Do Patients Diagnosed with Metastatic Pancreatic Cancer Benefit from Primary Tumor Surgery? A Propensity-Adjusted, Population-Based Surveillance, Epidemiology and End Results (SEER) Analysis

Identifying Optimal Candidates for Primary Tumor Resection Among Metastatic Pancreatic Cancer Patients: A Population-Based Predictive Model

BACKGROUND

There is a controversy about whether surgery should proceed among metastatic pancreatic cancer (mPC) patients. A survival benefit was observed in mPC patients who underwent primary tumor resection; however, determining which patients would benefit from surgery is complex. For this purpose, we created a model to identify mPC patients who may benefit from primary tumor excision.

METHODS

Patients with mPC were extracted from the Surveillance, Epidemiology, and End Results database, and separated into surgery and nonsurgery groups based on whether the primary tumor was resected. Propensity score matching (PSM) was applied to balance confounding factors between the two groups. A nomogram was developed using multivariable logistic regression to estimate surgical benefit. Our model is evaluated using multiple methods.

RESULTS

About 662 of 14,183 mPC patients had primary tumor surgery. Kaplan-Meier analyses showed that the surgery group had a better prognosis. After PSM, a survival benefit was still observed in the surgery group. Among the surgery cohort, 202 patients survived longer than 4 months (surgery-beneficial group). The nomogram discriminated better in training and validation sets under the receiver operating characteristic (ROC) curve (AUC), and calibration curves were consistent. Decision curve analysis (DCA) revealed that it was clinically valuable. This model is better at identifying candidates for primary tumor excision.

CONCLUSION

A helpful prediction model was developed and validated to identify ideal candidates who may benefit from primary tumor resection in mPC.

Construction and validation of a nomogram model for predicting early death in patients with metastatic pancreatic adenocarcinoma based on SEER database

BACKGROUND

Pancreatic adenocarcinoma is a highly aggressive malignancy that presents a considerable risk of early death (survival time ≤ 3 mo). As such, it is of great significance to develop an effective nomogram for predicting the likelihood of early death in patients with metastatic pancreatic adenocarcinoma.

AIM

To construct and validate a predictive nomogram model for early death in patients with metastatic pancreatic adenocar-cinoma.

METHODS

We extracted data from the SEER database of 18603 eligible patients with metastatic pancreatic adenocarcinoma from 2010 to 2015, and randomly divided them into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed on the training cohort to identify the risk factors for early death, based on which a nomogram was constructed. The performance of the nomogram was verified by receiver operating characteristic (ROC) curve and calibration curve analyses in both the training and validation cohorts. The clinical practicability of the nomogram was evaluated by decision curve analysis (DCA).

RESULTS

Age, sex, primary site, grade, T stage, N stage, brain metastasis, bone metastasis, liver metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy were identified as independent risk factors for early death in patients with metastatic pancreatic adenocarcinoma. Based on these variables, a nomogram was constructed. The areas under the ROC curves of the nomogram in the training and validation cohorts were 0.810 (95% confidence interval [CI]: 0.802-0.811) and 0.802 (95%CI: 0.790-0.813), respectively, indicating good discrimination. The calibration curves showed good calibration degrees in both cohorts, and the DCA results demonstrated that the nomogram had better clinical net benefit in predicting early mortality compared with TNM stage.

CONCLUSION

The constructed nomogram has good predictive ability for early death in patients with metastatic pancreatic adeno-carcinoma. This will help clinicians develop individualized treatment plans for these patients.

Epidemiology and prognostic analysis of patients with pancreatic signet ring cell carcinoma: a population-based study

Background

Pancreatic signet ring cell carcinoma (PSRCC) is a rare tumour subtype with poorly understood epidemiological characteristics and prognosis. We attempted to comprehensively characterise the epidemiology and survival outcomes of PSRCC.

Methods

Patients diagnosed with PSRCC between 2000 and 2018 were identified using Surveillance, Epidemiology and End Results Stat 8.3.9.2 software. Age-adjusted incidence and survival were calculated. Survival curves were plotted using the Kaplan–Meier method, and the differences between survival curves were compared using the log-rank test. Cox proportional hazards models were used to evaluate factors that independently predict overall survival. The primary analysis was a complete case analysis; multiple imputations were employed in a sensitivity analysis.

