Solid pancreatic tumors with unilocular cyst-like appearance on CT: differentiation from unilocular cystic tumors using CT

Differential Diagnosis of Pancreatic Cancer and its Mimicking Lesions

Pancreatic cancer is usually detected through contrast-enhanced CT and MRI. However, pancreatic cancer is occasionally overlooked because of its small size or is misdiagnosed as other conditions due to atypical imaging features that present diagnostic challenges. Considering the rapid growth and poor prognosis associated with pancreatic cancer, the ability to accurately detect and differentiate pancreatic lesions is crucial for appropriate surgical intervention. Reviewing diverse challenging cases of pancreatic cancer at an early stage and other mimicking lesions may help us accurately interpret the imaging features of pancreatic cancer on CT and MRI scans. Therefore, we aimed to illustrate various imaging features of pancreatic cancer and its mimicking lesions and provide valuable insights for differential diagnosis.

Diagnostic Anatomic Imaging for Neuroendocrine Neoplasms: Maximizing Strengths and Mitigating Weaknesses

ABSTRACT

Neuroendocrine neoplasms are a heterogeneous group of gastrointestinal and lung tumors. Their diverse clinical manifestations, variable locations, and heterogeneity present notable diagnostic challenges. This article delves into the imaging modalities vital for their detection and characterization. Computed tomography is essential for initial assessment and staging. At the same time, magnetic resonance imaging (MRI) is particularly adept for liver, pancreatic, osseous, and rectal imaging, offering superior soft tissue contrast. The article also highlights the limitations of these imaging techniques, such as MRI's inability to effectively evaluate the cortical bone and the questioned cost-effectiveness of computed tomography and MRI for detecting specific gastric lesions. By emphasizing the strengths and weaknesses of these imaging techniques, the review offers insights into optimizing their utilization for improved diagnosis, staging, and therapeutic management of neuroendocrine neoplasms.

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network

BACKGROUND

At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs.

PURPOSE

A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs).

MATERIALS AND METHODS

This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard.

RESULTS

Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs.

CONCLUSION

The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.

Laparoscopic Resection and Pre-Operative Imaging of Primary Retroperitoneal Mucinous Neoplasms: A Retrospective Case Series

Purpose

The purpose of this paper is to present the clinicopathological features of primary retroperitoneal mucinous neoplasms (PRMNs), to evaluate the diagnostic role of pre-operative radiological examinations, and to determine the feasibility of total resection using the laparoscopic approach.

Methods

We retrospectively analysed the clinicopathological and radiological features of 10 PRMN cases who underwent surgical resection from January 2000 to December 2019.

Results

Ten PRMN cases were evaluated in this study, including 1 malignant case, 4 borderline cases, and 5 benign cases. The most common primary complaints were pain (60%) and palpable mass (60%). Of the 8 cases with pre-operative CT results, 75% of the borderline or malignant cysts and none of the benign cysts were large in size (size>11.6 cm). Half of the non-benign masses and none of the benign ones were lobulated. Thick wall (≥3 mm) and irregular wall were found in 25% and 50% of the non-benign cases, respectively. Wall enhancement was detected in 75% of the non-benign cases and 25% of the benign cases. Enhanced mural nodules were identified in 50% of the non-benign patients. Septa were found in 25% of the non-benign cases. Complete dissection of the tumour without rupture or post-operative complications was achieved laparoscopically in 90% of the cases. All patients were alive with no recurrence at the last follow-up at an average of 28.1±26.6 months.

Conclusion

Larger size and lobulation of the cyst, presence and contrast enhancement of thick and irregular cyst walls, mural nodules, and internal septa may be imaging features suggestive of malignancy. Complete resection of PRMN through the laparoscopic approach is feasible in experienced institutions.

What Is New in the 2017 World Health Organization Classification and 8th American Joint Committee on Cancer Staging System for Pancreatic Neuroendocrine Neoplasms?

The diagnosis and management of pancreatic neuroendocrine neoplasms (NENs) have evolved significantly in recent years. There are several diagnostic and therapeutic challenges and controversies regarding the management of these lesions. In this review, we focus on the recent significant changes and controversial issues regarding the diagnosis and management of NENs and discuss the role of imaging in the multidisciplinary team approach.

Pancreatic neuroendocrine tumor: review of heterogeneous spectrum of CT appearance

OBJECTIVE

Pancreatic neuroendocrine tumors (PanNETs) are uncommon pancreatic neoplasms and can be a diagnostic challenge with heterogeneous spectrum of CT appearance. We review CT findings of PanNETs and other mimics.

CONCLUSION

PanNETs are typically hypervascular and have avid enhancement on arterial and venous phase images. However, dedicated pancreas protocol may be needed due to their sometimes atypical appearance including transient enhancement. Careful evaluation of CT findings will help differentiate PanNETs from their mimics, and can be used to establish the diagnosis of a PanNETs. Although an accurate diagnosis can be based on serological, urine, and CT scan findings, confirmation is made via pathological examination.

