Establishment and validation of prognostic nomograms in first-line metastatic gastric cancer patients

Analysis of risk factors and development of a nomogram prediction model for renal tubular acidosis in primary Sjogren syndrome patients

OBJECTIVE

To investigate the risk factors of renal tubular acidosis (RTA) in patients with primary Sjögren's syndrome (pSS) and create a personalized nomogram for predicting pSS-RTA patients.

METHOD

Data from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis. In addition, we graded the severity of pSS-RTA patients and used univariate analysis to assess the relationship between pSS-RTA severity and risk factors.

RESULTS

A multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model's reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value.This study did not find an association between the severity of pSS-RTA and risk factors.

DISCUSSION

We developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients.

A prognostic nomogram to predict the cancer-specific survival of patients with initially diagnosed metastatic gastric cancer: a validation study in a Chinese cohort

BACKGROUND

Few studies have been designed to predict the survival of Chinese patients initially diagnosed with metastatic gastric cancer (mGC). Therefore, the objective of this study was to construct and validate a new nomogram model to predict cancer-specific survival (CSS) in Chinese patients.

METHODS

We collected 328 patients with mGC from Northern Jiangsu People's Hospital as the training cohort and 60 patients from Xinyuan County People's Hospital as the external validation cohort. Multivariate Cox regression was used to identify risk factors, and a nomogram was created to predict CSS. The predictive performance of the nomogram was evaluated using the consistency index (C-index), the calibration curve, and the decision curve analysis (DCA) in the training cohort and the validation cohort.

RESULTS

Multivariate Cox regression identified differentiation grade (P < 0.001), T-stage (P < 0.05), N-stage (P < 0.001), surgery (P < 0.05), and chemotherapy (P < 0.001) as independent predictors of CSS. Nomogram of chemotherapy regimens and cycles was also designed by us for the prediction of mGC. Thus, these factors are integrated into the nomogram model: the C-index value was 0.72 (95% CI 0.70-0.85) for the nomogram model and 0.82 (95% CI 0.79-0.89) and 0.73 (95% CI 0.70-0.86) for the internal and external validation cohorts, respectively. Calibration curves and DCA also demonstrated adequate fit and ideal net benefit in prediction and clinical applications.

CONCLUSIONS

We established a practical nomogram to predict CSS in Chinese patients initially diagnosed with mGC. Nomograms can be used to individualize survival predictions and guide clinicians in making therapeutic decisions.

Predicting postoperative delirium after percutaneous transluminal angioplasty and stenting in patients with intracranial atherosclerotic stenosis

OBJECTIVE

Known as a major surgical complication, postoperative delirium (POD) has not been well studied in patients with intracranial atherosclerotic stenosis (ICAS). This study aimed to investigate the correlation between perioperative clinical characteristics and the occurrence of POD.

METHODS

Patients' demographic characteristics and perioperative testing data were collected. Binary logistic regression was conducted for assessing related risk factors. A nomogram was developed to predict the occurrence of POD after percutaneous transluminal angioplasty and stenting (PTAS) in patients with ICAS.

RESULTS

The occurrence of POD in this study was 30.67%. Among all the clinical and laboratory characteristics in patients, age (OR = 1.234, 95%CI = 1.004-1.517, p = 0.046), gender (OR = 5.676, 95%CI = 1.028-31.334, p = 0.046), preoperative MMSE scores (OR = 2.298, 95%CI = 1.005-5.259, p = 0.049), the degree of stenosis (OR = 6.294, 95%CI = 1.043-37.974, p = 0.045), operating time (OR = 1.088, 95%CI = 1.023-1.157, p = 0.006), and HbA1c levels (OR = 2.226, 95%CI = 1.199-4.130, p = 0.011) were the independent risk factors.

CONCLUSION

Male patients with advanced-age, lower preoperative MMSE scores, severe stenosis, longer operating time, and higher HbA1c levels are closely related to POD after PTAS. Fully perioperative assessments may play an important role in predicting the occurrence of POD.

A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis

Background

The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system.

Methods

The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve.

Results

Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified.

Conclusion

The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.

A dynamic online nomogram for predicting the heterogeneity trajectories of frailty among elderly gastric cancer survivors

BACKGROUND

Frailty is very common among older people with gastric cancer and seriously affects their prognosis. The development of frailty is continuous and dynamic, increasing the difficulty and burden of care.

OBJECTIVES

The aims of this study were to delineate the developmental trajectory of frailty in older people with gastric cancer 1 year after surgery, identify heterogeneous frailty trajectories, and further explore their predictors to construct a nomogram for prediction.

DESIGN

We conducted a prospective longitudinal observation study. Clinical evaluation and data collection were performed at discharge, and at 1, 3, 6, and 12 months.

SETTING AND PARTICIPANTS

This study was conducted in a tertiary hospital and 381 gastric cancer patients over 60 years who underwent radical gastrectomy completed the 1-year follow-up.

