A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer.

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan-Meier (K-M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K-M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

Analysis of survival benefit from primary tumor resection and individualized prediction of overall survival for lung cancer patients with bone metastasis: Worth it or not?

BACKGROUND: The clinical management of lung cancer (LC) patients with bone metastasis (BM) is still a significant challenge. This study aimed to explore the role of primary tumor resection (PTR) on survival outcome of LC patients with BM and to develop two web-based nomograms for predicting the overall survival (OS) of LC patients with BM who received PTR and those who did not. METHODS: We enrolled LC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Propensity score matching (PSM) was then conducted to balance the baseline characteristics of covariates between patients in surgery and non-surgery groups. Next, Kaplan-Meier analysis with log-rank test was performed to evaluate the survival benefit of PTR before and after PSM methods and to explore the impact of surgical resection extent on the prognosis of LC patients with BM and clinical outcomes in patients with different metastatic patterns. Cox proportional hazard regression analysis was then applied to determine the independent prognostic factors for OS of patients receiving PTR and did not receiving PTR, respectively. Subsequently, we constructed two individualized nomograms for predicting the 12-, 18- and 24-months OS. Finally, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were generated to evaluate discrimination, accuracy and clinical utility of the nomograms. RESULTS: A total of 7747 eligible patients were included in this analysis. The survival analysis revealed that PTR was closely associated with better survival outcome among LC patients with BM(P < 0.05), while the survival benefit of PTR was suboptimal in patients presented with multiple metastases(P > 0.05). Besides, lobectomy shows best survival benefit. Two nomograms were then constructed based on independent prognostic factors of patients in the surgery group and the non-surgery group. The ROC curves showed good discrimination of the two nomograms, with the area under curve (AUC) of each time point being higher than 0.7 in both the training set and testing set. The calibration curves also demonstrated satisfactory consistency between actual survival and nomogram-predicted OS of both nomograms. The DCA showed high benefit of nomogram in a clinical context. Moreover, the study population was stratified into three groups based on the scores of the nomogram, and the survival analysis showed that this prognostic stratification was statistically significant (p < 0.05). CONCLUSIONS: This study showed that surgical resection of the primary site strategy can prolong survival of LC patients with BM to some extent, depending on different sites of metastasis and highly selected patients. Furthermore, the web-based nomograms showed significant accuracy in predicting OS for patients with or without surgery, which may provide valuable insights for patients' counseling and individualized decision-making for clinicians.

A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer.

Background: The impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery. Methods: BC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness. Results: This study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group. Conclusion: Our study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates.

Clinical characteristic-assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score-matched analysis.

Background: Primary tumor resection (PTR) is the standard treatment for patients with primary malignant bone neoplasms (PMBNs). However, it remains unclear whether patients with advanced PMBNs still benefit from PTR. This study aimed to develop a prediction model to estimate the beneficial probability of PTR for this population. Methods: This study extracted data from patients diagnosed with advanced PMBNs, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database, with the period from 2004 to 2015. The patient cohort was then bifurcated into two groups: those who underwent surgical procedures and the non-surgery group. Propensity score matching (PSM) was utilized to mitigate any confounding factors in the study. The survival rates of patients from both the surgical and non-surgery groups were evaluated using Kaplan-Meier (K-M) curves analysis. Moreover, the study used this method to assess the capacity of the nomogram to distinguish patients likely to derive benefits from surgical intervention. The study was grounded in the hypothesis that patients who underwent PTR and survived beyond the median overall survival (OS) time would potentially benefit from the surgery. Subsequently, logistic regression analysis was performed to ascertain significant predictors, facilitating the development of a nomogram. This nomogram was subjected to both internal and external validation using receiver operating characteristic curves, area under the curve analysis, calibration plots, and decision curve analysis. Results: The SEER database provided a total of 839 eligible patients for the study, among which 536 (63.9%) underwent PTR. Following a 2:1 PSM analysis, patients were classified into two groups: 364 patients in the surgery group and 182 patients in the non-surgery group. Both K-M curves and multivariate Cox regression analysis revealed that patients who received PTR had a longer survival duration, observed both before and after PSM. Crucial factors such as age, M stage, and tumor size were identified to be significantly correlated with surgical benefits in patients with advanced PMBNs. Subsequently, a nomogram was developed that uses these independent predictors. The validation of this predictive model confirmed its high accuracy and excellent discrimination ability of the nomogram to distinguish patients who would most likely benefit from surgical intervention. Conclusion: In this study, we devised a user-friendly nomogram to forecast the likehood of surgical benefits for patients diagnosed with advanced PMBNs. This tool facilitates the identification of the most suitable candidates for PTR, thus promoting more discerning and effective use of surgical intervention in this patient population.

Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection.

BACKGROUND: Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery. METHODS: Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC. RESULTS: A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy. CONCLUSIONS: Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.

Two novel clinical tools to predict the risk of bone metastasis and overall survival in esophageal cancer patients: a large population-based retrospective cohort study.

BACKGROUND: The aim of this study was to construct two web-based nomograms to predict the probability of bone metastasis (BM) in esophageal cancer (EC) patients and the prognostic of EC patients with BM (ECBM). METHODS: We collected the data of EC and ECBM patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015. Independent risk variables for the development of BM in EC patients were identified using univariate and multivariate logistic regression analyses. Univariate and multivariate Cox regression analyses were used to assess independent prognostic variables in ECBM patients. And then, constructed two nomograms to predict the risk of bone metastases and overall survival (OS) of ECBM patients. Survival differences were studied by Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical applicability of these two nomograms were assessed by using receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: We selected a total of 6839 patients with EC, of which 326 (4.77%) had BM at the time of initial diagnosis. The results of K-M survival and Cox regression analysis showed significant effects of BM on the OS in EC patients. Age, N stage, tumor size and brain/liver/lung organ metastasis were identified as BM-related risk variables. Chemotherapy and brain/liver organ metastasis were identified as ECBM-related prognostic variables. The ROC, AUC, calibration curves and DCA of two nomograms all showed excellent predictive efficacy and clinical applicability. CONCLUSIONS: These two nomograms were constructed and validated, which could objectively predict the risk of BM in EC patients and the prognostic in ECBM patients. These tools are expected to make valuable contributions in clinical work, informing surgeons in making decisions about patient care.

Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study.

Background: Elderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms. Methods: We downloaded the data of all KC patients aged more than 65 years during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA). Results: A final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms. Conclusion: Two new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population.

Extensive spinal epidural abscess caused by Staphylococcus epidermidis: A case report and literature review.

Background: Extensive spinal epidural abscess (SEA) is an exceptional and threatening condition that requires prompt recognition and proper management to avoid potentially disastrous complications. We aimed to find key elements of early diagnosis and rational treatments for extensive SEA. Case presentation: A 70-year-old man complained of intense pain in the cervical-thoracic-lumbar spine that radiated to the lower extremity. Laboratory test results revealed a marked increase in all indicators of infection. The spinal magnetic resonance imaging (MRI) revealed a ventral SEA extending from C2 to L4. Owing to the patient's critical condition, laminectomy, drainage, and systemic antibiotic therapy were administered. And the multidrug-resistant Staphylococcus epidermidis was detected in the purulent material from this abscess. Results: Postoperative MRI revealed diminished epidural abscess, and the clinical symptoms were dramatically and gradually relieved after two rounds of surgery and systemic antibiotic therapy involving the combination of ceftriaxone, linezolid, and rifampicin. Conclusions: A comprehensive emergency assessment based on neck or back pain, neurological dysfunctions, signs of systemic infection, and MRI are important for early diagnosis of extensive SEA. Further, the combination of laminectomy, drainage, and systemic antibiotic therapy may be a rational treatment choice for patients with SEA, especially for extensive abscess or progressive neurological dysfunction.

The vertebral Hounsfield units can quantitatively predict the risk of adjacent vertebral fractures after percutaneous kyphoplasty.

