Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective.

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.

Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis.

INTRODUCTION: Brain metastasis (BM) is an aggressive complication with an extremely poor prognosis in patients with small-cell lung cancer (SCLC). A well-constructed prognostic model could help in providing timely survival consultation or optimizing treatments. METHODS: We analyzed clinical data from SCLC patients between 2000 and 2018 based on the Surveillance, Epidemiology, and End Results (SEER) database. We identified significant prognostic factors and integrated them using a multivariable Cox regression approach. Internal validation of the model was performed through a bootstrap resampling procedure. Model performance was evaluated based on the area under the curve (AUC) and calibration curve. RESULTS: A total of 2,454 SCLC patients' clinical data was collected from the database. It was determined that seven clinical parameters were associated with prognosis in SCLC patients with BM. A satisfactory level of discrimination was achieved by the predictive model, with 6-, 12-, and 18-month AUC values of 0.726, 0.707, and 0.737 in the training cohort; and 0.759, 0.742, and 0.744 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. Furthermore, prognostic scores were found to significantly alter the survival curves of different risk groups. We then deployed the prognostic model onto a website server so that users can access it easily. CONCLUSIONS: In this study, a nomogram and a web-based predictor were developed to predict overall survival in SCLC patients with BM. It may assist physicians in making informed clinical decisions and determining the best treatment plan for each patient.

Identification of a visualized web-based nomogram for overall survival prediction in patients with limited stage small cell lung cancer.

Small-cell lung cancer (SCLC) is an aggressive lung cancer subtype with an extremely poor prognosis. The 5-year survival rate for limited-stage (LS)-SCLC cancer is 10-13%, while the rate for extensive-stage SCLC cancer is only 1-2%. Given the crucial role of the tumor stage in the disease course, a well-constructed prognostic model is warranted for patients with LS-SCLC. The LS-SCLC patients' clinical data extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were reviewed. A multivariable Cox regression approach was utilized to identify and integrate significant prognostic factors. Bootstrap resampling was used to validate the model internally. The Area Under Curve (AUC) and calibration curve evaluated the model's performance. A total of 5463 LS-SCLC patients' clinical data was collected from the database. Eight clinical parameters were identified as significant prognostic factors for LS-SCLC patients' OS. The predictive model achieved satisfactory discrimination capacity, with 1-, 2-, and 3-year AUC values of 0.91, 0.88, and 0.87 in the training cohort; and 0.87, 0.87, and 0.85 in the validation cohort. The calibration curve showed a good agreement with actual observations in survival rate probability. Further, substantial differences between survival curves of the different risk groups stratified by prognostic scores were observed. The nomogram was then deployed into a website server for ease of access. This study developed a nomogram and a web-based predictor for predicting the overall survival of patients with LS-SCLC, which may help physicians make personalized clinical decisions and treatment strategies.

A visualized dynamic prediction model for overall survival in patients diagnosed with brain metastases from lung squamous cell carcinoma.

INTRODUCTION: Patients presenting with brain metastases (BMs) from lung squamous cell carcinoma (LUSC) often encounter an extremely poor prognosis. A well-developed prognostic model would assist physicians in patient counseling and therapeutic decision-making. METHODS: Patients with LUSC who were diagnosed with BMs between 2000 and 2018 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. Using the multivariate Cox regression approach, significant prognostic factors were identified and integrated. Bootstrap resampling was used to internally validate the model. An evaluation of the performance of the model was conducted by analyzing the area under the curve (AUC) and calibration curve. RESULTS: A total of 1812 eligible patients' clinical data was retrieved from the database. Patients' overall survival (OS) was significantly prognosticated by five clinical parameters. The nomogram achieved satisfactory discrimination capacity, with 3-, 6-, and 9-month AUC values of 0.803, 0.779, and 0.760 in the training cohort and 0.796, 0.769, and 0.743 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. There was also a substantial difference in survival curves between the different prognostic groups stratified by prognostic scores. For ease of access, the model was deployed on a web-based server. CONCLUSIONS: In this study, a nomogram and a web-based predictor were developed to assist physicians with personalized clinical decisions and treatment of patients who presented with BMs from LUSC.

Prognostic Nomogram for Overall Survival in Small Cell Lung Cancer Patients Treated with Chemotherapy: A SEER-Based Retrospective Cohort Study.

INTRODUCTION: Small cell lung cancer (SCLC) is known for its rapid clinical progression and poor prognosis. In this study, we sought to establish a prognostic nomogram among SCLC patients who received chemotherapy. METHODS: We obtained 4971 SCLC patients' clinical information from the Surveillance, Epidemiology, and End Results (SEER) database for the period between 2004 and 2015. Patients were divided into training and validation sets. Two nomograms were established based on limited stage (LS) and extensive stage (ES) SCLC patients to predict 1-, 2-, and 3-year overall survival (OS) incorporating superior parameters from multivariate Cox regression. Receiver-operating characteristic curves (ROCs) were applied to assess the discrimination ability of the nomogram while the calibration plots were applied to verify the model. Kaplan-Meier method was applied to find survival curves. Decision curve analysis (DCA) was applied to compare OS between the nomograms and 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system. RESULTS: Four and six clinical parameters were identified as significant prognostic factors for LS-SCLC and ES-SCLC patient's OS, respectively. The ROC curves indicated satisfactory discrimination capacity of the nomogram, with 1-, 2-, and 3-year area under curve (AUC) values of 0.89, 0.81, and 0.79 in LS-SCLC patients and 0.71, 0.66, and 0.66 in ES-SCLC patients, respectively. Calibration curves indicated that the nomogram showed good agreement with actual observations in survival rate probability. The survival curves among the LS-SCLC and ES-SCLC cohorts were consistent with the high-risk group having a worse prognosis than the low-risk group. Moreover, ROC and DCA curves showed our nomograms had more benefits than the 7th AJCC-TNM staging system. CONCLUSIONS: We established two nomograms that can present individual predictions of OS among LS-SCLC and ES-SCLC patients who received chemotherapy. These proposed nomograms may aid clinicians in treatment strategy and design of clinical trials.