METHODS

A growth mixture model (GMM) was used to delineate the frailty trajectories, and identify heterogeneous trajectories. A regression model was performed to determine their predictors and further construct a nomogram based on the predictors. Bootstrap with 1000 resamples was used for internal validation of nomogram, a receiver operating characteristic (ROC) curve to evaluate discrimination, calibration curves to evaluate calibration and decision curve analysis (DCA) to evaluate the clinical value.

RESULTS

GMM identified three classes of frailty trajectories: "frailty improving", "frailty persisting" and "frailty deteriorating". The latter two were referred to as heterogeneous frailty trajectories. Regression analysis showed 8 independent predictors of heterogeneous frailty trajectories and a nomogram was constructed based on these predictors. The area under ROC curve (AUC) of the nomogram was 0.731 (95 % CI = 0.679-0.781), the calibration curves demonstrated that probabilities predicted by the nomogram agreed well with the actual observation with a mean absolute error of 0.025, and the DCA of nomogram indicated that the net benefits were higher than that of the other eight single factors.

CONCLUSIONS

Older gastric cancer patients have heterogeneous frailty trajectories of poor prognosis during one-year postoperative survival. Therefore, early assessment to predict the occurrence of heterogeneous frailty trajectories is essential to improve the outcomes of elderly gastric cancer patients. Scientific and effective frailty interventions should be further explored in the future to improve the prognosis of older gastric cancer patients.

CONTRIBUTION OF THE PAPER STATEMENTS

This study constructed a static and dynamic online nomogram with good discrimination and calibration, which can help to screen high-risk patients, implement preoperative risk stratification easily and promote the rational allocation of medical resources greatly.

REGISTRATION

ClinicalTrials.gov (Number: NCT05982899).

TWEETABLE ABSTRACT

Our findings identified three frailty trajectories and constructed a nomogram to implement preoperative risk stratification and improve patient outcomes.

Prognostic value of patient reported outcomes in advanced gastro-oesophageal cancer: a systematic review

To summarise the prognostic value of patient-reported outcomes (PROs) in advanced gastro-oesophageal (GO) cancer. We systematically searched multiple databases using search terms related to advanced GO cancer, PRO and prognosis. Studies examining the relationship between baseline PROs and prognosis were included. Two reviewers independently screened articles and extracted data on study design, survival and associations between PROs and survival, in both univariable and multivariable analyses. QUIPS was used for quality assessment. From 3004 studies screened, seven studies were eligible, comprising PRO data from 2761 of 3408 (81%) participants. Median survival times ranged from 4.5 to 9.5 months. Among participants with oesophageal squamous cell carcinoma (SCC), physical functioning, social functioning and fatigue (QLQ-C30) were associated with overall survival (OS) in one univariable analysis. Among three studies of participants with adenocarcinoma, univariable analyses revealed associations between OS and global quality of life (QOL), physical functioning, role functioning and social functioning; two studies showed association with pain. There was an association between emotional functioning, fatigue, lack of mobility, lack of self-care, appetite loss/anorexia and OS in one study. One multivariable analysis among participants with oesophageal SCC showed physical and social functioning was associated with OS. Among participants with adenocarcinoma, multivariable analyses showed associations between OS and physical functioning/lack of mobility, appetite loss/anorexia (three studies), global QOL, role functioning/lack of self-care, pain (two studies) and social functioning (one study). Physical functioning, role functioning, social functioning, pain, anorexia and global QOL were associated with OS in advanced GO cancer.

Development of nomograms for predicting the survival of intestinal-type gastric adenocarcinoma patients after surgery

Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation.

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

Background

Health-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC.

Methods

A total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram's performance with and without the consideration of comorbidities.

Results

Four variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074-0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004-0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities.

Conclusions

This study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.

Nomogram for predicting survival of patients with gastric cancer and multiple primary malignancies: a real-world retrospective analysis using the Surveillance, Epidemiology and End Results database

OBJECTIVES

Gastric cancer combined with multiple primary malignancies (GCM) is increasingly common. This study investigated GCM clinical features and survival time.

METHODS

Patients with GCM and GC only (GCO) were selected from the Surveillance, Epidemiology and End Results (SEER) database. Survival was compared between GCM and GCO groups using propensity score matching. Then, the GCM group was divided into a training cohort and a validation cohort. These cohorts were used to establish a nomogram for survival prediction in patients with GCM.

RESULTS

Survival time was significantly longer in the GCM group than in the GCO group. All-subsets regression was used to identify four variables for nomogram establishment: age, gastric cancer sequence, N stage, and surgery. The concordance index and time-dependent receiver operating characteristic curve indicated that the nomogram had favorable discriminative ability. Calibration plots of predicted and actual probabilities showed good consistency in both the training and validation cohorts. Decision curve analysis and risk stratification showed that the nomogram was clinically useful; it had favorable discriminative ability to recognize patients with different levels of risk.

