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Flow-chart for different histological types of lung cancer patients selection. SCLC = small cell lung cancer; LC = large cell; NSCLC/ NOS = non-small cell lung cancer/not otherwise specified
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Abstract Background The purpose of the present study was to characterize the prevalence, associated factors, and to construct a nomogram for predicting bone metastasis (BM) with different histological types of lung cancer. Patients and methods This study was a descriptive study that basing on the invasive lung cancer patients diagnosed between 2010...
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Background
Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualiz...
Citations
... Furthermore, the National Comprehensive Cancer Network (NCCN) screening guidelines in the United States similarly discourage imaging assessments for asymptomatic patients. 8,9 Consequently, there is a pressing need for an effective and easily accessible tool for the early diagnosis and risk assessment of NSCLC bone metastasis. ...
Background
Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients.
Methods
The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019–2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019–2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan–Meier survival curves.
Findings
Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95–0.99) for internal validation. External validation further confirmed the RF model’s effectiveness, yielding an AUC of 0.97 (95% CI 0.94–0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients.
Interpretation
This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies.
Funding
This work is supported by the 10.13039/501100001809National Natural Science Foundation of China (NO.81974315); 10.13039/501100003399Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).
... Patients were randomly assigned to the training cohort (n=351) and the validation cohort (n=150) in a 7:3 ratio. Previous studies have identified gender, age, tumor diameter, serum carbohydrate antigen 125 (CA125), alkaline phosphatase (ALP), and degree of differentiation as independent risk factors for BM in lung cancer (28)(29)(30). Based on this, we selected clinical factors including general clinical information, histopathological information, and traditional CT features. ...
Background
The occurrence of bone metastasis (BM) will seriously shorten the survival time of lung adenocarcinoma patients and aggravate the suffering of patients. Computed tomography (CT)-based clinical radiomics nomogram may help clinicians stratify the risk of BM in lung adenocarcinoma patients, thereby enabling personalized individualized clinical decision making.
Methods
A total of 501 patients with lung adenocarcinoma from March 2017 to March 2019 were enrolled in the study. Based on plain chest CT images, 1130 radiomics features were extracted from each lesion. One-way analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO) algorithm were used for radiomics features selection. Univariate and multivariate analyses were used to screen for clinical characteristics and identify independent predictors of BM. Three models (radiomics model, clinical model and combined model) were constructed to predict BM in lung adenocarcinoma patients. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the three models. The DeLong test was used to compare the performance of the models.
Results
Finally, the clinical model for predicting BM in lung adenocarcinoma patients was constructed based on 5 independent predictors: cytokeratin 19-fragments (CYFRA21-1), stage, Ki-67, edge, and lobulation. The radiomics model was constructed based on 5 radiomics features. The combined model incorporating clinical independent predictors and radiomics was constructed. In the validation cohort, the area under the curve (AUC) of the clinical model, radiomics model and combined model was 0.824, 0.842 and 0.866, respectively. Delong test showed that in the training cohort, the AUC values of the radiomics model and the combined model were statistically different (P=0.03), and the AUC values of the other models were not statistically different. DCA showed that the nomogram had a highest net clinical benefit.
Conclusions
The CT-based clinical radiomics nomogram can be used as a non-invasive and quantitative method to help clinicians stratify the risk of BM in patients with lung adenocarcinoma, thereby enabling personalized clinical decision making.
... 26,27 The recent SEER database study indicated that lung cancer prognosis worsens with an increase in the number of metastatic sites. 28 In bone metastatic cancer, the RANK/RANKL pathway plays a vital role in bone metabolism, immunity, and tumorigenesis. 29 Its expression has been identified as a T A B L E 2 The baseline clinical characteristics of EGFR-mutated NSCLC patients with bone metastasis (BoM) who received or did not receive denosumab. ...
Background
This study aimed to examine the clinical characteristics of bone metastasis (BoM) in patients with non‐small cell lung cancer (NSCLC) who have an epidermal growth factor receptor (EGFR) mutation and to identify the most effective treatment strategy using EGFR–tyrosine kinase inhibitors (TKIs).
Methods
The study included patients with stage IV EGFR‐mutated NSCLC who were receiving first‐line treatment with EGFR–TKIs between January 2014 and December 2020. These patients were divided into two groups based on the presence or absence of BoM at the time of initial diagnosis. The BoM group was further subdivided based on whether they received denosumab or not.
