A predictive model for early death in elderly patients with gastric cancer: A population-based study

Early death prediction model for breast cancer with synchronous lung metastases: an analysis of the SEER database

Background

Breast cancer with lung metastases (BCLM) is a serious condition that often leads to early death. This study aims to screen the risk factors of early death in BCLM patients and establish a simple and accurate nomogram prediction model. Identifying prognostic markers and developing accurate prediction models can help guide clinical decision-making.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was used to analyze a sizable sample of data, encompassing 4,238 BCLM patients diagnosed between 2010 and 2015. Stepwise regression was used to manage the collinearity of variables and to construct a prediction model based on the histogram. The results were subjected to internal validation and contrasted with those of related investigations.

Results

Of the 4,238 BCLM patients in this study, 3,232 did not die early. Of the 1,006 premature deaths, 891 were cancer specific. Lymph node involvement, tumor size, age, and race were all recognized as prognostic markers for premature mortality. A nomogram was constructed based on these factors to reliably predict cancer-specific death and early all-cause death.

Conclusions

This study gives new insights into the prognosis of individuals with BCLM and finds critical prognostic variables for early mortality. The created nomogram might assist physicians in identifying individuals at high risk of early mortality and making treatment options.

Comparison of the predictive performance of three lymph node staging systems for late-onset gastric cancer patients after surgery

Introduction

Lymph node (LN) status is a vital prognostic factor for patients. However, there has been limited focus on predicting the prognosis of patients with late-onset gastric cancer (LOGC). This study aimed to investigate the predictive potential of the log odds of positive lymph nodes (LODDS), lymph node ratio (LNR), and pN stage in assessing the prognosis of patients diagnosed with LOGC.

Methods

The LOGC data were obtained from the Surveillance, Epidemiology, and End Results database. This study evaluated and compared the predictive performance of three LN staging systems. Univariate and multivariate Cox regression analyses were carried out to identify prognostic factors for overall survival (OS). Three machine learning methods, namely, LASSO, XGBoost, and RF analyses, were subsequently used to identify the optimal LN staging system. A nomogram was built to predict the prognosis of patients with LOGC. The efficacy of the model was demonstrated through receiver operating characteristic (ROC) curve analysis and decision curve analysis.

Results

A total of 4,743 patients with >16 removed lymph nodes were ultimately included in this investigation. Three LN staging systems demonstrated significant performance in predicting survival outcomes (P < 0.001). The LNR exhibited the most important prognostic ability, as evidenced by the use of three machine learning methods. Utilizing independent factors derived from multivariate Cox regression analysis, a nomogram for OS was constructed.

Discussion

The calibration, C-index, and AUC revealed their excellent predictive performance. The LNR demonstrated a more powerful performance than other LN staging methods in LOGC patients after surgery. Our novel nomogram exhibited superior clinical feasibility and may assist in patient clinical decision-making.

A practical nomogram for predicting early death in elderly small cell lung cancer patients: A SEER-based study

This study aimed to identify risk factors for early death in elderly small cell lung cancer (SCLC) patients and develop nomogram prediction models for all-cause and cancer-specific early death to improve patient management. Data of elderly patients diagnosed with SCLC were extracted from the SEER database, then randomly divided into training and validation cohorts. Univariate and stepwise multivariable Logistic regression analyses were performed on the training cohort to identify independent risk factors for early death in these patients. Nomograms were developed based on these factors to predict the overall risk of early death. The efficacy of the nomograms was validated using various methods, including ROC analysis, calibration curves, DCA, NRI, and IDI. Among 2077 elderly SCLC patients, 773 died within 3 months, 713 due to cancer-specific causes. Older age, higher AJCC staging, brain metastases, and lack of surgery, chemotherapy, or radiotherapy increase the risk of all-cause early death, while higher AJCC staging, brain metastases, lung metastases, and lack of surgery, chemotherapy, or radiotherapy increase the risk of cancer-specific death (P < .05). These identified factors were used to construct 2 nomograms to predict the risk of early death. The ROC indicated that the nomograms performed well in predicting both all-cause early death (AUC = 0.823 in the training cohort and AUC = 0.843 in the validation cohort) and cancer-specific early death (AUC = 0.814 in the training cohort and AUC = 0.841 in the validation cohort). The results of calibration curves, DCAs, NRI and IDI also showed that the 2 sets of nomograms had good predictive power and clinical utility and were superior to the commonly used TNM staging system. The nomogram prediction models constructed in this study can effectively assist clinicians in predicting the risk of early death in elderly SCLC patients, and can also help physicians screen patients at higher risk and develop personalized treatment plans for them.

[Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma]

Objective

To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM).

Methods

The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020.

Results

According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models.

