Surgical Impacts of Metastatic Non-small Cell Lung Cancer to the Thoracic and Lumbar Spine

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).

JAK2 Loss Arising From Tumor-Spread-Through-Air-Spaces (STAS) Promotes Tumor Progression by Suppressing CD8+ T Cells in Lung Adenocarcinoma: A Machine Learning Approach

BACKGROUND

Tumor spread through air spaces (STAS) is a recently discovered risk factor for lung adenocarcinoma (LUAD). The aim of this study was to investigate specific genetic alterations and anticancer immune responses related to STAS. By using a machine learning algorithm and drug screening in lung cancer cell lines, we analyzed the effect of Janus kinase 2 (JAK2) on the survival of patients with LUAD and possible drug candidates.

METHODS

This study included 566 patients with LUAD corresponding to clinicopathological and genetic data. For analyses of LUAD, we applied gene set enrichment analysis (GSEA), in silico cytometry, pathway network analysis, in vitro drug screening, and gradient boosting machine (GBM) analysis.

RESULTS

The patients with STAS had a shorter survival time than those without STAS (P < 0.001). We detected gene set-related downregulation of JAK2 associated with STAS using GSEA. Low JAK2 expression was related to poor prognosis and a low CD8+ T-cell fraction. In GBM, JAK2 showed improved survival prediction performance when it was added to other parameters (T stage, N stage, lymphovascular invasion, pleural invasion, tumor size). In drug screening, mirin, CCT007093, dihydroretenone, and ABT737 suppressed the growth of lung cancer cell lines with low JAK2 expression.

CONCLUSION

In LUAD, low JAK2 expression linked to the presence of STAS might serve as an unfavorable prognostic factor. A relationship between JAK2 and CD8+ T cells suggests that STAS is indirectly related to the anticancer immune response. These results may contribute to the design of future experimental research and drug development programs for LUAD with STAS.

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Objective

The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.

Methods

Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.

Results

A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.

Conclusion

This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.

Prognostic factors and outcomes of surgical intervention for patients with spinal metastases secondary to lung cancer: an update systematic review and meta analysis

PURPOSE

Lung cancer is one of the most common malignant tumors. Most patients develop spinal metastases during the course of cancer and suffer skeletal-related events. Currently, no consensus has been reached on the prognostic factors in patients undergoing surgeries. This study aimed to answer two questions: (1) what are the effects of surgical intervention, and (2) what are the factors associated with postoperative survival.

METHODS

Searches were performed on electronic databases including PubMed, Ovid/MEDLINE, Cochrane, and Scopus for articles published before February of 2022, involving the survival factors of patients with spinal metastasis. Multiple data items were considered, such as baseline demographics, surgical details, clinical outcome, and prognostic factors. The analysis was performed in Review Manager (RevMan) 5.5. The prognostic factors of survival were analyzed with univariate and multivariate cox regression analysis.

RESULTS

Finally, 14 studies with 813 patients were identified. Their 6, 12, and 24 months survival rates ranged from 18 to 58%, 18 to 22.4%, and 0 to 58.5%, respectively. The pooled hazard ratio of preoperative ambulatory status and the number of involved vertebrae demonstrated statistical significance, while no significant prognostic effect on the overall survival was found for targeted therapy, visceral metastases, chemotherapy, radiotherapy, or postoperative ambulatory status.

CONCLUSION

Overall, surgical intervention could achieve significant pain relief and neurological function improvements. For patients receiving surgery for spinal metastasis from lung cancer, preoperative ambulatory status and the number of involved vertebrae were significant prognostic factors associated with their survival.

Radiotherapy and Surgical Advances in the Treatment of Metastatic Spine Tumors: A Narrative Review

Spine tumors encompass a wide range of diseases with a commensurately broad spectrum of available treatments, ranging from radiation for spinal metastases to highly invasive en bloc resection for primary vertebral column malignancies. This high variability in treatment approaches stems both from variability in the goals of surgery (e.g., oncologic cure vs. symptom palliation) and from the significant advancements in surgical technologies that have been made over the past 2 decades. Among these advancements are improvements in surgical technique, namely minimally invasive approaches, increased availability of focused radiation modalities (e.g., proton therapy and linear accelerator devices), and new surgical technologies, such as carbon fiber-reinforced polyether ether ketone rods. In addition, several groups have described nonsurgical interventions, such as vertebroplasty and kyphoplasty for spinal instability secondary to pathologic fracture, and lesion ablation with spinal laser interstitial thermoablation, radiofrequency ablation, or cryoablation. We provide an overview of the latest technological advancements in spinal oncology and their potential usefulness for modern spinal oncologists.