Development and Validation of Nomograms to Predict Overall Survival and Cancer-Specific Survival in Patients With Pancreatic Adenosquamous Carcinoma

A case of pancreatic adenosquamous cell carcinoma with a pseudocyst following curative surgery

BACKGROUND

Pancreatic adenosquamous cell carcinoma (PASC) is a relatively rare histological type of pancreatic malignancy, and preoperative diagnosis is difficult because of its rarity. PASC accounts for 1-4% of all pancreatic cancers, and even after curative surgery, its prognosis is poorer than that of ordinary pancreatic adenocarcinoma. Pathologically, it shows glandular and squamous differentiation of cells. Complete resection is the only method to achieve a good long-term prognosis, and an increasing doubling time of PASC is considered to indicate early recurrence after surgery. Here, we report a rare case of PASC with an infected pancreatic cyst that was difficult to treat, along with a review of the literature.

CASE PRESENTATION

A woman in her 80s with a history of breast cancer presented with pericardial pain. Computed tomography revealed a 20-mm hypovascular tumor in the body of the pancreas and a 27-mm pseudocyst. Endoscopic retrograde cholangiopancreatography showed a severe main pancreatic duct stenosis in the body of the pancreas that made cannulation impossible, and contrast media extravasation was due to pancreatic duct disruption in the pancreatic tail. Endoscopic fine-needle aspiration revealed that the tumor was a PASC. Because the patient had an infected pancreatic cyst, central intravenous nutrition and antibiotics were administered, which stabilized her general condition. She was diagnosed with resectable PASC and underwent distal pancreatectomy with lymphadenectomy. The postoperative course was uneventful. Immunohistochemical analysis of the resected specimen confirmed T2N0M0 stage IB. Systemic adjuvant chemotherapy with S-1 is ongoing.

CONCLUSION

Appropriate preoperative management and preoperative accurate staging (T2N0M0 stage IB) of PASC with curative surgery can ensure predictable outcomes.

How to use the Surveillance, Epidemiology, and End Results (SEER) data: research design and methodology

In the United States (US), the Surveillance, Epidemiology, and End Results (SEER) program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data. This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field. Currently, the SEER program covers approximately half of the total cancer patients in the US. A growing number of clinical studies have applied the SEER database in various aspects. However, the intrinsic features of the SEER database, such as the huge data volume and complexity of data types, have hindered its application. In this review, we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database. Therefore, the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.

Development and validation of prognostic nomogram for patients with metastatic gastric adenocarcinoma based on the SEER database

The aim of this study was to investigate the prognostic factors affecting overall survival in patients with metastatic gastric adenocarcinoma and to establish a nomogram prediction model for comprehensive clinical application. Data from 2370 patients with metastatic gastric adenocarcinoma between 2010 and 2017 were retrieved from the surveillance, epidemiology, and end results database. They were randomly divided into a training set (70%) and a validation set (30%), univariate and multivariate Cox proportional hazards regressions were used to screen important variables that may affect overall survival and to establish the nomogram. The nomogram model was evaluated using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Internal validation was performed to test the accuracy and validity of the nomogram. Univariate and multivariate Cox regression analyses revealed that, age, primary site, grade, and American joint committee on cancer. T, bone metastasis, liver metastasis, lung metastasis, tumor Size, and chemotherapy were identified as independent prognostic factors for overall survival and were included in the prognostic model to construct a nomogram. The prognostic nomogram showed good overall survival risk stratification ability for the area under the curve, calibration plots, and decision curve analysis in both the training and validation sets. Kaplan-Meier curves further showed that patients in the low-risk group had better overall survival. This study synthesizes the clinical, pathological, therapeutic characteristics of patients with metastatic gastric adenocarcinoma, establishes a clinically effective prognostic model, and that can help clinicians to better evaluate the patient's condition and provide accurate treatment.

Research advances and treatment perspectives of pancreatic adenosquamous carcinoma

BACKGROUND

As a malignant tumor, pancreatic cancer has an extremely low overall 5-year survival rate. Pancreatic adenosquamous carcinoma (PASC), a rare pancreatic malignancy, owns clinical presentation similar to pancreatic ductal adenocarcinoma (PDAC), which is the most prevalent pancreatic cancer subtype. PASC is generally defined as a pancreatic tumor consisting mainly of adenocarcinoma tissue and squamous carcinoma tissue. Compared with PDAC, PASC has a higher metastatic potential and worse prognosis, and lacks of effective treatment options to date. However, the pathogenesis and treatment of PASC are not yet clear and are accompanied with difficulties.

CONCLUSION

The present paper systematically summarizes the possible pathogenesis, diagnosis methods, and further suggests potential new treatment directions through reviewing research results of PASC, including the clinical manifestations, pathological manifestation, the original hypothesis of squamous carcinoma and the potential regulatory mechanism. In short, the present paper provides a systematic review of the research progress and new ideas for the development mechanism and treatment of PASC.

Establishment of predictive nomogram and web-based survival risk calculator for malignant pleural mesothelioma: A SEER database analysis

Background

Malignant pleural mesothelioma (MPM) is an uncommon condition with limited available therapies and dismal prognoses. The purpose of this work was to create a multivariate clinical prognostic nomogram and a web-based survival risk calculator to forecast patients’ prognoses.

Methods

Using a randomization process, training and validation groups were created for a retrospective cohort study that examined the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 for individuals diagnosed with MPM (7:3 ratio). Overall survival (OS) and cancer-specific survival (CSS) were the primary endpoints. Clinical traits linked to OS and CSS were identified using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, which was also utilized to develop nomogram survival models and online survival risk calculators. By charting the receiver operating characteristic (ROC), consistency index (C-index), calibration curve, and decision curve analysis (DCA), the model’s performance was assessed. The nomogram was used to classify patients into various risk categories, and the Kaplan-Meier method was used to examine each risk group’s survival rate.

Results

The prognostic model comprised a total of 1978 patients. For the total group, the median OS and CSS were 10 (9.4-10.5) and 11 (9.4-12.6) months, respectively. As independent factors for OS and CSS, age, gender, insurance, histology, T stage, M stage, surgery, and chemotherapy were chosen. The calibration graphs demonstrated good concordance. In the training and validation groups, the C-indices for OS and CSS were 0.729, 0.717, 0.711, and 0.721, respectively. Our nomogram produced a greater clinical net benefit than the AJCC 7th edition, according to DCA and ROC analysis. According to the cut-off values of 171 for OS and 189 for CSS of the total scores from our nomogram, patients were classified into two risk groups. The P-value < 0.001 on the Kaplan-Meier plot revealed a significant difference in survival between the two patient groups.

Conclusions

Patient survival in MPM was correctly predicted by the risk evaluation model. This will support clinicians in the practice of individualized medicine.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Simple Summary

Surgery is the main treatment to cure pancreatic cancer (PC). However, the 5-year survival rate of surgical resection is only 10–20%. The aim of our study was to develop a prediction model with the novel machine learning algorithm random survival forest (RSF) and to offer easy-to-use prediction tools, including risk stratification and individual prognosis. The study would benefit patients and physicians in postoperative management and facilitate personalized medicine.

Abstract

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model’s favorable calibration and the decision curve analysis illustrated the model’s value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.