The "extracapsular cystic" sign in pancreatic serous cystic neoplasms: A clinicopathologic study of 177 patients with cystic pancreatic lesions

OBJECTIVE

To assess a new imaging feature that we have named the extracapsular cystic sign which can make a constructive contribution towards differentiating serous cystic neoplasms (SCNs) from other pancreatic cystic lesions.

MATERIAL AND METHODS

We retrospectively reviewed 177 CTs/MRIs of patients who underwent pancreatic resection of cystic lesions at two institutions from January 2011/2013, to September 2017. For each patient, demographic information, clinical presentation, especially imaging features were carefully investigated by two experienced abdominal radiologists, retrospectively. All statistical analyses were performed using SPSS V.23.0.

RESULTS

Twenty-one lesions had extracapsular cystic signs which were newly discovered, 17 (28.3%) of 60 SCNs and 4 (3.4%) (mucinous cystic neoplasm = 1, walled-off necrosis = 2, retention cyst = 1) of 117 Non-SCNs were included, from which indicating that the extracapsular cystic sign was more often detected on SCNs. As for 21 lesions, 86% (n = 18) were females, and mean age at diagnosis was 51.2 years. 71% (n = 15) located in the pancreatic body and tail. Average size was 27.2 mm (23.7-53.4), mean (SD) ratio of biggest daughter cyst to mother cyst was 0.51[0.14] (p = 0.99), average (SD) angle between two of them was 105.5° [14.9] (p = 0.84). The average time interval between last imaging examination and surgery was 8.4 days.

CONCLUSIONS

The new sign named the extracapsular cystic sign in SCNs may help differentiate SCNs from other pancreatic cystic lesions. Furthermore, this study supports an original diagnosis for SCNs when the sign of extracapsular cyst appears.

Selection and Reporting of Statistical Methods to Assess Reliability of a Diagnostic Test: Conformity to Recommended Methods in a Peer-Reviewed Journal

Objective

To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test.

Materials and Methods

Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis.

Results

Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies.

Conclusion

Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.

Does the Reporting Quality of Diagnostic Test Accuracy Studies, as Defined by STARD 2015, Affect Citation?

Objective

To determine the rate with which diagnostic test accuracy studies that are published in a general radiology journal adhere to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015, and to explore the relationship between adherence rate and citation rate while avoiding confounding by journal factors.

Materials and Methods

All eligible diagnostic test accuracy studies that were published in the Korean Journal of Radiology in 2011–2015 were identified. Five reviewers assessed each article for yes/no compliance with 27 of the 30 STARD 2015 checklist items (items 28, 29, and 30 were excluded). The total STARD score (number of fulfilled STARD items) was calculated. The score of the 15 STARD items that related directly to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 was also calculated. The number of times each article was cited (as indicated by the Web of Science) after publication until March 2016 and the article exposure time (time in months between publication and March 2016) were extracted.

Results

Sixty-three articles were analyzed. The mean (range) total and QUADAS-2-related STARD scores were 20.0 (14.5–25) and 11.4 (7–15), respectively. The mean citation number was 4 (0–21). Citation number did not associate significantly with either STARD score after accounting for exposure time (total score: correlation coefficient = 0.154, p = 0.232; QUADAS-2-related score: correlation coefficient = 0.143, p = 0.266).

Conclusion

The degree of adherence to STARD 2015 was moderate for this journal, indicating that there is room for improvement. When adjusted for exposure time, the degree of adherence did not affect the citation rate.

Comparison of Multidetector CT and Gadobutrol-Enhanced MR Imaging for Evaluation of Small, Solid Pancreatic Lesions

OBJECTIVE

To compare multidetector computed tomography (MDCT) and MRI for lesion conspicuity, as well as the detection and characterization of small solid pancreatic lesions (SPLs).

MATERIALS AND METHODS

193 patients with small SPLs (< 3 cm) and 52 patients with normal pancreas who underwent both multiphasic MDCT and gadobutrol-enhanced MRI were included in our study. Two radiologists blinded to the pathologic diagnoses independently reviewed those images, and determined the detection of "SPL per se" and "SPL in consideration of secondary features", the lesion conspicuity, the probability of pancreatic ductal adenocarcinoma (PDAC), and the most likely specific diagnosis.

RESULTS

The sensitivity of MRI for "detection of SPL per se" was significantly higher than that of CT in both reviewers: 92.7% (179/193) and 97.9% (189/193), respectively, for reviewer 1 (p = 0.031) and 90.7% (175/193) and 99.5% (192/193), respectively, for reviewer 2 (p < 0.001). In addition, MRI provided better lesion conspicuity than MDCT for both reviewers (p < 0.001). However, CT and MRI did not show significant difference in sensitivity for "detection of SPL in consideration of secondary features", specificity for SPL detection, and differentiation of PDAC vs. non-PDAC (p > 0.05). The accuracies of CT and MRI for making a specific diagnosis were as follows: 85.7% (210/245) vs. 86.9% (213/245), respectively, for reviewer 1 (p = 0.736), and 91.8% (225/245) vs. 93.5% (229/245), respectively, for reviewer 2 (p = 0.454).

CONCLUSION

MRI showed better lesion conspicuity than MDCT, but did not show significantly different diagnostic performance compared with MDCT for detecting and characterizing small SPLs.