Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building on this insight, we developed sex-specific 3-year survival predictive models alongside a single comprehensive model. Despite smaller sample sizes, the sex-specific models outperformed the general model. We further refined our models by selecting the most important features from the initial models. The refined sex-specific predictive models achieved higher accuracy and consistently outperformed the refined comprehensive model, highlighting the value of sex-specific analysis. To ensure robustness, all refined sex-specific models were calibrated and then evaluated using an independent dataset. Random Forest models emerged as the most effective predictors, achieving accuracies of 90.33 % for males and 90.40 % for females on the training dataset, and 81.25 % for males and 89.47 % for females on the independent test dataset. These top-performing models were integrated into Gap-App, a web application that leverages individual gene expression profiles and sex information for personalized survival predictions. As the first online tool bridging complex genomic data with clinical application, Gap-App facilitates more precise, individualized cancer care, marking a significant step in personalized prognosis prediction. This study underscores the importance of incorporating sex differences in predictive modeling and sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The Gap-App service is freely available for patients and clinicians at www.gap-app.org.

Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy

INTRODUCTION

About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of diagnosis. These patients carry all the risks of significant morbidity with no survival advantage when compared to nonsurgical options. We aimed to determine if machine learning models have superior accuracy to traditional regression models at predicting futile surgery in patients with PDAC.

METHODS

We analyzed data from patients in the National Cancer Database undergoing PD for PDAC between 2004 and 2020. PD was defined as futile if the patient died within 12 mo of cancer diagnosis. We trained predictive models using 80% of the dataset and 16 preoperative input variables. Models included logistic regression, multilayer perceptron, decision tree, random forest, and gradient boosting classifiers. Models were tested on a 20% test set using area under the receiver operating characteristic curve and Brier scores.

RESULTS

Of the 66,331 patients identified, 34,260 (51.7%) were men, with a median age of 67 y (interquartile range, 59 to 74 y). A total of 16,772 (25.3%) patients met the criteria for futile surgery. The gradient boosting model outperformed other models with an area under the receiver operating characteristic curve of 0.689, followed by logistic regression (0.679), random forest (0.675), and decision tree (0.664). Key predictors of futile PD included advanced age (> 79 y), tumor size ≥ 4 cm, and poor differentiation. Neoadjuvant therapy was associated with lower futility risk.

CONCLUSIONS

We demonstrated the ability of machine learning models to predict the odds of futile PD with moderate accuracy. Although similar analyses are needed on more granular datasets, our study has important implications for shared decision-making and optimized care for patients with PDAC.

A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

Integrating traditional machine learning with qPCR validation to identify solid drug targets in pancreatic cancer: a 5-gene signature study

Background

Pancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.

Materials and methods

Using traditional machine learning methods, including random-effects meta-analysis and forward-search optimization, we developed a robust signature validated across 14 publicly available datasets, achieving a summary AUC of 0.99 in training datasets and 0.89 in external validation datasets. To further validate its clinical relevance, we analyzed 55 peripheral blood samples from pancreatic cancer patients and healthy controls using qPCR.

Results

This study identifies and validates a novel five-gene transcriptomic signature (LAMC2, TSPAN1, MYO1E, MYOF, and SULF1) as both diagnostic biomarkers and potential drug targets for pancreatic cancer. The differential expression of these genes was confirmed, demonstrating their utility in distinguishing cancer from normal conditions with an AUC of 0.83. These findings establish the five-gene signature as a promising tool for both early, non-invasive diagnostics and the identification of actionable drug targets.

Conclusion

A five-gene signature is established robustly and has utility in diagnostics and therapeutic targeting. These findings lay a foundation for developing diagnostic tests and targeted therapies, potentially offering a pathway toward improved outcomes in pancreatic cancer management.

Bayesan Model to Predict R Status After Neoadjuvant Therapy in Pancreatic Cancer

OBJECTIVE

To build a Bayesian approach-based model to predict the success of surgical exploration post-neoadjuvant treatment.

BACKGROUND

Pancreatic cancer (PDAC) is best treated with radical surgery and chemotherapy, offering the greatest chance of survival. Surgery after neoadjuvant treatment (NAT) is indicated in the absence of progression, knowing the limits in accurately predicting resectability with traditional radiology. R Status being a pathological parameter, it can be assessed only after surgery.

METHOD

Patients successfully resected for histologically confirmed PDAC after NAT for BR and LA disease were included, with attention to the predictors of R status from the existing literature. The Bayesian logistic regression model was estimated for predicting the R1 status. The area under curve (AUC) of the average posterior probability of R1 was calculated and results were reported considering the 95% posterior credible intervals for the odds ratios, along with the probability of direction.

RESULTS

The final model demonstrated a commendable AUC value of 0.72, indicating good performance. The likelihood of positive margins was associated with older age, higher ASA score, the presence of venous and/or arterial involvement at preoperative radiology, tumor location within the pancreatic body, a lack of tumor size reduction post-NAT, and the persistence of an elevated Ca19.9 value.

CONCLUSIONS

A Bayesian approach using only preoperative items is firstly used with good performance to predict R Status in pancreatic cancer patients who underwent resection after neoadjuvant therapy.

Prognostic implication of extra-pancreatic organ invasion in resectable pancreas ductal adenocarcinoma in the pancreas tail

OBJECTIVES

To assess the prognostic significance of extra-pancreatic organ invasion in patients with resectable pancreatic ductal adenocarcinoma (PDAC) in the pancreas tail.

MATERIALS & METHODS

This retrospective study included patients with resectable PDAC in the pancreas tail who received upfront surgery between 2014 and 2020 at a tertiary institution. Preoperative pancreas protocol computed tomography (CT) scans evaluated tumor size, peripancreatic tumor infiltration, suspicious metastatic lymph nodes, and extra-pancreatic organ invasion. The influence of extra-pancreatic organ invasion, detected by CT or postoperative pathology, on pathologic resection margin status was evaluated using logistic regression. The impact on recurrence-free survival (RFS) was analyzed using multivariable Cox proportional hazard models (clinical-CT and clinical-pathologic).

RESULTS

The study included 158 patients (mean age, 65 years ± 8.8 standard deviation; 93 men). Extra-pancreatic organ invasion identified by either CT (p = 0.92) or pathology (p = 0.99) was not associated with a positive resection margin. Neither CT (p = 0.42) nor pathological (p = 0.64) extra-pancreatic organ invasion independently correlated with RFS. Independent predictors for RFS included suspicious metastatic lymph node (hazard ratio [HR], 2.05; 95 % confidence interval [CI], 1.08-3.9; p = 0.03) on CT in the clinical-CT model, pathological T stage (HR, 2.97; 95 % confidence interval [CI], 1.39-6.35; p = 0.005 for T2 and HR, 3.78; 95 % CI, 1.64-8.76; p = 0.002 for T3) and adjuvant therapy (HR, 0.62; 95 % confidence interval [CI], 0.42-0.92; p = 0.02) in the clinical-pathologic model.

CONCLUSION

Extra-pancreatic organ invasion does not independently influence pathologic resection margin status and RFS in patients with resectable PDAC in the pancreas tail after curative-intent resection; therefore, it should not be considered a high-risk factor.

