scholarly journals Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3656
Author(s):  
Mohamed Zaid ◽  
Lauren Widmann ◽  
Annie Dai ◽  
Kevin Sun ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
External Validation ◽  
Classification Model ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Quantitative Classification ◽  
Biologically Relevant ◽  
High Q ◽  
Improved Outcomes ◽  
Treatment Naïve

Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.

Characterization of undiagnosed pancreatic ductal adenocarcinoma on CT scans.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 380-380
Author(s):  
John Chang ◽  
Madelyn Bartels ◽  
Kelsey Beyer ◽  
Ashley Maitland ◽  
Richard Taft Peterson ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
The Body ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Imaging Features ◽  
Cognitive Errors ◽  
Radiologic Findings ◽  
Stage 1 ◽  
Improved Technology ◽  
Radiologic Features

380 Background: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths. At present, the best 5-year survival is 25% for resectable PDAC. For small (1 cm) stage 1 PDAC, resection has resulted in much better survival. The goal of this study was to evaluate the appearance and location of early undiagnosed PDAC on computed tomography scans (CT) prior to diagnosis with the goal of minimizing missing early PDAC. We also categorize the errors as either perceptive or cognitive. Methods: PDAC cases were retrospectively reviewed from 1/1/2012 through 12/31/2018 from our tumor registry, identifying 81 cases with paired CT scans both at the time of and prior to diagnosis. Among these, 31 contained imaging features considered diagnostic or suspicious for early PDAC(38%). These “errors” were classified by radiologic features and as well as by location. In addition, errors were classified into “perceptive errors" when the first study was read as normal, and as “cognitive errors” when the report noted an abnormality but failed to note suspicion for malignancy. Results: Among the 31 undiagnosed PDAC, 18 had features of an identifiable mass (58%), 9 had pancreatic ductal dilatation (29%), and 4 had evidence of perivascular soft tissue (13%). 44% of undiagnosed tumors were located in the head-neck, 39% in the body, and 17% in the tail. Perceptive errors were found in 58% and 42% were cognitive. No significant differences were seen between perceptive and cognitive errors based on suspicious features. Conclusions: Radiologic findings of early PDAC was retrospectively evident in more than one third of cases in which prior imaging was performed. These findings are most often masses or ductal dilatation. Location of these undiagnosed tumors were distributed throughout the gland. This study identifies the radiologic features of undiagnosed PDAC which may provide an opportunity for future prospective studies and improved technology which may improve early detection of pancreatic cancer.

scholarly journals Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma Exhibit Differential Growth and Metabolic Patterns in the Pre-Diagnostic Period: Implications for Early Detection

2020 ◽  
Vol 10 ◽  
Author(s):  
Mohamed Zaid ◽  
Dalia Elganainy ◽  
Prashant Dogra ◽  
Annie Dai ◽  
Lauren Widmann ◽  
...  
Keyword(s):  
Growth Rate ◽  
Early Detection ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cross Validation ◽  
Abdominal Fat ◽  
Metabolic Effects ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Tumor Growth Rate ◽  
Average Growth

BackgroundPreviously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis.Materials and methodsRetrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value <0.05 was considered significant.ResultsCompared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month−1 vs. 0.088 month−1, p<0.0001) and longer average initiation-times (14 years vs. 5 years, p<0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month−1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p <0.001), and BG disturbance (p<0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p<0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors.ConclusionImaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.

Discovery and validation of a quantitative, stromal-associated imaging biomarker of pancreatic ductal adenocarcinoma (PDAC).

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 228-228 ◽  
Author(s):  
Mohamed Zaid ◽  
Baishali Chaudhury ◽  
Gauri R. Varadhachary ◽  
Matthew H. G. Katz ◽  
Joseph M. Herman ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Proportional Hazards ◽  
Quantitative Imaging ◽  
Tumor Biology ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Imaging Biomarker ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Treatment Naïve

