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PURPOSE: Delay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass. METHODS: Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas. RESULTS: Forty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semi-quantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels. CONCLUSION: Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.
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Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Inteligência Artificial , Processamento de Imagem Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Redes Neurais de Computação , Pâncreas/diagnóstico por imagem , Neoplasias Pancreáticas/diagnóstico por imagemRESUMO
PURPOSE: A wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist. METHODS: In this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis. RESULTS: 214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist. CONCLUSION: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.
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Cisto Pancreático , Neoplasias Pancreáticas , Humanos , Estudos Retrospectivos , Neoplasias Pancreáticas/patologia , Radiologistas , ComputadoresRESUMO
OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.
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Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/mortalidade , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/mortalidade , Cuidados Pré-Operatórios , Tomografia Computadorizada por Raios X , Adulto , Idoso , Idoso de 80 Anos ou mais , Carcinoma Ductal Pancreático/cirurgia , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Neoplasias Pancreáticas/cirurgia , Prognóstico , Estudos Retrospectivos , Análise de Sobrevida , Carga TumoralRESUMO
Hepatic angiosarcoma is a rare, highly aggressive mesenchymal liver malignancy with poor prognosis that stems from the endothelial cells that line the walls of blood or lymphatic vessels. It is the third most common primary liver malignancy and is most prevalent among older males. It is difficult to diagnose due to various clinical presentations from asymptomatic to abdominal pain, pleural effusion, and liver failure. The diagnosis of liver angiosarcoma is suspected on imaging features and confirmed by histopathological assessment. Primary management is determined based on the stage of tumor from surgery to palliative care such as chemotherapy or tumor transarterial embolization. We report a 51-year-old female who presented with stage 4 liver angiosarcoma and a history of childhood Wilms tumor. We focus on tumor management using radiological modalities and pathological analysis and discuss secondary liver tumors in survivors of childhood Wilms tumor.
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Lymphangioma is a rare, benign congenital malformation of the lymphatic system that usually affects the neck and head in children. Intra-abdominal lymphangioma accounts for less than 5 percent of all cases of lymphangioma. The clinical presentation of intra-abdominal lymphangioma can vary from asymptomatic to nausea, vomiting, and abdominal pain. The diagnosis of intra-abdominal lymphangioma is based on imaging modalities and histopathological examination. The definitive treatment is surgical resection. Here we describe the interesting and rare case of a 29-year-old woman with lymphangioma of the retroperitoneum extending to the root of the mesentery. We focus on the diagnosis and management of this rare tumor by the application of radiological modalities and pathological analysis.
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Desmoid tumors are rare, benign, and locally aggressive neoplasms that stem from connective tissue that have high rates of recurrence after surgery. Intra-abdominal desmoid-type fibromatosis can arise in 2 forms: sporadic or hereditary (associated with familial adenomatous polyposis and Gardner syndrome). The diagnosis of desmoid-type tumors is based on imaging modalities and histopathological examination. The primary treatment is resection surgery. We report a 64-year-old male with a distal pancreatic desmoid tumor. We focus on tumor management by the application of radiological modalities and pathological analysis.
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Cardiac catheterization through the radial artery has reduced the percentage of complications seen previously in the femoral approach. Computed tomographic angiography is a noninvasive technique that can accurately diagnose pseudoaneurysms and other vascular pathologies. A 93-year-old female presented to the emergency department with pain and swelling of her right distal arm and wrist after a transradial cardiac catheterization procedure, as part of a transcatheter aortic valve replacement evaluation for severe aortic stenosis. Angiography is the standard diagnosing technique for aneurysms and pseudoaneurysms, but it is an invasive procedure. We show this case in order to highlight the importance of computed tomographic angiography as a noninvasive approach in diagnosing uncommon complications in the clinical setting.
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Recurrent acute pancreatitis is characterized by frequent episodes of inflammation in the pancreas. The most common causes are alcohol abuse and gallstones but approximately 30% of cases are defined as idiopathic because initial evaluation fails to detect the etiology. In these patients, extensive laboratory and imaging evaluations usually lead to the uncovering of an occult biliary duct stone or sphincter of Oddi dysfunction as the main reason for frequent pancreatitis. We report a 42-year-old female with a longstanding history of acute recurrent pancreatitis in the setting of pancreas divisum due to its rarity. We focus on appropriate investigations by application of specialized laboratory and radiological modalities because determining the etiology is a crucial step in patient management.