CONCLUSIONS

Compared with GCO, GCM is a relatively indolent malignancy. The nomogram developed in this study can help clinicians to assess GCM prognosis.

The AGAMENON-SEOM model for prediction of survival in patients with advanced HER2-positive oesophagogastric adenocarcinoma receiving first-line trastuzumab-based therapy

Background

Trastuzumab and chemotherapy is the standard first-line treatment in human epidermal growth factor receptor 2 (HER2)-positive advanced gastro-oesophageal cancer. The objective was to develop a predictive model for overall survival (OS) and progression-free survival (PFS) in patients treated with trastuzumab.

Methods

Patients with HER2-positive advanced gastro-oesophageal adenocarcinoma (AGA) from the Spanish Society of Medical Oncology (SEOM)-AGAMENON registry and treated first line with trastuzumab and chemotherapy between 2008 and 2021 were included. The model was externally validated in an independent series (The Christie NHS Foundation Trust, Manchester, UK).

Results

In all, 737 patients were recruited (AGAMENON-SEOM, n = 654; Manchester, n = 83). Median PFS and OS in the training cohort were 7.76 [95% confidence interval (CI), 7.13-8.25] and 14.0 months (95% CI, 13.0-14.9), respectively. Six covariates were significantly associated with OS: neutrophil-to-lymphocyte ratio, Eastern Cooperative Oncology Group performance status, Lauren subtype, HER2 expression, histological grade and tumour burden. The AGAMENON-HER2 model demonstrated adequate calibration and fair discriminatory ability with a c-index for corrected PFS/OS of 0.606 (95% CI, 0.578-0.636) and 0.623 (95% CI, 0.594-0.655), respectively. In the validation cohort, the model is well calibrated, with a c-index of 0.650 and 0.683 for PFS and OS, respectively.

Conclusion

The AGAMENON-HER2 prognostic tool stratifies HER2-positive AGA patients receiving trastuzumab and chemotherapy according to their estimated survival endpoints.

Prognostic Nomogram Combining Preoperative Neutrophil to Lymphocyte Ratio and Clinicopathologic Features for Gastric Cancer Patients after Distal Radical Gastrectomy: Based on Propensity Score Matching

Background: Preoperative inflammatory status has been widely used in assessing the prognosis of malignant tumor. This study aimed to establish a novel nomogram combining preoperative inflammatory factors and clinicopathologic features to predict the prognosis of gastric cancer (GC) patients after distal radical gastrectomy. Methods: A total of 522 GC patients from Fujian Provincial Hospital were retrospectively reviewed. Propensity score matching was performed and Cox regression models were used to analyze the clinical and pathological factors to determine their impact on survival. A prognostic nomogram was established and validated based on these factors. Results: The multivariate analysis indicated that tumor stage, pathological type, and neutrophil to lymphocyte ratio (NLR) were independent risk factors for the prognosis of GC patients. The nomogram was established based on these factors. In the primary cohort, the concordance index (C-index) of the nomogram was 0.753 (95% CI 0.647-0.840), which was higher than that of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage. The calibration curve showed the actual overall survival (OS) probabilities were in good keeping with those predicted by the nomogram. Furthermore, we divided the patients into two distinct risk groups for OS according to the nomogram points: low and high risk. The OS rates were significantly different among the subgroups (p ˂ 0.001). Conclusions: We proposed a novel nomogram combining preoperative NLR and clinicopathologic features that is economical, routinely available, and highly predictive of OS in GC patients after distal radical gastrectomy. Compared with the current AJCC TNM staging, this model was more accurate in prognostic prediction.

Low-grade hepatocellular carcinoma characteristics, a practical nomogram and risk stratification system: a SEER population-based study

BACKGROUND

The purpose of this study is to establish a nomogram and risk stratification system to predict OS in patients with low-grade HCC.

RESEARCH DESIGN AND METHODS

Data were extracted from the SEER database. C-index, time-dependent AUCs, and calibration plots were used to evaluate the effective performance of the nomogram. NRI, IDI, and DCA curves were adopted to compare the clinical utility of nomogram with AJCC.

RESULTS

3415 patients with low-grade HCC were available. The C-indices for the training and validation cohorts were 0.773 and 0.772. The time-dependent AUCs in the training cohort were 0.821, 0.817, and 0.846 at 1, 3 and 5 years. Calibration plots for 1-, 3- and 5-year OS showed good consistency between actual observations and that predicted by the nomogram. The values of NRI at 1, 3, and 5 years were 0.37, 0.66, and 0.64. The IDI values at 1, 3, and 5 years were 0.11, 0.16, and 0.23 (P< 0.001). DCA curves demonstrated that the nomogram showed better ability of predicting 1-, 3-, and 5-year OS probabilities than AJCC.

CONCLUSIONS

A nomogram and risk stratification system for predicting OS in patients with low-grade HCC were established and validated.

