Development and Validation of a Prognostic Nomogram to Guide Decision-Making for High-Grade Digestive Neuroendocrine Neoplasms

Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors

BACKGROUND

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.

MATERIALS AND METHODS

We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.

RESULTS

By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.

CONCLUSIONS

A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.

Development and validation of prognostic nomograms for early-onset colon cancer in different tumor locations: a population-based study

OBJECTIVE

The prevalence of early-onset colon cancer (EOCC) among individuals below the age of 50 has shown a marked upward trend in recent years. The embryology, clinical symptoms, incidence, molecular pathways, and oncologic outcomes differ between right-sided and left-sided colon cancers. However, the differences have not been fully researched in EOCC. Our study aims to develop and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for EOCC in different tumor locations based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

Using the SEER database, a total of 5,588 patients with EOCC were extracted and divided into development and validation cohorts in a random allocation ratio of 7:3 across three groups. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors influencing OS and CSS outcomes. These factors were then utilized to construct nomogram models. The prognostic capabilities of the three models were assessed through various evaluation metrics, including the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and validation cohorts respectively. Additionally, survival curves of the low- and high-risk groups were calculated using the Kaplan-Meier method together with the log-rank test.

RESULTS

Significant differences in clinical features were observed between right-sided and left-sided EOCCs, particularly in terms of OS (52 months vs 54 months) as demonstrated by Kaplan-Meier curves. Transverse-sided EOCCs exhibited clinical characteristics similar to right-sided EOCCs, suggesting a potential shared tumor microenvironment and therapeutic considerations. Advanced stage, liver metastasis, poor grade, elevated pretreatment carcinoembryonic antigen (CEA) level, chemotherapy, and perineural invasion were identified as independent prognostic factors across all three tumor locations and were incorporated into the nomogram model. Nomograms were constructed to predict the probability of 3- and 5-year OS and CSS. The C-index and calibration plots showed that the established nomograms had good consistency between actual clinical observations and predicted outcomes. ROC curves with calculated area under the curve (AUC) values exceeded 0.8 for all three groups in both the development and validation cohorts, indicating robust predictive performance for OS and CSS. Furthermore, decision curve analysis (DCA) plots revealed a threshold probability range of 0.1 to 0.9, within which the nomogram model exhibited maximum benefit. Kaplan-Meier curves exhibited significant differences between the low- and high-risk groups in EOCC for all three tumor locations in OS and CSS, further validating the prognostic value of the nomogram models.

CONCLUSIONS

We successfully developed three precise nomogram models for EOCCs in different tumor locations, providing valuable support for clinicians in guiding clinical treatments and facilitating further prospective follow-up studies.

Lymph node ratio and hematological parameters predict relapse-free survival in patients with high grade rectal neuroendocrine neoplasms after radical resection: a multicenter prognostic study

BACKGROUND

The prognostic nutritional index (PNI), alkaline phosphatase (ALP), and lymph node ratio (LNR) are reportedly related to prognosis. The aim of this study was to elucidate the clinical importance of the LNR and hematological parameters in patients with high grade rectal neuroendocrine neoplasms (HG-RNENs) who were undergoing radical resection.

METHODS

We reviewed the medical records of patients with HG-RNENs from 17 large-scale medical centers in China (January 1, 2010-April 30, 2022). A nomogram was constructed by using a proportional hazard model. Bootstrap method was used to draw calibration plots to validate the reproducibility of the model. Concordance index (C-Index), decision curve analysis (DCA), and time-dependent area under the receiver operating characteristic curve (TD-AUC) analysis were used to compare the prognostic predictive power of the new model with American Joint Committee on Cancer (AJCC) TNM staging and European Neuroendocrine Tumor Society (ENETS) TNM staging.

RESULTS

A total of 85 patients with HG-RNENs were enrolled in this study. In the 45 patients with HG-RNENs who underwent radical resection, PNI ≤ 49.13 (HR: 3.997, 95% CI: 1.379-11.581, P = 0.011), ALP > 100.0 U/L (HR: 3.051, 95% CI: 1.011-9.205, P = 0.048), and LNR > 0.40 (HR: 6.639, 95% CI: 2.224-19.817, P = 0.0007) were independent predictors of relapse-free survival. The calibration plots suggested that the nomogram constructed based on the three aforementioned factors had good reproducibility. The novel nomogram revealed a C-index superior to AJCC TNM staging (0.782 vs 0.712) and ENETS TNM staging (0.782 vs 0.657). Also, the new model performed better compared to AJCC TNM staging and ENETS TNM staging in DCA and TD-AUC analyses.

CONCLUSIONS

LNR, ALP, and PNI were independent prognostic factors in patients with HG-RNENs after radical resection, and the combined indicator had better predictive efficacy compared with AJCC TNM staging and ENETS TNM staging.

A novel prognostic model for patients with colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.

METHODS

Using the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan-Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).

