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 prognosis or outcome based on a set of specific variables, thereby aiding in clinical decision-making11. Many models for the treatment of pulmonary nodules were established on the basis of certain epidemiological variables and CT scan results. However, clinical findings such as blood biomarkers are also very important for the diagnosis of lung cancer 12. Moreover, for some of these variables, such as GGO, the surgical criteria are not well defined such that treatments are often conducted according to surgeons’ own experience13, 14. As such, we herein sought to develop a new nomogram capable of predicting the relative risk of malignancy when evaluating patients with pulmonary nodules.
Here, 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 lung cancer 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 malignancy11.
Prior studies have confirmed that hypertension is a common comorbidity in cancer patients15. Several mechanisms may explain this observation, including the fact that hypertension can increase VEGF levels in the plasma16. 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 past17, 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 malignant18, potentially explaining the significance of plasma fibrinogen levels in our model. Dovell et al. demonstrated that higher SUA levels were associated with a somewhat higher risk of overall cancer incidence, including lung cancer19. Xie et al. also found hyperuricemia to be linked to increased rates of cancer and associated mortality20, potentially explaining the inclusion of SUA in our predictive risk model. HDL levels have been shown to be negatively correlated with the risk of lung cancer in one cohort study21, with other studies having similarly supported the existence of lower HDL levels in lung cancer patients relative to healthy individuals22. HDL levels are also readily measured in a clinical context. TG levels have also been reported to be positively correlated with lung cancer incidence in an analysis conducted by Lin et al. of 4673 lung cancer patients in a cohort of 685,852 individuals18. Low and high TG levels have been linked to higher rates of lung cancer in a prospective cohort study23. With respect to spicule sign, Fang et al. previously conducted a case-control study demonstrating that stage I lung adenocarcinoma patients were significantly more likely to exhibit this finding24. 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 evident25. Tu et al. found CT density to be a valuable feature when differentiating between nodules that were malignant and benign26. Qiu et al. further determined that solitary ground-glass opacity nodule size and density upon high-resolution T evaluation were associated with invasive adenocarcinoma risk27. Nodule size may be the most important variable included in our predictive module, given that nodule diameter is a key determinant of treatment under the British Thoracic Society guidelines28 and Fleischner Society Guidelines29. For nodules ≥ 10 mm in diameter, the odds of malignancy in the NELSON screening study were 15.2% 30. As such, we included nodule diameter as the size variable in the present study.
Herein, we found that pulmonary modules > 8 mm in size were more likely to be malignant than smaller nodules (57.53% vs. 40.63%), suggesting that a predictive model including this parameter, after being appropriately calibrated, may aid in improving lung cancer patient outcomes by providing individualized predictions of risk. Herein, we thus developed 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 selecting subjects 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 prognosis.
Accurate prognostic evaluations 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.
There are multiple limitations to this study. For one, the sample size of this study was limited, and all patients were enrolled from a single center over a relatively limited study period. However, nomograms established by Chen et al.31 and Luo et al.32, with the training and validation cohorts (61/101 and 32/43 patients, respectively), exhibited good accuracy. Additionally, risk factor analyses did not incorporate all possible risk factors that may be relevant to the differentiation between benign and malignant nodules. Other relevant factors not included in this analysis included the number of nodules and specific comorbidity incidence rates. Lastly, while a bootstrap testing approach was used to validate our nomogram, the patients used for this validation approach may not be sufficient to ensure the generalizability of these data to patients from other countries or regions. As such, further external validation in a wider pulmonary nodule patient population will be essential in the future.