Assessment of Liver Metastases Using CT and MRI Scans in Patients with Pancreatic Ductal Adenocarcinoma: Effects of Observer Experience on Diagnostic Accuracy

Tsurusaki, Masakatsu; Numoto, Isao; Oda, Tatsuya; Wakana, Miyuki; Suzuki, Ayako; Yagyu, Yukinobu; Matsuki, Mitsuru; Ishii, Kazunari

doi:10.20944/preprints202005.0490.v1

Cited by 4 publications

(4 citation statements)

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“…The diagnostic performance of the combination of CECT and gadoxetic acid‐enhanced MRI for liver metastases from PDAC in our study was in line with previous publications, with a reported sensitivity of 76%–94% and PPV of 82%–100% 5,6,15 . Our results also revealed that an abbreviated or standard gadoxetic acid‐enhanced MRI protocol can provide more accurate therapeutic decisions than CECT alone by detecting liver metastases in patients with potentially resectable PDAC.…”

Section: Discussionsupporting

confidence: 91%

Abbreviated Gadoxetic Acid‐Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma

Yamaguchi

Sofue

Ueshima

et al. 2022

Magnetic Resonance Imaging

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BackgroundGadoxetic acid‐enhanced magnetic resonance imaging (MRI) is useful in detecting liver metastases from pancreatic ductal adenocarcinoma (PDAC). However, the long examination time limits its utility in the initial workup of patients with PDAC.PurposeTo evaluate the incremental value of an abbreviated gadoxetic acid‐enhanced MRI for the detection of liver metastases in patients with PDAC.Study TypeRetrospective.PopulationPatients (N = 130) with potentially resectable PDAC (women, 58 [44.6%]).Field Strength/Sequence1.5 T and 3 T; gradient dual‐echo T1‐weighted (in‐phase and opposed‐phase), fat‐suppressed fast spin‐echo T2‐weighted, single‐shot echo‐planar diffusion‐weighted, and three‐dimensional fat‐suppressed T1‐weighted gradient‐echo dynamic contrast‐enhanced and hepatobiliary phase sequences, as well as contrast‐enhanced computed tomography (CECT).AssessmentThree radiologists independently reviewed three different image sets to detect liver metastases: set 1, CECT alone; set 2, CECT and abbreviated MRI comprising fat‐suppressed T2‐weighted, diffusion‐weighted, and hepatobiliary phase images; and set 3, CECT and standard gadoxetic acid‐enhanced MRI.Statistical TestsFigure of merit (FOM) was compared using the jackknife alternative free‐response receiver operating characteristics, and other per‐lesion and per‐patient diagnostic parameters for each image set were compared using McNemar's and Fisher's test. P < 0.05 was considered statistically significant.ResultsA total of 43 liver metastases were identified in 13 patients. Reader‐averaged FOM to detect liver metastases were significantly higher for sets 2 (0.884) and 3 (0.886) than for set 1 (0.609), while they were comparable between sets 2 and 3 (P = 0.96). The mean per‐patient sensitivities, negative predictive values, and accuracies were significantly higher for sets 2 and 3 than for set 1, while those between sets 2 and 3 were not significantly different (not applicable, P > 0.99, and P > 0.99, respectively).Data ConclusionGadoxetic acid‐enhanced MRI combined with CECT had higher diagnostic performance than CECT alone for the detection of liver metastases in patients with PDAC. The incremental values were comparable for the abbreviated MRI and standard MRI.Level of Evidence3Technical EfficacyStage 2

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Section: Discussionsupporting

confidence: 91%

Abbreviated Gadoxetic Acid‐Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma

Yamaguchi

Sofue

Ueshima

et al. 2022

Magnetic Resonance Imaging

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“…In this study, only 7% patients were found with distant metastasis during exploration, so the cost-effectiveness seemed to be even lower. This may be owing to the fact that most of patients in this study underwent MRI pre-operation, which was superior to MD-CT in detecting small liver metastases (24). What's more, we should add the fact that laparoscopy exploration cost patients less than 300 American dollars in China.…”

Section: Discussionmentioning

confidence: 93%

A nomogram of preoperative predictors for occult metastasis in patients with PDAC during laparoscopic exploration

Ge¹,

Li²,

Ma³

et al. 2021

Gland Surg

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Background: Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC). However, most of patients lose the chance of surgery due to the unresectable disease at the time of diagnosis. Despite the improvement of radiological imaging, a portion of patients intended for radical resection were proven to be unresectable at surgical exploration due to occult metastasis.Methods: Patients who were aimed to undergo radical pancreatectomy for PDAC from 2010 to 2019 were reviewed retrospectively. All patients included underwent diagnostic laparoscopic exploration. Patients were divided into two groups depending on whether distant metastasis were encountered during exploration.Univariate and multivariate logistic regression analyses were used to identify risk factors for occult metastasis.A nomogram to predict occult metastasis of PDAC on exploration was developed and evaluated.Results: A total of 273 patients who underwent diagnostic laparoscopic exploration were included in this study. Nineteen (7.0%) patients were found with distant metastasis during exploration. Multivariate logistic regression analysis showed that ALT>40U/L, CA19-9, CA125 and regional nodes enlargement were independent predictors for occult metastasis. Incorporating these four factors, the nomogram achieved concordance index of 0.799, with a well-fitted calibration curve.Conclusions: Occult metastasis is not unusual during surgical exploration in patients with resectable or borderline resectable PDAC. The nomogram could achieve a personal prediction of unexpected distant metastasis on exploration. It may help to sift through patients with PDAC who would benefit from laparoscopic exploration.

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“…However, segmentation accuracy and speed are still unsatisfactory for complex scenes (16)(17)(18). In addition, the use of fine-grained classification in liver segmentation is complicated by the small inter-class differences that arise from the close similarities between subcategories and by the large intra-class variabilities in position, scale, and orientation (19,20). Indeed, finegrained recognition is popular in computer vision and pattern recognition applications (21,22); it facilitates the learning of object parts and helps differentiate between object subclasses, and hence can be employed to learn liver patterns more accurately (23).…”

Section: Original Articlementioning

confidence: 99%

Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks

Chen¹,

Wei²,

Tang³

et al. 2021

Ann Transl Med

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Background: Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients.Methods: To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences. This method uses an enhanced mask region-based convolutional neural network (Mask R-CNN) with graph-cut segmentation. Specifically, the k-nearest neighbor (k-NN) algorithm is employed to cluster the target liver pixels in order to get an appropriate aspect ratio. Then, anchors are adapted to the liver size using the ratio information. Thus, high-accuracy liver localization can be achieved using the anchors and rotation-invariant object recognition. Next, a fully convolutional network (FCN) is used to segment the foreground objects, and local fine-grained liver detection is realized by pixel prediction. Finally, a whole liver mask is obtained by Mask R-CNN proposed in this paper.Results: We proposed a Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN algorithms in term of the dice similarity coefficient (DSC), and the Medical Image Computing and Computer-Assisted Intervention (MICCAI) metrics.Conclusions: Our experimental results demonstrate that the improved Mask R-CNN architecture has good performance, accuracy, and robustness for liver segmentation in CT image sequences.

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Assessment of Liver Metastases Using CT and MRI Scans in Patients with Pancreatic Ductal Adenocarcinoma: Effects of Observer Experience on Diagnostic Accuracy

Cited by 4 publications

References 0 publications

Abbreviated Gadoxetic Acid‐Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma

Abbreviated Gadoxetic Acid‐Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma

A nomogram of preoperative predictors for occult metastasis in patients with PDAC during laparoscopic exploration

Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks

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