Clinical usefulness of C-reactive protein-albumin-lymphocyte (CALLY) index as a prognostic biomarker in patients undergoing surgical resection of pancreatic cancer

PURPOSE

The C-reactive protein-albumin-lymphocyte (CALLY) index, which simultaneously evaluates the nutritional, immunological, and inflammatory statuses, is a new prognostic biomarker in patients with various cancers; however, no study has reported the clinical significance of the CALLY index in patients with pancreatic cancer. This study aimed to investigate whether the preoperative CALLY index is a prognostic biomarker in patients undergoing surgical resection of pancreatic cancer.

METHODS

We retrospectively enrolled 461 patients with pancreatic cancer who underwent surgical resection between January 2013 and December 2022. The overall survival (OS) and relapse-free survival (RFS) rates were calculated using the Kaplan-Meier method. Univariate and multivariate analyses were performed using Cox proportional hazards regression models.

RESULTS

The optimal cut-off value for the preoperative CALLY index was 1.9. In the low CALLY group, patients were older (p = 0.012), more patients underwent pancreaticoduodenectomy (p = 0.002), the median tumor size was larger (p < 0.001), more patients had pathologically confirmed metastatic lymph nodes (p = 0.015) and worse pathological stage (p = 0.015), and fewer patients received adjuvant chemotherapy (p = 0.003). A low CALLY index was associated with decreased OS (22.1 vs. 37.9 months) and RFS (12.4 vs. 16.4 months). Univariate and multivariate analyses showed that the preoperative CALLY index was an independent prognostic factor for OS (p < 0.001) and RFS (p = 0.045).

CONCLUSION

The preoperative CALLY index is a prognostic biomarker for both OS and RFS in patients undergoing surgery for pancreatic cancer.

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.

Predicting Recurrence-Free Survival After Upfront Surgery in Resectable Pancreatic Ductal Adenocarcinoma: A Preoperative Risk Score Based on CA 19-9, CT, and 18F-FDG PET/CT

OBJECTIVE

To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Patients with resectable PDAC who underwent upfront surgery between 2014 and 2017 (development set) or between 2018 and 2019 (test set) were retrospectively evaluated. In the development set, a risk-scoring system was developed using the multivariable Cox proportional hazards model, including variables associated with RFS. In the test set, the performance of the risk score was evaluated using the Harrell C-index and compared with that of the postoperative pathological tumor stage.

RESULTS

A total of 529 patients, including 335 (198 male; mean age ± standard deviation, 64 ± 9 years) and 194 (103 male; mean age, 66 ± 9 years) patients in the development and test sets, respectively, were evaluated. The risk score included five variables predicting RFS: tumor size (hazard ratio [HR], 1.29 per 1 cm increment; P < 0.001), maximal standardized uptake values of tumor ≥ 5.2 (HR, 1.29; P = 0.06), suspicious regional lymph nodes (HR, 1.43; P = 0.02), possible distant metastasis on 18F-FDG PET/CT (HR, 2.32; P = 0.03), and CA 19-9 (HR, 1.02 per 100 U/mL increment; P = 0.002). In the test set, the risk score showed good performance in predicting RFS (C-index, 0.61), similar to that of the pathologic tumor stage (C-index, 0.64; P = 0.17).

CONCLUSION

The proposed risk score based on preoperative CA 19-9, CT, and 18F-FDG PET/CT variables may have clinical utility in selecting high-risk patients with resectable PDAC.

Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

BACKGROUND AND AIM

Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study.

MATERIALS AND METHODS

In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm.

RESULTS

The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction.

CONCLUSION

The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background

Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models.

Methods

To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models.

Results

We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154).

Conclusion

The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

The pan - COVID - AGICT study. The impact of COVID-19 pandemic on surgically treated pancreatic cancer patients. A multicentric Italian study

BACKGROUND

In this article we aimed to perform a subgroup analysis using data from the COVID-AGICT study, to investigate the perioperative outcomes of patients undergoing surgery for pancreatic cancers (PC) during the COVID-19 pandemic.

METHODS

The primary endpoint of the study was to find out any difference in the tumoral stage of surgically treated PC patients between 2019 and 2020. Surgical and oncological outcomes of the entire cohort of patients were also appraised dividing the entire peri-pandemic period into six three-month timeframes to balance out the comparison between 2019 and 2020.

RESULTS

Overall, a total of 1815 patients were surgically treated during 2019 and 2020 in 14 Italian surgical Units. In 2020, the rate of patients treated with an advanced pathological stage was not different compared to 2019 (p = 0.846). During the pandemic, neoadjuvant chemotherapy (NCT) has dropped significantly (6.2% vs 21.4%, p < 0.001) and, for patients who didn't undergo NCT, the latency between diagnosis and surgery was shortened (49.58 ± 37 days vs 77.40 ± 83 days, p < 0.001). During 2020 there was a significant increase in minimally invasive procedures (p < 0.001). The rate of postoperative complication was the same in the two years but during 2020 there was an increase of the medical ones (19% vs 16.1%, p = 0.001).

CONCLUSIONS

The post-pandemic dramatic modifications in healthcare provision, in Italy, did not significantly impair the clinical history of PC patients receiving surgical resection. The present study is one of the largest reports available on the argument and may provide the basis for long-term analyses.

Can preoperative Carbohydrate Antigen 19-9 predict metastatic pancreatic cancer? Results of a systematic review and meta-analysis

BACKGROUND

To investigate the relationship between preoperative Carbohydrate Antigen19-9(CA19-9)and pancreatic cancer occult metastasis.

METHODS

Systematic search of MEDLINE, CENTRAL, Web of Science and bibliographic reference lists were conducted. All comparative observational studies investigating the predictive ability of preoperative CA 19-9 in patients with pancreatic cancer were considered. Mean CA-19-9 value in the pancreatic cancer patients with and without metastasis were evaluated. Best cut-off value of CA 19-9 for metastasis was determined using ROC analysis.

RESULTS

Ten comparative observational studies reporting a total of 1431 pancreatic cancer patients with (n = 496) and without (n = 935) metastasis were included. Subsequent meta-analysis demonstrated that mean preoperative CA 19-9 level was significantly higher in patients with metastases compared to those without (MD: 904.4; 95 % CI, 642.08-1166.74, P < 0.0001). The between-study heterogeneity was significant (I2: 99 %, P < 0.00001). ROC analysis yielded a cut-off CA 19-9 level of 336 with a sensitivity and specificity for predicting metastasis of 90 % and 80 %, respectively (AUC = 0.90).

CONCLUSIONS

CA 19-9 level is significantly higher in patients with metastatic pancreatic cancer. A preoperative CA 19-9 value of 336 should be considered as an acceptable cut-off value to design prospective studies.

Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features

OBJECTIVES

To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer.

METHODS

We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity.

RESULTS

Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort.

CONCLUSIONS

The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.

