Establishment of a regression model of bone metabolism markers for the diagnosis of bone metastases in lung cancer

Sensing the future: A review on emerging technologies for assessing and monitoring bone health

Bone health is crucial at all stages of life. Several medical conditions and changes in lifestyle affect the growth, structure, and functions of bones. This may lead to the development of bone degenerative disorders, such as osteoporosis, osteoarthritis, rheumatoid arthritis, etc., which are major public health concerns worldwide. Accurate and reliable measurement and monitoring of bone health are important aspects for early diagnosis and interventions to prevent such disorders. Significant progress has recently been made in developing new sensing technologies that offer non-invasive, low-cost, and accurate measurements of bone health. In this review, we have described bone remodeling processes and common bone disorders. We have also compiled information on the bone turnover markers for their use as biomarkers in biosensing devices to monitor bone health. Second, this review details biosensing technology for bone health assessment, including the latest developments in various non-invasive techniques, including dual-energy X-ray absorptiometry, magnetic resonance imaging, computed tomography, and biosensors. Further, we have also discussed the potential of emerging technologies, such as biosensors based on nano- and micro-electromechanical systems and application of artificial intelligence in non-invasive techniques for improving bone health assessment. Finally, we have summarized the advantages and limitations of each technology and described clinical applications for detecting bone disorders and monitoring treatment outcomes. Overall, this review highlights the potential of emerging technologies for improving bone health assessment with the potential to revolutionize clinical practice and improve patient outcomes. The review highlights key challenges and future directions for biosensor research that pave the way for continued innovations to improve diagnosis, monitoring, and treatment of bone-related diseases.

Worldwide research trends on bone metastases of lung cancer: a bibliometric analysis

Background

Lung cancer has the highest fatality rate among all malignancies worldwide. Within this disease, bone metastasis (BM) emerges as a particularly deleterious site of metastatic dissemination, marked by a dismal prognosis. The objective of this investigation is to shed light on the current international research efforts and the development trajectory on lung cancer BM through a bibliometric analysis (performance and visualization analysis).

Method

Data were obtained from the Web of Science Core Collection repository on lung cancer BM from 1 January 2012 to 1 January 2022. Subsequently, the collected data underwent scrutiny using the VOSviewer software to reveal patterns of co-authorship, co-citation, and keyword analysis, while the CiteSpace software facilitated the generation of keyword cluster maps and performed burst analyses.

Results

The study included 327 papers of 2,154 authors, 587 organizations, and 41 countries, and explored the cooperation between them and the relationships between citations. Over the past decade, published papers showed a steady growth trend. China had the highest production with 189 papers, and USA had the highest collaboration with other countries, with 43 total link strength. Lung Cancer exhibited the highest frequency of co-cited journals, with a co-citation time of 412 and an IF/JCR partition of 6.081/Q1 in 2021. The most frequently co-cited article, authored by Tsuya A and published in Lung Cancer in 2007, amassed 70 co-citations. High-frequency keywords were categorized into four clusters: pathogenesis, treatment and clinical manifestations, prognosis, and diagnosis. In recent years, there has been a significant increase in the strong citation burst strength of keywords such as "predictor," "skeletal-related events," "efficacy," "migration," "docetaxel," and "impact." Lung adenocarcinoma is the most common type of tumor.

Conclusion

This bibliometric study provides a comprehensive analysis of lung cancer BM in the recent 10 years. The field of early diagnosis, pathogenesis, and new treatments is entering a phase of rapid development and remains valuable for future research.

Establishment and verification of a predictive model for bone metastasis in patients with non-small cell lung cancer based on peripheral blood CX3CL and CCL28

To establish a predictive model of bone metastasis in patients with non-small cell lung cancer (NSCLC) using peripheral blood CX3CL and CCL28, and to verify its application value. We retrospectively gathered clinical data from 210 patients with NSCLC treated at our institution between April 2021 and December 2023. These patients were stratified into two groups based on the presence of bone metastases: a bone metastasis group (n = 49) and a non-bone metastasis group (n = 161). A logistic regression model was developed to predict bone metastasis and to evaluate the model's predictive performance. Multivariate logistic regression analysis identified age (OR = 6.689, P < 0.001), carcinoembryonic antigen (CEA, OR = 5.699, P < 0.001), CX3CL1 (OR = 5.418, P < 0.001), and CCL28 (OR = 7.692, P < 0.001) as independent predictors of bone metastasis in NSCLC patients. The receiver operating characteristic (ROC) curve analysis yielded an area under the curve (AUC) of 0.794 for both the modeling and validation cohorts. Decision curve analysis (DCA) indicated a superior net benefit of the model. Calibration curves confirmed close concordance between predicted and observed probabilities of bone metastasis. The Hosmer-Lemeshow test yielded a chi-square statistic of 4.743 with a P-value of 0.178, suggesting a good fit. The predictive model utilizing serum levels of CX3CL1 and CCL28 demonstrates robust predictive accuracy and efficacy for bone metastasis in patients with NSCLC.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

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 National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study

Background

Adenocarcinoma, the most prevalent histological subtype of non-small cell lung cancer, is associated with a significantly higher likelihood of bone metastasis compared to other subtypes. The presence of bone metastasis has a profound adverse impact on patient prognosis. However, to date, there is a lack of accurate bone metastasis prediction models. As a result, this study aims to employ machine learning algorithms for predicting the risk of bone metastasis in patients.

Method

We collected a dataset comprising 19,454 cases of solitary, primary lung adenocarcinoma with pulmonary nodules measuring less than 3 cm. These cases were diagnosed between 2010 and 2015 and were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing clinical feature indicators, we developed predictive models using seven machine learning algorithms, namely extreme gradient boosting (XGBoost), logistic regression (LR), light gradient boosting machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP) and support vector machine (SVM).

Results

The results demonstrated that XGBoost exhibited superior performance among the four algorithms (training set: AUC: 0.913; test set: AUC: 0.853). Furthermore, for convenient application, we created an online scoring system accessible at the following URL: https://www.xsmartanalysis.com/model/predict/?mid=731symbol=7Fr16wX56AR9Mk233917, which is based on the highest performing model.

Conclusion

XGBoost proves to be an effective algorithm for predicting the occurrence of bone metastasis in patients with solitary, primary lung adenocarcinoma featuring pulmonary nodules below 3 cm in size. Moreover, its robust clinical applicability enhances its potential utility.

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.