Background: Measuring the Hounsfield units (HU) of the vertebrae may yield diagnostic information for fracture risk. This study aimed to measure HU of vertebrae in percutaneous kyphoplasty (PKP) patients using computed tomography (CT) imaging to determine the HU measurements threshold for adjacent vertebral fracture and to assess the relationship between HU measurements and the risk of adjacent vertebral fracture. Methods: A retrospective study was conducted on consecutive patients who underwent PKP between January 2019 and October 2021 in the China-Japan Union Hospital of Jilin University. The HU of the vertebra was measured on the reconstructed CT images by 2 independent spine surgeons. The HU measurements of adjacent vertebrae and the ratio of HU measurements between the surgical vertebra and adjacent vertebrae were statistically analyzed to determine the best critical value and evaluate the prediction effectiveness and accuracy of the best critical value. Results: A total of 105 patients were identified with complete imaging and follow-up information. Of these, 47 patients (44.8%) had evidence of an adjacent vertebral fracture on follow-up imaging. The mean HU measurements of the fractured adjacent vertebra were significantly different from the mean HU measurements of the unfractured adjacent vertebra (50.94±20.59 vs. 81.74±18.97 HU; P<0.001). There was a significant difference in the ratio of HU measurements between the surgical vertebra and the fractured adjacent vertebra and between the surgical vertebra and the unfractured adjacent vertebra (26.34±17.52 vs. 14.53±9.40; P<0.001). Interactive scatter plots and receiver operating characteristic (ROC) curve showed that a HU measurement of 66.9 and a HU measurements ratio of 15.18 were the best thresholds for predicting the risk of fracture of adjacent vertebrae after PKP surgery, with an area under the curve (AUC) of 0.901 [95% confidence interval (CI): 0.822-0.953; P<0.001] and 0.874 (95% CI: 0.790-0.934; P<0.001), respectively. The prediction accuracy was 90.4% and 84.0%, respectively. Conclusions: A low mean HU measurements of adjacent vertebrae or a high ratio of the mean HU measurements of the operated vertebrae to the adjacent vertebrae are risk factors for the vulnerability of adjacent vertebrae to fracture. The risk of fracture in the adjacent vertebrae after PKP can be predicted by measuring HU.

The Clinical Characteristics, Risk Classification System, and Web-Based Nomogram for Primary Spinal Ewing Sarcoma: A Large Population-Based Cohort Study.

BACKGROUND: The goal of this study was to determine the clinical characteristics of patients with primary spinal Ewing sarcoma (PSES) and to create a prognostic nomogram. METHODS: Clinical information related to patients diagnosed with PSES between 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using univariate and multivariate Cox analyses to construct nomograms predicting overall survival in patients with PSES. Calibration curves and receiver operating characteristic curves were used to assess the model's prediction accuracy, while decision curve analysis was used to assess the model's clinical utility. RESULTS: The overall number of 314 patients with PSES were screened from the SEER database between 2004 and 2015. Race, chemotherapy, age, and disease stage were found to be independent predictive factors for overall survival in both univariate and multivariate Cox analyses. The training and validation cohorts' calibration curves, receiver operating characteristic curves, and decision curve analysis showed that the nomogram has strong discrimination and clinical value. Furthermore, a new risk classification system has been constructed that can divide all patients into 2 risk groups. CONCLUSIONS: Based on a broad population, the research demonstrates statistical evidence for the clinical features and prognostic variables of patients with PSES. The constructed prognostic nomogram provides a more precise prediction of prognosis for PSES patients.

Construction of a Tool to Predict Overall Survival of Patients With Primary Spinal Tumors After Surgical Resection: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database.

