Nonenhanced Chemical Exchange Saturation Transfer Cardiac Magnetic Resonance Imaging in Patients With Amyloid Light-Chain Amyloidosis.

BACKGROUND: Chemical exchange saturation transfer (CEST) is an emerging metabolic MRI technique to map creatine distribution in the myocardium. PURPOSE: To investigate the feasibility of using a contrast-free CEST technique to evaluate cardiac involvement in amyloid light-chain (AL) amyloidosis. STUDY TYPE: Prospective. POPULATION: Forty patients with biopsy-proven AL amyloidosis (age 57.6 ± 9.1 years, 31 males) and 20 healthy controls (age 42.8 ± 13.8 years, 13 males). FIELD STRENGTH/SEQUENCE: A 3.0 T, CEST imaging using a single-shot FLASH sequence, T1 mapping with a modified Look-Locker inversion recovery sequence and late gadolinium enhancement (LGE) imaging with a phase-sensitive inversion recovery gradient echo sequence. ASSESSMENT: The average CEST was calculated in the basal short-axis slice of the entire left ventricle and septum. LGE was assessed subjectively (none/patchy/global) and extracellular volume (ECV), CEST and T1 maps generated. STATISTICAL TESTS: Comparison between patient groups and healthy controls was performed by one-way analysis of variance with post hoc Bonferroni correction. Correlation was assessed using the Pearson's r correlation or Spearman ρ correlation. Statistical significance was defined as P < 0.05. RESULTS: Global (0.09 ± 0.03 vs. 0.11 ± 0.02) and septal (0.09 ± 0.03 vs. 0.11 ± 0.03) basal short-axis CEST was significantly decreased in patients with AL amyloidosis compared to the controls. Global CEST correlated significantly with Mayo stage (ρ = -0.508), NYHA Class (ρ = -0.430), LVEF (r = 0.511), mass index (r = -0.373), LGE (ρ = -0.537), ECV (r = -0.544), and T2 (r = -0.396). Septal CEST correlated significantly with LVEF (r = 0.395), LGE (ρ = -0.330), and ECV (r = -0.391). DATA CONCLUSIONS: This study highlights the potential of CEST MRI to identify cardiac involvement and evaluate disease burden and to give insight into cellular changes intermediary between function and structure in AL amyloidosis patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images.

Purpose: Accurate segmentation of the pancreas using abdominal computed tomography (CT) scans is a prerequisite for a computer-aided diagnosis system to detect pathologies and perform quantitative assessment of pancreatic disorders. Manual outlining of the pancreas is tedious, time-consuming, and prone to subjective errors, and thus clearly not a viable solution for large datasets. Approach: We introduce a multiphase morphology-guided deep learning framework for efficient three-dimensional segmentation of the pancreas in CT images. The methodology works by localizing the pancreas using a modified visual geometry group-19 architecture, which is a 19-layer convolutional neural network model that helped reduce the region of interest for more efficient computation and removed most of the peripheral structures from consideration during the segmentation process. Subsequently, soft labels for segmentation of the pancreas in the localized region were generated using the U-net model. Finally, the model integrates the morphology prior of the pancreas to update soft labels and perform segmentation. The morphology prior is a single three-dimensional matrix, defined over the general shape and size of the pancreases from multiple CT abdominal images, that helps improve segmentation of the pancreas. Results: The system was trained and tested on the National Institutes of Health dataset (82 CT scans of the healthy pancreas). In fourfold cross-validation, the system produced an average Dice-SØrensen coefficient of 88.53% and outperformed state-of-the-art techniques. Conclusions: Localizing the pancreas assists in reducing segmentation errors and eliminating peripheral structures from consideration. Additionally, the morphology-guided model efficiently improves the overall segmentation of the pancreas.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.