Conclusion

The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Post-Radiotherapy Exosomal Non-Coding RNA and Hemograms for Early Death Prediction in Patients with Cervical Cancer

Concurrent chemo-radiotherapy (CCRT) is linked with accelerated disease progression and early death (ED) in various cancers. This study aimed to assess the association of plasma levels of exosomal non-coding ribonucleic acid (RNA) (ncRNA) and blood cell dynamics with ED prediction in patients with cervical cancer undergoing CCRT. Using propensity score matching, a comparison of complete blood counts (CBCs) was performed among 370 CCRT-treated patients. Differences in ncRNA and messenger RNA (mRNA) expression before and after CCRT in 84 samples from 42 patients (cohort 2) were represented as logarithmic fold change (log2FC). Networks were constructed to link the CBCs to the RNAs whose expression correlated with ED. From the key RNAs selected using multiple regression of all RNA combinations in the network, CBC dynamics-associated ncRNAs were functionally characterized using an enrichment analysis. Cohort 1 (120 patients) exhibited a correlation between elevated absolute neutrophil counts (ANC) and ED. Cohort 2 exhibited a prevalence of microRNA (miR)-574-3p and long intergenic non-protein coding (LINC)01003 ncRNA, whose expression correlated with ANC and hemoglobin values, respectively. Conversely, acyl-coenzyme A thioesterase 9 (ACOT9) mRNA was relevant to all CBC components. An integrative analysis of post-CCRT ncRNA levels and CBC values revealed that the patients with miR-574-3p-LINC01003-ACOT9 log2FC) < 0 had a better prospect of 30-month disease-specific survival. These findings indicate that miR-574-3p and LINC01003 could serve as ED prognostic biomarkers.

Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study

Background: Owing to an aging society, there has been an observed increase in the average age of patients diagnosed with hepatocellular carcinoma (HCC). Consequently, this study is centered on identifying the prognostic factors linked with early death among this elderly demographic diagnosed with HCC. Additionally, our focus extends to developing nomograms capable of predicting such outcomes. Methods: The Surveillance, Epidemiology and End Results (SEER) database underpinned this study, showcasing participants aged 75 and above diagnosed with HCC within the timeframe from 2010 to 2015. These participants were divided randomly, at a 7:3 ratio, into training and validation cohorts. Univariable and multivariable logistic regressions were applied to the training cohort in the identification of prognostic indicators of early death, forming the basis for nomogram development. To measure the efficacy of these nomograms within both cohorts, we resorted to Receiver Operating Characteristic (ROC) curves, along with GiViTI calibration belt and Decision Curve Analysis (DCA). Results: The study involved 1,163 elderly individuals diagnosed with HCC, having reported instances of 397 all-cause early deaths and 356 HCC-specific early deaths. The sample group was divided into two cohorts: a training group consisting of 815 individuals, and a validation cohort, comprised of 348 individuals. Multifactorial analysis identified grade, T-stage, surgery, radiation, chemotherapy, bone and lung metastasis as significant predictors of mortality from all causes. Meanwhile, race, grade, T-stage, surgery, radiation, chemotherapy, and bone metastasis were revealed to be estimative factors for cancer-specific mortality. Subsequently, these factors were used to develop nomograms for prediction. GiViTI calibration belt corroborated the acceptable coherence of the nomograms, DCA confirmed their valuable clinical applicability, and ROC curves evidenced satisfactory discriminative capacity within both training and validation cohorts. Conclusion: The nomograms utilized in this study proved instrumental in detecting early death among elderly individuals afflicted with HCC. This tool could potentially assist physicians in formulating individualized treatment strategies.

Prognostic value of the preoperative albumin-bilirubin score among patients with stages I-III gastric cancer undergoing radical resection: A retrospective study

The albumin-bilirubin (ALBI) score was originally used to accurately assess liver dysfunction and predict the prognoses of patients with hepatocellular carcinoma. Following its more recent application to patients with gastrointestinal tumors, this study analyzed the prognostic value of the ALBI score in Chinese patients with advanced resectable (tumor-node-metastasis [TNM] stages I-III) gastric cancer (GC). This study investigated 1185 patients with advanced GC, including 429 with TNM stage I. The patients were divided into training and verifications groups (593 and 592 patients, respectively) in which these patients were classified as high risk (ALBI score ≥ -2.65) or low risk (ALBI score < -2.65). Univariate and multivariate Cox regression analyses were performed, and a visual survival prediction model (nomogram) was created. On Kaplan-Meier analysis, patients who were low-risk and high-risk according to their ALBI scores had significantly different survival rates in both the training and verification groups (p < 0.01). The difference was also significant when analyzing only patients with TNM stage I GC (p = 0.031). Univariate and multivariate analyses showed that the ALBI score (p = 0.014), age (p < 0.001), Nutritional Risk Screening 2002 score (p = 0.024), sarcopenia (p = 0.049), tumor differentiation (p = 0.027), and TNM stage (p < 0.001) were independent risk factors for survival. Our survival prediction model that incorporated the ALBI score accurately predicted the 5-year survival rate of Chinese patients with GC. Therefore, the ALBI score is a valid clinical indicator and good predictor of survival after surgery for progressive GC. Moreover, this score is simple to derive and does not burden patients with additional costs.