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Cushing syndrome is a disorder that occurs when the body is exposed to a higher than normal level of the hormone cortisol. It is most commonly caused by exogenous glucocorticoids, and less commonly due to endogenous sources. Ectopic adrenocorticotropic hormone (ACTH) syndrome is one of the rare causes of endogenous Cushing syndrome engendered by oversecretion of ACTH from a tumor outside of the pituitary or adrenal glands. We present a case of a 74-year-old male with uncontrolled type 2 diabetes mellitus who was suspected of having Cushing syndrome on chest CT due to increasing mediastinal lipomatosis and enlarging bilateral adrenal glands. Cushing syndrome was confirmed based on clinical features and biochemical tests. Further investigation revealed an ACTH-producing functional neuroendocrine tumor of the pancreas causing ectopic Cushing syndrome.
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Uterine artery pseudoaneurysm is an uncommon cause of vaginal bleeding that can occur after cesarean, hysterectomy, myomectomy, uncomplicated vaginal delivery, as well as gynecologic surgery. A 29-year-old woman (G4P1122) who underwent stat cesarean section and intrauterine device placement was found to have a 1.6-cm left uterine artery pseudoaneurysm on follow-up ultrasound. The patient presented to interventional radiology for angiography and uterine artery embolization to minimize the risk of spontaneous hemorrhage. This is the rare reported case, to our knowledge, of a uterine artery pseudoaneurysm associated with a recent cesarean section. Computed tomography angiogram, with multiplanar and maximal intensity projection images, can optimally display the pseudoaneurysm and the feeding vessel, which can provide valuable information for image-guided catheter embolization.
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Various conditions such as inflammation, malignancy, surgical manipulations, and radiation therapy can lead to the development of urinary bladder fistulae. Although many of these fistulae are suspected clinically, imaging plays a major role in confirmation and planning of subsequent treatment. Computed tomography (CT) cystoscopy provides useful details regarding the fistulous track and the anatomy of the region. This article aims to provide a succinct review of bladder fistulae and the role of CT cystography in their management.
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Cistografia , Bexiga Urinária , Cistoscopia , Humanos , Tomografia Computadorizada por Raios X , Bexiga Urinária/diagnóstico por imagemRESUMO
Acute pancreatitis (AP) is caused by acute inflammation of the pancreas and adjacent tissue and is a common source of abdominal pain. The current CT and MRI evaluation of AP is mostly based on morphologic features. Recent advances in image acquisition and analysis offer the opportunity to go beyond morphologic features. Advanced MR techniques such as diffusion-weighted imaging, as well as T1 and T2 mapping, can potentially quantify signal changes reflective of underlying tissue abnormalities. Advanced analytic techniques such as radiomics and artificial neural networks (ANNs) offer the promise of uncovering imaging biomarkers that can provide additional classification and prognostic information. The purpose of this article is to review recent advances in imaging acquisition and analytic techniques in the evaluation of AP.
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Inteligência Artificial , Diagnóstico por Imagem/tendências , Pancreatite/diagnóstico por imagem , Humanos , Pancreatite/classificação , Prognóstico , Índice de Gravidade de DoençaRESUMO
Although conventional radiographic cystography has been traditionally considered the reference standard in detecting bladder injuries, computed tomography (CT) cystography has become the initial imaging method of choice in the acute setting. CT cystography has been shown to provide comparable accuracy as conventional cystography, and can be easily performed in conjunction with trauma CT surveys in patients with suspected bladder injuries. Despite increasing enthusiasm toward CT cystography in dealing with patients with suspected bladder injuries, there is little information in this regard in the literature. This article aims to discuss the role of CT cystography in the evaluation of bladder injuries.
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Cistografia/métodos , Tomografia Computadorizada por Raios X/métodos , Bexiga Urinária/diagnóstico por imagem , Bexiga Urinária/lesões , HumanosAssuntos
Carcinoma Ductal Pancreático/diagnóstico por imagem , Aprendizado Profundo , Imageamento Tridimensional , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/patologia , Tomografia Computadorizada por Raios X/métodos , Automação , Carcinoma Ductal Pancreático/patologia , Estudos de Casos e Controles , Feminino , Previsões , Humanos , Masculino , Melhoria de Qualidade , Estudos Retrospectivos , Fluxo de TrabalhoRESUMO
OBJECTIVE. The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas. MATERIALS AND METHODS. In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated. RESULTS. Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%. CONCLUSION. Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.