BACKGROUND: We aim to construct a practical clinical prediction model to accurately evaluate the overall survival (OS) of patients with primary spinal tumors after primary tumor resection, thereby aiding clinical decision-making. METHODS: A total of 695 patients diagnosed with a primary spinal tumor, selected from the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. The Cox regression algorithm was applied to the training cohort to build the prognostic nomogram model. The nomogram's performance in terms of discrimination, calibration, and clinical usefulness was also assessed in the internal SEER validation cohort. The fitted prognostic nomogram was then used to create a web-based calculator. RESULTS: Four independent prognostic factors were identified to establish a nomogram model for patients with primary spinal tumors who had undergone surgical resection. The C-index (.757 for the training cohort and .681 for the validation cohort) and the area under the curve values over time (both >.68) showed that the model exhibited satisfactory discrimination in both the SEER cohort. The calibration curve revealed that the projected and actual survival rates are very similar. The decision curve analysis also revealed that the model is clinically valuable and capable of identifying high-risk patients. CONCLUSIONS: After developing a nomogram and a web-based calculator, we were able to reliably forecast the postoperative OS of patients with primary spinal tumors. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions about patient care after surgery.

Early distinction of lymph node metastasis in patients with soft tissue sarcoma and individualized survival prediction using the online available nomograms: A population-based analysis.

Background: The presence of metastatic tumor cells in regional lymph nodes is considered as a significant indicator for inferior prognosis. This study aimed to construct some predictive models to quantify the probability of lymph node metastasis (LNM) and survival rate of patients with soft tissue sarcoma (STS) with LNM. Methods: Research data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017, and data of patients with STS from our medical institution were collected to form an external testing set. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for developing LNM. On the basis of the identified variables, we developed a diagnostic nomogram to predict the risk of LNM in patients with STS. Those patients with STS presenting with LNM were retrieved to build a cohort for identifying the independent prognostic factors through univariate and multivariate Cox regression analysis. Then, two nomograms incorporating the independent prognostic predictors were developed to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with STS with LNM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. Moreover, validations of these nomograms were performed by the receiver operating characteristic curves, the area under the curve, calibration curves, and the decision curve analysis (DCA). Results: A total of 16,601 patients with STS from the SEER database were enrolled in our study, of which 659 (3.97%) had LNM at the initial diagnosis. K-M survival analysis indicated that patients with LNM had poorer survival rate. Sex, histology, primary site, grade, M stage, and T stage were found to be independently related with development of LNM in patients with STS. Age, grade, histology, M stage, T stage, chemotherapy, radiotherapy, and surgery were identified as the independent prognostic factors for OS of patients with STS with LNM, and age, grade, M stage, T stage, radiotherapy, and surgery were determined as the independent prognostic factors for CSS. Subsequently, we constructed three nomograms, and their online versions are as follows: https://tyxupup.shinyapps.io/probabilityofLNMforSTSpatients/, https://tyxupup.shinyapps.io/OSofSTSpatientswithLNM/, and https://tyxupup.shinyapps.io/CSSofSTSpatientswithLNM/. The areas under the curve (AUCs) of diagnostic nomogram were 0.839 in the training set, 0.811 in the testing set, and 0.852 in the external testing set. For prognostic nomograms, the AUCs of 24-, 36-, and 48-month OS were 0.820, 0.794, and 0.792 in the training set and 0.759, 0.728, and 0.775 in the testing set, respectively; the AUCs of 24-, 36-, and 48-month CSS were 0.793, 0.777, and 0.775 in the training set and 0.775, 0.744, and 0.738 in the testing set, respectively. Furthermore, calibration curves suggested that the predicted values were consistent with the actual values. For the DCA, our nomograms showed a superior net benefit across a wider scale of threshold probabilities for the prediction of risk and survival rate for patients with STS with LNM. Conclusion: These newly proposed nomograms promise to be useful tools in predicting the risk of LNM for patients with STS and individualized survival prediction for patients with STS with LNM, which may help to guide clinical practice.

Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis.

