scholarly journals Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble

Author(s):  
Yingda Xia ◽  
Qihang Yu ◽  
Wei Shen ◽  
Yuyin Zhou ◽  
Elliot K. Fishman ◽  
...  
2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 380-380
Author(s):  
John Chang ◽  
Madelyn Bartels ◽  
Kelsey Beyer ◽  
Ashley Maitland ◽  
Richard Taft Peterson ◽  
...  

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.


2020 ◽  
Vol 10 ◽  
Author(s):  
Mohamed Zaid ◽  
Dalia Elganainy ◽  
Prashant Dogra ◽  
Annie Dai ◽  
Lauren Widmann ◽  
...  

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.


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16754-e16754
Author(s):  
Raphael Louie ◽  
Gabriel Aleixo ◽  
Allison Mary Deal ◽  
Emily Damone ◽  
Jaclyn Tremont-Portelli ◽  
...  

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.


Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3656
Author(s):  
Mohamed Zaid ◽  
Lauren Widmann ◽  
Annie Dai ◽  
Kevin Sun ◽  
Jie Zhang ◽  
...  

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.


2020 ◽  
Vol 9 (12) ◽  
pp. 4013
Author(s):  
Sebastian Ziegelmayer ◽  
Georgios Kaissis ◽  
Felix Harder ◽  
Friederike Jungmann ◽  
Tamara Müller ◽  
...  

The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.


2000 ◽  
Vol 15 (11) ◽  
pp. 1333-1338 ◽  
Author(s):  
Koji Uno ◽  
Takeshi Azuma ◽  
Masatsugu Nakajima ◽  
Kenjiro Yasuda ◽  
Takanobu Hayakumo ◽  
...  

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