Presurgical Evaluation of Pancreatic Cancer: A Comprehensive Imaging Comparison of CT Versus MRI

Chen, Fang‐Ming; Ni, Jianming; Zhang, Zhuiyang; Zhang, Lei; Li, Bin; Jiang, Chunming

doi:10.2214/ajr.15.15236

Cited by 91 publications

(58 citation statements)

References 32 publications

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“…Several studies have proven that CT-based and MRI-based radiomics features both have the ability to discriminate different tumor phenotypes ( 29 – 31 ). A study found that both CT-based and MRI-based radiomics models can detect lymph node metastases in cervical cancer ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

et al. 2020

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Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures. Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

et al. 2020

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“…Although multidetector CT is most widely used, magnetic resonance (MR) imaging is an excellent modality for the workup of pancreatic carcinoma. On the basis of the limited available data, it has been suggested that MR imaging and CT are equivalent for assessment of vascular invasion (17) and have similar performance in the preoperative evaluation of PDAC (18). Compared with multidetector CT, MR imaging has demonstrated significantly greater tumor conspicuity of small PDACs (P < .05), which may be isoattenuating or slightly hypoattenuating on multidetector CT images (19).…”

Section: Assessment Of Resectability Pdac Stagingmentioning

confidence: 99%

Tumor-Vessel Relationships in Pancreatic Ductal Adenocarcinoma at Multidetector CT: Different Classification Systems and Their Influence on Treatment Planning

Zaky¹,

Wolfgang²,

Weiss³

et al. 2017

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Treatment of pancreatic ductal adenocarcinoma (PDAC) remains a challenge, given its propensity for early systemic spread and growth into the adjacent vital vascular structures. With the advent of newer surgical techniques and chemoradiation therapies, multidetector computed tomography (CT) plays a crucial role in the identification of patients with borderline resectable disease who may benefit from such treatments. Stage III PDAC is divided into two categories-locally advanced, defined by arterial encasement or nonreconstructible portovenous axis involvement; and borderline resectable, defined by limited arterial involvement and/or reconstructible portovenous involvement. A consensus definition for stage III borderline resectable PDAC has been proposed by the Americas Hepato-Pancreato-Biliary Association, the Society of Surgical Oncology, and the Society for Surgery of the Alimentary Tract and has gained widespread use. Evaluation of borderline resectable disease involves the identification of the circumferential and longitudinal relationship of the tumor with its neighboring vessels, markers of vascular invasion, and aberrant anatomic structures that alter the surgical approach. Furthermore, the use of template-based radiology reporting may increase the objectivity of the evaluation and mandate the provision of all of the key descriptors required for a comprehensive evaluation of the disease. In this review, the staging of PDAC at multidetector CT is described, with reference to the evaluation of the tumor-vessel interface as it guides treatment planning, along with a discussion of the key descriptors of PDAC at multidetector CT and their importance. Examples are provided of the imaging findings of borderline resectable disease and different surgical approaches, along with a discussion on the importance of standardized terminology and template-based reporting. RSNA, 2016.

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“…To the best of our knowledge, no study has explored differences in imaging features between PDAC and PNEC. MRI, particularly in diffusion-weight imaging (DWI), has been used to differentiate pancreatitis and pancreatic cancer [25–28], and MRI has a similar or better performance in PDAC evaluation [29, 30]. The present study assessed the value of MRI, including DWI and dynamic contrast-enhanced imaging, for differentiating PNEC and PDAC.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma using magnetic resonance imaging: The value of contrast-enhanced and diffusion weighted imaging

et al. 2017

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Pancreatic neuroendocrine carcinoma (PNEC) is often misdiagnosed as pancreatic ductal adenocarcinoma (PDAC). This retrospective study differentiated PNEC from PDAC using magnetic resonance imaging (MRI), including contrast-enhanced (CE) and diffusion-weighted imaging (DWI). Clinical data and MRI findings, including the T1/T2 signal, tumor boundary, size, enhancement degree, and apparent diffusion coefficient (ADC), were compared between 37 PDACs and 13 PNECs. Boundaries were more poorly defined in PDAC than PNEC (97.3% vs. 61.5%, p<0.01). Hyper-/isointensity was more common in PNEC than PDAC at the arterial (38.5% vs. 0.0), portal (46.2% vs. 2.7%) and delayed phases (46.2% vs. 5.4%) (all p<0.01). Lymph node metastasis (97.3% vs. 61.5%, p<0.01) and local invasion/distant metastasis (86.5% vs. 46.2%, p<0.01) were more common in PDAC than PNEC. Enhancement degree via CE-MRI was higher in PNEC than PDAC at the arterial and portal phases (p<0.01). PNEC ADC values were lower than those of normal pancreatic parenchyma (p<0.01) and PDAC (p<0.01). Arterial and portal phase signal intensity ratios and ADC values showed the largest areas under the receiver operating characteristic curve and good sensitivities (92.1%–97.2%) and specificities (76.9%–92.3%) for differentiating PNEC from PDAC. Thus the enhancement degree at the arterial and portal phases and the ADC values may be useful for differentiating PNEC from PDAC using MRI.

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Presurgical Evaluation of Pancreatic Cancer: A Comprehensive Imaging Comparison of CT Versus MRI

Abstract: MRI and CT had similar performance in the presurgical evaluation of pancreatic cancer.

Cited by 91 publications

References 32 publications

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

Tumor-Vessel Relationships in Pancreatic Ductal Adenocarcinoma at Multidetector CT: Different Classification Systems and Their Influence on Treatment Planning

Differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma using magnetic resonance imaging: The value of contrast-enhanced and diffusion weighted imaging

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