Background: The prognosis of patients with primary osseous spinal neoplasms (POSNs) presented with distant metastases (DMs) is still poor. This study aimed to evaluate the independent risk and prognostic factors in this population and then develop two web-based models to predict the probability of DM in patients with POSNs and the overall survival (OS) rate of patients with DM. Methods: The data of patients with POSNs diagnosed between 2004 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistics regression analyses were used to study the risk factors of DM. Based on independent DM-related variables, we developed a diagnostic nomogram to estimate the risk of DM in patients with POSNs. Among all patients with POSNs, those who had synchronous DM were included in the prognostic cohort for investigating the prognostic factors by using Cox regression analysis, and then a nomogram incorporating predictors was developed to predict the OS of patients with POSNs with DM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. In addition, validation of these nomograms were performed by using receiver operating characteristic (ROC) curves, the area under curves (AUCs), calibration curves, and decision curve analysis (DCA). Results: A total of 1345 patients with POSNs were included in the study, of which 238 cases (17.70%) had synchronous DM at the initial diagnosis. K-M survival analysis and multivariate Cox regression analysis showed that patients with DM had poorer prognosis. Grade, T stage, N stage, and histological type were found to be significantly associated with DM in patients with POSNs. Age, surgery, and histological type were identified as independent prognostic factors of patients with POSNs with DM. Subsequently, two nomograms and their online versions (https://yxyx.shinyapps.io/RiskofDMin/ and https://yxyx.shinyapps.io/SurvivalPOSNs/) were developed. The results of ROC curves, calibration curves, DCA, and K-M survival analysis together showed the excellent predictive accuracy and clinical utility of these newly proposed nomograms. Conclusion: We developed two well-validated nomograms to accurately quantify the probability of DM in patients with POSNs and predict the OS rate in patients with DM, which were expected to be useful tools to facilitate individualized clinical management of these patients.

Clinical Characteristics, Prognostic Factor and a Novel Dynamic Prediction Model for Overall Survival of Elderly Patients With Chondrosarcoma: A Population-Based Study.

Background: Chondrosarcoma is the most common primary bone sarcoma among elderly population. This study aims to explore independent prognostic factors and develop prediction model in elderly patients with CHS. Methods: This study retrospectively analyzed the clinical data of elderly patients diagnosed as CHS between 2004 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. We randomly divided enrolled patients into training and validation group, univariate and multivariate Cox regression analyses were used to determine independent prognostic factors. Based on the identified variables, the nomogram was developed and verified to predict the 12-, 24-, and 36-month overall survival (OS) of elderly patients with CHS. A k-fold cross-validation method (k=10) was performed to validate the newly proposed model. The discrimination, calibration and clinical utility of the nomogram were assessed using the Harrells concordance index (C-index), receiver operating characteristic (ROC) curve and the area under the curve (AUC), calibration curve, decision curve analysis (DCA), the integrated discrimination improvement (IDI) and net reclassification index (NRI). Furthermore, a web-based survival calculator was developed based on the nomogram. Results: The study finally included 595 elderly patients with CHS and randomized them into the training group (419 cases) and validation group (176 cases) at a ratio of 7:3. Age, sex, grade, histology, M stage, surgery and tumor size were identified as independent prognostic factors of this population. The novel nomogram displayed excellent predictive performance, which can be accessible by https://nomoresearch.shinyapps.io/elderlywithCHS/, with a C-index of 0.800 for the training group and 0.789 for the validation group. The value AUC values at 12-, 24-, and 36-month of 0.866, 0.855, and 0.860 in the training group and of 0.839, 0.856, and 0.840 in the validation group, respectively. The calibration curves exhibited good concordance from the predicted survival probabilities to actual observation. The ROC curves, IDI, NRI, and DCA showed the nomogram was superior to the existing AJCC staging system. Conclusion: This study developed a novel web-based nomogram for accurately predicting probabilities of OS in elderly patients with CHS, which will contribute to personalized survival assessment and clinical management for elderly patients with CHS.

Construction, Validation, and Visualization of Two Web-Based Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Elderly Patients with Primary Osseous Spinal Neoplasms.

