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Objectives This study aimed to investigate the high-resolution CT (HRCT) characteristics of anti-melanoma differentiation-associated gene 5 (MDA5) antibody positive dermatomyositis-associated interstitial lung disease (anti-MDA5 DM-ILD), and to clarify the underlying mechanisms of the clinical phenomenon. Methods Clinical data and HRCT patterns were compared between anti-MDA5 DM-ILD (n  = 32) and antisynthetase syndrome-associated ILD (ASS-ILD) (n  = 29). RNA sequencing of whole-blood samples from the two groups, and in vitro experiments using human embryonic lung fibroblasts (HELFs) were conducted to explore the potential mechanisms of the clinical findings. Results The anti-MDA5 DM-ILD subset had a significantly higher incidence of rapidly progressive ILD (RPILD) than ASS-ILD (65.6% vs 37.9%; P  = 0.031). The relative percentage of the lung fibrosis HRCT pattern was significantly lower in the anti-MDA5 DM-ILD group, especially the RPILD subgroup (P  = 0.013 and 0.003, respectively). RNA sequencing detected the upregulated genes including interferon-induced helicase C domain 1 (encoding MDA5), and a trend towards downregulated expression of TGF-β signalling components in anti-MDA5 DM-ILD. In vitro culture of HELFs revealed that upregulated expression of MDA5 in HELFs was correlated with the downregulated expression of alpha smooth muscle actin, connective tissue growth factor, collagen I and collagen III by suppressing the TGF-β signalling pathway. Conclusions Anti-MDA5 DM-ILD patients have significantly less lung fibrosis and elevated MDA5 expression. The upregulated expression of MDA5 has relations with the suppression of the pro-fibrotic function of fibroblasts via the TGF-β signalling pathway, which may partially explain the mechanism of the clinical phenomenon. [ABSTRACT FROM AUTHOR]

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. [ABSTRACT FROM AUTHOR]

Background: Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients' multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Methods: Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model. Results: The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors' consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors' consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model's generalization ability. Conclusions: We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer. [ABSTRACT FROM AUTHOR]