RESULTS

A total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.

CONCLUSION

In this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies.

Construction and validation of the prognostic model for patients with neuroendocrine cervical carcinoma: a competing risk nomogram analysis

Purpose

Neuroendocrine cervical carcinoma (NECC) is an uncommon malignancy of the female reproductive system. This study aimed to evaluate cancer-specific mortality and to construct prognostic nomograms for predicting the survival of patients with NECC.

Methods

we assembled the patients with NECC diagnosed between 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Meanwhile, we identified other patients with NECC from the Wenling Maternal and Child Health Care Hospital between 2002 to 2017. Fine and Gray’s test and Kaplan–Meier methods were used to evaluate cancer-specific mortality and overall survival (OS) rates, respectively. Nomograms were constructed for predicting cancer-specific survival (CSS) and OS for patients with NECC. The developed nomograms were validated both internally and externally.

Results

a total of 894 patients with NECC were extracted from the SEER database, then classified into the training cohort (n = 628) and the internal validation cohort (n = 266). Besides, 106 patients from the Wenling Maternal and Child Health Care Hospital served as an external validation cohort. Nomograms for predicting CSS and OS were constructed on clinical predictors. The validation of nomograms was calculated by calibration curves and concordance indexes (C-indexes). Furthermore, the developed nomograms presented higher areas under the receiver operating characteristic (ROC) curves when compared to the FIGO staging system.

Conclusions

we established the first competing risk nomograms to predict the survival of patients with NECC. Such a model with high predictive accuracy could be a practical tool for clinicians.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09104-9.

Combined Identification of Novel Markers for Diagnosis and Prognostic of Classic Hodgkin Lymphoma

BACKGROUND

An effective diagnostic and prognostic marker based on the gene expression profile of classic Hodgkin lymphoma (cHL) has not yet been developed. The aim of the present study was to investigate potential markers for the diagnosis and prediction of cHL prognosis.

METHODS

The gene expression profiles with all available clinical features were downloaded from the Gene Expression Omnibus (GEO) database. Then, multiple machine learning algorithms were applied to develop and validate a diagnostic signature by comparing cHL with normal control. In addition, we identified prognostic genes and built a prognostic model with them to predict the prognosis for 130 patients with cHL which were treated with first-line treatment (ABVD chemotherapy or an ABVD-like regimen).

RESULTS

A diagnostic prediction signature was constructed and showed high specificity and sensitivity (training cohort: AUC=0.981,95% CI 0.933-0.998, P<0.001, validation cohort: AUC=0.955,95% CI 0.895-0.986, P<0.001). Additionally, nine prognostic genes (LAMP1, STAT1, MMP9, C1QB, ICAM1, CD274, CCL19, HCK and LILRB2) were screened and a prognostic prediction model was constructed with them, which had been confirmed effectively predicting prognosis (P<0.001). Furthermore, the results of the immune infiltration assessment indicated that the high scale of the fraction of CD8 + T cells, M1 macrophages, resting mast cells associated with an adverse outcome in cHL, and naive B cells related to prolonged survival. In addition, a nomogram that combined the prognostic prediction model and clinical characteristics is also suggested to have a good predictive value for the prognosis of patients.

CONCLUSION

The new markers found in this study may be helpful for the diagnosis and prediction of the prognosis of cHL.

Risk Factors and Predictive Score Model for Early Recurrence After Curative Surgery in Patients With Poorly Differentiated Gastrointestinal Neuroendocrine Neoplasms

Purpose: Studies on early recurrence in gastrointestinal neuroendocrine carcinoma (NEC) and mixed adenoneuroendocrine carcinoma (MANEC) are lacking and risk factors related to early recurrence are not clear. We evaluated risk factors for early recurrence in such patients and developed a predictive scoring model.

Methods: Patients undergoing curative surgery for GI-NEC or MANEC between January 2010 and January 2019 were included. Early recurrence was defined as recurrence within 12 months after surgery. Risk factors for early recurrence were identified using logistic regression.

Results: Of the 80 included patients, 27 developed early recurrence and 53 had no early recurrence. Independent risk factors associated with early recurrence included tumor location in the midgut/hindgut [odds ratio (OR) = 5.077, 95% confidence interval (CI) 1.058–24.352, p = 0.042], alkaline phosphatase (ALP) >80 (OR = 5.331, 95% CI 1.557–18.258, p = 0.008), and lymph node ratio (LNR) >0.25 (OR = 6.578, 95% CI 1.971–21.951, p = 0.002). Risk scores were assigned to tumor location (foregut, 0; midgut/hindgut, 1), ALP (≤80, 0; >80, 1), and LNR (≤0.25, 0; >0.25, 1). Patients with a high risk (score 2–3) for early recurrence had significantly shorter disease-free survival and overall survival than those with low- (score 0) and intermediate risks (score 1) (both p < 0.001). The novel scoring model had superior predictive efficiency for early recurrence over TNM staging (area under the curve 0.795 vs. 0.614, p = 0.003).

