Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

Gender impact on pancreatic neuroendocrine neoplasm (PanNEN) prognosis according to survival nomograms

PURPOSE

Personalizing care and outcome evaluation are important aims in the field of NEN and nomograms may represent useful tools for clinicians. Of note, gender difference is being progressively more considered in NEN care, as it may also impact on survival. This systematic review aims to describe and analyze the available nomograms on pancreatic NENs (PanNENs) to identify if gender differences are evaluated and if they could impact on patients' management and prognosis.

METHODS

We performed an electronic-based search using PubMed updated until June 2024, summarizing the available evidence of gender impact on PanNEN survival outcomes as emerges from published nomograms.

RESULTS

34 articles were identified regarding prognostic nomograms in PanNEN fields. The most included variables were age, tumor grade, tumor stage, while only 5 papers (14.7%) included sex as one of the key model variables with a significant impact on patients' prognosis. These 5 studies analyzed a total of 18,920 PanNENs. 3 studies found a significant impact of sex on overall survival (OS), whereas the remaining 2 studies showed no significant impact of sex on OS.

CONCLUSIONS

Gender difference is being progressively more considered in PanNEN diagnosis, care and survival. Nomograms represent a potentially useful tool in patients' management and in outcomes prediction in the field of PanNENs. A key role of sex in the prognosis of PanNENs has been found in few models, while definitive conclusions couldn't be drawn. Future studies are needed to finally establish gender impact on PanNEN prognosis.

Evaluating endoscopic ultrasound-guided tissue acquisition for diagnosis of small pancreatic neuroendocrine neoplasms

Background and study aims Although small hypervascular tumors are suspected to be pancreatic neuroendocrine tumors (p-NENs), their diagnosis and treatment are challenging. This study evaluated the usefulness of endoscopic ultrasound-guided tissue acquisition (EUS-TA) for diagnosis of small p-NENs. Methods All p-NEN lesions that underwent EUS-TA at our hospital between April 2018 and December 2023 were retrospectively analyzed. The diagnostic sensitivity of EUS-TA and the concordance rate of grading with EUS-TA and surgical specimens were examined. The lesions were grouped by size. Results The diagnostic sensitivity of EUS-TA was analyzed for 82 lesions, of which 44 were compared with postoperative specimens for grading. The definitive diagnosis was neuroendocrine tumor (NET) in 75 lesions, neuroendocrine carcinoma in five lesions, and mixed neuroendocrine non-neuroendocrine neoplasm in two lesions. Thirty tumors were ≤10 mm, 30 were 10 to 20 mm, and 22 were >20 mm, and the diagnostic sensitivities were 96.7%, 96.7%, and 90.9%, respectively. Concordance rates for grading were 94.4%, 82.4%, and 77.8% for tumors ≤10 mm, 10 to 20 mm, and ≥20 mm, respectively, with Cohen's kappa coefficients of 0.64, 0.48, and 0.40, respectively. Conclusions EUS-TA showed adequate diagnostic sensitivity and grading agreement for p-NENs of all sizes, allowing for determination of appropriate treatment.

Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation

RATIONALE AND OBJECTIVES

We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups.

RESULTS

PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively.

CONCLUSION

Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics

BACKGROUND

Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.

METHODS

Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model's performance was validated in both internal and external test cohorts.

RESULTS

Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).

CONCLUSION

A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification

Objective

This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.

Methods

242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.

Results

The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.

Conclusion

The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Epidemiologic characteristics and a prognostic nomogram for patients with vulvar cancer: results from the Surveillance, Epidemiology, and End Results (SEER) program in the United States, 1975 to 2016

OBJECTIVE

To elucidate clinical characteristics and build a prognostic nomogram for patients with vulvar cancer.

METHODS

The study population was drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned to training and validation sets. Cox proportional hazards model and competing risk model were used to identify the prognostic parameters of overall survival (OS) and cancer-specific survival (CSS) to construct a nomogram. The nomogram was assessed by concordance index (C-index), area under the curve (AUC), calibration plot, and decision curve analysis (DCA).