Construction of a Prognostic Model in Lung Adenocarcinoma Based on Ferroptosis-Related Genes.

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma. Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observed that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score. Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes. Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways.

Prevalence and Mortality due to COVID-19 in HIV Co-Infected Population: A Systematic Review and Meta-Analysis.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) was defined as a species of beta coronavirus causing atypical respiratory disease in humans. The COVID-19 pandemic has resulted in an unprecedented health and economic crisis worldwide. Little is known about the specifics of its influence on people living with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) (PLWHA). In this study, we aim to investigate the prevalence and mortality in PLWHA co-infected with COVID-19. METHODS: The databases PUBMED, EMBASE, BioRxiv, and medRxiv were searched up to 9 March 2021 to explore the prevalence and mortality rate of COVID-19 in PLWHA. Cohort studies and case series meeting the inclusion criteria were included in this review. RESULTS: We identified 14 eligible studies, 9 of which were cohort and 5 were case series. A total of 203,761 patients with COVID-19 were identified (7718 PLWHA vs. 196,043 non-PLWHA). Meta-analyses estimated the prevalence and mortality rate of COVID-19 in PLWHA was 0.774% [95% confidence interval (CI) 0.00393-0.01517] and 8.814% (95% CI 0.05766-0.13245) respectively. COVID-19 co-infected PLWHA do not seem to be associated with higher mortality, as compared to non-PLWHA [relative risk (RR) 0.96 (95% CI 0.88-1.06)]. The presence of comorbidities such as diabetes mellitus, RR 5.2 (95% CI 4.25-6.36), hypertension and chronic cardiac disease, RR 4.2 (95% CI 1.09-16.10), and chronic kidney disease, RR 8.43 (95% CI 5.49-12.93) were associated with an increased mortality in COVID-19 co-infected PLWHA. CONCLUSION: The estimated prevalence and mortality rate of COVID-19 in PLWHA were 0.774% and 8.814%, respectively. Since most of the included studies used unmatched populations, comparisons between PLWHA and non-PLWHA should be interpreted with caution. Further investigations are needed for a more comprehensive understanding of the relationship between cluster of differentiation 4 cell count, HIV viral load, antiretroviral therapy, and COVID-19 related prognosis in PLWHA.

Pulmonary nocardiosis in a patient with pemphigus foliaceus: case report and literature review.

BACKGROUND: Nocardiosis is an uncommon opportunistic infection seen in immunocompromised patients or those with a dysfunctional immune system. Nocardia asteroides infection in patients with Pemphigus foliaceus (PF) has never been reported. CASE PRESENTATION: We report an interesting case of nocardiosis-characterized by pulmonary intra-cavitary infection, in a 54-year-old man with PF and diabetes mellitus. The man finally recovered from the infection. CONCLUSIONS: This is the first case reporting pulmonary nocardiosis in a patient with PF. We recommend that physicians be aware of nocardiosis in patients with pemphigus as a possible cause of underlying infectious disease to avoid misdiagnoses and mismanagement.

Efficacy and Safety of Opioids in Treating Cancer-Related Dyspnea: A Systematic Review and Meta-Analysis Based on Randomized Controlled Trials.

BACKGROUND: Dyspnea is one of the most distressing symptoms encountered by advanced cancer patients. In this study, we aimed to evaluate the role of opioids in the management of cancer-related dyspnea. METHODS: A systematic review and meta-analysis based on Randomized Controlled Trials was conducted in the databases PUBMED, EMBASE, and Cochrane Central Register of Controlled Trials testing the effect of opioids in relieving cancer-related dyspnea. Subgroup and sensitivity analyses were performed to evaluate various types of opioids in dyspnea management and stabilization of the study respectively. RESULTS: Eleven RCTs fulfilled the eligibility criteria and had a total of 290 participants. Nine of these studies were included in meta-analyses. Compared with control, opioid therapy showed a small positive effect in dyspnea, SMD-0.82 (95%CI = -1.54 to -0.10) and Borg score, WMD-0.95 (95%CI = -1.83 to -0.06); Opioid therapy did not increase the risk of somnolence, OR0.93 (95%CI = 0.34 to 2.58), whereas a negative effect on respiratory rate was observed,WMD-1.89 (95%CI = -3.36 to -0.43); Also, there was no evidence to suggest improved performance of the 6MWT test, WMD6.49 (95%CI = -34.23 to 47.21), or the level of peripheral oxygen saturation, WMD0.33 (95%CI = -0.59 to 1.24) after opioid therapy. Subgroup analysis yielded a small positive effect for morphine on dyspnea, SMD-0.78 (95%CI = -1.45 to -0.10), whereas fentanyl showed no improvement in dyspnea, SMD-0.44 (95%CI = -0.89 to 0.02). Sensitivity analysis showed no changes in the direction of effect when any one study was excluded from the meta-analyses. CONCLUSIONS: Our systematic review and meta-analysis indicated low quality evidence for a small positive effect of opioids in cancer-related dyspnea. Evidence for safety is insufficient as comprehensive adverse events were not adequately reported in studies.