A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

doi:10.21203/rs.3.rs-5239380/v1

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A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-5239380/v1

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Rationale and Objectives:

Bone metastasis (BM) significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. Currently, no effective clinical model exists for predicting early BM in category T1 LUAD. This study aims to develop a model for timely BM detection by analyzing relevant influencing factors.

Materials and Methods

This retrospective study analyzed data from 478 patients with category T1 LUAD from August 2017 to August 2023. Of these, 334 patients were assigned to a training cohort and 144 to an internal validation cohort. Univariate and multivariate analyses identified BM risk factors, leading to a nomogram model. Model performance was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). An online calculator was also created to assess BM risk.

Results

Multivariate analysis revealed that alkaline phosphatase (ALP), carcinoembryonic antigen (CEA), nodule type, CT-reported N staging, and pleural effusion are independent BM risk factors. The nomogram showed strong accuracy, with AUC values of 0.929 in the training cohort and 0.954 in the validation cohort. Calibration analyses confirmed reliability, with DCA indicating high clinical benefit for both cohorts.

Conclusion

This nomogram effectively identifies high-risk patients for BM in category T1 LUAD, aiding personalized clinical decision-making.

Health sciences/Diseases/Cancer/Lung cancer

Health sciences/Diseases/Cancer/Metastases

Health sciences/Diseases/Cancer

Lung adenocarcinoma

category T1

bone metastasis

computed tomography

nomogram

Lung cancer is a prominent malignancy worldwide and stands as a leading cause of cancer-related mortality [1, 2]. The advent of low-dose spiral computed tomography (LDCT) screening has increased the detection of lung cancers, particularly nodules measuring 3 centimeters or less [3, 4], which generally have a favorable prognosis. However, category T1 lung cancer does not always signify early-stage disease, as it can display high invasiveness [5, 6]. Research indicates that over 20% of category T1 lung cancers may present with lymph node or extra-thoracic metastases [6, 7]. Distant metastases considerably worsen prognosis, especially in patients with adenocarcinoma, the most prevalent lung cancer subtype [8]. Lung adenocarcinoma (LUAD) shows a higher incidence of bone metastases (BM) compared to other subtypes [9], resulting in a median overall survival of less than one year post-BM diagnosis [10]. Patients with bone metastatic disease are prone to skeletal-related events, such as pathological fractures, spinal cord compression, or hypercalcemia, significantly affecting survival and quality of life [10, 11].

The diagnosis of BM typically relies on imaging techniques [10]. Bone scintigraphy is the preferred initial method for suspected BM, though its detection capabilities are limited due to low specificity [12]. Alternative imaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT), are recommended for their superior sensitivity and specificity [13, 14]. However, the high costs and limited availability of these advanced technologies hinder their routine use in bone metastasis screening. Consequently, the National Comprehensive Cancer Network screening guidelines do not advocate for imaging assessments in asymptomatic lung cancer patients [15]. Thus, there is an urgent need for an effective and accessible tool for the early diagnosis and risk assessment of BM in patients with category T1 LUAD.

Computed tomography (CT) scanning is extensively utilized for the diagnosis, staging, and therapeutic assessment of Lung cancer [16, 17]. CT imaging features can provide insights into the invasiveness of LUAD [18]. Additionally, serological indicators have demonstrated associations with the staging of LUAD [19]. This study aims to develop and validate a nomogram model that integrates clinical indicators and CT features to facilitate the early diagnosis of BM in category T1 LUAD.

2.1 Study design and participants

This study retrospectively reviewed data from 2114 patients diagnosed with category T1 LUAD at our institution from August 2017 to August 2023. The inclusion criteria were as follows: (1) confirmed to have LUAD through surgical or percutaneous biopsy; (2) accessible chest CT imaging before surgery; (3) classified as T1 stage (maximum tumor diameter ≤ 3cm on CT imaging). Exclusion criteria included: (1) with a history of anti-tumor treatment before chest CT examination; (2) with other primary malignant tumors; (3) with incomplete clinical data; (4) bone metastasis could not be confirmed. A total of 478 patients met the inclusion criteria. Among these patients, 111 were diagnosed with bone metastases based on Single Photon Emission Computed Tomography imaging and MRI or PET-CT or biopsy of suspicious regions, defining the bone metastasis group. The remaining 367 patients, who exhibited no evidence of bone metastases as determined by Single Photon Emission Computed Tomography imaging and MRI or PET-CT or biopsy of suspicious regions, comprised the non-bone metastasis group. Figure 1 illustrates the patient recruitment process. Patients were randomly allocated to the training cohort (n = 334) and the validation cohort (n = 144) at a ratio of 7:3. This study obtained approval from the Ethics Committee of Zhongnan Hospital of Wuhan University (approval number:2021057), and the research was conducted in accordance with relevant guidelines and regulations. Due to the retrospective nature of this study, the Medical Ethics Committee of Zhongnan Hospital of Wuhan University waived the need of obtaining informed consent.