RESULTS

A total of 20,716 patients were included in epidemiological analysis, of whom 7,025 patients were selected in survival analysis, including 4,215 and 2,810 in training and validation sets, respectively. The multivariate Cox model showed that the predictors for OS were age, marital status, histopathology, differentiation and tumor node metastasis (TNM) stages, whether to undergo surgery and chemotherapy. However, the predictors for CSS were age, race, differentiation and TNM stages, whether to undergo surgery and radiation. The C-index for OS and CSS in the training set were 0.76 and 0.80. The AUC in the training set for 1-, 3- and 5-year OS and CSS were 0.84, 0.81, 0.80 and 0.88, 0.85, 0.83, respectively, which was similar in the validation set. The calibration curves showed good agreement between prediction and actual observations. DCA revealed that the nomogram had a better discrimination than TNM stages.

CONCLUSIONS

The nomogram showed accurate prognostic prediction in OS and CSS for vulvar cancer, which could provide guidance to clinical practice.

Risk prediction model of polypharmacy for community-dwelling elderly patients: An assessment tool for early detection

Background: Polypharmacy has become a major and growing public health issue, with significant implications for health outcomes and expenditure on healthcare resources. In this study, a risk prediction model of polypharmacy represented by a nomogram for community-dwelling elderly patients based on the Chinese population was constructed.

Methods: A cross-sectional study was conducted in Shanghai, China. The variables data affecting polypharmacy were fetched from the information system database of health government departments in Shanghai. The Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the predictor variables, and multivariate logistic regression was used to establish the prediction model. A visual tool of the nomogram was established for predicting the risk of polypharmacy in the elderly population. In addition, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to estimate the performance of the model.

Results: A total of 80,012 elderly patients were included in this study. Eight variables, containing age, residential area, preferred medical institutions, number of visits to tertiary hospitals, number of visits to secondary hospitals, number of visits to community health centers, number of diagnoses, and main types of disease, were included in the risk prediction model of nomogram. The area under the curve (AUC) of the nomogram was 0.782 in both sets, demonstrating that the model has a good discriminant ability. The calibration chart shows that the prediction model fits well with the validation set. DCA results displayed that the threshold probabilities of the two sets in the prediction model reached up to 90%, implying that the model had a preferable application value.

Conclusion: This study explored the risk factors for polypharmacy among the elderly in Shanghai, China, and applied the nomogram to establish a predictive model via eight variables, which provided an effective tool for early screening and timely prevention of polypharmacy. Family physicians or pharmacists could scientifically use the tool to closely observe community-dwelling elderly patients, decreasing the adverse health effects caused by medication for the elderly.

A Practical Nomogram and Risk Stratification System Predicting Cancer-Specific Survival for Hepatocellular Carcinoma Patients With Severe Liver Fibrosis

Objective

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis.

Methods

Data on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system.

Results

Seven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767–0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779–0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P < 0.001). Moreover, DCA also showed that the nomogram was more practical and had better recognition.

Conclusion

A nomogram for predicting CSS for HCC patients with severe liver fibrosis was established and validated, which provided a new system of risk stratification as a practical tool for individualized treatment and management.

Development and validation of a novel nomogram for predicting survival rate in pancreatic neuroendocrine neoplasms

BACKGROUND

Over the past decades, the incidence and prevalence of pancreatic neuroendocrine neoplasms (pNENs) have steadily increased. However, accurate prediction of the prognosis and treatment of this condition are currently challenging. This study aims to develop and validate a personalized nomogram to predict the survival of patients with pNENs.

MATERIALS AND METHODS

A total of 9739 patients with pNENs were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, the patients were randomly assigned to a derivation cohort (n = 6874) and a validation cohort (n = 2865). The survival of patients was assessed using the Cox proportional hazards (PHs) regression analysis. Then, the nomogram that predicted 3-and 5-year survival rates were developed in the derivation cohort. Further, the predictive performance of the nomogram was evaluated through discrimination and calibration.

RESULTS

The Cox regression analysis revealed that age, differentiation, the extent of tumor, M staging, and surgery were independent prognostic predictors for pNENs. The nomogram showed superior discrimination capability than AJCC staging in both derived and validation cohorts (C-index: 0.874 versus 0.721 and 0.833 versus 0.721). The calibration curves showed that the practical and predicted survival rates effectively coincided, specifically for the 3-year survival rate.

CONCLUSION

Our nomogram is a valuable tool for the prediction of the survival rate for patients with pNENs; this may promote individualized prognostic evaluation and treatment.

Construction of a risk prediction model for Alzheimer's disease in the elderly population

BACKGROUND

Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population.

METHODS

Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model.

RESULTS

Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set).

CONCLUSION

The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.