Results
The final analysis included 247 patients. Those with BoM at initial diagnosis had shorter progression‐free survival (12.6 vs. 10.5 months, p = 0.002) and overall survival (OS) (49.7 vs. 30.9 months, p = 0.002) compared to those without BoM. There was a difference in the location of metastatic sites between the two groups, with a higher incidence of extrathoracic metastasis in the BoM group ( p < 0.001). The incidence of T790M was higher in patients with BoM than in those without (47.4% vs. 33.9%, p = 0.042). Multivariate Cox regression analysis revealed that sequential osimertinib treatment and the addition of antiangiogenic therapy (AAT) and denosumab therapy improved OS in patients with BoM.
Conclusions
The presence of BoM is a negative prognostic factor for NSCLC patients with an EGFR mutation, possibly due to the presence of extrathoracic metastases. However, adding AAT and denosumab, along with sequential osimertinib, to the treatment regimen for patients with BoM can improve survival outcomes.
... Although the anatomical system may be a potential choice due to the similar symptoms and pathogenic mechanism, pieces of studies in vain to verify the similar BM patterns even in different histological types of same cancer. 11,14 Genetics may also have deep value in forecasting BM risk, while the invasive inspection method, high-cost and precision equipment-dependent characteristics limit its wide application in the clinical practice. [15][16][17] The identification of associated factors for BM will play an important role in the prediction of BM risk. ...
... Hence, we hypothesize that the high proportion of synchronous BM may be partly derived from the rapid cancer progression caused by the relatively higher malignancy degree and the inadequate and overdue BM screening. 11,14,21 To provide timely and individualized BM screening, we explore the associated factors for BM occurrence and constructed a predicting nomogram. ...
Background
Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan‐cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk.
Methods
Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence‐based cancer classification system (BM‐CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM‐CCS and the nomogram, respectively.
Results
A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%–17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%–5.6%), and category C (1.2%; 95% CI: 1.1%–1.4%) ( p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM‐CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%–88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%–87.1%). The decision curve analysis also demonstrated the clinical usefulness.
Conclusion
The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM.
... This not only aids in expeditious and accurate diagnosis but also facilitates the anticipation of its progression, predating existing imaging methods (e.g., skeletal scintigraphy, computerized tomography (CT), Positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI)) [7][8][9]. So far, the incidence of BoM has shown positive correlations with various factors such as male gender, married status, younger age (≤50), adenocarcinoma type, clinical stage (III-IV), TNM stage (T1-T3, N2-N3), fibrinogen, activated partial thromboplastin time, D-Dimer, alkaline phosphatase, metabolic tumor volume (MTV) of the whole body (MTVwb), and MTV of thorax (MTVtho) [3,[10][11][12][13][14][15]. Intriguingly, patients with primary lung tumors in the lower lobe exhibited a higher propensity for BoM compared to those with tumors in the main bronchus, suggesting that different primary tumor locations may influence the pattern of distant metastasis in patients with advanced NSCLC [16]. ...
... 17 Previous studies have demonstrated that distinct histological subtypes of the same tumor exhibit varying rates of metastasis in different organs. 18,19 The disparate incidences of liver, lung, and hepato-lung metastases observed may partially reflect the heterogeneity and homogeneity of distant metastasis in EOCRC. This study reveals both heterogeneity and homogeneity among factors associated with distant metastasis at different sites in EOCRC. ...
Background
Identifying the risk factors for distant metastasis in early‐onset colorectal cancer (EOCRC) is crucial for elucidating its etiology and facilitating preventive treatment. This study aims to characterize the variability in EOCRC incidence and discern both heterogeneous and homogeneous risk factors associated with synchronous liver, lung, and hepato‐lung metastases.
Methods
This study included patients with EOCRC enrolled in the SEER database between 2010 and 2015 and divided patients into three groups by synchronous liver, lung, and hepato‐lung metastases. Each group of patients with different metastasis types was randomly assigned to the development and validation cohort in a ratio of 7:3. Logistic regression was used to analyze the heterogeneous and homogenous risk factors for synchronous liver, lung, and hepato‐lung metastases in the development cohort of patients. Nomograms were built to calculate the risk of metastasis, and the receiver operating characteristic (ROC) curve and calibration curve were used to quantitatively evaluate their performance.
Results
A total of 16,336 eligible patients with EOCRC were included in this study, of which 17.90% (2924/16,336) had distant metastases. The overall incidences of synchronous liver, lung, and hepato‐lung metastases were 11.90% (1921/16,146), 2.42% (390/16,126), and 1.50% (241/16,108), respectively. Positive CEA values before treatment, increased lymphatic metastases, and deeper invasion of intestinal wall were positively correlated with three distant types of metastases. On the contrary, the correlation of age, ethnicity, location of primary tumor, and histologic grade among the three types was inconsistent. The ROC curve and calibration curve proved to have fine performance in predicting distant metastases of EOCRC.