Conclusion: Tumor location, preoperative ALP, and LNR were independent factors associated with early recurrence after curative surgery for GI-NEC or MANEC. The risk scoring model developed based on these three factors shows superior predictive efficiency.

Biliary Neuroendocrine Neoplasms: Analysis of Prognostic Factors and Development and Validation of a Nomogram

Background

For this study, we explored the prognostic profiles of biliary neuroendocrine neoplasms (NENs) patients and identified factors related to prognosis. Further, we developed and validated an effective nomogram to predict the overall survival (OS) of individual patients with biliary NENs.

Methods

We included a total of 446 biliary NENs patients from the SEER database. We used Kaplan-Meier curves to determine survival time. We employed univariate and multivariate Cox analyses to estimate hazard ratios to identify prognostic factors. We constructed a predictive nomogram based on the results of the multivariate analyses. In addition, we included 28 biliary NENs cases from our center as an external validation cohort.

Results

The median survival time of biliary NENs from the SEER database was 31 months, and the value of gallbladder NENs (23 months) was significantly shorter than that of the bile duct (45 months) and ampulla of Vater (33.5 months, p=0.023). Multivariate Cox analyses indicated that age, tumor size, pathological classification, SEER stage, and surgery were independent variables associated with survival. The constructed prognostic nomogram demonstrated good calibration and discrimination C-index values of 0.783 and 0.795 in the training and validation dataset, respectively.

Conclusion

Age, tumor size, pathological classification, SEER stage, and surgery were predictors for the survival of biliary NENs. We developed a nomogram that could determine the 3-year and 5-year OS rates. Through validation of our central database, the novel nomogram is a useful tool for clinicians in estimating individual survival among biliary NENs patients.

Epidemiological Analysis and Prognosis of Conjunctival Cancer in the Past Twenty Years: A Population-Based Retrospective Study Using SEER Data

Epidemiological studies of malignant primary conjunctival tumors are rare. We extracted data pertaining to primary site-labeled conjunctival cancer patients present within the Surveillance, Epidemiology, and End Results (SEER) database from 1992 to 2001 and from 2002 to 2011. The Kaplan-Meier approach was used for comparisons of overall survival (OS) between patients, while OS-related risk factors were identified via a Cox proportional hazards regression approach. We then constructed a nomogram that could be used to predict the 3- and 5-year OS, with the accuracy of this predictive model based on receiver operating characteristic (ROC) curve. We observed a significant reduction in age-adjusted incidence of conjunctival cancer in the 50-69-year-old age group of the 2002-2011 cohort relative to the 1992-2001 cohort (APC, P < 0.05). There were no significant differences in OS between the 1992-2001 and 2002-2011 conjunctival cancer patient cohorts. Being ≥30 years old (P < 0.05), male (P < 0.001), single (P < 0.05), divorced (P < 0.001), or widowed (P < 0.001) were all associated with an increased OS-related risk of primary conjunctival cancer (1992-2011). Our nomogram was able to accurately predict 3- and 5-year OS in conjunctival cancer patients. In verification mode, the 3-year area under the curve (AUC) was 0.697 and the 5-year AUC was 0.752. We found that age, sex, and marital status were all associated with primary conjunctival cancer survival. Our results further suggest that conjunctival cancer incidence and survival rates have been relatively stable over the last two decades, and using these data, we were able to generate a satisfactory risk prediction model for this disease.

Development and Validation of Prognostic Nomograms for Patients with Duodenal Neuroendocrine Neoplasms

Background

This study was designed to predict prognosis of patients with primary duodenal neuroendocrine neoplasms (D-NENs) by developing nomograms.

Material/Methods

Patients diagnosed with D-NENs between 1988 and 2015 were queried from the SEER database and a total of 965 appropriate cases were randomly separated into the training and validation sets. Kaplan-Meier analysis was used to generated survival curves, and the difference among the groups was assessed by the log-rank test. Independent prognostic indicators were acquired by Cox regression analysis, and were used to develop predictive overall survival (OS) and cancer-specific survival (CSS) nomograms. Harrell’s concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the efficacy of nomograms. Tumor stage was regarded as a benchmark in predicting prognostic compared with the nomograms built in this study.

Results

The C-index was 0.739 (0.690–0.788) and 0.859 (0.802–0.916) for OS and CSS nomograms, respectively. Calibration curves exhibited obvious consistency between the nomograms and the actual observations. In addition, C-index, AUC, and DCA were better than tumor stage in the evaluative performance of nomograms.

Conclusions

The nomograms were able to predict the 1-, 5-, and 10-year OS and CSS for D-NENs patients. The good performance of these nomograms suggest that they can be used for evaluating the prognosis of patients with D-NENs and can facilitate individualized treatment in clinical practice.