Development and external validation of a dynamic nomogram to predict the survival for adenosquamous carcinoma of the pancreas

Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years.

Methods

Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.

Results

Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.

Conclusions

We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.

Metastatic pattern and prognosis in patients with lung adenosquamous carcinoma: A surveillance, epidemiology, and end results-based population study

Background

Lung adenosquamous carcinoma (ASC) is a rare tumor with high invasive and metastatic potential. Few studies have explored metastatic patterns in patients with advanced-stage ASC.

Methods

Patients diagnosed with ASC in the Surveillance, Epidemiology, and End Results database from 2010 to 2015 were selected. Descriptive statistics were obtained to characterize the metastatic sites of the study participants. The Kaplan-Meier method was applied to compare survival curves among patients with different metastatic patterns. Cox regression analysis was performed to evaluate risk factors for metastasis.

Results

A total of 858 eligible patients with ASC were enrolled; the mean age was 71.5 years (standard deviation ± 7.8 years). There was a slightly higher proportion of male patients (54.0 %). A total of 63.2 % of patients harbored single-site metastasis (median OS: 5 months), 23.6 % of patients had two-site metastasis (median OS: 4 months), and approximately 10 % of patients harbored three or more sites metastasis (median OS: 3 months). Bone (56.9 %) was the most frequent site of metastasis (median OS: 4 months), followed by lung metastasis (37.6 %, median OS: 5 months), liver metastasis (22.1 %, median OS: 5 months), and brain metastasis (21.4 %, median OS: 4 months). Chemotherapy decreased the risk of death by 70 % (HR = 0.296, 95 % CI 0.241-0.363), 70 % (HR = 0.302, 95 % CI 0.224-0.406), 78 % (HR = 0.218, 95 % CI 0.151-0.314), and 70 % (HR = 0.302, 95 % CI 0.231-0.396) in patients harboring bone, liver, brain and lung metastases, respectively. The brain increased the risk of death by 50 % (HR = 1.501, 95 % CI 1.209-1.865), 64 % (HR = 1.644, 95 % CI 1.126-2.402), and 128 % (HR = 2.284, 95 % CI 1.653-3.157) in patients harboring bone, liver and lung metastases, respectively.

Conclusion

Patients with advanced-stage ASC have unfavorable outcomes. Early detection and aggressive treatment can improve patients outcomes.

Development and validation of a nomogram for predicting overall survival in patients with sinonasal mucosal melanoma

BACKGROUND

Sinonasal mucosal melanoma (SNMM) is a relatively rare malignant tumour with a poor prognosis. This study was designed to identify prognostic factors and establish a nomogram model to predict the overall survival (OS) of patients with SNMM.

METHODS

A total of 459 patients with SNMM were selected from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort. Univariate and multivariate Cox regression analyses were used to screen for independent factors associated with patient prognosis and develop the nomogram model. In addition, external validation was performed to evaluate the effectiveness of the nomogram with a cohort of 34 patients with SNMM from Peking Union Medical College Hospital.

RESULTS

The median OS in the cohort from the SEER database was 28 months. The 1-year, 3-year and 5-year OS rates were 69.8%, 40.4%, and 30.0%, respectively. Multivariate Cox regression analysis indicated that age, T stage, N stage, surgery and radiotherapy were independent variables associated with OS. The areas under the receiver operating characteristic curves (AUCs) of the nomograms for predicting 1-, 3- and 5-year OS were 0.78, 0.71 and 0.71, respectively, in the training cohort. In the validation cohort, the area under the curve (AUC) of the nomogram for predicting 1-, 3- and 5-year OS were 0.90, 0.75 and 0.78, respectively. Patients were classified into low- and high-risk groups based on the total score of the nomogram. Patients in the low-risk group had a significantly better survival prognosis than patients in the high-risk group in both the training cohort (P < 0.0001) and the validation cohort (P = 0.0016).

CONCLUSION

We established and validated a novel nomogram model to predict the OS of SNMM patients stratified by age, T stage, N stage, surgery and radiotherapy. This predictive tool is of potential importance in the realms of patient counselling and clinical decision-making.

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma

Background

Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use.

Methods

In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM.

Results

The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy.

Conclusions

Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

Development and validation of a predictive model for febrile seizures

Febrile seizures (FS) are the most prevalent type of seizures in children. Existing predictive models for FS exhibit limited predictive ability. To build a better-performing predictive model, a retrospective analysis study was conducted on febrile children who visited the Children's Hospital of Shanghai from July 2020 to March 2021. These children were divided into training set (n = 1453), internal validation set (n = 623) and external validation set (n = 778). The variables included demographic data and complete blood counts (CBCs). The least absolute shrinkage and selection operator (LASSO) method was used to select the predictors of FS. Multivariate logistic regression analysis was used to develop a predictive model. The coefficients derived from the multivariate logistic regression were used to construct a nomogram that predicts the probability of FS. The calibration plot, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used to evaluate model performance. Results showed that the AUC of the predictive model in the training set was 0.884 (95% CI 0.861 to 0.908, p < 0.001) and C-statistic of the nomogram was 0.884. The AUC of internal validation set was 0.883 (95% CI 0.844 to 0.922, p < 0.001), and the AUC of external validation set was 0.858 (95% CI 0.820 to 0.896, p < 0.001). In conclusion, the FS predictive model constructed based on CBCs in this study exhibits good predictive ability and has clinical application value.

Optimal treatment strategy and prognostic analysis for hypopharyngeal squamous-cell carcinoma patients with T3-T4 or node-positive: A population-based study

PURPOSE

To explore the optimal treatment strategy and relevant prognostic analysis for hypopharyngeal squamous-cell carcinoma patients (HSCC) with T3-T4 or node-positive.

METHODS AND MATERIALS

From 2004 to 2018, data for 2574 patients from the Surveillance, Epidemiology, and End Results database (SEER) and 66 patients treated at our center from 2013 to 2022 with T3-T4 or N + HSCC were collected. Patients in the SEER cohort were randomly assigned to the training set or validation set at a 7:3 ratio. Variables with statistically significant (P < 0.05) in univariate COX regression analysis or clinical significance were included in the multivariate COX regression model and subsequently used to construct the nomogram.

RESULTS

The 3-year OS (52.9%vs44.4%, P < 0.01) and 3-year CSS rate (58.7%vs51.5%, P < 0.01) rates in the surgery combined with postoperative adjuvant therapy (S + ADT) group were superior to the radiotherapy combined with chemotherapy (CRT) group. The multivariate Cox regression analysis of the training group showed that age, race, marital status, primary site, T stage, N stage, and treatment modalities were correlated with OS and CSS. Based on those variables, we constructed nomograms for OS and CSS. Both the internal and external validation showed high prediction accuracy of the nomogram.

CONCLUSION

Among patients with T3-T4 or node-positive, S + ADT was associated with superior OS and CSS compared to those treated with primary CRT, while the survival rate in the CRT group was comparable to S + ADT group in T2-T3 disease. The internal and external verification shows that the prognostic model has good discrimination ability and accuracy.

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.

Genetic characteristics and clinical-specific survival prediction in elderly patients with gallbladder cancer: a genetic and population-based study

Background

Biliary system cancers are most commonly gallbladder cancers (GBC). Elderly patients (≥ 65) were reported to suffer from an unfavorable prognosis. In this study, we analyzed the RNA-seq and clinical data of elderly GBC patients to derive the genetic characteristics and the survival-related nomograms.

Methods

RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.

Results

Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.

Conclusion

Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.