Background: Primary osseous spinal neoplasms (POSNs) are the rarest tumor type in the spine. Very few studies have presented data on elderly patients with POSNs specifically. The present study was aimed at exploring the prognostic factors and developing two web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for this population. Method: The data of elderly patients with POSNs was extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Cox regression analyses were performed to determine independent prognostic factors for OS and CSS, these prognostic factors were incorporated to establish nomograms. The discrimination of the nomograms was evaluated by the receiver operating characteristic (ROC) curve and the value of area under the curve (AUC). Calibration curve was plotted to assess the predictive accuracy of model. Decision curve analysis (DCA) was conducted to determine the net clinical benefit. Furthermore, two web-based survival rate calculators were developed. Result: A total of 430 patients were finally selected into this study and were randomly assigned to the training set (302 cases) and validation set (128 cases). Of these, 289 patients were further considered for the analysis of CSS and were randomized into training set (205 cases) and validation set (84 cases). Based on the results of univariate and multivariate Cox analyses, variables that significantly correlated with survival outcomes were used to establish nomograms for OS and CSS prediction. Two established nomograms demonstrated good predictive performance. In the training set, the AUCs of the nomogram for predicting 12-, 24-, and 36-month OS were 0.849, 0.903, and 0.889, respectively, and those for predicting 12-, 24-, and 36-month CSS were 0.890, 0.880, and 0.881, respectively. Two web-based survival rate calculators were developed to estimate OS (https://research1.shinyapps.io/DynNomappOS/) and CSS (https://research1.shinyapps.io/DynNomappCSS/). Conclusion: Novel nomograms based on identified clinicopathological factors were developed and can be used as a tool for clinicians to predict OS and CSS in elderly patients with POSNs. These models could help facilitate a personalized survival evaluation for this population.

Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System.

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

Comparison of Percutaneous Transforaminal Endoscopic Decompression and Transforaminal Lumbar Interbody Fusion in the Treatment of Single-Level Lumbar Disc Herniation with Modic Type I Changes.

BACKGROUND: Modic changes (MC) are generally considered to be related to degenerative disc disease, and there is no uniform standard for surgical methods for lumbar disc herniation (LDH) accompanied by Modic type I changes (MC I). The purpose of this study was to observe the clinical results of percutaneous transforaminal endoscopic decompression (PTED) and transforaminal lumbar interbody fusion (TLIF) for treatment of LDH accompanied by MC I. METHODS: Of the 53 consecutive patients included, 29 underwent PTED and 24 underwent TLIF. All patients were followed up for at least 24 months. Preoperative demographic characteristics, perioperative outcomes, and clinical outcomes were recorded. Visual analog scale (VAS) scores, Oswestry disability index (ODI) scores, and modified Macnab criteria were used to assess clinical results. RESULTS: The mean age was 53.7±9.2 years in the PTED group and 53.6±9.6 years in the TLIF group. The scores of VAS legs, VAS back and ODI in the two groups after operation were significantly improved compared with those before operation (P<0.05). Notably, the VAS back pain score and ODI in the PTED group showed an increasing trend with time. And the VAS back pain scores and ODI of the two groups were statistically different at 1 year and 2 years postoperatively (P<0.05). In addition, compared with the TLIF group, the PTED group showed less operation time, blood loss, and postoperative hospital stay (P<0.05). At the final follow-up, the excellent rates were 91.7% and 86.2% in the fusion and PTED groups, respectively. CONCLUSION: Both PTED and TLIF procedures significantly improved the clinical symptoms of single-level LDH patients with MC I. Compared with TLIF, MC I may affect the improvement of low back pain and functional status after PTED.

Comparison of percutaneous transforaminal endoscopic decompression and short-segment fusion in the treatment of elderly degenerative lumbar scoliosis with spinal stenosis.

