Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 201

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images

Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results

Biological function and clinical implication of coagulation proteins during malignant transformation of pancreatic cells

The premalignant pancreatic cellular genotype can remain stable for years before rapid malignant transformation, often associated with inflammation. Tissue factor (TF) is an inflammatory modulator regulated by factor VIIa (fVIIa) for its levels and activity. The presence of TF in PDAC and its role in cell proliferation, angiogenesis, and metastasis suggests that TF may be a marker of the inflammatory microenvironment driving precursor lesions of pancreatic cancer. This study examined the in vitro influence of TF on pancreatic epithelial cells and its clinical value in detecting malignant transformation within pancreatic cyst fluid (PCyF). PCyF from 27 patients with pancreatic cystic lesions was analysed in a blinded fashion. TF and fVIIa levels were measured (ELISA), and the fVIIa:TF ratios were calculated. A cut-off value for TF concentration was determined and compared to the conventional assessment parameters (radiological features, CEA and amylase). Patients were categorised into four groups based on cytopathology and two groups based on indication for resection (‘resective’). Significant histological stage-dependent increases in TF levels were observed. Mean TF concentration was significantly higher (p=0.006) in the resective (high-grade dysplasia & malignant; 1.17 ng/ml, 95% CI 0.68, 1.67) vs non-resective group (benign & low-grade dysplasia; 0.27 ng/ml, 95% CI 0.1, 0.44), with a strong positive correlation (r= 0.746, p <0.001, TF cut-off 0.75 ng/ml, AUC 0.877, p=0.002). The fVIIa:TF ratio did not add further value. Incubation of pancreatic cells with recombinant TF resulted in increased expression of a marker of epithelial to mesenchymal transition (Vimentin). This influence was moderated by supplementation with fVIIa in benign (hTERT-HPNE) but not overtly malignant pancreatic cells (AsPC-1). Cyst-associated TF levels appear to correlate with cytological progression to the malignant phenotype and may allow better discrimination (specificity 94%) of the ‘resective’ lesion, reduce healthcare costs and offer a more nuanced tool for monitoring indeterminate cystic lesions

デジタルPCRを用いた膵嚢胞液中のテロメラーゼ活性測定は嚢胞性膵腫瘍の良悪性の鑑別診断に有用である

Tohoku University海野　倫明課

Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book

Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo

Diagnostic accuracy of transabdominal ultrasound in chronic pancreatitis

The performance of transabdominal ultrasound (US) in chronic pancreatitis (CP) following the advances in US technology made during recent decades has not been explored. Our aim in this prospective study was to evaluate the diagnostic accuracy of modern abdominal US compared with the Mayo score in CP. One hundred thirty-four patients referred for suspected CP were included in the study. Fifty-four patients were assigned the diagnosis CP. After inclusion, transabdominal US was performed. Ductal features (calculi, dilations and caliber variations, side-branch dilations and hyper-echoic duct wall margins) and parenchymal features (calcifications, cysts, hyper-echoic foci, stranding, lobulation and honeycombing) were recorded. Features were counted and scored according to a weighting system defined at the international consensus meeting in Rosemont, Illinois (Rosemont score). Diagnostic performance indices (95% confidence interval) of US were calculated: The unweighted count of features had a sensitivity of 0.69 (0.54–0.80) and specificity of 0.97 (0.90–1). The Rosemont score had a sensitivity of 0.81 (0.69–0.91) and specificity of 0.97 (0.90–1). Exocrine pancreatic failure was most pronounced in Rosemont groups I and II (p < 0.001). We conclude that using both unweighted and weighted scores, the diagnostic accuracy of modern transabdominal US is good. The extent of pancreatic changes detected by the method is correlated with exocrine pancreatic function.publishedVersio

Magnetic resonance imaging 3t and total fibrotic volume in autosomal dominant polycystic kidney disease

INTRODUCTION: Autosomal dominant polycystic kidney disease (ADPKD) is the most common renal hereditary disorder. Several authors have attempted to identify a kidney damage marker for predicting the prognosis and the effectiveness of therapy in ADPKD. The aim of this study was to identify and quantify in ADPKD, through a novel MR protocol with 3 Tesla (MRI 3Tesla), the presence of parenchymal fibrotic tissue at early stage of disease, able to correlate the glomerular filtrate and to predict the loss of the function renal. MATERIAL AND METHODS: 15 ADPKD patients undergone to renal MRI 3Tesla at T0 and revaluated after follow up (T1) of 5 years. We have evaluated renal function, plasma aldosterone concentration (PAC), insulin resistance and surrogate markers of atherosclerosis (carotid intima media thickness (IMT), ankle/brachial index (ABI) and left ventricular mass index (LVMI). RESULTS: Our study showed a significant negative correlation between total kidney volume and estimated glomerular filtration rate (eGFR) during observational observation (p&lt;0.02). Moreover, we showed a negative correlation between eGFR with Total Fibrotic Volume (TFV) (p&lt;0.04) and Total Perfusion Volume/Total kidney Volume(&lt;0.02). Moreover TFV was correlated positively with PAC (p&lt;0.05), insulin values (p&lt;0.05), ABI (p &lt;0.05) and LVMI(p&lt;0.01). CONCLUSIONS: The MRI 3Tesla, despite the high costs, could be considered an useful and non-invasive method in the evaluation of fibrotic tissue and progression of the disease in ADPKD patients. Further clinical trials on larger group are due to confirm the results of this pilot study, suggesting that MRI 3Tesla can be useful to evaluate the effectiveness of new therapeutic strategies. This article is protected by copyright. All rights reserved