228 Background: As pancreatic ductal adenocarcinoma (PDAC) remains highly lethal, biomarkers are needed to identify patients who may benefit from specific therapeutic strategies. We previously described a qualitative computed tomography (CT) based biomarker - delta classification, whereby high delta tumors showed lower stromal content, more aggressive biology and poorer outcomes, than their counterparts. Here, we describe a quantitative method to differentiate these patients and predict outcomes. Methods: We retrospectively identified 101 treatment naïve patients who underwent pancreatectomy as a discovery cohort and 90 patients who underwent preoperative gemcitabine-based chemoradiation for validation. All patients underwent a pre-therapy pancreatic protocol CT and were classified as high or low delta, as described before. We semi-automatically segmented the tumors, chose normal pancreatic (NP) tissue and abdominal fat as references, then measured relative enhancement values using Philips IntelliSpace8 multimodality tumor tracking. We then analyzed the arterial and portal-venous phases separately using ROC and cox proportional hazards. Results: Delta class significantly associated with normalized enhancement values (NEV) in the arterial phase referenced to NP (P<0.0001, AUC =90%). A cutoff of 0.72 was identified that also distinguished high and low delta groups in the validation cohort (P<0.0001). As a continuous variable, the NEV was associated with distant metastasis free survival (DMFS) and overall survival (OS) on uni and multivariate analyses, accounting for traditional survival covariates. Using cutoff of 0.72, patients with high NEV had longer median OS (39 and 35.9 months) compared to those with low NEV (17.5 and 17.6 months, P=<0.0001) in discovery and validation cohorts, respectively. Similarly, patients with high NEV had longer median DMFS (46.6 and 62.2 months) compared to those with low NEV (15.6 and 13.1 months, P=0.005) in discovery and validation cohorts, respectively. Conclusions: The NEV measurement on baseline CT scans may serve as a quantitative imaging biomarker that can objectively reflect tumor biology and provide prognostic insight.

Myosteatosis to predict postoperative morbidity in pancreatic ductal adenocarcinoma patients receiving neoadjuvant chemotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16754-e16754
Author(s):  
Raphael Louie ◽  
Gabriel Aleixo ◽  
Allison Mary Deal ◽  
Emily Damone ◽  
Jaclyn Tremont-Portelli ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Postoperative Morbidity ◽  
Tube Feeding ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Baseline Body Mass Index ◽  
Cross Sectional ◽  
Student’S T ◽  
Morbidity Risk

e16754 Background: Myosteatosis (adipose deposits in muscle) can be detected on cross-sectional imaging through variations in Skeletal Muscle Density (SMD). Patients with myosteatosis tend to have lower overall survival, increased chemotherapy toxicity, and shorter progression-free intervals across cancer types. We investigated whether changes in myosteatosis during neoadjuvant chemotherapy can predict postoperative morbidity risk in patients with pancreatic ductal adenocarcinoma (PDAC). Methods: This is a retrospective cohort study from 2014-2019 of patients with biopsy-proven PDAC who completed neoadjuvant chemotherapy and R0/1 resection (R1: margin < 1mm or microscopically positive). We obtained preoperative patient (age at diagnosis, baseline body mass index (BMI), sex, race, comorbidities) and treatment data (neoadjuvant chemotherapy regimen and duration, time from completion of systemic therapy to surgery, type of operation). Primary outcomes were postoperative complications and 90-day readmission. Average SMD was measured using imaging analysis software at the L3 level on axial abdominal CT scans at the time of diagnosis and at completion of neoadjuvant therapy (SliceOmatic TomoVision QC, Can). We defined SMDΔ as the decrease in SMD during neoadjuvant chemotherapy. Descriptive statistics and Student’s t-test were performed with STATA. Results: We identified 44 patients who received neoadjuvant chemotherapy, achieved a R0/1 resection, and had available CT scans for body composition evaluation. The postoperative complication rate was 43% (n = 19) and 90-day readmission rate was 30% (n = 13). Lower SMD at diagnosis was associated with increased postoperative delirium (p < 0.01) and 90-day readmission (p = 0.02). Greater SMDΔ was associated with increased ICU utilization (p < 0.01) and tube feeding upon discharge (p = 0.03). There was no significant association between preoperative BMI or albumin and our primary outcomes. Conclusions: Preoperative SMD and SMDΔ, rather than albumin or BMI, can predict postoperative morbidity in PDAC patients who received neoadjuvant chemotherapy. This study provides the framework for future studies to develop and validate a tool to predict postoperative morbidity risk in these patients.

scholarly journals A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

2021 ◽  
Vol 9 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Mindi Ma ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Dna Replication ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

scholarly journals Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

2020 ◽  
Author(s):  
Zengyu Feng ◽  
Minmin Shi ◽  
Kexian Li ◽  
Lingxi Jiang ◽  
Hao Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Calibration Curves ◽  
Risk Patients

Abstract Background Cancer stem cells (CSCs) are crucial to malignant behavior and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established and reported in PDAC. Methods This signature was developed and validated in seven independent PDAC datasets. MTAB-6134 cohort was used as training set, while one Chinese local cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and its predictive performance was evaluated by Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics. Results A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It could classify patients into high-risk and low-risk groups, and high risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) compared with low risk patients. Calibration curves and Cox regression analysis demonstrated the powerful predictive performance. ROC curves showed an improved survival prediction provided by this model. Functional analysis revealed positive association between risk score and CSC marker. These results had cross-dataset compatibility. Conclusions We established a novel four-gene signature based on CSC-related genes which could serve as a powerful prognostic tool in PDAC. Impact This signature can offer potential help for further improving current TNM staging system, and providing data for the development of novel personalized therapeutic strategies in the future.

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