BACKGROUND: Degenerative lumbar scoliosis (DLS) combined with spinal stenosis is increasingly being diagnosed in the elderly. However, the appropriate surgical approach remains somewhat controversial. The aim of this study was to compare the results of percutaneous transforaminal endoscopic decompression (PTED) and short-segment fusion for the treatment of mild degenerative lumbar scoliosis combined with spinal stenosis in older adults over 60 years of age. METHODS: Of the 54 consecutive patients included, 30 were treated with PTED and 24 were treated with short-segment open fusion. All patients were followed up for at least 12 months (12-24 months). Patient demographics, and perioperative and clinical outcomes were recorded. Visual analog scale (VAS) scores, Oswestry disability index (ODI) scores, and modified Macnab criteria were used to assess clinical outcomes. At the same time, changes in disc height, segmental lordosis, coronal Cobb angle, and lumbar lordosis were compared. RESULTS: The mean age was 68.7 ± 6.5 years in the PTED group and 66.6 ± 5.1 years in the short-segment fusion group. At 1 year postoperatively, both groups showed significant improvement in VAS and ODI scores compared with preoperative scores (p < 0.05), with no statistically significant difference between groups. However, VAS-Back and ODI were lower in the PTED group at 1 week postoperatively (p < 0.05). According to the modified Macnab criteria, the excellent rates were 90.0 and 91.6% in the PTED and short-segment fusion groups, respectively. However, the PTED group had a significantly shorter operative time, blood loss, postoperative hospital stay, postoperative bed rest, and complication rate. There was no significant difference in radiological parameters between the two groups preoperatively. At the last follow-up, there were significant differences in disc height, segmental lordosis at the L4-5 and L5-S1 levels, and Cobb angle between the two groups. CONCLUSION: Both PTED and short-segment fusion for mild degenerative lumbar scoliosis combined with spinal stenosis have shown good clinical results. PTED under local anesthesia may be an effective supplement to conventional fusion surgery in elderly patients with DLS combined with spinal stenosis.

Sites of Synchronous Distant Metastases, Prognosis, and Nomogram for Small Cell Lung Cancer Patients with Bone Metastasis: A Large Cohort Retrospective Study.

BACKGROUND: Small cell lung cancer (SCLC) is often associated with metastases at the time of diagnosis, and the bone is one of the most common sites. The primary aim of this study was to investigate the site of synchronous distant metastasis to other organs in SCLC patients with bone metastasis (BM) and develop a robust predictive prognostic model. METHODS: We retrospectively analyzed the data from patients diagnosed with SCLC with BM in the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. A prognostic nomogram was constructed and evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Then, according to the sites of metastasis and treatment modality, all patients were stratified into several subgroups. The relationship among sites of metastasis, treatment modality, and overall survival was then analyzed. RESULTS: A total of 6253 patients were included. Independent prognostic factors for SCLC with BM were age, sex, primary site, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and marital status. Calibration, ROC curves, and DCA indicated the excellent performance of the prognostic nomogram. The liver is the most common organ for extraskeletal metastases, followed by the lung. Patients with only BM had the longest mean survival time (9.30 ± 0.31 months). In the subgroup analysis, chemotherapy was an independent prognostic factor for all subgroups. In contrast, radiotherapy showed a positive effect on the prognosis of patients in all subgroups except those with bone and brain metastases and those with bone, lung, and brain metastases. CONCLUSIONS: The prognostic nomogram is expected to be an accurate and personalized tool for predicting the prognosis of SCLC patients with BM. Additionally, the determination of the sites of synchronous extraskeletal metastases and the associated prognosis helps in treatment selection.

Successful treatment of continuous ossification of the posterior longitudinal ligament in the lumbar spine using percutaneous transforaminal endoscopic spinal decompression: a case report.

Ossification of the posterior longitudinal ligament (OPLL) of the lumbar spine is rare relative to that of the cervical spine but is often associated with more severe symptoms. Continuous lumbar OPLL is extremely rare. We herein describe a 48-year-old Chinese woman with lumbar spinal stenosis caused by continuous OPLL. She presented with a 5-year history of lower back pain and intermittent claudication. We performed percutaneous transforaminal endoscopic decompression by the posterolateral approach to achieve adequate decompression of the spinal canal up to the lower 1/3 level (0.9 cm) of the L1 vertebral body and down to the upper 1/2 level (1.3 cm) of the L2 vertebral body. After surgery, the patient's neurological function substantially improved, and her visual analog scale scores for the lower back and both lower extremities and her Oswestry disability index were significantly lower than those in the preoperative period. During the 12-month clinical follow-up period, the patient's neurological function was fully restored, and she regained her ability to walk normally. No surgery-related complications were observed. This case report describes a novel surgical approach that may be an effective treatment alternative for continuous lumbar OPLL.