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

Spirulina is an effective dietary source of zeaxanthin to humans

Zeaxanthin is a predominant xanthophyll in human eyes and may reduce the risk of cataracts and age-related macular degeneration. Spirulina is an algal food that contains a high concentration of zeaxanthin. In order to determine the zeaxanthin bioavailability of spirulina for dietary supplementation in humans, spirulina was grown in nutrient solution with 2H2O for carotenoid labelling. Single servings of 2H-labelled spirulina (4·0-5·0g) containing 2·6-3·7mg zeaxanthin were consumed by fourteen healthy male volunteers (four Americans and ten Chinese) with 12g dietary fat. Blood samples were collected over a 45d period. The serum concentrations of total zeaxanthin were measured using HPLC, and the enrichment of labelled zeaxanthin was determined using LC-atmospheric pressure chemical ionisation-MS (LC-APCI-MS). The results showed that intrinsically labelled spirulina zeaxanthin in the circulation was detected at levels as low as 10% of the total zeaxanthin for up to 45d after intake of the algae. A single dose of spirulina can increase mean serum zeaxanthin concentration in humans from 0·06 to 0·15μmol/l, as shown in our study involving American and Chinese volunteers. The average 15 d area under the serum zeaxanthin response curve to the single dose of spirulina was 293nmol×d/μmol (range 254-335) in American subjects, and 197nmol×d/μmol (range 154-285) in Chinese subjects. It is concluded that the relative bioavailability of spirulina zeaxanthin can be studied with high sensitivity and specificity using 2H labelling and LC-APCI-MS methodology. Spirulina can serve as a rich source of dietary zeaxanthin in human

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

Uncovering hidden in vivo resonances using editing based on localized TOCSY

A novel single-shot spectral editing technique for in vivo proton NMR is proposed to recover resonances of low-concentration metabolites obscured by very strong resonances. With this new method, editing is performed by transferring transverse magnetization to J-coupled spins from selected coupling partners using a homonuclear Hartmann-Hahn polarization transfer with adiabatic pulses. The current implementation uses 1D-TOCSY with single-voxel localization based on LASER to recover the H1 proton of beta-glucose at 4.63 ppm from under water and the lactate methyl resonances from beneath a strong lipid signal. The method can be extended to further spin systems where conventional editing methods are difficult to perform

Interactions between microplastics, pharmaceuticals and personal care products: implications for vector transport

Microplastics are well known for vector transport of hydrophobic organic contaminants, and there are growing concerns regarding their potential adverse effects on ecosystems and human health. However, recent studies focussing on hydrophilic compounds, such as pharmaceuticals and personal care products (PPCPs), have shown that the compounds ability to be adsorbed onto plastic surfaces. The extensive use of PPCPs has led to their ubiquitous presence in the environment resulting in their cooccurrence with microplastics. The partitioning between plastics and PPCPs and their fate through vector transport are determined by various physicochemical characteristics and environmental conditions of specific matrices. Although the sorption capacities of microplastics for different PPCP compounds have been investigated extensively, these findings have not yet been synthesized and analyzed critically. The specific objectives of this review were to synthesize and critically assess the various factors that affect the adsorption of hydrophilic compounds such as PPCPs on microplastic surfaces and their fate and transport in the environment. The review also focuses on environmental factors such as pH, salinity, and dissolved organics, and properties of polymers and PPCP compounds, and the relationships with sorption dynamics and mechanisms. Furthermore, the ecotoxicological effects of PPCP-sorbed microplastics on biota and human health are also discussed

Chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM)

PURPOSETo review imaging findings in chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM) on computed tomography (CT)/magnetic resonance imaging (MRI) and its association with tumor burden.METHODSWe performed a retrospective chart review to identify patients with hepatic metastases who received chemotherapy and subsequent follow-up imaging where CT or MRI showed morphological changes in the liver. The morphological changes searched for were nodularity, capsular retraction, hypodense fibrotic bands, lobulated outline, atrophy or hypertrophy of segments or lobes, widened fissures, and one or more features of portal hypertension (splenomegaly/venous collaterals/ascites). The inclusion criteria were as follows: a) no known chronic liver disease; b) availability of CT or MRI images before chemotherapy that showed no morphological signs of chronic liver disease; c) at least one follow-up CT or MRI image demonstrating CALMCHeM after chemotherapy. Two radiologists in consensus graded the initial hepatic metastases tumor burden according to number (≤10 and >10), lobe distribution (single or both lobes), and liver parenchyma volume affected (10 in 64.4% of patients. The volume of liver involved was <50% in 79.8% and ≥50% in 20.2% of cases. The severity of CALMCHeM at the first imaging follow-up was associated with a larger number of metastases (P = 0.002) and volume of the liver affected (P = 0.015). The severity of CALMCHeM had progressed to moderate to severe changes in 85.9% of patients, and 72.5% of patients had one or more features of portal hypertension at the last follow-up. The most common features at the final follow-up were nodularity (95.0%), capsular retraction (93.4%), atrophy (66.2%), and ascites (65.7%). The Cox proportional hazard model showed metastases affected ≥50% of the liver (P = 0.033), and the female gender (P = 0.004) was independently associated with severe CALMCHeM.CONCLUSIONCALMCHeM can be observed with a wide variety of malignancies, is progressive in severity, and the severity correlates with the initial metastatic liver disease burden

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT0104237