Acquired and intrinsic gemcitabine resistance in pancreatic cancer therapy: Environmental factors, molecular profile and drug/nanotherapeutic approaches

A high number of cancer patients around the world rely on gemcitabine (GEM) for chemotherapy. During local metastasis of cancers, surgery is beneficial for therapy, but dissemination in distant organs leads to using chemotherapy alone or in combination with surgery to prevent cancer recurrence. Therapy failure can be observed as a result of GEM resistance, threatening life of pancreatic cancer (PC) patients. The mortality and morbidity of PC in contrast to other tumors are increasing. GEM chemotherapy is widely utilized for PC suppression, but resistance has encountered its therapeutic impacts. The purpose of current review is to bring a broad concept about role of biological mechanisms and pathways in the development of GEM resistance in PC and then, therapeutic strategies based on using drugs or nanostructures for overcoming chemoresistance. Dysregulation of the epigenetic factors especially non-coding RNA transcripts can cause development of GEM resistance in PC and miRNA transfection or using genetic tools such as siRNA for modulating expression level of these factors for changing GEM resistance are suggested. The overexpression of anti-apoptotic proteins and survival genes can contribute to GEM resistance in PC. Moreover, supportive autophagy inhibits apoptosis and stimulates GEM resistance in PC cells. Increase in metabolism, glycolysis induction and epithelial-mesenchymal transition (EMT) stimulation are considered as other factors participating in GEM resistance in PC. Drugs can suppress tumorigenesis in PC and inhibit survival factors and pathways in increasing GEM sensitivity in PC. More importantly, nanoparticles can increase pharmacokinetic profile of GEM and promote its blood circulation and accumulation in cancer site. Nanoparticles mediate delivery of GEM with genes and drugs to suppress tumorigenesis in PC and increase drug sensitivity. The basic research displays significant connection among dysregulated pathways and GEM resistance, but the lack of clinical application is a drawback that can be responded in future.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9

OBJECTIVES

To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model.

RESULTS

The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort).

CONCLUSIONS

The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making.

CLINICAL RELEVANCE STATEMENT

We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments.

KEY POINTS

• Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Accuracy of models to prognosticate survival after surgery for pancreatic cancer in the era of neoadjuvant therapy

BACKGROUND

Outcomes for pancreatic adenocarcinoma (PDAC) remain difficult to prognosticate. Multiple models attempt to predict survival following the resection of PDAC, but their utility in the neoadjuvant population is unknown. We aimed to assess their accuracy among patients that received neoadjuvant chemotherapy (NAC).

METHODS

We performed a multi-institutional retrospective analysis of patients who received NAC and underwent resection of PDAC. Two prognostic systems were evaluated: the Memorial Sloan Kettering Cancer Center Pancreatic Adenocarcinoma Nomogram (MSKCCPAN) and the American Joint Committee on Cancer (AJCC) staging system. Discrimination between predicted and actual disease-specific survival was assessed using the Uno C-statistic and Kaplan-Meier method. Calibration of the MSKCCPAN was assessed using the Brier score.

RESULTS

A total of 448 patients were included. There were 232 (51.8%) females, and the mean age was 64.1 years (±9.5). Most had AJCC Stage I or II disease (77.7%). For the MSKCCPAN, the Uno C-statistic at 12-, 24-, and 36-month time points was 0.62, 0.63, and 0.62, respectively. The AJCC system demonstrated similarly mediocre discrimination. The Brier score for the MSKCCPAN was 0.15 at 12 months, 0.26 at 24 months, and 0.30 at 36 months, demonstrating modest calibration.

CONCLUSIONS

Current survival prediction models and staging systems for patients with PDAC undergoing resection after NAC have limited accuracy.

Preoperative C-Reactive Protein-to-Albumin Ratio and Its Ability to Predict Outcomes of Pancreatic Cancer Resection: A Systematic Review

OBJECTIVES

To evaluate the ability of the c-reactive protein-to-albumin ratio (CAR) in predicting outcomes in patients undergoing pancreatic cancer resection.

METHODS

A systematic search of electronic information sources and bibliographic reference lists was conducted. Survival outcomes and perioperative morbidity were the evaluated outcome parameters.

RESULTS

Eight studies reporting a total of 1056 patients undergoing pancreatic cancer resection were identified. The median cut-off value for CAR was 0.05 (range 0.0003-0.54). Using multivariate analysis, all studies demonstrated that a higher CAR value was an independent and significant predictor of poor overall survival in patients undergoing pancreatic cancer resection. The estimated hazard ratio (HR) ranged from 1.4 to 3.6. Although there was a positive correlation between the reported cut-off values for CAR and HRs for overall survival, it was weak and non-significant (r = 0.36, n = 6, p = 0.480). There was significant between-study heterogeneity.

CONCLUSIONS

Preoperative CAR value seems to be an important prognostic score in predicting survival outcomes in patients undergoing pancreatic cancer resection. However, the current evidence does not allow the determination of an optimal cut-off value for CAR, considering the heterogeneous reporting of cut-off values by the available studies and the lack of knowledge of their sensitivity and specificity. Future research is required.

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

BACKGROUND

When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes toward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma.

METHODS

Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neoadjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neoadjuvant therapy.

RESULTS

The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P < .001). The regret of the commission was diametrically opposite to the regret of omission (P ≤ .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P ≤ .001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy.

CONCLUSION

Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

OBJECTIVE

We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.

MATERIALS AND METHODS

We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS).

RESULTS

Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated.

CONCLUSIONS

Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

Survival prediction for patients with malignant biliary obstruction caused by pancreatic cancer undergoing biliary drainage: the COMBO-PaS model

BACKGROUND

Patients with pancreatic cancer-caused biliary obstruction (PC-BO) have poor prognosis, but we lack of tools to predict survival for clinical decision-making. This study aims to establish a model for survival prediction among patients with PC-BO.

METHODS

A total of 172 patients with PC-BO treated with percutaneous biliary drainage were randomly divided into a training group (n = 120) and a validation group (n = 52). The independent risk factors for overall survival were selected to develop a Cox model. The predictive performance of M stage, hepatic metastases, cancer antigen 199, and the Cox model was determined. Naples prognostic score (NPS), the prognostic nutritional index (PNI), and the controlling nutritional status (CONUT) for 1-month mortality risk were compared with the Cox model.

RESULTS

The Cox model was developed based on total cholesterol, direct bilirubin, hepatic metastases, cancer antigen 199, stenosis type, and preprocedural infection (all P < 0.05), which named "COMBO-PaS." The COMBO-PaS model had the highest area under the curves (AUC) (0.801-0.933) comparing with other predictors (0.506-0.740) for 1-, 3-, and 6-month survival prediction. For 1-month mortality risk prediction, the COMBO-PaS model had the highest AUC of 0.829 comparing with NPS, PNI, and CONUT.

CONCLUSION

The COMBO-PaS model was useful for survival prediction among patients with PC-BO.

Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19.

METHODS

The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance.

RESULTS

In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09).

CONCLUSION

The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.

Trial sequential analysis of randomized controlled trials on neoadjuvant therapy for resectable pancreatic cancer

INTRODUCTION

Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence but can suffer from type I (false-positive) and II (false-negative) errors, which can be estimated through trial sequential analysis (TSA) demonstrating eventual credibility of results. Aim of the study was to establish through TSA which strategy between neoadjuvant approach or upfront surgery provides best results when treating potentially resectable pancreatic adenocarcinoma.