Results

We identified 585 eligible patients with PSRCC. The overall annual incidence from 2000 to 2018 was 0.349 (95% CI, 0.321–0.379) per million population. The incidence increased significantly in patients over 55 years of age and peaked at about 80 years of age (2.12 per million). Males and Black patients had the highest incidence. The observed survival rates at 1, 2 and 5 years were 20.1, 8.3 and 3.4%, respectively. Survival analysis revealed that primary surgery and chemotherapy are effective treatments for patients with PSRCC (P < 0.05). According to multivariate Cox regression analysis, early stage and receiving surgery and chemotherapy were favourable factors (P < 0.05). Similar conclusions were drawn from the interpolated data.

Conclusions

PSRCC is a highly malignant tumour that predominates in elderly, male and Black patients. The prognosis is poor with a 5-year survival rate of 3.4%; however, multivariate analysis and adjusted models accounting for missing data revealed that early diagnosis, surgery and chemotherapy are effective in improving the prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02543-z.

Risk Assessment of Liver Metastasis in Pancreatic Cancer Patients Using Multiple Models Based on Machine Learning: A Large Population-Based Study

Background

A more accurate prediction of liver metastasis (LM) in pancreatic cancer (PC) would help improve clinical therapeutic effects and follow-up strategies for the management of this disease. This study was to assess various prediction models to evaluate the risk of LM based on machine learning algorithms.

Methods

We retrospectively reviewed clinicopathological characteristics of PC patients from the Surveillance, Epidemiology, and End Results database from 2010 to 2018. The logistic regression, extreme gradient boosting, support vector, random forest (RF), and deep neural network machine algorithms were used to establish models to predict the risk of LM in PC patients. Specificity, sensitivity, and receiver operating characteristic (ROC) curves were used to determine the discriminatory capacity of the prediction models.

Results

A total of 47,919 PC patients were identified; 15,909 (33.2%) of which developed LM. After iterative filtering, a total of nine features were included to establish the risk model for LM based on machine learning. The RF showed the most promising results in the prediction of complications among the models (ROC 0.871 for training and 0.832 for test sets). In risk stratification analysis, the LM rate and 5-year cancer-specific survival (CSS) in the high-risk group were worse than those in the intermediate- and low-risk groups. Surgery, radiotherapy, and chemotherapy were found to significantly improve the CSS in the high- and intermediate-risk groups.

Conclusion

In this study, the RF model constructed could accurately predict the risk of LM in PC patients, which has the potential to provide clinicians with more personalized clinical decision-making recommendations.

Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.

Methods

Patients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.

Results

A total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.

Conclusion

The nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.

Worth it or not? Primary tumor resection for stage IV pancreatic cancer patients: A SEER-based analysis of 15,836 cases

BACKGROUND

Primary tumor resection (PTR) as a treatment option for patients with stage IV pancreatic cancer (PC) is controversial.

PATIENTS AND METHODS

Stage IV PC patients, with treatment data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER), were screened. The main outcomes were overall survival (OS) and cancer-specific survival (CSS).

RESULTS

We enrolled 15,836 stage IV PC patients in this study. Propensity score-matched analyses revealed improved OS and CSS of patients receiving chemotherapy plus PTR versus chemotherapy (median survival time [MSTOS ]: 13 vs. 9 months, p = 0.024; MSTCSS : 14 vs. 10 months, p = 0.035), and chemoradiotherapy plus PTR versus chemoradiotherapy (MSTOS : 14 vs. 7 months, p = 0.044; MSTCSS : 14 vs. 7 months, p = 0.066). Multivariate adjusted analyses further confirmed these results. Stratified with different metastatic modalities, multivariate analyses suggested that PTR significantly improved the OS and CSS among patients with ≤1 metastatic organ, and that patients with brain metastasis might not benefit from chemotherapy treatment.

CONCLUSION

PTR improves the OS and CSS of stage IV PC patients on the basis of chemotherapy or chemoradiotherapy, provided that the metastases involve ≤1 organ. Chemotherapy, however, should be carefully considered in patients with metastases involving the brain.

