CT Abnormalities of the Pancreas Associated With the Subsequent Diagnosis of Clinical Stage I Pancreatic Ductal Adenocarcinoma More Than 1 Year Later: A Case-Control Study

Toshima, Fumihito; Watanabe, Ryosuke; Inoue, Dai; Yoneda, Norihide; Yamamoto, Tatsuya; Sasahira, Naoki; Sasaki, Takashi; Matsuyama, Masato; Minehiro, Kaori; Tateishi, Ukihide; Gabata, Toshifumi

doi:10.2214/ajr.21.26014

Cited by 34 publications

(11 citation statements)

References 40 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Focal parenchymal atrophy may be a less known PDAC-related imaging feature, but was observed on CT and MRI in 46%–49% of cases and only in one control patient. These results confirm the conclusion of recently published papers, who recognized focal atrophy as one of the first radiological features of early-stage PDAC [ 25 , 26 ]. Current practice may underestimate the importance of these secondary findings, especially in the absence of a distinct mass.…”

Section: Discussionsupporting

confidence: 92%

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case–control study

et al. 2022

View full text Add to dashboard Cite

Purpose To characterize the prevalence of missed pancreatic masses and pancreatic ductal adenocarcinoma (PDAC)-related findings on CT and MRI between pre-diagnostic patients and healthy individuals. Materials and methods Patients diagnosed with PDAC (2010–2016) were retrospectively reviewed for abdominal CT- or MRI-examinations 1 month—3 years prior to their diagnosis, and subsequently matched to controls in a 1:4 ratio. Two blinded radiologists scored each imaging exam on the presence of a pancreatic mass and secondary features of PDAC. Additionally, original radiology reports were graded based on the revised RADPEER criteria. Results The cohort of 595 PDAC patients contained 60 patients with a pre-diagnostic CT and 27 with an MRI. A pancreatic mass was suspected in hindsight on CT in 51.7% and 50% of cases and in 1.3% and 0.9% of controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. On MRI, a mass was suspected in 70.4% and 55.6% of cases and 2.9% and 0% of the controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. Pancreatic duct dilation, duct interruption, focal atrophy, and features of acute pancreatitis is strongly associated with PDAC (p < .001). In cases, a RADPEER-score of 2 or 3 was assigned to 56.3% of the CT-reports and 71.4% of MRI-reports. Conclusion Radiological features as pancreatic duct dilation and interruption, and focal atrophy are common first signs of PDAC and are often missed or unrecognized. Further investigation with dedicated pancreas imaging is warranted in patients with PDAC-related radiological findings. Graphical abstract

show abstract

Section: Discussionsupporting

confidence: 92%

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case–control study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…4,19 Toshima et al have identified morphological abnormalities on CT, including focal pancreatic parenchyma atrophy, faint focal enhancement, and focal ductal changes, which precede the diagnosis of stage 1 PDAC by one year. 26 Current studies suggest that AI would help detect small PDAC, 24 with deep-learning models yielding AUC of 0.9632 for the diagnosis of PDAC 27 or sensitivity reaching 98.3% compared to 92.9% for the radiologists ( P = .014). 28 However, the latter deep-learning model achieved only 63.1% sensitivity (95% confidence interval [CI]: 50.2-74.7%) for PDAC < 20 mm using an external test set.…”

Section: Lesion Detectionmentioning

confidence: 97%

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

et al. 2022

View full text Add to dashboard Cite

Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

show abstract

“…For instance, accurate pancreas segmentation is often the first step in the investigation of radiomics and deep learning for early detection of pancreatic cancer on prediagnostic CT scans. This is because the pancreas tends to be morphologically normal on prediagnostic CTs in majority of patients with pancreatic cancer 12,30–32 . There is also increasing interest in the application of AI to cross-sectional imaging studying for endocrine diseases such as diabetes 24,25 .…”

Section: Discussionmentioning

confidence: 99%

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists

Khasawneh

Patra

Rajamohan

et al. 2022

J Comput Assist Tomogr

View full text Add to dashboard Cite

Purpose: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability.Methods: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared.Results: Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [P < 0.0001]).Conclusions: Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

show abstract

CT Abnormalities of the Pancreas Associated With the Subsequent Diagnosis of Clinical Stage I Pancreatic Ductal Adenocarcinoma More Than 1 Year Later: A Case-Control Study

Cited by 34 publications

References 40 publications

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case–control study

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case–control study

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists

Contact Info

Product

Resources

About