Dataset Information

Preoperative contrast-enhanced computed tomographic characterisation of pancreatic cystic lesions: A prospective study.

ABSTRACT: Background:Characterisation of pancreatic cystic lesions has a direct role in their management and computed tomography is the mainstay of investigation for diagnosing and characterising them. Objectives:The aim of this study was to prospectively assess the diagnostic accuracy of contrast-enhanced computed tomography (CECT) in preoperative characterisation of pancreatic cystic lesions with histopathology as the reference standard. Method:A total of 38 patients with cystic pancreatic lesions diagnosed after clinical, laboratory and sonographic evaluation, irrespective of age, were preoperatively evaluated with CECT. Images were reviewed for the general characteristics of the lesions on pre-contrast and portal venous phase images and overall diagnostic accuracy calculated. Imaging findings were compared with histopathology, or cytology and/or intra-operative findings. Results:Serous cystadenoma (SCA) was the most common cystic pancreatic lesion found in 31.6% of patients followed by mucinous cystadenoma (MCA) (26.3%), solid pseudo-papillary tumour (SPT) (21.1%) and intra-ductal papillary mucinous neoplasm (IPMN) (10.5%). Three patients (7.9%) had simple cysts and one patient (2.6%) had a lymphangioma. The diagnostic accuracy of CECT for pancreatic cystic lesions was found to be 72.5. Conclusion:The diagnostic accuracy of computed tomography (CT) was high for SCA, IPMN and pancreatic cysts, and low for MCA and SPT. Combination of a multiloculated cystic lesion with locule size of less than 20 mm, septal enhancement with relative lack of wall enhancement, central scar and lobulated outline are highly specific for SCA. Unilocular or macro-cystic pattern with locule size of more than 20 mm, female gender and wall enhancement with smooth external contour are pointers towards MCA. Solid cystic pancreatic head lesions in young females may be suggestive of SPT. A dilated main pancreatic duct in a cystic lesion with internal septations may point towards IPMN. Fluid attenuation lesions with imperceptible non-enhancing wall indicate pancreatic cysts. Lastly, pseudocysts and neuroendocrine tumours with cystic components are great mimickers of pancreatic cystic lesions, and a history of pancreatitis and hormonal profile of patients should always be sought.

SUBMITTER: Saleem DM

PROVIDER: S-EPMC6837796 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Preoperative contrast-enhanced computed tomographic characterisation of pancreatic cystic lesions: A prospective study.

Saleem Dar M DM Haseeb Wani A WA Parry Arshed H AH Irfan Robbani R Muzaffar Najar M NM Tariq Gojwari G Javed Shah O SO Feroz Imza I

SA journal of radiology 20190610 1

<h4>Background</h4>Characterisation of pancreatic cystic lesions has a direct role in their management and computed tomography is the mainstay of investigation for diagnosing and characterising them.<h4>Objectives</h4>The aim of this study was to prospectively assess the diagnostic accuracy of contrast-enhanced computed tomography (CECT) in preoperative characterisation of pancreatic cystic lesions with histopathology as the reference standard.<h4>Method</h4>A total of 38 patients with cystic pa ...[more]

PMID: 31754534

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Project description:Background: Serous cystadenoma (SCA), mucinous cystadenoma (MCN), and intraductal papillary mucinous neoplasm (IPMN) are three subtypes of pancreatic cystic neoplasm (PCN). Due to the potential of malignant-transforming, patients with MCN and IPMN require radical surgery while patients with SCA need periodic surveillance. However, accurate pre-surgery diagnosis between SCA, MCN, and IPMN remains challenging in the clinic. Methods: This study enrolled 164 patients including 76 with SCA, 40 with MCN and 48 with IPMN. Patients were randomly split into a training cohort (n = 115) and validation cohort (n = 41). We performed statistical analysis and Boruta method to screen significantly distinct clinical factors and radiomics features extracted on pre-surgery contrast-enhanced computed tomography (CECT) images among three subtypes. Three reliable machine-learning algorithms, support vector machine (SVM), random forest (RF) and artificial neural network (ANN), were utilized to construct classifiers based on important radiomics features and clinical parameters. Precision, recall, and F1-score were calculated to assess the performance of the constructed classifiers. Results: Nine of 547 radiomics features and eight clinical factors showed a significant difference among SCA, MCN, and IPMN. Five radiomics features (Histogram_Entropy, Histogram_Skeweness, LLL_GLSZM_GLV, Histogram_Uniformity, HHL_Histogram_Kurtosis), and four clinical factors, including serum carbohydrate antigen 19-9, sex, age, and serum carcinoembryonic antigen, were identified important by Boruta method. The SVM classifier achieved an overall accuracy of 73.04% in training cohort and 71.43% in validation cohort, respectively. The RF classifier achieved overall accuracy of 84.35 and 79.59%, respectively. The constructed ANN model showed an overall accuracy of 77.39% in the training dataset and 71.43% in the validation dataset. All the three classifiers showed high F1 score for differentiation among the three subtypes. Conclusion: Our study proved the feasibility and translational value of CECT-based radiomics classifiers for differentiation among SCA, MCN, and IPMN.