2.2 Data collection

We collected clinical information from the electronic medical records system, encompassing demographic details such as age, gender, smoking history, family history of malignancy, preoperative or pre-puncture serum carcinoembryonic antigen (CEA), serum carbohydrate antigen 125 (CA125), neuron-specific enolase (NSE), serum squamous cell carcinoma (SCC), cytokeratin-19-fragment (CA211), and alkaline phosphatase (ALP). Additionally, we collected lung CT image features, including emphysema (present/absent), primary tumor location (upper left/lower left/upper right/middle right/lower right), tumor type (central/peripheral), tumor diameter, nodule type(solid/subsolid), CT-reported N staging (N0-1/N2-3), spiculation (present/absent), lobulation (present/absent), cystic airspace (present/absent), air bronchogram (present/absent), bronchial cutoff sign (present/absent), pleural retraction (present/absent), and pleural effusion (present/absent). The CT features were independently observed by two radiologists with 10 years of experience each, who were blinded to the BM status. Any inconsistencies were resolved through consultation with a senior radiologist with 15 years of diagnostic expertise.

2.3 Statistical analysis

Using R software for statistical analysis, we initially conducted the Shapiro-Wilk test to verify the normal distribution of continuous variables. Subsequently, skewed continuous variables were converted into categorical variables. The continuous data of laboratory parameters was categorized into discrete variables according to clinical reference standards as follows: ALP was categorized as > 120 U/L or 30–120 U/L; NSE as > 16.3 ng/ml or ≤ 16.3 ng/ml; CEA as > 5.0 ng/ml or ≤ 5.0 ng/ml; CA125 as > 35 U/ml or ≤ 35 U/ml; SCC as ≥ 1.5 ng/ml or < 1.5 ng/ml; CA211 as > 3.3 ng/ml or 0-3.3 ng/ml. A logistic regression model was constructed to predict BM in the training cohort. Variables with significance in univariate analysis (P < 0.05) were included in multivariate logistic regression analysis. Based on the multivariate logistic regression results, a nomogram model was developed to predict BM in category T1 LUAD. Receiver operating characteristic (ROC) curves were plotted to evaluate the model's discriminative ability, with the area under the curve (AUC) serving as a measure of the nomogram model's diagnostic performance. The AUC values after ten-fold cross-validation were used to evaluate the generalization ability of the model in the training and the validation cohorts. The calibration curves and decision curve analysis (DCA) were used to evaluate the performance and clinical utility of the model.

3.1 Baseline characteristics of the study population

The study enrolled a total of 478 patients with category T1 LUAD, comprising 245 (51.3%) males and 233 (48.7%) females. In these 478 LUAD patients, a total of 478 lung nodules were evaluated, including 339 (70.9%) solid nodules and 139 (29.1%) subsolid nodules. Among the 111 patients with bone metastases, there were 2 (1.8%) subsolid nodules and 109 (98.2%) solid nodules; conversely, in the 367 patients without bone metastases, 137 (37.3%) were subsolid nodules and 230 (62.7%) were solid nodules. The study population was divided into a training cohort (n = 334) and a validation cohort (n = 144). No significant differences were discerned in baseline parameters (P > 0.05). The baseline characteristics of the training and validation cohorts are presented in Table 1.

3.2 Univariate and multivariate analysis of variables in the training cohort

Univariate analysis revealed that ALP, NSE, CEA, CA125, CA211, tumor type, tumor diameter, nodule type, CT-reported N staging, spiculation, air bronchogram, bronchial cut-off sign, and pleural effusion were associated with BM in the training cohort (all P < 0.05). Significant variables identified in the univariate analysis were included in a multivariate logistic regression analysis. The results of the multivariate analysis indicated that ALP, CEA, nodule type, CT-reported N staging, and pleural effusion significantly influenced the occurrence of BM in category T1 LUAD (P < 0.05). The results of univariate analysis and multivariate logistic regression analysis are presented in Table 2.

3.3 Construction of the nomogram model

The nomogram model was constructed using predictive variables filtered through multivariate logistic regression analysis in the training cohort (Fig. 2). Each point on the nomogram corresponds to the score of a specific variable. The total score, obtained by summing the scores of each variable, evaluates the risk of bone metastasis (BM) on a scale from 0.01 to 0.99. Additionally, to facilitate the widespread use of our prediction nomogram among clinicians, we have developed a web-based interactive calculator using the R package Shiny. Users can conveniently obtain the predicted probability of bone metastasis by entering or selecting variables in the graphical user interface available (https://bmofluad.shinyapps.io/model/).

