Diffuse pancreatic ductal adenocarcinoma: Characteristic imaging 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.

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Prediction of tumour grade and survival outcome using pre-treatment PET- and MRI-derived imaging features in patients with resectable pancreatic ductal adenocarcinoma

European Radiology ◽

10.1007/s00330-020-07191-z ◽

2020 ◽

Author(s):

Vincent Dunet ◽

Nermin Halkic ◽

Christine Sempoux ◽

Nicolas Demartines ◽

Michael Montemurro ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Survival Outcome ◽

Ductal Adenocarcinoma ◽

Imaging Features ◽

Tumour Grade ◽

Pre Treatment

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Imaging features of pancreatic metastases: A comparison with pancreatic ductal adenocarcinoma

Clinical Imaging ◽

10.1016/j.clinimag.2018.01.016 ◽

2018 ◽

Vol 51 ◽

pp. 76-82 ◽

Cited By ~ 5

Author(s):

Massimo Galia ◽

Domenico Albano ◽

Dario Picone ◽

Maria Chiara Terranova ◽

Antonino Agrusa ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Ductal Adenocarcinoma ◽

Imaging Features ◽

Pancreatic Metastases

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High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis

10.31487/j.aco.2021.02.03 ◽

2021 ◽

Author(s):

Talayna Leonard ◽

◽

Robert Lemme ◽

Cati Kral ◽

Briana Santiago ◽

...

Keyword(s):

Soft Tissue ◽

Pancreatic Ductal Adenocarcinoma ◽

Clinical Diagnosis ◽

Cross Sectional Study ◽

Ductal Adenocarcinoma ◽

Imaging Features ◽

Cross Sectional ◽

Tumor Registry ◽

One Year ◽

Improve Patient

Objective: Pancreatic ductal adenocarcinoma (PDAC) has the best survival when detected early with 5-year survival near 40% for small, resectable PDAC. We evaluate the undiagnosed PDAC imaging features on routine CT and their impact on resectability. Methods: 76 of the screened 134 CTs from 1/1/2012 to 12/31/2018 using our tumor registry were obtained prior to PDAC diagnosis for other indications at least one month before presentation. Each cross-sectional study was reviewed for features of early PDAC: pancreatic mass, pancreatic ductal dilatation, perivascular/peripancreatic soft-tissue infiltration, omental lesions/ascites, and lymphadenopathy. When such features were detectible by the reviewing radiologists, the original CT readings were classified as concordant/discrepant. Descriptive statistics are reported for discrepant reads, tumor resectability, and tumor size. Results: Of the 76 cases from 46 unique subjects (30 male/16 female), 25 CTs (33%) had undetected PDAC imaging features: masses (15/19 unreported), ductal dilatation (16/20 unreported), and peripancreatic/perivascular soft-tissue infiltration (20/36 unreported). 63% of early PDAC features were not identified initially. One year before clinical diagnosis, 75-80% of the PDAC cases were resectable; at < 6 months before clinical diagnosis, only 29% were resectable. Conclusion: Improving early detection of key PDAC features on routine CT examinations can potentially improve patient outcomes.

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CT texture analysis for the presurgical prediction of superior mesenteric–portal vein invasion in pancreatic ductal adenocarcinoma: comparison with CT imaging features

Clinical Radiology ◽

10.1016/j.crad.2021.01.003 ◽

2021 ◽

Author(s):

F. Chen ◽

Y. Zhou ◽

X. Qi ◽

W. Xia ◽

R. Zhang ◽

...

Keyword(s):

Portal Vein ◽

Pancreatic Ductal Adenocarcinoma ◽

Texture Analysis ◽

Ct Imaging ◽

Ductal Adenocarcinoma ◽

Imaging Features ◽

Portal Vein Invasion ◽

Ct Texture Analysis

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Pancreatic Ductal Adenocarcinoma: Ultrasound, Computed Tomography, and Magnetic Resonance Imaging Features

Seminars in Ultrasound CT and MRI ◽

10.1053/j.sult.2007.06.001 ◽

2007 ◽

Vol 28 (5) ◽

pp. 330-338 ◽

Cited By ~ 13

Author(s):

Eric P. Tamm ◽

Priya R. Bhosale ◽

Jeffrey H. Lee

Keyword(s):

Magnetic Resonance Imaging ◽

Computed Tomography ◽

Magnetic Resonance ◽

Pancreatic Ductal Adenocarcinoma ◽

Ductal Adenocarcinoma ◽

Imaging Features ◽

Resonance Imaging ◽

Ultrasound Computed Tomography

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Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters

Journal of Clinical Medicine ◽

10.3390/jcm9051250 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1250

Author(s):

Georgios A. Kaissis ◽

Friederike Jungmann ◽

Sebastian Ziegelmayer ◽

Fabian K. Lohöfer ◽

Felix N. Harder ◽

...

Keyword(s):

Machine Learning ◽

Pancreatic Ductal Adenocarcinoma ◽

Molecular Subtype ◽

Cox Proportional Hazards ◽

Ductal Adenocarcinoma ◽

Imaging Biomarkers ◽

Lymph Node Status ◽

Survival Prediction ◽

Imaging Features ◽

True Value

Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.

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