MATERIALS AND METHODS

RCTs were searched until September 2021. Intention-to-treat (ITT) overall survival, resection rate, ITT R0 and N0 rates and per-protocol R0 and N0 rates were the outcomes considered. Fixed-effect model was applied. TSA assumed an alpha = 5% and a power = 80%.

RESULTS

Four RCTs were identified accruing 325 patients for the ITT analyses and 242 for the per-protocol analyses. Neoadjuvant did not improve survival (p = 0.167) and TSA supported that this result was underpowered, requiring additional 1514 patients to prove credibility. Neoadjuvant reduced resection rate (p = 0.044) but type I error was not avoided. Neoadjuvant credibly increased per-protocol R0 and N0 rates (p = 0.003 and p < 0.001), and TSA showed that these were true-positive findings. Neoadjuvant did not increase ITT R0 rate since randomization (p = 0.169) but TSA showed lack of power. Neoadjuvant credibly increased the ITT N0 rate (p < 0.001) and TSA supported that this was a true positive finding.

CONCLUSIONS

Neoadjuvant strategy credibly demonstrated superiority over upfront surgery in determine per-protocol R0 resection and N0 rates, as well as ITT N0 rate. For the remaining outcomes, TSA suggested the need of larger samples to exclude type I and II errors.

A predictive model for recurrence after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC) by using preoperative clinical data and CT characteristics

BACKGROUND

The overall survival for patients with resectable PDAC following curative surgical resection hasn't been improved significantly, as a considerable proportion of patients develop recurrence within a year. The purpose of this study was to develop and validate a predictive model to assess recurrence risk in patients with PDAC after upfront surgery by using preoperative clinical data and CT characteristics.

METHODS

The predictive model was developed based on a retrospective set of 141 pancreatic cancer patients after surgery. A separate set of 77 patients was used to validate model. Between January 2017 and December 2019, all patients underwent multidetector pancreatic CT and upfront surgery. Univariable and multivariate Cox regression was used to determine the risk factors related to recurrence and then establish a nomogram to estimate the 1-year recurrence probability. The Harrell C-index was employed in evaluating the discrimination and calibration of the model.

RESULTS

A total of 218 patients in this retrospective cohort. A recurrence model in nomogram form was developed with predictors including tumor size (hazard ratio [HR], 1.277; 95% CI 1.098, 1.495; P = 0.002), tumor density in the portal vein phase (HR, 0.598; 95% CI 0.424, 0.844; P = 0.003), peripancreatic infiltration (HR, 4.151; 95% CI 2.077, 8.298; P < 0.001), suspicious metastatic lymph node (HR, 2.561; 95% CI 1.653, 3.967; P < 0.001), Neutrophils/Lymphocytes ratio (HR, 1.111; 95% CI 1.016, 1.215; P = 0.020). The predictive nomogram had good discrimination capability with these predictors with an area under curve at 1 year of 0.84 (95%CI 0.77, 0.91) in the development set and 0.82 (95% CI 0.72, 0.92) and 0.84 (95% CI 0.74, 0.94) in the validation set for two radiologists reading respectively.

CONCLUSIONS

The model developed based on preoperative clinical data and CT characteristics of resectable pancreatic ductal adenocarcinoma patients, which can helpfully estimate the recurrence-free survival. It may be a useful tool for clinician to select optimal candidates for upfront surgery or neoadjuvant therapy.

Controlling nutritional status (CONUT) score-based nomogram to predict overall survival of patients with pancreatic cancer undergoing radical surgery

BACKGROUND

/Objective: As a new immune-nutritional marker, the controlled nutritional status (CONUT) score has been reported to predict the prognosis of cancer patients. We aimed to elucidate the prognostic value of preoperative CONUT score in pancreatic cancer patients undergoing radical surgery, and to construct a nomogram based on CONUT score to predict individual survival.

METHODS

Preoperative CONUT scores were calculated prospectively in 382 patients with pancreatic cancer who underwent radical surgery. Evaluated the relationship between CONUT score and pancreatic cancer prognosis. Cox proportional hazard models were used to determine predictors of survival and a new nomogram was established to predict pancreatic cancer overall survival (OS).

RESULTS

The area under curve of CONUT score was higher than other immune-nutritional indexes. The OS of the high-CONUT group were significantly lower than that of low-CONUT group. Multivariate analysis showed that CONUT score, gender, AJCC stage, complications and reoperation were independent prognostic factors for OS. Nomogram based on these variables has better discriminant ability in predicting survival compared with other traditional staging systems.

CONCLUSIONS

Preoperative CONUT score is an effective independent predictor of OS in pancreatic cancer patients undergoing radical surgery. This new CONUT based nomogram provides accurate, individualized survival prediction for pancreatic cancer.

Prognostic stratification in patients with pancreatic ductal adenocarcinoma after curative resection based on preoperative pancreatic contrast-enhanced CT findings

PURPOSE

To establish a prognostic stratification model for predicting prognosis in patients with pancreatic ductal adenocarcinoma (PDAC) after curative resection based on preoperative contrast-enhanced computed tomography (CECT) findings.

METHOD

From January 2014 to June 2020, 126 patients with radically resected PDAC were reviewed and divided into a development cohort (n = 90) and a validation cohort (n = 36). In the development cohort, clinicopathological parameters and preoperative CECT findings associated with recurrence-free survival (RFS) and overall survival (OS) were identified by using univariate and multivariate analyses. Nomograms were constructed based on Cox proportional hazards regression models. New prognostic nomograms were certificated in the validation cohort. Model performance was evaluated based on calibration, discrimination, and clinical utility.

RESULTS

Tumor size >4 cm, adjacent organs invasion, suspicious lymph nodes, and rim enhancement were independently associated with worse RFS and OS (all P values were < 0.05). In the validation cohort, the nomograms based on pancreatic CECT showed good discrimination capability and outperformed the TNM staging system in outcomes prediction. Patients were stratified into low- and high-risk groups based on nomograms, and RFS and OS rates in the low-risk group were significantly higher than those in the high-risk group (P < 0.001 and <0.01, respectively).

CONCLUSIONS

Nomograms based on preoperative pancreatic CECT findings can predict RFS and OS for PDAC patients after curative resection and facilitate further prognostic stratification.

Development and external validation of a prediction model for overall survival after resection of distal cholangiocarcinoma

BACKGROUND

Various prognostic factors are associated with overall survival (OS) after resection of distal cholangiocarcinoma (dCCA). The objective of this study was to develop and validate a prediction model for 3-year OS after pancreatoduodenectomy for dCCA.

METHODS

The derivation cohort consisted of all patients who underwent pancreatoduodenectomy for dCCA in the Netherlands (2009-2016). Clinically relevant variables were selected based on the Akaike information criterion using a multivariate Cox proportional hazards regression model, with model performance being assessed by concordance index (C-index) and calibration plots. External validation was performed using patients from the Belgium Cancer Registry (2008-2016), and patients from two university hospitals of Southampton (U.K.) and Verona (Italy).