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

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Nomograms constructed by overall survival and cancer-specific survival can predict more accurately than AJCC stage system for GCLM patients.

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The study includes the prognostic factor as many as possible and evaluated all of them in the cohort.

•

In our cohort, surgery is a beneficial factor associated with survival.

Background

Gastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.

Methods

From January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.

Results

There was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.

Conclusion

Both nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.

Development of a Risk Nomogram Model for Identifying Interstitial Lung Disease in Patients With Rheumatoid Arthritis

The clinical features of rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) (RA-ILD) usually manifest to an advanced stage of lung disease, which leads the challenge of early diagnosis and the difficulty in guiding treatments for patients with RA-ILD in clinical settings. The aim of this study was to construct a nomogram for identifying ILD in RA patients. Through the incorporation of the level of matrix metalloproteinase-3 (MMP-3) in plasma, demographics, clinical feature, and laboratory parameters of 223 RA patients (85 RA-ILD) which were grouped as training cohorts and validation cohorts, an identifying nomogram of RA-ILD was built. Candidate variables for the nomogram were screened using univariable analysis and multivariable logistic regression analysis. The accuracy of the diagnostic nomogram was measured via concordance index (C-index), calibration plots, and decision curve analysis (DCA). Results showed that plasma MMP-3 protein was elevated in RA-ILD patients compared with non-ILD RA patients in both training cohorts (p = 0.0475) and validation cohorts (p = 0.0006). Following a final regression analysis, the gender of male, current smoking state, levels of circulating rheumatoid factor (RF), C-reactive protein (CRP), and MMP-3 were identified as risk factors for the construction of the nomogram. The calibration plots further showed a favorable consistency between the identifying nomogram and actual clinical findings. In consistence, the C-index (0.826 for both training cohorts and validation cohorts) indicated the satisfactory discriminative ability of the nomogram. Although the incorporation of MMP-3 failed to significantly improve identified outcomes of the nomogram as determined by DCA, including the level of circulating MMP-3 increased the diagnostic accuracy of the nomogram for ILD in RA patients. Thus, our proposed model can serve as a non-invasive tool to identify ILD in RA patients, which may assist physicians to make treatment decisions for RA patients.

A Novel Nomogram for Prediction and Evaluation of Lymphatic Metastasis in Patients With Renal Cell Carcinoma

Background

Lymphatic metastasis is an important mechanism of renal cell carcinoma (RCC) dissemination and is an indicator of poor prognosis. Therefore, we aimed to identify predictors of lymphatic metastases (LMs) in RCC patients and to develop a new nomogram to assess the risk of LMs.

Methods

This study included patients with RCC from 2010 to 2018 in the Surveillance, Epidemiology, and Final Results (SEER) database into the training cohort and included the RCC patients diagnosed during the same period in the Second Affiliated Hospital of Dalian Medical University into the validation cohort. Univariate and multivariate logistic regression analysis were performed to identify risk factors for LM, constructing a nomogram. The receiver operating characteristic (ROC) curves were generated to assess the nomogram’s performance, and the concordance index (C-index), area under curve value (AUC), and calibration plots were used to evaluate the discrimination and calibration of the nomogram. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA), probability density function (PDF) and clinical utility curve (CUC). Furthermore, Kaplan-Meier curves were performed in the training and the validation cohort to evaluate the survival risk of the patients with lymphatic metastasis or not. Additionally, on the basis of the constructed nomogram, we obtained a convenient and intuitive network calculator.

Results

A total of 41837 patients were included for analysis, including 41,018 in the training group and 819 in the validation group. Eleven risk factors were considered as predictor variables in the nomogram. The nomogram displayed excellent discrimination power, with AUC both reached 0.916 in the training group (95% confidence interval (CI) 0.913 to 0.918) and the validation group (95% CI 0.895 to 0.934). The calibration curves presented that the nomogram-based prediction had good consistency with practical application. Moreover, Kaplan-Meier curves analysis showed that RCC patients with LMs had worse survival outcomes compared with patients without LMs.

Conclusions

The nomogram and web calculator (https://liwenle0910.shinyapps.io/DynNomapp/) may be a useful tool to quantify the risk of LMs in patients with RCC, which may provide guidance for clinicians, such as identifying high-risk patients, performing surgery, and establishing personalized treatment as soon as possible.

Development and Validation of a Nomogram for Predicting Overall Survival in Patients with Second Primary Small Cell Lung Cancer After Non-Small Cell Lung Cancer: A SEER-Based Study

Background

Non-small cell lung cancer (NSCLC) survivors are at an increased risk of developing second primary malignancies, such as small cell lung cancer. This paper sought to establish a prognostic nomogram to assess overall survival (OS) in patients with second primary small cell lung cancer (SPSCLC) after NSCLC.

