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

Development of a Composite Score Based on Carbohydrate Antigen 19-9 Dynamics to Predict Survival in Carbohydrate Antigen 19-9-Producing Patients With Pancreatic Ductal Adenocarcinoma After Neoadjuvant Treatment

PURPOSE

Dynamics of carbohydrate antigen 19-9 (CA19-9) often inform treatment decisions during and after neoadjuvant chemotherapy (NAT) of patients with pancreatic ductal adenocarcinoma (PDAC). However, considerable dispute persists regarding the clinical relevance of specific CA19-9 thresholds and dynamics. Therefore, we aimed to define optimal thresholds for CA19-9 values and create a biochemically driven composite score to predict survival in CA19-9-producing patients with PDAC after NAT.

METHODS

Patients with PDAC who underwent NAT and surgical resection from 2012 to 2022 were retrospectively identified from three high-volume centers. CA19-9 nonproducers and patients with 90-day mortality, and macroscopically incomplete resections were excluded. A composite score was created on the basis of relative CA19-9 change and newly defined optimal thresholds of pre- and postneoadjuvant values for overall survival (OS) using patients from two centers and validated using data from the third center.

RESULTS

A total of 492 patients met inclusion criteria in the development cohort. Optimal CA19-9 cutoff values for predicting a difference in OS were 202 U/mL for preneoadjuvant and 78 U/mL for postneoadjuvant levels. Furthermore, increase in CA19-9 during neoadjuvant treatment was associated with worse OS (median-OS, 17.5 months v 26.0 months; P = .008). Not surpassing any or only one of these thresholds (composite score of 0-1) was associated with improved OS compared with patients with 2-3 points (median-OS, 29.9 months v 15.8 months; P < .001). Major serological response (90% decrease of CA19-9) had a positive and negative predictive value of 32% and 88%, respectively.

CONCLUSION

The composite score consisting of CA19-9 levels at diagnosis, after neoadjuvant treatment, and its dynamics demonstrates prognostic discrimination between low and high scores. However, better predictive biomarkers are needed to facilitate treatment decisions during neoadjuvant treatment.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

The clinical implication of minimally invasive versus open pancreatoduodenectomy for non-pancreatic periampullary cancer: a systematic review and individual patient data meta-analysis

BACKGROUND

Most studies on minimally invasive pancreatoduodenectomy (MIPD) combine patients with pancreatic and periampullary cancers even though there is substantial heterogeneity between these tumors. Therefore, this study aimed to evaluate the role of MIPD compared to open pancreatoduodenectomy (OPD) in patients with non-pancreatic periampullary cancer (NPPC).

METHODS

A systematic review of Pubmed, Embase, and Cochrane databases was performed by two independent reviewers to identify studies comparing MIPD and OPD for NPPC (ampullary, distal cholangio, and duodenal adenocarcinoma) (01/2015-12/2021). Individual patient data were required from all identified studies. Primary outcomes were (90-day) mortality, and major morbidity (Clavien-Dindo 3a-5). Secondary outcomes were postoperative pancreatic fistula (POPF), delayed gastric emptying (DGE), postpancreatectomy hemorrhage (PPH), blood-loss, length of hospital stay (LOS), and overall survival (OS).

RESULTS

Overall, 16 studies with 1949 patients were included, combining 928 patients with ampullary, 526 with distal cholangio, and 461 with duodenal cancer. In total, 902 (46.3%) patients underwent MIPD, and 1047 (53.7%) patients underwent OPD. The rates of 90-day mortality, major morbidity, POPF, DGE, PPH, blood-loss, and length of hospital stay did not differ between MIPD and OPD. Operation time was 67 min longer in the MIPD group (P = 0.009). A decrease in DFS for ampullary (HR 2.27, P = 0.019) and distal cholangio (HR 1.84, P = 0.025) cancer, as well as a decrease in OS for distal cholangio (HR 1.71, P = 0.045) and duodenal cancer (HR 4.59, P < 0.001) was found in the MIPD group.

CONCLUSIONS

This individual patient data meta-analysis of MIPD versus OPD in patients with NPPC suggests that MIPD is not inferior in terms of short-term morbidity and mortality. Several major limitations in long-term data highlight a research gap that should be studied in prospective maintained international registries or randomized studies for ampullary, distal cholangio, and duodenum cancer separately.