RESULTS

Independent prognostic factors for OS in the derivation cohort of 454 patients after pancreatoduodenectomy for dCCA were age (HR 1.02, 95% CI 1.01-1.03), pT (HR 1.43, 95% CI 1.07-1.90) and pN category (pN1: HR 1.78, 95% CI 1.37-2.32; pN2: HR 2.21, 95% CI 1.63-3.01), resection margin status (HR 1.79, 95% CI 1.39-2.29) and tumour differentiation (HR 2.02, 95% CI 1.62-2.53). The prediction model was based on these prognostic factors. The optimism-adjusted C-indices were similar in the derivation cohort (0.69), and in the Belgian (0.66) and Southampton-Verona (0.68) validation cohorts. Calibration was accurate in the Belgian validation cohort (slope = 0.93, intercept = 0.12), but slightly less optimal in the Southampton-Verona validation cohort (slope = 0.88, intercept = 0.32). Based on this model, three risk groups with different prognoses were identified (3-year OS of 65.4%, 33.2% and 11.8%).

CONCLUSIONS

The prediction model for 3-year OS after resection of dCCA had reasonable performance in both the derivation and geographically external validation cohort. Calibration slightly differed between validation cohorts. The model is readily available via www. pancreascalculator.com to inform patients from Western European countries on their prognosis, and may be used to stratify patients for clinical trials.

Prognostic impact of para-aortic lymph node status in resected pancreatic ductal adenocarcinoma and invasive intraductal papillary mucinous neoplasm - Time to consider a reclassification?

BACKGROUND

Para-aortic lymph node (PALN) metastases in pancreatic ductal adenocarcinoma (PDAC) correlates with poor prognosis. The role of PALN in invasive intraductal papillary mucinous neoplasms (inv-IPMN) has not been well explored. The present study investigated the rate of metastatic PALN, lymph node ratio (LNR) and the overall nodal (N) status as prognostic factors in PDAC and inv-IPMN.

METHODS

This consecutive single-center series included patients with PDAC or inv-IPMN in the pancreatic head who underwent pancreatoduodenectomy or total pancreatectomy, including PALN resection between 2009 and 2018. Median overall survival (mOS) and impact of clinicopathological factors, including PALN status on survival, were evaluated.

RESULTS

403 patients were included, 314 had PDAC and 89 inv-IPMN. PALN were metastatic in 16% of PDAC and 17% of inv-IPMN. N0 status was present in 6% of the patients with PDAC and 16% of inv-IPMN patients (p = 0.007). LNR >15% was more common in PDAC (52%) than in inv-IPMN (34%) (p = 0.004). mOS was 12.7 months in the presence of PALN metastases and 22.7 months without (p < 0.0001). Age >70 years, CA19-9 >200 U/mL, PDAC and N2 status were significantly associated with worse survival in a multivariable analysis. PALN status and LNR were not independent prognostic factors. In N2 status mOS was similar regardless the presence of PALN metastases.

CONCLUSION

The frequency of PALN metastases was similar in PDAC and inv-IPMN. Although PALN positive status entailed a shorter mOS, it was not an independent risk factor for death, and did not influence survival in N2-staged disease. The M1-status for PALN positivity may need reconsideration.

Quantitative assessment of the impact of COVID-19 pandemic on pancreatic surgery: an Italian multicenter analysis of 1423 cases from 10 tertiary referral centers

Few evidences are present on the consequences of coronavirus disease 2019 (COVID-19) pandemic on pancreatic surgery. Aim of this study is to evaluate how COVID-19 influenced the diagnostic and therapeutic pathways of surgical pancreatic diseases. A comparative analysis of surgical volumes and clinical, surgical and perioperative outcomes in ten Italian referral centers was conducted between the first semester 2020 and 2019. One thousand four hundred and twenty-three consecutive patients were included in the analysis: 638 from 2020 and 785 from 2019. Surgical volume in 2020 decreased by 18.7% (p < 0.0001). Benign/precursors diseases (- 43.4%; p < 0.0001) and neuroendocrine tumors (- 33.6%; p = 0.008) were the less treated diseases. No difference was reported in terms of discussed cases at the multidisciplinary tumor board (p = 0.43), mean time between diagnosis and neoadjuvant treatment (p = 0.91), indication to surgery and surgical resection (p = 0.35). Laparoscopic and robot-assisted procedures dropped by 45.4% and 61.9%, respectively, during the lockdown weeks of 2020. No difference was documented for post-operative intensive care unit accesses (p = 0.23) and post-operative mortality (p = 0.06). The surgical volume decrease in 2020 will potentially lead, in the near future, to the diagnosis of a higher rate of advanced stage diseases. However, the reassessment of the Italian Health Service kept guarantying an adequate level of care in tertiary referral centers. Clinicaltrials.gov ID: NCT04380766.

Appraisal of multivariable prognostic models for post-operative liver decompensation following partial hepatectomy: a systematic review

BACKGROUND

Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable prognostic models for post-operative liver decompensation following partial hepatectomy.

METHODS

This study was designed using the CHARMS checklist. Following a comprehensive literature search, two reviewers independently screened candidate references for inclusion and abstracted relevant study details. Qualitative assessment was performed using the PROBAST tool.

RESULTS

We identified 36 prognostic modelling studies; 25 focused on development only, 3 developed and validated models, and 8 validated pre-existing models. None compared routine use of a prognostic model against standard clinical practice. Most studies used single-institution, retrospective cohort designs, conducted in Eastern populations. In total, 15 different outcome definitions for post-operative liver decompensation events were used. Statistical concerns surrounding model overfitting, performance assessment, and internal validation led to high risk of bias for all studies.

CONCLUSIONS

Current prognostic models for post-operative liver decompensation following partial hepatectomy may not be valid for routine clinical use due to design and methodologic concerns. Landmark resources and reporting guidelines such as the TRIPOD statement may assist researchers, and additionally, model impact assessment studies represent opportunities for future research.

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

OBJECTIVE

Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD.

RESULTS

Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]).

CONCLUSION

Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach

Simple Summary

If pancreatic adenocarcinoma is assessed to be technically resectable, curative surgery is still suggested as the primary treatment option; however, the recurrence rate can be very high even in this selected population. The aim of our retrospective study was to develop a preoperative model to accurately stratify upfront resectable patients according to the risk of early distant disease relapse after surgery (<12 months from index procedure). Through a machine learning-based approach, we identified one biochemical marker (serum level of CA19.9), one radiological finding (necrosis) and one radiomic feature (SurfAreaToVolumeRatio), all significantly associated with the early resurge of distant recurrence. A model composed of these three variables only allowed identification of those patients at high risk for early distant disease relapse (50% chance of developing metastases within 12 months after surgery), who would benefit from neoadjuvant chemotherapy instead of upfront surgery.

Abstract

Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (n = 94) and validation cohort (n = 53). Multivariable Cox regression analysis was first applied on the training cohort; the resulting prognostic index was then tested in the validation cohort. Clinical (serum level of CA19.9), radiological (necrosis), and radiomic (SurfAreaToVolumeRatio) features were significantly associated with the early resurge of distant recurrence. The model combining these three variables performed well in the training cohort (p = 0.0015, HR = 3.58, 95%CI = 1.98–6.71) and was then confirmed in the validation cohort (p = 0.0178, HR = 5.06, 95%CI = 1.75–14.58). The comparison of survival curves between low and high-risk patients showed a p-value <0.0001. Our model may help to better define resectability status, thus providing an actual aid for pancreatic adenocarcinoma patients’ management (upfront surgery vs. neoadjuvant chemotherapy). Independent validations are warranted.

