Dataset Information

Clinical and radiological features that predict malignant transformation in cystic lesions of the pancreas: a retrospective case note review.

ABSTRACT: Background: Pancreatic cystic lesions (PCL) are being detected with increasing frequency. Current methods of stratifying risk of malignant transformation are imperfect. This study aimed to determine the frequency of pancreatic malignancy in patients with PCL and define clinical and radiological features that predict malignant transformation in patients managed by surgery and/or surveillance. Methods: A retrospective cohort of adults who were evaluated in a tertiary hepatopancreaticobiliary centre between January 2000 - December 2013 with a confirmed PCL and followed up for at least 5 years. All cystic lesions were discussed at a weekly multidisciplinary meeting. Results: Of the 1,090 patients diagnosed with a PCL, 768 patients were included in the study: 141 patients were referred for immediate pancreatic resection, 570 entered surveillance while 57 had a malignant PCL which was unresectable at diagnosis (n=47) or were unfit for surgery (n=10). In those who were resected following presentation, malignancy was present in 38%. During follow-up 2% of those entering a surveillance programme underwent malignant transformation. Clinical and radiological features associated with a high-risk PCL included older age, symptoms, associated solid component or dilated main pancreatic duct. In intraductal papillary mucinous neoplasms, larger size was not a feature of malignant transformation (benign vs. malignant 30mm vs. 23mm; P= 0.012). Conclusion: The sensitivity of standard diagnostic tests leading to immediate surgery for high-risk PCL (malignant or mucinous) was 92% but with a specificity of just 5%. Surveillance of PCL without high-risk features within a multidisciplinary meeting was associated with a low incidence of cancer development, supporting the use of worrisome clinical and radiological features in the initial stratification of PCL.

SUBMITTER: Keane MG

PROVIDER: S-EPMC7176489 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Clinical and radiological features that predict malignant transformation in cystic lesions of the pancreas: a retrospective case note review.

Keane Margaret G MG Dadds Hannah R HR El Sayed Ghassan G Luong Tu Vinh TV Davidson Brian R BR Fusai Guiseppe K GK Thorburn Douglas D Pereira Stephen P SP

AMRC open research 20200101

<b>Background:</b> Pancreatic cystic lesions (PCL) are being detected with increasing frequency. Current methods of stratifying risk of malignant transformation are imperfect. This study aimed to determine the frequency of pancreatic malignancy in patients with PCL and define clinical and radiological features that predict malignant transformation in patients managed by surgery and/or surveillance. <b>Methods:</b> A retrospective cohort of adults who were evaluated in a tertiary hepatopancreatic ...[more]

PMID: 32322783

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Project description:Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature's reproducibility. Pearson correlation coefficients for normal distribution features and Spearman's rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

Project description:PurposeThe detection of pancreatic cystic lesions (PCL) causes uncertainty for physicians and patients, and international guidelines are based on low evidence. The extent and perioperative risk of resections of PCL in Germany needs comparison with these guidelines to highlight controversies and derive recommendations.MethodsClinical data of 1137 patients who underwent surgery for PCL between 2014 and 2019 were retrieved from the German StuDoQ|Pancreas registry. Relevant features for preoperative evaluation and predictive factors for adverse outcomes were statistically identified.ResultsPatients with intraductal papillary mucinous neoplasms (IPMN) represented the largest PCL subgroup (N = 689; 60.6%) while other entities (mucinous cystic neoplasms (MCN), serous cystic neoplasms (SCN), neuroendocrine tumors, pseudocysts) were less frequently resected. Symptoms of pancreatitis were associated with IPMN (OR, 1.8; P = 0.012) and pseudocysts (OR, 4.78; P < 0.001), but likewise lowered the likelihood of MCN (OR, 0.49; P = 0.046) and SCN (OR, 0.15, P = 0.002). A total of 639 (57.2%) patients received endoscopic ultrasound before resection, as recommended by guidelines. Malignancy was histologically confirmed in 137 patients (12.0%), while jaundice (OR, 5.1; P < 0.001) and weight loss (OR, 2.0; P = 0.002) were independent predictors. Most resections were performed by open surgery (N = 847, 74.5%), while distal lesions were in majority treated using minimally invasive approaches (P < 0.001). Severe morbidity was 28.4% (N = 323) and 30d mortality was 2.6% (N = 29). Increased age (P = 0.004), higher BMI (P = 0.002), liver cirrhosis (P < 0.001), and esophageal varices (P = 0.002) were independent risk factors for 30d mortality.ConclusionWith respect to unclear findings frequently present in PCL, diagnostic means recommended in guidelines should always be considered in the preoperative phase. The therapy of PCL should be decided upon in the light of patient-specific factors, and the surgical strategy needs to be adapted accordingly.

Dataset Information

Clinical and radiological features that predict malignant transformation in cystic lesions of the pancreas: a retrospective case note review.

Publications

Clinical and radiological features that predict malignant transformation in cystic lesions of the pancreas: a retrospective case note review.

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