Methods

420 patients who developed SPSCLC after NSCLC were randomly split into the training and validation groups. A nomogram was established by stepwise regression. Area under the curve (AUC) and calibration plots were applied to assess the prognostic performance of the nomogram. Concordance index (C-index), integrated discrimination improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA) were performed to compare the nomogram with the American Joint Committee on Cancer (AJCC) 8th staging system. Survival risk classification was constructed based on the nomogram.

Results

Five variables were chosen to construct the nomogram. The AUC showed that it had a satisfactory discrimination ability. All calibration plots displayed good concordance between nomogram and observation. The C-index, IDI, NRI and DCA showed the nomogram was superior to the AJCC 8th staging system. The Kaplan-Meier curves suggested huge differences in prognosis among the three risk groups.

Conclusion

This study build a nomogram and risk stratification system for predicting probabilities of OS in patients with SPSCLC after NSCLC, which can help clinicians in individualized survival assessment and treatment decisions.

Influence of Baseline Positron Emission Tomography in Metastatic Gastroesophageal Cancer on Survival and Response to Therapy

BACKGROUND

The value of baseline fluorodeoxyglucose-positron emission tomography-computed tomography (PET-CT) remains uncertain once gastroesophageal cancer is metastatic. We hypothesized that assessment of detailed PET-CT parameters (maximum standardized uptake value [SUVmax] and/or total lesion glycolysis [TLG]), and the extent of metastatic burden could aid prediction of probability of response or prognosticate.

METHODS

We retrospectively analyzed treatment-naive patients with stage 4 gastroesophageal cancer (December 2002-August 2017) who had initial PET-CT for cancer staging at MD Anderson Cancer Center. SUVmax and TLG were compared with treatment outcomes for the full cohort and subgroups based on metastatic burden (≤2 or >2 metastatic sites).

RESULTS

We identified 129 patients with metastatic gastroesophageal cancer who underwent PET-CT before first-line therapy. The median follow-up time was 61 months. The median overall survival (OS) was 18.5 months; the first progression-free survival (PFS) was 5.5 months. SUVmax or TLG of the primary tumor or of all metastases combined had no influence on OS or PFS, whether the number of metastases was ≤2 or >2. Overall response rates (ORRs) to first-line therapy were 48% and 45% for patients with ≤2 and >2 metastases, respectively (nonsignificant). ORR did not differ based on low or high values of SUVmax or TLG.

CONCLUSIONS

This is the first assessment of a unique set of PET-CT data and its association with outcomes in metastatic gastroesophageal cancer. In our large cohort of patients, detailed analyses of PET-CT (by SUVmax and/or TLG) did not discriminate any parameters examined. Thus, baseline PET-CT in untreated metastatic gastroesophageal cancer patients has limited or no utility.

The incidence, risk factors and predictive nomograms for early death among patients with stage IV gastric cancer: a population-based study

Background

Although advances in the treatment of stage IV gastric cancer (GC) patients, some patients were observed to die within 3 months of initial diagnosis. The present study aimed to explore the early mortality and risk factors for stage IV GC and further develop nomograms.

Methods

A total of 2,174 eligible stage IV GC patients were selected from the Surveillance, Epidemiology, and End Results database. Logistic regression analyses were used to determine the risk factors and develop the nomograms to predict all-cause early death and cancer-specific early death. The predictive performance of the nomograms was assessed by receiver operating characteristic curves (ROC), calibration plots and decision curve analyses (DCA) in both training and validation cohorts.

Results

Of 2,174 patients enrolled, 708 died within 3 months of initial diagnosis (n=668 for cancer-specific early death). Early mortality remained stable from 2010-2015. Non-Asian or Pacific Islander (API) race, poorer differentiation, middle sites of the stomach, no surgery, no radiotherapy, no chemotherapy, lung metastases and liver metastases were associated with high risk of both all-causes early death and cancer-specific early death. The nomograms constructed based on these factors showed favorable sensitivity, with the area under the ROC range of 0.816-0.847. The calibration curves and DCAs also exhibited adequate fit and ideal net benefit in prediction and clinical application.

Conclusions

Approximately one-third of stage IV GC patients experienced early death. These associated risk factors and predictive nomograms may help clinicians identify the patients at high risk of early death and be the reference for treatment choices.

Prognostic nomogram for predicting survival in patients with high grade endometrial stromal sarcoma: a Surveillance Epidemiology, and End Results database analysis

OBJECTIVE

High grade endometrial stromal sarcoma is a rare and highly malignant tumor that lacks a prognostic model. The aim of this study was to develop a prognostic nomogram predicting the overall survival of patients with high grade endometrial stromal sarcoma.

METHODS

Clinical data for patients were derived from the Surveillance Epidemiology, and End Results database. Cox analysis and Akaike's information criterion were used to construct the nomogram. The concordance index, time dependent receiver operating characteristic curve, and calibration plot were used to evaluate the discriminative and calibrating capability. The net reclassification index, integrated discrimination improvement, and concordance index change were also compared between the nomogram and the International Federation of Gynecology and Obstetrics (FIGO) stage. Clinical benefit was evaluated using decision curve analysis. The patients were separated into groups with low and high nomogram risk scores. Kaplan-Meier curve analysis and Cox analysis were used to investigate the survival difference between the two groups.