Complete circumferential lymphadenectomy around the superior mesenteric artery with preservation of nerve plexus reduces locoregional recurrence after pancreatoduodenectomy for resectable pancreatic ductal adenocarcinoma

BACKGROUND

Evaluation of recurrence pattern and risk factors for recurrence are essential for good rates of survival after upfront pancreatoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective study included 167 consecutive patients who underwent upfront PD for resectable PDAC between 2000 and 2018. Postoperative recurrences were classified into three patterns according to initial recurrence site: isolated locoregional, isolated distant, and simultaneous locoregional and distant recurrences.

RESULTS

This study found 114 patients who developed postoperative recurrence (68.3%), including 37 patients with isolated locoregional recurrence (32.5%), 67 patients with isolated distant recurrence (58.8%), and 10 patients with simultaneous locoregional and distant recurrences (6.0%). When locoregional recurrence was classified based on the location of recurrent lesions, locoregional recurrence most commonly occurred around the superior mesenteric artery (SMA) (70.2%), followed by around the hepatic artery (25.5%) and in the paraaortic region (14.9%). Multivariate analyses showed that complete circumferential lymphadenectomy around the SMA, including not only the right side, but also the left side, was an independent factor for reduction of locoregional recurrence (P = 0.019, odds ratio [OR]: 2.217). Lymph node metastasis was an independent risk factor for both locoregional (P < 0.001, OR: 3.686) and distant recurrences (P < 0.001, OR: 4.315). Non-completion of postoperative adjuvant therapy was a risk factor for distant recurrence (P < 0.001, OR: 3.748).

CONCLUSION

Based on our data, complete circumferential lymphadenectomy around the SMA might contribute to local control, and multidisciplinary treatment including neoadjuvant therapy might be needed for resectable PDAC with high risk for recurrence.

A Prognostic Score for Predicting Survival in Patients With Pancreatic Head Adenocarcinoma and Distal Cholangiocarcinoma

BACKGROUND/AIM

Survival of patients with pancreatic cancer remains poor despite improvements in therapeutic strategies. This study aims to create a novel preoperative score to predict prognosis in patients with tumors of the pancreaticobiliary head.

PATIENTS AND METHODS

Data on 190 patients who underwent to pancreaticoduodenectomy at Sapienza University of Rome from January 2010 to December 2018 were retrospectively analyzed. After exclusion criteria, 101 patients were considered eligible for retrospective study. Preoperative biological, clinical and radiological parameters were considered.

RESULTS

Pancreatic ductal adenocarcinoma [hazard ratio (HR)=1.995, 95% confidence intervaI (CI)=1.1-3.3; p=0.01], carbohydrate antigen 19.9 (CA 19.9) >230 U/ml (HR=2.414, 95% CI=2.4-1.5, p<0.0001) and Wirsung duct diameter >3 mm (HR=1.592, 95% CI=1.5-0.9; p=0.08) were the only parameters associated with poor prognosis. Through these parameters, a prognostic score (PHT score) was developed which predicted worst survival when exceeding 2 and better survival when ≤2.

CONCLUSION

The PHT score may have a potential impact on predicting overall survival and consequently modulate the timing and type of treatment (up-front surgery vs. neoadjuvant therapy) patients are offered.

The ultimate preoperative C-reactive protein-to-albumin ratio is a prognostic factor for survival after pancreatic cancer resection

Background

Emerging evidence indicates that an elevated C-reactive protein-to-albumin ratio (CAR) may be associated with a poor prognosis in pancreatic ductal adenocarcinoma (PDAC). Further evidence showing that this ratio has significant prognostic value could contribute to current prediction models and clinical decision-making.

Methods

Data were analysed of consecutive patients who underwent curative pancreatic resection between 2013 and 2018 and were histologically diagnosed with PDAC. We investigated the relation between the ultimate preoperative CAR and overall survival.

Results

A total of 163 patients were analysed. Median overall survival was 18 months (IQR 9–36). Multivariate analysis demonstrated that a higher CAR (HR 1.745, P = 0.004), a higher age (HR 1.062, P < 0.001), male sex (HR 1.977, P = 0.001), poor differentiation grade (HR 2.812, P < 0.001), and positive para-aortic lymph node(s) (HR 4.489, P < 0.001) were associated with a lower overall survival. Furthermore, a CAR ≥ 0.2 was associated with decreased overall survival (16 vs. 26 months, P = 0.003).

Conclusion

We demonstrated that an ultimate preoperative elevated CAR is an independent indicator of decreased overall survival after resection for PDAC. The preoperative CAR may be of additional value to the current prediction models.

Equipping the American Joint Committee on Cancer Staging for Resectable Pancreatic Ductal Adenocarcinoma with Tumor Grade: A Novel Staging System

Background

The 8th American Joint Committee on Cancer (AJCC) staging system for pancreatic ductal adenocarcinoma (PDAC) outperforms its previous version in reproducibility but not in survival discrimination. Tumor grade, an indicator of the aggressive biology of PDAC, has been suggested as a reliable prognostic factor. This study aimed to construct a novel staging system with greater prognostication for resectable PDAC by incorporating tumor grade into the 8th AJCC system.

Methods

A total of 9966 patients with resectable PDAC from the Surveillance Epidemiology and End Results (SEER) database were randomly separated into training and interval validation sets. Another 324 patients from our center were included as an external validation set. We proposed a novel staging system by sorting the substages yielded by a combination of T, N, and tumor grade based on their overall survival (OS) and grouping them into several stages. Prognostic homogeneity and discrimination were determined using the likelihood ratio χ ² and the linear trend χ ² test, respectively. Prognostic accuracies were evaluated by the area under the receiver operating characteristics curve (AUC).

Results

Using the 8th AJCC system, the prognosis of patients within the same stage was quite heterogeneous among different substages. The multivariate Cox model identified the tumor grade (hazard ratio 1.333, 95% confidence interval 1.250-1.423, p < 0.001) was an independent prognostic factor of the OS. In the training set, the AUC, homogeneity, and discriminatory ability were superior for the novel staging system than for the 8th AJCC system (0.642 vs. 0.615, 403.4 vs. 248.6, and 335.1 vs. 218.0, respectively). Similar results were observed in the internal and external validation sets.

Conclusions

The novel staging system incorporating tumor grade into the 8th AJCC system was associated with better prognostic accuracy, homogeneity, and discriminatory ability among resectable PDAC patients. Moreover, the novel staging system also allowed possibly adjuvant chemotherapy decisions.

Clinical characteristics and blood/serum bound prognostic biomarkers in advanced pancreatic cancer treated with gemcitabine and nab-paclitaxel

BACKGROUND

In recent years treatment options for advanced pancreatic cancer have markedly improved, and a combination regimen of gemcitabine and nab-paclitaxel is now considered standard of care in Sweden and elsewhere. Nevertheless, a majority of patients do not respond to treatment. In order to guide the individual patient to the most beneficial therapeutic strategy, simple and easily available prognostic and predictive markers are needed.

