Prediction of Survival Prognosis for Spinal Metastasis From Cancer of Unknown Primary: Derivation and Validation of a Nomogram Model

Risks for prolonged mechanical ventilation and reintubation after cervical malignant tumor surgery: a nested case-control study

PURPOSE

Prolonged mechanical ventilation (PMV) and reintubation are among the most serious postoperative adverse events associated with malignant cervical tumors. In this study, we aimed to clarify the incidence, characteristics, and risk factors for PMV and reintubation in target patients.

METHODS

This retrospective nested case-control study was performed between January 2014 and January 2020 at a large spinal tumor center in China. Univariate analysis was used to identify the possible risk factors associated with PMV and reintubation. Logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) with covariates of a probability < 0.05 in univariate analysis.

RESULTS

From a cohort of 560 patients with primary malignant (n = 352) and metastatic (n = 208) cervical tumors, 27 patients required PMV and 20 patients underwent reintubation. The incidence rates of PMV and reintubation were 4.82% and 3.57%, respectively. Three variables (all p < 0.05) were independently associated with an increased risk of PMV: Karnofsky Performance Status < 50 compared to ≥ 80, operation duration ≥ 8 h compared to < 6 h, and C4 nerve root encased by the tumor. Longer operative duration and preoperative hypercapnia (all p < 0.05) were independent risk factors for postoperative reintubation, both of which led to longer length of stay (32.6 ± 30.8 vs. 10.7 ± 5.95 days, p < 0.001), with an in-hospital mortality of 17.0%.

CONCLUSION

Our results demonstrate the risk factors for PMV or reintubation after surgery for malignant cervical tumors. Adequate assessment, early detection, and prevention are necessary for this high-risk population.

Effect of radiotherapy on local control and overall survival in spinal metastasis of non-small-cell lung cancer after surgery and systemic therapy

Aims

Radiotherapy is a well-known local treatment for spinal metastases. However, in the presence of postoperative systemic therapy, the efficacy of radiotherapy on local control (LC) and overall survival (OS) in patients with spinal metastases remains unknown. This study aimed to evaluate the clinical outcomes of post-surgical radiotherapy for spinal metastatic non-small-cell lung cancer (NSCLC) patients, and to identify factors correlated with LC and OS.

Methods

A retrospective, single-centre review was conducted of patients with spinal metastases from NSCLC who underwent surgery followed by systemic therapy at our institution from January 2018 to September 2022. Kaplan-Meier analysis and log-rank tests were used to compare the LC and OS between groups. Associated factors for LC and OS were assessed using Cox proportional hazards regression analysis.

Results

Overall, 123 patients with 127 spinal metastases from NSCLC who underwent decompression surgery followed by postoperative systemic therapy were included. A total of 43 lesions were treated with stereotactic body radiotherapy (SBRT) after surgery and 84 lesions were not. Survival rate at one, two, and three years was 83.4%, 58.9%, and 48.2%, respectively, and LC rate was 87.8%, 78.8%, and 78.8%, respectively. Histological type was the only significant associated factor for both LC (p = 0.007) and OS (p < 0.001). Treatment with targeted therapy was significantly associated with longer survival (p = 0.039). The risk factors associated with worse survival were abnormal laboratory data (p = 0.021), lesions located in the thoracic spine (p = 0.047), and lumbar spine (p = 0.044). This study also revealed that postoperative radiotherapy had little effect in improving OS or LC.

Conclusion

Tumour histological type was significantly associated with the prognosis in spinal NSCLC metastasis patients. In the presence of post-surgical systemic therapy, radiotherapy appeared to be less effective in improving LC, OS, or quality of life in spinal NSCLC metastasis patients.

Risk Factors for Poor Outcome after Palliative Surgery for Metastatic Spinal Tumors

Palliative surgery is performed to improve the quality of life of patients with spinal metastases. However, it is sometimes difficult to achieve the expected results because the patient's condition, and risk factors related to poor outcomes have not been well elucidated. This study aimed to evaluate the functional outcomes and investigate the risk factors for poor outcomes after palliative surgery for spinal metastasis. We retrospectively reviewed the records of 117 consecutive patients who underwent palliative surgery for spinal metastases. Neurological and ambulatory statuses were evaluated pre- and post-operatively. Poor outcomes were defined as no improvement or deterioration in functional status or early mortality, and the related risk factors were analyzed using multivariate logistic regression analysis. The results showed neurological improvement in 48% and ambulatory improvement in 70% of the patients with preoperative impairment, whereas 18% of the patients showed poor outcomes. In the multivariate analysis, low hemoglobin levels and low revised Tokuhashi scores were identified as risk factors for poor outcomes. The present results suggest that anemia and low revised Tokuhashi scores are related not only to life expectancy but also to functional recovery after surgery. Treatment options should be carefully selected for the patients with these factors.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Background

Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques.

Methods

This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator.

Results

The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001).

Conclusions

The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.