Locoregional Treatment of Metastatic Pancreatic Cancer Utilizing Resection, Ablation and Embolization: A Systematic Review

Simple Summary

Metastatic pancreatic ductal adenocarcinoma (mPDAC) has a dismal prognosis. In selected patients with limited metastatic disease, locoregional therapy, in addition to systemic chemotherapy, may improve survival. This systematic review sought to examine current evidence on the value of additional locoregional treatment, including resection, ablation and embolization, in patients with hepatic or pulmonary mPDAC. The results, although liable to substantial bias, demonstrated superior survival from metastatic diagnosis or treatment in a subset of patients after radical-intent local primary and metastatic treatment (hepatic mPDAC 7.8–19 months; pulmonary mPDAC 22.8–47 months) compared to chemotherapy or best supportive care (hepatic mPDAC 4.3–7.6 months; pulmonary mPDAC 11.8 months). However, as a consequence of the bias, definitive conclusions regarding the seemingly beneficial effect of locoregional treatment cannot be endorsed. Randomized controlled trials with strictly selected oligometastatic PDAC patients are required to deduce final recommendations on this notion.

Abstract

The prognosis of metastatic pancreatic ductal adenocarcinoma (mPDAC) remains universally poor, requiring new and innovative treatment approaches. In a subset of oligometastatic PDAC patients, locoregional therapy, in addition to systemic chemotherapy, may improve survival. The aim of this systematic review was to explore and evaluate the current evidence on locoregional treatments for mPDAC. A systematic literature search was conducted on locoregional techniques, including resection, ablation and embolization, for mPDAC with a focus on hepatic and pulmonary metastases. A total of 59 studies were identified, including 63,453 patients. Although subject to significant bias, radical-intent local therapy for both the primary and metastatic sites was associated with a superior median overall survival from metastatic diagnosis or treatment (hepatic mPDAC 7.8–19 months; pulmonary mPDAC 22.8–47 months) compared to control groups receiving chemotherapy or best supportive care (hepatic mPDAC 4.3–7.6 months; pulmonary mPDAC 11.8 months). To recruit patients that may benefit from these local treatments, selection appears essential. Most significant is the upfront possibility of local radical pancreatic and metastatic treatment. In addition, a patient’s response to neoadjuvant systemic chemotherapy, performance status, metastatic disease load and, to a lesser degree, histological differentiation grade and tumor marker CA19-9 serum levels, are powerful prognostic factors that help identify eligible subjects. Although the exact additive value of locoregional treatments for mPDAC patients cannot be distillated from the results, locoregional primary pancreatic and metastatic treatment seems beneficial for a highly selected group of oligometastatic PDAC patients. For definite recommendations, well-designed prospective randomized controlled trials with strict in- and exclusion criteria are needed to validate these results.

Metastatic pancreatic adenocarcinomas could be classified into M1a and M1b category by the number of metastatic organs

Background

With the improvement of treatment and prognosis for patients with late malignant diseases, certain malignancies with distant metastasis (M1 category) have been further classified into M1a (single metastatic site) and M1b (multiple metastatic sites) category in the staging system. We aimed to assess the feasibility of sub-classifying metastatic pancreatic adenocarcinoma (mPA) into M1a and M1b category depending on the number of metastatic organs.

Methods

Patient records were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). Univariable and multivariable analyses were performed using the Cox regression model. Then survival analysis was determined using the Kaplan–Meier method.

Results

A total of 11,885 patients were included in this analysis, including 9425 patients with single metastasis and 2460 patients with multiple metastases. Multivariable analysis showed that gender, age, marital status, grade, surgery, chemotherapy, and radiotherapy were independent prognostic factors for patients with single metastasis; gender, age, marital status, grade, chemotherapy and radiotherapy were independent prognostic factors for patients with multiple metastases. Notably, surgery was an independent prognostic factor for patients with single metastasis (P < 0.001) but not for patients with multiple metastases (P = 0.134). Kaplan–Meier analysis showed that patients with single metastasis (M1a) had better survival outcomes than patients with multiple metastases (M1b) (P < 0.001).

Conclusions

PA patients with M1 diseases could be divided into M1a (single metastasis) category and M1b (multiple metastases) category by the number of metastatic organs. The subclassification would facilitate individualized treatment for late PA patients. Surgery was associated with lower mortality in M1a patients but not significantly in M1b patients.