RESULTS

The training and validation cohorts had 461 and 195 patients, respectively. A nomogram that incorporated disease stage, age, surgery, lymph node status, radiotherapy, and chemotherapy for predicting overall survival was established and validated. The concordance index of the nomogram was 0.734 (0.708-0.761) in the training cohort and 0.705 (0.659-0.751) in the validation cohort. The calibration plots showed a favorable calibrating ability of the nomogram. The 1 year and 3 year time dependent receiver operating characteristic curves showed the better discriminative ability of the nomogram than the staging system. The concordance index change, net reclassification index, and integrated discrimination improvement also indicated a significantly (p<0.05) better predictive power of the nomogram over disease stage. Furthermore, decision curve analysis suggested that the nomogram was clinically useful and had a larger clinical net benefit than disease stage alone. Patients with a high risk score had distinctly poorer survival than those with low risk scores.

CONCLUSIONS

A prognostic nomogram in patients with high grade endometrial stromal sarcoma exhibited favorable prognostic discrimination and survival prediction ability compared with FIGO stage.

Practical guidance for the evaluation of disease progression and the decision to change treatment in patients with advanced gastric cancer receiving chemotherapy

After failure of first-line chemotherapy with fluoropyrimidines and platinum compounds for advanced gastric cancer, second-line chemotherapy with ramucirumab plus paclitaxel, which elicits a durable response, and third-line or later chemotherapy with nivolumab have been shown to lead to a more favorable prognosis in advanced gastric cancer patients. As new and more effective drugs are now available, sequential chemotherapy would contribute to prolonged survival. From this point of view, the patient's disease course should be frequently monitored in order to adapt treatment regimens. This review summarizes the points to note in regard to radiological assessment, and discusses the integration of prognostic factors, tumor markers, and clinical symptoms that need to be taken into account to change treatment at an appropriate timing.

Development and internal validation of a diagnostic score for gastric linitis plastica

BACKGROUND

There is no consensual definition for gastric linitis plastica (GLP). We aim to construct a diagnostic score to distinguish this rare tumor from usual gastric adenocarcinomas.

METHODS

In this retrospective study, all patients who had gastrectomy for cancer between 2007 and 2017 in French tertiary centers were included. The outcome was a diagnosis of GLP based on pathological review of the surgical specimen. The diagnostic score was created by using variables that were most frequently associated with GLP using penalized logistic regression on multiply imputed datasets. We used discrimination measures to assess the performances of the score. Internal validation was performed using bootstrapping methods to correct for over-optimism.

RESULTS

220 patients including 71 linitis plastica (female 49%, median age 57 years) were analyzed. The six parameters retained in the diagnosis score were the presence of large folds and/or parietal thickening on at least one segment, pangastric infiltration and presence of gastric stenosis on the upper endoscopy, circumferential thickening on at least one segment and thickening of the third hyperechogenic layer on endoscopic ultrasound and the presence of signet ring cells on endoscopic biopsies. The area under the ROC curve (AUC) was 0.967 with a sensitivity of 94% [89.9-97.3] and a specificity of 88.7% [81.7-95.8] for a threshold of 2.75. After internal validation, the corrected AUC was 0.959.

CONCLUSION

It is the first study validating a pre-therapeutic diagnostic score (Saint Louis linitis score) with an excellent ability to discriminate GLP from non-GLP adenocarcinomas. An external validation is necessary to confirm our data.

A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis

Background

Low‐grade endometrial stromal sarcoma (LG‐ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG‐ESS patients.

Methods

A total of 1172 patients confirmed to have LG‐ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C‐index), area under time‐dependent receiver operating characteristic curve (time‐dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria‐based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria‐based tumor staging. The risk stratifications of the nomogram and the FIGO criteria‐based tumor staging were compared.

Results

Seven variables were selected to establish the nomogram for LG‐ESS. The C‐index (0.814 for the training cohort and 0.837 for the validation cohort) and the time‐dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5‐year and 0.433 for 10‐year OS prediction; validation cohort: 0.310 for 5‐year and 0.383 for 10‐year OS prediction) and IDI (training cohort: 0.146 for 5‐year and 0.185 for 10‐year OS prediction; validation cohort: 0.177 for 5‐year and 0.191 for 10‐year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria‐based tumor staging alone (P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria‐based tumor staging.

Conclusions

A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG‐ESS patients.

A Novel Nomogram Integrated with Inflammation-Based Factors to Predict the Prognosis of Gastric Cancer Patients

INTRODUCTION

Gastric cancer (GC) is the fifth most common cancer worldwide, and every year approximately 950,000 individuals are diagnosed worldwide. Our study aimed to establish an effective nomogram to predict the prognosis of GC based on inflammation biomarkers.