PROTOCOL REGISTRATION

PROSPERO (CRD42021277495) on the 25th of October 2021.

Surgical Decision-Making in Pancreatic Ductal Adenocarcinoma: Modeling Prognosis Following Pancreatectomy in the Era of Induction and Neoadjuvant Chemotherapy

OBJECTIVE

To develop a predictive model of oncologic outcomes for patients with pancreatic ductal adenocarcinoma (PDAC) undergoing resection after neoadjuvant or induction chemotherapy use.

BACKGROUND

Early recurrence following surgical resection for PDAC is common. The use of neoadjuvant chemotherapy prior to resection may increase the likelihood of long-term systemic disease control. Accurately characterizing an individual's likely oncologic outcome in the perioperative setting remains challenging.

METHODS

Data from patients with PDAC who received chemotherapy prior to pancreatectomy at a single high-volume institution between 2007 and 2018 were captured in a prospectively collected database. Core clinicopathologic data were reviewed for accuracy and survival data were abstracted from the electronic medical record and national databases. Cox-proportional regressions were used to model outcomes and develop an interactive prognostic tool for clinical decision-making.

RESULTS

A total of 581 patients were included with a median overall survival (OS) and recurrence-free survival (RFS) of 29.5 (26.5-32.5) and 16.6 (15.8-17.5) months, respectively. Multivariable analysis demonstrates OS and RFS were associated with type of chemotherapeutic used andthe number of chemotherapy cycles received preoperatively. Additional factors contributing to survival models included: tumor grade, histopathologic response to therapy, nodal status, and administration of adjuvant chemotherapy. The models were validated using an iterative bootstrap method and with randomized cohort splitting. The models were well calibrated with concordance indices of 0.68 and 0.65 for the final OS and RFS models, respectively.

CONCLUSION

We developed an intuitive and dynamic decision-making tool that can be useful in estimating OS, RFS, and location-specific disease recurrence rates. This prognostic tool may add value to patient care in discussing the benefits associated with surgical resection for PDAC.

External Validation of a Risk Model for Severe Complications following Pancreatoduodenectomy Based on Three Preoperative Variables

BACKGROUND

Pancreatoduodenectomy (PD) is the only cure for periampullary and pancreatic cancer. It has morbidity rates of 40-60%, with severe complications in 30%. Prediction models to predict complications are crucial. A risk model for severe complications was developed by Schroder et al. based on BMI, ASA classification and Hounsfield Units of the pancreatic body on the preoperative CT scan. These variables were independent predictors for severe complications upon internal validation. Our aim was to externally validate this model using an independent cohort of patients.

METHODS

A retrospective analysis was performed on 318 patients who underwent PD at our institution from 2013 to 2021. The outcome of interest was severe complications Clavien-Dindo ≥ IIIa. Model calibration, discrimination and performance were assessed.

RESULTS

A total of 308 patients were included. Patients with incomplete data were excluded. A total of 89 (28.9%) patients had severe complications. The externally validated model achieved: C-index = 0.67 (95% CI: 0.60-0.73), regression coefficient = 0.37, intercept = 0.13, Brier score = 0.25.

CONCLUSIONS

The performance ability, discriminative power, and calibration of this model were acceptable. Our risk calculator can help surgeons identify high-risk patients for post-operative complications to improve shared decision-making and tailor perioperative management.

External validation of three lymph node ratio-based nomograms predicting survival using an international cohort of patients with resected pancreatic head ductal adenocarcinoma

INTRODUCTION

Lymph node ratio (LNR) is an important prognostic factor of survival in patients with pancreatic ductal adenocarcinoma (PDAC). This study aimed to validate three LNR-based nomograms using an international cohort.

MATERIALS AND METHODS

Consecutive PDAC patients who underwent upfront pancreatoduodenectomy from six centers (Europe/USA) were collected (2000-2017). Patients with metastases, R2 resection, missing LNR data, and who died within 90 postoperative days were excluded. The updated Amsterdam nomogram, the nomogram by Pu et al., and the nomogram by Li et al. were selected. For the validation, calibration, discrimination capacity, and clinical utility were assessed.