METHODS

The potential prognostic value of a range of blood/serum parameters, patient-, and tumour characteristics was explored in a retrospective cohort of 75 patients treated with gemcitabine/nab-paclitaxel (Gem/NabP) for advanced pancreatic ductal adenocarcinoma (PDAC) in the South Eastern Region of Sweden. Primary outcome was overall survival (OS) while progression free survival (PFS) was the key secondary outcome.

RESULT

Univariable Cox regression analysis revealed that high baseline serum albumin (> 37 g/L) and older age (> 65) were positive prognostic markers for OS, and in multivariable regression analysis both parameters were confirmed to be independent prognostic variables (HR 0.48, p = 0.023 and HR = 0.47, p = 0.039,). Thrombocytopenia at any time during the treatment was an independent predictor for improved progression free survival (PFS) but not for OS (HR 0.49, p = 0.029, 0.54, p = 0.073), whereas thrombocytopenia developed under cycle 1 was neither related with OS nor PFS (HR 0.87, p = 0.384, HR 1.04, p = 0.771). Other parameters assessed (gender, tumour stage, ECOG performance status, myelosuppression, baseline serum CA19-9, and baseline serum bilirubin levels) were not significantly associated with survival.

CONCLUSION

Serum albumin at baseline is a prognostic factor with palliative Gem/NabP in advanced PDAC, and should be further assessed as a tool for risk stratification. Older age was associated with improved survival, which encourages further studies on the use of Gem/NabP in the elderly.

Development and external validation of a prediction model for survival in patients with resected ampullary adenocarcinoma

INTRODUCTION

Ampullary adenocarcinoma (AAC) is a rare malignancy with great morphological heterogeneity, which complicates the prediction of survival and, therefore, clinical decision-making. The aim of this study was to develop and externally validate a prediction model for survival after resection of AAC.

MATERIALS AND METHODS

An international multicenter cohort study was conducted, including patients who underwent pancreatoduodenectomy for AAC (2006-2017) from 27 centers in 10 countries spanning three continents. A derivation and validation cohort were separately collected. Predictors were selected from the derivation cohort using a LASSO Cox proportional hazards model. A nomogram was created based on shrunk coefficients. Model performance was assessed in the derivation cohort and subsequently in the validation cohort, by calibration plots and Uno's C-statistic. Four risk groups were created based on quartiles of the nomogram score.

RESULTS

Overall, 1007 patients were available for development of the model. Predictors in the final Cox model included age, resection margin, tumor differentiation, pathological T stage and N stage (8th AJCC edition). Internal cross-validation demonstrated a C-statistic of 0.75 (95% CI 0.73-0.77). External validation in a cohort of 462 patients demonstrated a C-statistic of 0.77 (95% CI 0.73-0.81). A nomogram for the prediction of 3- and 5-year survival was created. The four risk groups showed significantly different 5-year survival rates (81%, 57%, 22% and 14%, p < 0.001). Only in the very-high risk group was adjuvant chemotherapy associated with an improved overall survival.

CONCLUSION

A prediction model for survival after curative resection of AAC was developed and externally validated. The model is easily available online via www.pancreascalculator.com.

Estimating Recurrence after Upfront Surgery in Patients with Resectable Pancreatic Ductal Adenocarcinoma by Using Pancreatic CT: Development and Validation of a Risk Score

Background No preoperative model is available for predicting postsurgical prognosis of patients with resectable pancreatic ductal adenocarcinoma (PDAC). Purpose To develop and validate a preoperative risk scoring system using clinical and CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable PDAC. Materials and Methods In this retrospective study, consecutive patients with resectable PDAC underwent upfront surgery from January 2014 to December 2015 (development set) and from January 2016 to January 2017 (test set). In the development set, multivariable Cox proportional hazard modeling with bootstrapping was used to select clinical and CT variables associated with RFS and to construct a risk scoring system. The discrimination capability of the risk score was assessed by using the Harrell C-index and compared with that of pathologic American Joint Committee on Cancer tumor stage. The risk score was validated in the test set. Results A total of 395 patients were evaluated, including 262 patients (mean age ± standard deviation, 64 years ± 10; 155 men) in the development set and 133 (mean age, 64 years ± 9; 79 men) in the test set. Five independent variables predicted risk of recurrence or death: tumor size (hazard ratio [HR], 1.23; 95% confidence interval [CI]: 1.05, 1.44; P = .009), hypodense tumor in the portal venous phase (HR, 1.66; 95% CI: 1.01, 2.73; P = .04), tumor necrosis (HR, 2.04; 95% CI: 1.38, 3.03; P < .001), peripancreatic tumor infiltration (HR, 1.50; 95% CI: 1.07, 2.11; P = .02), and suspicious metastatic lymph nodes (HR, 1.94; 95% CI: 1.38, 2.72; P < .001). In the test set, the risk score showed good discrimination capability (C-index of 0.68; 95% CI: 0.63, 0.74) and outperformed the pathologic tumor stage (C-index of 0.60; 95% CI: 0.55, 0.66; P = .03). Patients were categorized into favorable, intermediate, and poor prognosis groups with 1-year RFS of 0.87, 0.58, and 0.26, respectively. Conclusion The presented preoperative risk score can predict recurrence-free survival after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Pandharipande and Anderson in this issue.

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model

Background

Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed.

Methods

This population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005–2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0–5 years after resection was calculated using the Kaplan–Meier method. A prediction model was constructed.

Results

Overall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17–18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics.

Conclusions

This population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via www.pancreascalculator.com to inform patients and caregivers.

Electronic supplementary material

The online version of this article (10.1245/s10434-020-08235-w) contains supplementary material, which is available to authorized users.

International validation and update of the Amsterdam model for prediction of survival after pancreatoduodenectomy for pancreatic cancer

BACKGROUND

The objective of this study was to validate and update the Amsterdam prediction model including tumor grade, lymph node ratio, margin status and adjuvant therapy, for prediction of overall survival (OS) after pancreatoduodenectomy for pancreatic cancer.

METHODS

We included consecutive patients who underwent pancreatoduodenectomy for pancreatic cancer between 2000 and 2017 at 11 tertiary centers in 8 countries (USA, UK, Germany, Italy, Sweden, the Netherlands, Korea, Australia). Model performance for prediction of OS was evaluated by calibration statistics and Uno's C-statistic for discrimination. Validation followed the TRIPOD statement.

RESULTS

Overall, 3081 patients (53% male, median age 66 years) were included with a median OS of 24 months, of whom 38% had N2 disease and 77% received adjuvant chemotherapy. Predictions of 3-year OS were fairly similar to observed OS with a calibration slope of 0.72. Statistical updating of the model resulted in an increase of the C-statistic from 0.63 to 0.65 (95% CI 0.64-0.65), ranging from 0.62 to 0.67 across different countries. The area under the curve for the prediction of 3-year OS was 0.71 after updating. Median OS was 36, 25 and 15 months for the low, intermediate and high risk group, respectively (P < 0.001).

CONCLUSIONS

This large international study validated and updated the Amsterdam model for survival prediction after pancreatoduodenectomy for pancreatic cancer. The model incorporates readily available variables with a fairly accurate model performance and robustness across different countries, while novel markers may be added in the future. The risk groups and web-based calculator www.pancreascalculator.com may facilitate use in daily practice and future trials.

