary nodule as being benign or malignant based on previous studies [1, 7‐9].

By analyzing epidemiological, clinical, and CT-related factors for patients with pulmonary nodules that had undergone surgical treatment, we sought to develop a simple but robust predictive model that would enable the relative assessment of NSCLC risk based only upon characteristics that can be readily assessed prior to surgery or other therapeutic interventions.

Nomograms are valuable predictive tools that have been widely utilized in oncology and other clinical and research fields, offering a user-friendly approach to intuitively assessing the odds of a given diagnosis or outcome based on a set of specific variables, thereby aiding in clinical decision-making [11]. Many models for the treatment of pulmonary nodules were established based upon certain epidemiological variables and CT scan results. However, clinical findings such as hematological biomarkers are also very important for the diagnosis of lung cancer [1]. Moreover, for some of these variables, such as GGO, the surgical criteria are not well defined such that treatments are often conducted according to the experience of the operating surgeons [12, 13]. As such, we herein sought to develop a new nomogram capable of predicting the relative risk of malignancy when evaluating patients with pulmonary nodules.

We designed and validated a novel predictive model capable of assessing the risk of a given lung nodule being benign or malignant based on analysis of data from patients that had undergone pulmonary nodule resection. The resultant model incorporated demographic, disease-, and treatment-related features to easily predict the odds of a given pulmonary nodule corresponding to a NSCLC diagnosis. The model developed herein was accurate, and exhibited good calibration and discrimination in our validation cohort. The C-index value in this validation cohort was also high, indicating that the nomogram can be accurately used to gauge patient risk of pulmonary nodule malignancy.

Prior studies have confirmed that hypertension is a common comorbidity in cancer patients [14]. Several mechanisms may explain this observation, including the fact that hypertension can increase VEGF levels in the plasma [15]. We identified hypertension as a risk factor for lung nodule malignancy. Fibrinogen has also been significantly linked to the risk of lung cancer in the past [16], with Kuang et al. having demonstrated that a combination of the beta and gamma chains of fibrinogen may offer value as a sensitive biomarker for differentiating between lung nodules that are benign and malignant [17], potentially explaining the significance of plasma fibrinogen levels in our model. One research indicated that the value of BUN to seralbumin ratio might predict patients with serious pulmonary cancer [18]. BUN had a positive relationship with pulmonary tumor risk and was included in risk prediction model therefore [19]. Some researches demonstrated that the maximum diameter of nodules > 8 mm was independent risk factors for malignancy [20] and presence of solid element in the GGO nodules might cause lymph node metastasis [21]. GGO findings have been reported to be associated with cancer rates as high as 63%, with many surgeons believing that GGO nodules should be resected, particularly if they grow in size. Persistent GGO nodules may be indicative of a greater risk of malignancy when solid components are evident [12]. Tu et al. found CT density to be a valuable feature when differentiating between nodules that were malignant and benign [22]. Qiu et al. further determined that solitary ground-glass opacity nodule size and density upon high-resolution T evaluation were associated with invasive adenocarcinoma risk [23]. Nodule size may be the most important variable included in our predictive model, given that nodule diameter is a key determinant of treatment under the British Thoracic Society guidelines [24] and Fleischner Society Guidelines [25]. For nodules ≥ 10 mm in diameter, the odds of malignancy in the NELSON screening study were 15.2% [26]. As such, we included nodule diameter as the size variable in the present study. As the comparison of the AUC value between different models had certain limitations, we calculated the NRI and IDI of the two models to explain the improvement of our model.

Herein, we thus designed a risk nomogram that may aid clinicians in differentiating between patients with benign or malignant lung nodules. It may also aid in the optimal selection of pulmonary nodules in the context of clinical research. For example, this model might be used to aid investigators in selecting patients with larger nodules and other risk-related findings when identifying candidates for surgical procedures or other interventions. Early interventions including CT scans, biochemical analyses of blood samples, and family support can better benefit low-risk patients, while regular clinical examination can ensure the appropriate monitoring of lung nodules to better guide the appropriate assessment of patient diagnosis.

Previous classical models based on large-scale screening experiments have been widely used for clinical evaluation. However, people who go to different hospitals for treatment are inevitably screened by human factors. For example, as a tertiary hospital, our hospital serves for many patients come from subordinate hospitals, which may express the high proportion of patients with ≥ 8 mm and malignant nodules in our cohorts. Therefore, it is necessary to develop clinical assessment models for pulmonary nodules based on different groups of patients. Accurate predictive evaluation can aid surgeons in predicting lung cancer risk in individual patients, ensuring timely intervention for high-risk patients while reducing the need for interventional treatment in low-risk patients. Accurately predicting the risk of lung cancer in a given patient is very challenging, and appropriate measurements together with multifaceted interventional approaches are thus the most reliable approach to detecting and evaluating patients with pulmonary nodules. Further research on this topic is warranted as the accurate detection of pulmonary nodules alone is necessary but insufficient for treating affected patients, underscoring directions for future study.

Although our model showed good accuracy and stability in different validation cohorts. Among the variables included in the model, BUN demonstrated statistical significance solely within the training cohort, while it did not exhibit significance in both the external validation cohort and external validation cohort 2. This suggests potential instability of this index and highlights room for improvement within the model. The presence of these findings indicates that there is still scope for enhancing the current study's model, which is currently limited by its inclusion of a restricted number of variables. With the continuous advancement of artificial intelligence technology, we believe that future research endeavors will benefit from larger training cohorts encompassing more diverse variables, thereby facilitating the establishment of more precise and straightforward prediction models.

In summary, we herein designed a novel nomogram with good accuracy that offers value as a means of differentiating between benign and malignant pulmonary nodules, enabling clinicians to better plan patient treatment. Such individualized risk analyses offer clinicians an opportunity to appropriately monitor and treat patients. However, further work will be needed to validate this nomogram in larger patient populations and to establish whether the treatment decisions made based on this nomogram will reduce rates of incorrect diagnosis and treatment planning for patients with pulmonary nodules.