METHODS

We retrospectively analysed GC patients from the Sun Yat-sen University Cancer Center. The nomogram was developed with a primary cohort (n = 1067), and 537 patients were included in the validation cohort. The univariate survival analyses included 19 biomarkers.

RESULTS

The multivariate analysis showed that tumour stage, metastasis stage and C-reactive protein (CRP), albumin (ALB), carcinoembryonic antigen (CEA) and carbohydrate antigen-199 (CA199) levels as well as the lymphocyte (LYM) count were independent risk factors for the prognosis of GC patients. The nomogram was based on the above factors. In the primary cohort, the nomogram had a concordance index (C-index) of 0.825 (95% CI 0.796-0.854), which was higher than the C-index of the AJCC TNM stage and that of the other biomarkers (CEA and CA199). The calibration plot suggested good agreement between the actual and nomogram-predicted overall survival (OS) probabilities, and the decision curve analyses showed that the nomogram model had a higher overall net benefit in predicting OS than the AJCC TNM stage. Moreover, we divided the patients into the following three distinct risk groups for OS based on the nomogram points: low, middle and high risk. The differences in OS rates were significant among the subgroups (P < 0.001).

CONCLUSION

A novel nomogram integrated with inflammatory prognostic factors was proposed, which is highly predictive of OS in GC patients.

The expression of epidermal growth factor receptor 1 and human epidermal growth factor receptor 2 based on tumor location affect survival in gastric cancer

To investigate the different expression of epidermal growth factor receptor 1 (EGFR) and human epidermal growth factor receptor 2 (HER2) in gastric cancer based on tumor locations and its impact on patients survival.Gastric cancer is heterogeneous disease, recent years have established a molecular classification and described distribution of molecular subtypes in stomach. However, the difference of EGFR and HER-2 expression among tumor location is still unknown.Between January 2010 and August 2014, 2477 consecutive patients with gastric cancer were treated in our surgery department. The tumor locations were classified into 4 groups: cardia, fundus, corpus, and antrum. Based on tumor locations, the clinicopathologic characteristics, EGFR and HER-2 expression, and follow-up data were analyzed by univariant analysis and Kaplan-Meier analysis retrospectively.There were difference of gender, age, Borrmann type, pathological type, differentiation, T-stage, tumor size, gastrectomy method, and complications among the locations. The positive rate of EGFR expression in fundus was 18.18%, which was lower than cardia (46.21%), corpus (43.62%), and antrum (48.83%) (P < .001). The 5-year survival rate in EGFR positive patients was 50.8%, which was significantly lower than EGFR negative patients (64.0%, P = .021). The positive rate of HER-2 expression in cardia was 48.15%, which was significantly higher than fundus (37.5%), corpus (35.45%), and antrum (38.54%) (P = .009), but HER-2 expression did not correlate with 5-year survive (P = .548).Our results suggest that there exist difference of EGFR and HER-2 expression based on tumor locations, and the distribution of EGFR impact on patients survival. Emphasizing the role of EGFR and HER-2 in the context of location contribute to make appropriate treatment strategy and improve prognosis of gastric cancer.

Nomogram-based prediction of survival in unresectable or metastatic gastric cancer patients with good performance status who received first-line chemotherapy

Background

Good performance status (PS) is widely acknowledged to have a high prognostic ability, although the prognostic parameters of cancer patients with good PS are still uncertain. This study was conducted to establish and validate a point-based nomogram to assist with predicting prognosis in unresectable or metastatic gastric cancer (GC) patients who had good PS and underwent first-line chemotherapy.

Methods

At random, a total of 309 patients with GC were split into 2 cohorts: a training cohort (n=259) and an internal validation cohort (n=50). An independent external validation cohort comprising 147 patients was also recruited. Both univariate and multivariate Cox regression analyses were used to evaluate patients based on the overall survival (OS) to develop the nomogram, which was subsequently validated using the concordance index (c-index), calibration curve, and decision curve analysis (DCA).

Results

The nomogram contained 3 independent prognostic variables in the training cohort: the number of distant metastatic sites (P<0.001), carbohydrate antigen 199 (CA199) level (P=0.002), and fibrinogen (P=0.020). The nomogram predicted an OS with a c-index of 0.623 (95% CI, 0.58–0.67) in the training cohort. The internal validation showed that the nomogram had a c-index of 0.614 (95% CI, 0.51–0.72). For external validation, the c-index was 0.638 (95% CI, 0.58–0.70).

Conclusions

A reliable point-based nomogram for predicting the prognosis of patients who had unresectable or metastatic GC and good PS who underwent first-line chemotherapy was developed and validated.

Keywords

Nomogram-based prediction; overall survival; unresectable gastric cancer; metastatic gastric cancer; good performance status; first-line chemotherapy

Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review

Background

This study was designed to review the methodology and reporting of gastric cancer prognostic models and identify potential problems in model development.