3.4 Evaluation and validation of the nomogram model's performance

Subsequently, we plotted the receiver operating characteristic (ROC) curve of the nomogram model. The area under the curve (AUC) was 0.929 (95% CI: 0.898–0.959) in the training cohort (Fig. 3a), and 0.954 (95% CI: 0.920–0.987) in the validation cohort (Fig. 3b). According to the best threshold, the sensitivity of the model in the training cohort was 0.797 with a specificity of 0.910, while in the validation cohort, the sensitivity was 0.938 with a specificity of 0.830. These findings indicate the robust discriminative capability of the model in both the training and validation cohorts. After ten-fold cross-validation, the AUC values for the training set and validation set were 0.921 and 0.952, respectively, demonstrating that the model has good generalization ability. The calibration curve showed high consistency between the bias-corrected curve and the ideal curve (45-degree line) in both the training and validation cohorts, demonstrating accurate prediction of risk and good calibration (Figs. 4a, b). Moreover, the mean average error rates of the model in the training and validation cohorts were 0.013 (Fig. 4a) and 0.029 (Fig. 4b) respectively, indicating good stability of the nomogram model. To assess whether the model has potential clinical benefits, decision curves were plotted separately for the training and validation cohorts. DCA showed that the model is beneficial in a wide range of threshold probability values, both in the training and validation cohorts (Figs. 5a, b).

BM is a significant adverse prognostic factor in patients with lung adenocarcinoma. Many patients with BM lack specific clinical manifestations in the early stages, and metastasis is often incidentally detected during routine screening or investigation of elevated tumor markers. Unfortunately, by the time patients experience skeletal-related events, the optimal window for early treatment has passed [15]. To timely prevent skeletal-related events through bone-targeted therapy and reduce the toxicity of subsequent treatments, it is necessary to screen for metastasis in lung cancer patients at high risk of BM. Several studies have focused on the diagnosis and risk prediction factors of BM in NSCLC patients. Teng et al. utilized machine learning techniques to analyze serum markers closely associated with BM and constructed an early diagnostic model for bone metastasis in NSCLC patients [20]. Su et al. developed a clinical radiomics nomogram model to predict the occurrence of BM in LUAD patients during the course of the disease [21]. Compared to the study by Su et al, our study focuses on category T1 LUAD, which generally has a relatively better prognosis, and we selected baseline synchronous BM patients as the study population. A comprehensive nomogram model was constructed through univariate and multivariate logistic regression analyses of 10 clinical features and 13 CT image features to achieve early identification and diagnosis of bone metastasis in category T1 LUAD. Our study revealed that elevated ALP levels, elevated CEA levels, solid nodules, N2-3 in CT-reported N staging, and present of pleural effusion are risk factors for BM in category T1 LUAD.

CEA, initially identified in the bloodstream of colorectal cancer patients in 1965, serves as a widely utilized serum glycoprotein marker across various cancer types. In the context of NSCLC, CEA holds significance as a serum biomarker in clinical settings, facilitating risk stratification and potentially informing disease management strategies. Notably, several studies have indicated that CEA reflects the extent of disease progression and is an adverse prognostic factor in NSCLC patients [22, 23]. Prior investigation has delineated a close correlation between elevated levels of serum CEA or cerebrospinal fluid CEA and the occurrence of brain metastasis in lung cancer, attributed to CEA's ability to traverse the blood-brain barrier [24]. Additionally, serum CEA has demonstrated predictive utility for mediastinal lymph node metastasis in lung cancer patients [25], and analogous associations have been established between serum CEA expression and bone metastasis in NSCLC [26]. In our study, elevated CEA levels demonstrated a strong correlation in multivariate logistic regression, indicating its value in diagnosing BM in T1-stage LUAD. ALP is primarily derived from tissues such as the liver and bone and is an important indicator of bone metabolism. Elevations in serum ALP expression among lung cancer patients coincide with escalated osteoblast activity, accelerated bone formation, and heightened risk of BM due to aberrant bone metabolism mechanisms [27].

Subsolid nodules can be categorized into part-solid nodules (which include ground-glass opacity (GGO) and solid components) and pure GGO nodules (which lack solid components) [28]. GGO is a nonspecific radiological manifestation characterized by a hazy opacity that does not obscure underlying pulmonary vessels or bronchial structures. Lung cancers predominantly presenting as GGO are typically non-invasive or minimally invasive low-grade adenocarcinomas [29]. Compared to solid lesions, lung adenocarcinomas with GGO components exhibit higher survival rates [30]. In this study, among 111 patients with lung adenocarcinoma and bone metastasis, only 2 exhibited pulmonary lesions showing GGO. Pleural effusion is a common clinical issue associated with over 50 causes [31]. In cancer patients, malignant pleural effusion is often due to lymphatic obstruction and increased vascular permeability caused by pleural cancer or metastases [32, 33]. Malignant effusion typically indicates disseminated or advanced disease, with a shortened life expectancy. Lung cancer is a major cause of malignant pleural effusion, accounting for more than a third of cases. However, pleural effusion can also result from obstructive pneumonia, atelectasis, pulmonary embolism, hypoalbuminemia, among other causes [31]. In this study, pleural effusion on CT images in patients with lung adenocarcinoma is identified as a risk factor for bone metastasis and typically indicates a poor prognosis. Higher levels of lymph node involvement are associated with an increased incidence of BM in LUAD patients, consistent with previous research findings [34, 35].