Clinical Implications of Naples Prognostic Score in Patients with Resected Pancreatic Cancer

BACKGROUND

Nutritional and immunological statuses are attracting increasing attention for their ability to predict surgical outcomes in various cancers. The Naples prognostic score (NPS) consists of the serum albumin level, total cholesterol level, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio and could be useful for predicting survival.

PATIENTS AND METHODS

We retrospectively analyzed 196 patients with pancreatic cancer who underwent curative R0/R1 resection with a surgery-first strategy between June 2003 and August 2016. The NPS of the patients was calculated from preoperative data, and the patients were then divided into three groups based on their NPS. Clinicopathological characteristics, surgical outcomes, and long-term survival were compared, and multivariate analysis of overall survival was conducted.

RESULTS

Of a total of 196 patients, 22 were classified into group 0 (NPS 0), 113 into group 1 (NPS 1 or 2), and 61 into group 2 (NPS 3 or 4). Median survival time was 103.4 months in group 0, 33.3 months in group 1, and 21.3 months in group 2. Significant survival differences were observed among the 3 groups (group 1 vs. 2, group 0 vs. 2, P = 0.0380, P = 0.0022, respectively). On multivariate analysis, NPS was identified as an independent prognostic factor [hazard ratio (HR) = 1.78; P = 0.0131]; however, there were no significant differences in the incidence of postoperative morbidity among the NPS groups.

CONCLUSIONS

The NPS could be an easy scoring system and an independent preoperative predictor of survival.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Clinical characteristics and disease-specific prognostic nomogram for primary gliosarcoma: a SEER population-based analysis

Because the study population with gliosarcoma (GSM) is limited, the understanding of this disease is insufficient. In this study, the authors aimed to determine the clinical characteristics and independent prognostic factors influencing the prognosis of GSM patients and to develop a nomogram to predict the prognosis of GSM patients after craniotomy. A total of 498 patients diagnosed with primary GSM between 2004 and 2015 were extracted from the 18 Registries Research Data of the Surveillance, Epidemiology, and End Results (SEER) database. The median disease-specific survival (DSS) was 12.0 months, and the postoperative 0.5-, 1-, and 3-year DSS rates were 71.4%, 46.4% and 9.8%, respectively. We applied both the Cox proportional hazards model and the decision tree model to determine the prognostic factors of primary GSM. The Cox proportional hazards model demonstrated that age at presentation, tumour size, metastasis state and adjuvant chemotherapy (CT) were independent prognostic factors for DSS. The decision tree model suggested that age <71 years and adjuvant CT were associated with a better prognosis for GSM patients. The nomogram generated via the Cox proportional hazards model was developed by applying the rms package in R version 3.5.0. The C-index of internal validation for DSS prediction was 0.67 (95% confidence interval (CI), 0.63 to 0.70). The calibration curve at one year suggested that there was good consistency between the predicted DSS and the actual DSS probability. This study was the first to develop a disease-specific nomogram for predicting the prognosis of primary GSM patients after craniotomy, which can help clinicians immediately and accurately predict patient prognosis and conduct further treatment.

Racial and ethnic disparities in a state-wide registry of patients with pancreatic cancer and an exploratory investigation of cancer cachexia as a contributor to observed inequities

Pancreatic cancer (PC) is characterized by racial/ethnic disparities and the debilitating muscle-wasting condition, cancer cachexia. Florida ranks second in the number of PC deaths and has a large and understudied minority population. We examined the primary hypothesis that PC incidence and mortality rates may be highest among Black Floridians and the secondary hypothesis that biological correlates of cancer cachexia may underlie disparities. PC incidence and mortality rates were estimated by race/ethnicity, gender, and county using publicly available state-wide cancer registry data that included approximately 2700 Black, 25 200 Non-Hispanic White (NHW), and 3300 Hispanic/Latino (H/L) Floridians diagnosed between 2004 and 2014. Blacks within Florida experienced a significantly (P < 0.05) higher incidence (12.5/100 000) and mortality (10.97/100 000) compared to NHW (incidence = 11.2/100 000; mortality = 10.3/100 000) and H/L (incidence = 9.6/100 000; mortality = 8.7/100 000), especially in rural counties. To investigate radiologic and blood-based correlates of cachexia, we leveraged data from a subset of patients evaluated at two geographically distinct Florida Cancer Centers. In Blacks compared to NHW matched on stage, markers of PC-induced cachexia were more frequent and included greater decreases in core musculature compared to corresponding healthy control patients (25.0% vs 10.1% lower), greater decreases in psoas musculature over time (10.5% vs 4.8% loss), lower baseline serum albumin levels (3.8 vs 4.0 gm/dL), and higher platelet counts (332.8 vs 268.7 k/UL). Together, these findings suggest for the first time that PC and cachexia may affect Blacks disproportionately. Given its nearly universal contribution to illness and PC-related deaths, the early diagnosis and treatment of cachexia may represent an avenue to improve health equity, quality of life, and survival.

Ping-Pong-Tumor and Host in Pancreatic Cancer Progression

Metastasis is the main cause of high pancreatic cancer (PaCa) mortality and trials dampening PaCa mortality rates are not satisfying. Tumor progression is driven by the crosstalk between tumor cells, predominantly cancer-initiating cells (CIC), and surrounding cells and tissues as well as distant organs, where tumor-derived extracellular vesicles (TEX) are of major importance. A strong stroma reaction, recruitment of immunosuppressive leukocytes, perineural invasion, and early spread toward the peritoneal cavity, liver, and lung are shared with several epithelial cell-derived cancer, but are most prominent in PaCa. Here, we report on the state of knowledge on the PaCIC markers Tspan8, alpha6beta4, CD44v6, CXCR4, LRP5/6, LRG5, claudin7, EpCAM, and CD133, which all, but at different steps, are engaged in the metastatic cascade, frequently via PaCIC-TEX. This includes the contribution of PaCIC markers to TEX biogenesis, targeting, and uptake. We then discuss PaCa-selective features, where feedback loops between stromal elements and tumor cells, including distorted transcription, signal transduction, and metabolic shifts, establish vicious circles. For the latter particularly pancreatic stellate cells (PSC) are responsible, furnishing PaCa to cope with poor angiogenesis-promoted hypoxia by metabolic shifts and direct nutrient transfer via vesicles. Furthermore, nerves including Schwann cells deliver a large range of tumor cell attracting factors and Schwann cells additionally support PaCa cell survival by signaling receptor binding. PSC, tumor-associated macrophages, and components of the dysplastic stroma contribute to perineural invasion with signaling pathway activation including the cholinergic system. Last, PaCa aggressiveness is strongly assisted by the immune system. Although rich in immune cells, only immunosuppressive cells and factors are recovered in proximity to tumor cells and hamper effector immune cells entering the tumor stroma. Besides a paucity of immunostimulatory factors and receptors, immunosuppressive cytokines, myeloid-derived suppressor cells, regulatory T-cells, and M2 macrophages as well as PSC actively inhibit effector cell activation. This accounts for NK cells of the non-adaptive and cytotoxic T-cells of the adaptive immune system. We anticipate further deciphering the molecular background of these recently unraveled intermingled phenomena may turn most lethal PaCa into a curatively treatable disease.