Conclusions
There are significant differences in the incidence of distant metastases in EOCRC, and related risk factors are heterogeneous and homogenous. Although limited risk factors were incorporated in this study, the established nomograms indicated good predictive performance.
... The calibration curve serves as a valuable tool for assessing the degree of fit between the model's prediction results and the actual situation [37][38][39][40]. It can be used to evaluate the fitting degree between the nomogram and the change in EGCG content under abiotic stress. ...
To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.
... Due to the advances in treatment strategies and improvement in diagnosis, the prevalence of bone metastasis (BM) has been increasing in recent years [1,2]. It was reported that BM occurred in approximately 70% of cancer patients with metastatic disease [3,4]. ...
Background
The aim of study was to evaluate survival outcome and limb function in cancer patients with proximal limbs metastasis. Associated factors on survival outcome and limb function were identified. The comparative analysis between intramedullary nailing and prosthesis surgery in cancer patients with proximal limb metastasis was performed.
Methods
In this five-center retrospective study, patients diagnosed with limbs metastasis were collected. Descriptive statistics was used and log-rank test was performed to analyze the survival in subgroups. The Cox proportional hazards regression analysis was performed to identify the independent prognostic factors. The Musculoskeletal Tumor Society (MSTS) scoring system was used to evaluate limb function after surgery, and t test or analysis of variance (ANOVA) was utilized in subgroup analysis.
Results
A total of 316 patients with limb metastasis were included with mean age at 61.0 years. The most common primary tumor was breast, followed by renal cancer and lung cancer. The median overall survival was 24.0 months and the 1-, 3- and 5-year survival rates were 86.9%, 34.7% and 6.8%, respectively. Primary tumor type, visceral metastasis and chemotherapy were proved to be the independent prognostic factors. The mean Musculoskeletal Tumor Society (MSTS) score was 20.5, significant difference was observed in subgroup of solitary/multiple bone metastasis, with/without pathological fracture, and type of surgery.
Conclusion
The present study concluded that primary tumor type, visceral metastasis and chemotherapy were three factors affecting the survival of patients. Compared with intramedullary nailing, the patients underwent prosthesis surgery showed better limb function, this procedure should be encouraged in patients with indication.
... Currently, the independent risk factors for bone metastatic breast cancer (BMBC) are inconsistent, and a dedicated prediction tool for BMBC is lacking. A nomogram is a reliable and convenient prognostic tool, and it is widely used in oncology prediction because of its incorporation of quantitative analysis of risk variables (17)(18)(19). However, since the prognostic nomograms of patients with MBC were created using mostly the Surveillance, Epidemiology, and End Results (SEER) database, the possibility of extending these nomograms to the Chinese population is unclear (20,21). ...
Objectives
The purpose of this study was to determine the independent risk factors for bone metastasis in breast cancer and to establish a nomogram to predict the risk of bone metastasis in early stages through clinicopathological characteristics and hematological parameters.
Methods
We selected 1042 patients with breast cancer from the database of Shandong Cancer Hospital for retrospective analysis, and determined independent risk factors for bone metastatic breast cancer (BMBC). A BMBC nomogram based on clinicopathological characteristics and hematological parameters was constructed using logistic regression analysis. The performance of the nomograph was evaluated using the receiver operating characteristic (ROC) and calibration curves. The clinical effect of risk stratification was tested using Kaplan-Meier analysis.
Results
BMBC patients were found to be at risk for eight independent risk factors based on multivariate analysis: age at diagnosis, lymphovascular invasion, pathological stage, pathological node stage, molecular subtype, platelet count/lymphocyte count, platelet count * neutrophil count/lymphocyte count ratio, Systemic Immunological Inflammation Index, and radiotherapy. The prediction accuracy of the BMBC nomogram was good. In the training set, the area under the ROC curve (AUC) was 0.909, and in the validation set, it was 0.926, which proved that our model had good calibration. The risk stratification system can analyze the risk of relapse in individuals into high- and low-risk groups.
Conclusion
The proposed nomogram may predict the possibility of breast cancer bone metastasis, which will help clinicians optimize metastatic breast cancer treatment strategies and monitoring plans to provide patients with better treatment.
... In several studies, tumor size is an independent risk factor for cancer metastasis (39)(40)(41)(42)(43). The results of this study's multivariable logistic regression and machine learning models are consistent with them. ...
Background
Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer.
Methods
In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models’ performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer.
Result
Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model’s wider adoption ( https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py ).
Conclusion
In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.