RESULTS

After exclusion of 176 patients, 1'113 patients were included. Median overall survival (OS) of the cohort was 23 months (95% CI: 21-25). For the three nomograms, Kaplan-Meier curves showed significant OS diminution with increasing scores (p < 0.01). All nomograms showed good calibration (non-significant Hosmer-Lemeshow tests). For the Amsterdam nomogram, area under the ROC curve (AUROC) for 3-year OS was 0.64 and 0.67 for 5-year OS. Sensitivity and specificity for 3-year OS prediction were 65% and 59%. Regarding the nomogram by Pu et al., AUROC for 3- and 5-year OS were 0.66 and 0.70. Sensitivity and specificity for 3-year OS prediction were 68% and 53%. For the Li nomogram, AUROC for 3- and 5-year OS were 0.67 and 0.71, while sensitivity and specificity for 3-year OS prediction were 63% and 60%.

CONCLUSION

The three nomograms were validated using an international cohort. Those nomograms can be used in clinical practice to evaluate survival after pancreatoduodenectomy for PDAC.

Surgical data science - from concepts toward clinical translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Database: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP)

Background/Aims

Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.

Methods

Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated.

Results

Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively.

Conclusions

The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

Preoperative CTC-Detection by CellSearch® Is Associated with Early Distant Metastasis and Impaired Survival in Resected Pancreatic Cancer

In patients with presumed pancreatic ductal adenocarcinoma (PDAC), biomarkers that may open for personalised, risk-adapted treatment are lacking. The study analysed the impact of CTCs-presence on the patterns of recurrence and survival in 98 patients resected for PDAC with 5-10 years of follow-up. Preoperative samples were analysed by the CellSearch^® system for EpCAM+/DAPI+/CK+/CD45-CTCs. CTCs were detected in 7 of the 98 patients. CTCs predicted a significantly shorter median disease-free survival (DFS) of 3.3 vs. 9.2 months and a median cancer specific survival (CSS)of 6.3 vs. 18.5 months. Relapse status was confirmed by imaging for 87 patients. Of these, 58 patients developed distant metastases (DM) and 29 developed isolated local recurrence (ILR) as the first sign of cancer relapse. All patients with CTCs experienced DM. pN-status and histological grade >2 were other independent risk factors for DM, but only CTCs predicted significantly shorter cancer-specific, disease-free and post-recurrence survival. Preoperative parameters did not affect clinical outcome. We conclude that CTC presence in resected PDAC patients predicted early distant metastasis and impaired survival. Preoperative CTCs alone or in combination with histopathological factors may guide initial treatment decisions in patients with resectable PDAC in the future.

Pancreatectomy with Vascular Resection After Neoadjuvant FOLFIRINOX: Who Survives More Than a Year After Surgery?

BACKGROUND

Experienced pancreatic surgeons, for whom complexity is not an issue, must decide at the end of neoadjuvant therapy whether to continue or discontinue surgery, when pancreatectomy with vascular resection is planned in patients with pancreatic ductal adenocarcinoma (PDAC).

OBJECTIVE

Our study aimed to determine preoperative factors that can predict short postoperative survival in such situations.

METHODS

Overall, 105 patients with borderline or locally advanced PDAC received neoadjuvant FOLFIRINOX (followed by chemoradiation in 22% of patients) and underwent pancreatectomy with segmental venous and/or arterial resection at two high-volume centers. The primary endpoint was overall survival (OS) of < 1 year after surgery for patients who did not die from the surgery.

RESULTS

Tumors were classified as borderline in 78% of cases and locally advanced in 22% of cases. Mean CA19-9 at diagnosis was 934 U/mL, which significantly decreased to 213 U/mL (p < 0.01) after a median of six cycles of FOLFIRINOX. Pancreaticoduodenectomy was performed most often (76%). The vast majority of patients underwent venous resection (92%), and a simultaneous arterial resection was performed in 16 patients (15%). The severe morbidity rate and 30- and 90-day mortality rates were 21%, 8.5%, and 10.4%, respectively. The median OS after surgery was 23 months. In the multivariate analysis, preoperative CA19-9 ≥ 450 U/mL was the only preoperative factor independently associated with OS of < 1 year (p = 0.044).

CONCLUSION

The preoperative CA19-9 value should be considered in the clinical decision-making process when complex vascular resection is required.

Artificial Intelligence-Assisted Surgery: Potential and Challenges

BACKGROUND

Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field.

SUMMARY

This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery.

KEY MESSAGES

AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.

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.