Project description:BackgroundThe presence of a few renal cysts is considered of little relevance in healthy adults, although acquired renal cystic disease occurs in advanced kidney failure. The objective of this study was to detail renal cystic and solid lesions and identify any association with clinical characteristics.Study designClinical-pathologic correlation.Setting & participantsPotential kidney donors undergoing a standardized evaluation at the Mayo Clinic in 2000-2008.PredictorsAge, kidney function, and chronic kidney disease risk factors.MeasurementsRenal cystic and solid lesions by contrast-enhanced computed tomographic images.OutcomesCyst number, diameter, and location.ResultsAfter excluding 8 with cystic disease, 7 of whom had autosomal dominant polycystic kidney disease, there were 1,948 potential kidney donors (42% men; mean age, 43 years). A cortical, medullary, or parapelvic cyst ≥5 mm was present in 12%, 14%, or 2.8%. For ages 19-49 years, 39%, 22%, 7.9%, and 1.6% had a cortical or medullary cyst ≥2, ≥5, ≥10, and ≥20 mm in diameter. For ages 50-75 years, 63%, 43%, 22%, and 7.8% had a cortical or medullary cyst ≥2, ≥5, ≥10, and ≥20 mm in diameter. The 97.5th percentile for number of cortical and medullary cysts ≥5 mm increased with age (10 for men and 4 for women in the 60- to 69-year group). After age and sex adjustment, cortical and medullary cysts ≥5 mm were associated with higher 24-hour urine albumin excretion, as well as increased body surface area, hypertension, and higher glomerular filtration rate in some analyses. Angiomyolipomas, hyperdense cysts, and enhancing masses or cysts with concerning features for malignancy occurred in 2.2%, 1.2%, and 0.6% and were associated with older age (P ≤ 0.05 for each).LimitationsPersons with known chronic kidney disease were excluded.ConclusionsRenal cysts are common, particularly in older men, and may be a marker of early kidney injury because they associate with albuminuria, hypertension, and hyperfiltration.

Project description:BackgroundSerous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), and intraductal papillary mucinous neoplasm (IPMN) comprise a large proportion of pancreatic cystic neoplasms (PCNs). Patients with MCN and IPMN require surgery due to the potential of malignant transformation, whereas those with SCN require periodic surveillance. However, the differential diagnosis of patients with PCNs before treatment remains a great challenge for all surgeons. Therefore, the establishment of a reliable diagnostic tool is urgently required for the improvement of precision diagnostics.MethodsBetween February 2015 and December 2020, 143 consecutive patients with PCNs who were confirmed by postoperative pathology were retrospectively included in the study cohort, then randomized into development and test cohorts at a ratio of 7:3. The predictors of preoperative clinical-radiologic parameters were evaluated by univariate and multivariable logistic regression analyses. A total of 1,218 radiomics features were computationally extracted from the enhanced computed tomography (CT) scans of the tumor region, and a radiomics signature was established by the random forest algorithm. In the development cohort, multi- and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the 3 types of PCNs. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficiency of the model. An independent internal test cohort was applied to validate the classification models.ResultsAll preoperative prediction models were built by integrating the radiomics signature with 13 diagnosis-related radiomics features and 3 important clinical-radiologic parameters: age, sex, and tumor diameter. The multiclass prediction model presented an overall accuracy of 0.804 in the development cohort and 0.707 in the test cohort. The binary-class prediction models displayed higher overall accuracies of 0.853, 0.866, and 0.928 in the development dataset and 0.750, 0.839, and 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances {AUC =0.914 [95% confidence interval (CI): 0.786 to 1.000], 0.863 (95% CI: 0.714 to 0.941), and 0.926 (95% CI: 0.824 to 1.000)} than the multiclass radiomics model [AUC =0.850 (95% CI: 0.696 to 1.000)], with a large net benefit in the decision curve analysis (DCA). The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCNs.ConclusionsThe proposed radiomics models with clinical-radiologic parameters and radiomics features help to predict the accurate diagnosis among PCNs to advance personalized medicine.

Project description:BackgroundCorrect preoperative estimation of the malignant extent is crucial for optimal planning of breast cancer surgery. The sensitivity of mammography is lower in dense breasts, and additional imaging techniques are sometimes warranted. Contrast-enhanced mammography (CEM) has shown similar sensitivity and in some cases better specificity, than magnetic resonance imaging (MRI) in small, observational studies. CEM may be more cost-effective than MRI, and may provide better identification of the tumor extent, however, no randomized trials have been performed to date to investigate the added value of CEM. In a feasibility study, we found that the treatment was changed in 10/47 (21%) cases after additional CEM. The purpose of the present study is to evaluate the added value of CEM in preoperative staging of breast cancer in a randomized study.MethodThis prospective randomized study will include 440 patients with strongly suspected or established diagnosis of breast malignancy, based on assessment with mammography, ultrasound and core biopsy/cytology, and for whom primary surgery is planned. Patients will be randomized 1:1 using a web-based randomization tool to additional investigation with CEM or no further imaging. The CEM findings will be taken into consideration, which may lead to changes in primary treatment, which is the primary endpoint of this study. Secondary endpoints include rate of reoperation and number of avoidable mastectomies, as well as a cost-benefit analysis of additional CEM. Patient-reported health-related quality of life will be investigated at 1 year with the validated Breast-Q™ questionnaire. The rate of local recurrence or new cancer ipsi- or contralaterally within 5 years will be assessed from medical records and pathology reports.DiscussionThe aim of this trial is to explore the added value of CEM in preoperative staging of breast cancer. The results obtained from this study will contribute to our knowledge on CEM as an additional imaging method to standard investigation with digital mammography and ultrasound. The findings may also provide additional information on which patient groups would benefit from CEM, and on the economic aspects of CEM in standard preoperative practice.Trial registrationThis trial is registered at clinicaltrials.gov , registration no: NCT04437602 , date of registration: June 18, 2020.

Dataset Information

Preoperative contrast-enhanced computed tomographic characterisation of pancreatic cystic lesions: A prospective study.

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Preoperative contrast-enhanced computed tomographic characterisation of pancreatic cystic lesions: A prospective study.

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