Increasing evidence supports that nomograms provide a visual and easily interpretable method for predicting disease risk. While several models have been proposed to predict bone metastases (BM) in lung cancer, none have specifically addressed the risk of synchronous bone metastases in T1 stage lung adenocarcinoma. Compared to existing BM prediction models, our model presents several advantages. Firstly, we have visualized this predictive model as a nomogram and developed an interactive web interface (https://bmofluad.shinyapps.io/model/), significantly optimizing the computational process and enhancing its clinical usability. Secondly, our clinical model leverages preoperatively accessible clinical and CT features to predict the risk of bone metastases. Thirdly, our nomogram model consistently maintains an AUC value exceeding 0.90 even after internal cross-validation. Finally, we conducted a decision curve analysis (DCA) to assess the clinical utility of the nomogram, which demonstrated significant net benefits within a wide range of threshold, underscoring its considerable clinical value.

The nomogram model developed in this study aids clinicians in evaluating the risk of bone metastases in patients with T1 stage LUAD using preoperative data, thereby enhancing clinical decision-making. For high-risk patients, preventive interventions can be implemented to mitigate the incidence of skeletal-related events and improve patient outcomes., while avoiding unnecessary interventions and associated costs for low-risk patients. For example, if a patient is identified as high-risk prior to surgery, further imaging modalities such as ECT or PET may be recommended to confirm the presence of bone metastases and facilitate early targeted treatment. Conversely, for low-risk patients, we do not recommend routine screening in the absence of symptoms indicative of bone metastases. Furthermore, informing high-risk patients about the potential for extended hospitalizations and increased costs can improve their acceptance of medical decisions.

Our study exhibits several limitations. Firstly, this study was conducted with patients from a single medical institution, which presents challenges in mitigating selection and information biases inherent to single-center samples. The model has undergone only internal validation; thus, prospective multi-center studies are necessary for external validation prior to clinical implementation. Secondly, the data were sourced from a tertiary hospital, and given that CEA fluid/serum level testing is not universally routine, the applicability of our novel web-based nomogram model may be restricted in regions where such testing is not standard. Additionally, the limited sample size in this study may introduce unavoidable biases. Future research will aim to increase the sample size to enhance the model's reliability and stability. Consequently, there is an urgent need for multi-center validation involving larger populations to provide robust evidence for future clinical applications. This will facilitate the refinement of the model and enable its integration into electronic health systems, thereby advancing clinical decision-making.

In conclusion, we have developed a novel nomogram model to aid in identifying individuals at high risk for BM in T1 LUAD patients. This nomogram demonstrates strong accuracy and discriminative power. Clinicians can employ this tool to predict bone metastasis within this specific population, thereby facilitating improved clinical decision-making.

Conflicts of interests

The authors declare no competing interests.

Human Ethics and Consent to Participate declarations

This study obtained approval from the Ethics Committee of Zhongnan Hospital of Wuhan University (approval number:2021057), and the research was conducted in accordance with relevant guidelines and regulations. Due to the retrospective nature of this study, the Medical Ethics Committee of Zhongnan Hospital of Wuhan University waived the need of obtaining informed consent.

Author Contribution

T.L. was responsible for the statistics analysis of data, charm making, and the writing of the manuscript. T. G. was responsible for collecting the clinical data. J.T.W. and Y. L. participated in assessing chest CT imaging. K.M. Z. and Y.G. were responsible for data collection and literature searching. M.Y. L. was in charge of the proposal of scientific problems, and the design and organization of the subject. All authors read and approved the final manuscript.

Data Availability

The raw data supporting the conclusions of this article can be obtained from the corresponding author upon reasonable request.

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Tables 1 and 2 are available in the Supplementary Files section.

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You are reading this latest preprint version

A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

Status:

Version 1

Abstract

Rationale and Objectives:

Materials and Methods

Results

Conclusion

Figures

1 Introduction

2 Materials and methods

2.1 Study design and participants

2.2 Data collection

2.3 Statistical analysis

3 Results

3.1 Baseline characteristics of the study population

3.2 Univariate and multivariate analysis of variables in the training cohort

3.3 Construction of the nomogram model

3.4 Evaluation and validation of the nomogram model's performance

4 Discussion

5 Conclusion

Declarations

Conflicts of interests

Human Ethics and Consent to Participate declarations

Author Contribution

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1