Methods

This systematic review was conducted following the CHARMS checklist. MEDLINE and EMBASE were searched. Information on patient characteristics, methodological details, and models' performance was extracted. Descriptive statistics was used to summarize the methodological and reporting quality.

Results

In total, 101 model developments and 32 external validations were included. The median (range) of training sample size, number of death, and number of final predictors were 360 (29 to 15320), 193 (14 to 9560), and 5 (2 to 53), respectively. Ninety-one models were developed from routine clinical data. Statistical assumptions were reported to be checked in only nine models. Most model developments (94/101) used complete-case analysis. Discrimination and calibration were not reported in 33 and 55 models, respectively. The majority of models (81/101) have never been externally validated. None of the models have been evaluated regarding clinical impact.

Conclusions

Many prognostic models have been developed, but their usefulness in clinical practice remains uncertain due to methodological shortcomings, insufficient reporting, and lack of external validation and impact studies.

Impact

Future research should improve methodological and reporting quality and emphasize more on external validation and impact assessment.

Developing a Nomogram for Risk Prediction of Severe Hand-Foot-and-Mouth Disease in Children

OBJECTIVE

Early recognition of children with severe Hand-Foot-and-Mouth disease (HFMD) is especially important, as severe cases are associated with poor prognosis. To accomplish this, authors designed a quantitative assessment tool to build a nomogram to assist in clinical diagnosis.

METHODS

A total of 2332 HFMD patients were enrolled in this study; 1750 cases in the mild group and 582 cases in the severe group. Analysis of all of the data was performed using R software version 3.4.3. Multivariate logistic regression was utilized to screen predictors to construct a nomogram model. Finally, predictive performance of the model was evaluated using a receiver operating characteristic (ROC) curve and classifier calibration plot.

RESULTS

A nomogram was constructed with five variables: age, peak temperature, fever duration, pathogen, and vomiting. For the nomogram, the area under the curve was 0.87, and the model prediction accuracy rate was 85.2%. Depending upon the comparison of the area under the ROC curve, the nomogram model was superior to the traditional pediatric clinical illness score (PCIS). With the help of the Hosmer-Lemeshow test and resampling model calibration curve, the fitting performance of the nomogram was stable.

CONCLUSIONS

With advantages such as simplicity, intuitiveness, and practicality, the nomogram (including age, peak temperature, fever duration, pathogen, and vomiting) is capable of predicting severe HFMD and has certain auxiliary value in clinical applications.

Nomogram application to predict overall and cancer-specific survival in osteosarcoma

Purpose

A prognostic nomogram was applied to predict survival in osteosarcoma patients.

Patients and methods

Data collected from 2,195 osteosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database between 1983 and 2014 were analyzed. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These were incorporated into a nomogram to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Internal and external data were used for validation. Concordance indices (C-indices) were used to estimate nomogram accuracy.

Results

Patients were randomly assigned into a training cohort (n=1,098) or validation cohort (n=1,097). Age at diagnosis, tumor site, histology, tumor size, tumor stage, use of surgery, and tumor grade were identified as independent prognostic factors via univariate and multivariate Cox analyses (all P<0.05) and then included in the prognostic nomogram. C-indices for OS and CSS prediction in the training cohort were 0.763 (95% CI 0.761–0.764) and 0.764 (95% CI 0.762–0.765), respectively. C-indices for OS and CSS prediction in the external validation cohort were 0.739 (95% CI 0.737–0.740) and 0.740 (95% CI, 0.738–0.741), respectively. Calibration plots revealed excellent consistency between actual survival and nomogram prediction.

Conclusion

Nomograms were constructed to predict OS and CSS for osteosarcoma patients in the SEER database. They provide accurate and individualized survival prediction.

Serotonin and YAP/VGLL4 Balance Correlated with Progression and Poor Prognosis of Hepatocellular Carcinoma

YAP-TEAD complex plays an important role in tumorigenesis. 5-HT is proved to upregulate YAP expression by our previous study and VGLL4 is found to compete with YAP for binding to TEAD in several of cancers. Here, we investigated whether 5-HT could affect progression and prognosis of hepatocellular carcinoma (HCC) patients and regulate YAP/VGLL4 balance. We found that 5-HT and YAP/VGLL4 ratio were higher in HCC patients and closely related with progression and poor prognosis. Furthermore, 5-HT level, YAP/VGLL4 ratio and tumor size were proved as independent risk factors of HCC patients in our study. Based on the independent risk factors, nomogram was established to exactly predict prognosis of HCC patients. Additionally, the study revealed that a higher total point of the nomogram was closely correlated with poorer prognosis. As a result, 5-HT might contribute to the progression and poor prognosis of hepatocellular carcinoma via regulating YAP/VGLL4 balance. Therefore, the established nomogram based on the independent risk factors may become an important part of HCC prediction system and YAP/VGLL4 balance may be a potential therapeutic target in future.