scholarly journals Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

2021 ◽  
Vol 10 (9) ◽  
pp. 4217-4231
Author(s):  
Jing-Wen Ma ◽  
Meng Li
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Molecular Typing ◽  
Narrative Review ◽  
Research Progress ◽  
Ct Image

scholarly journals MA02.08 Computed Tomography Attenuation Value as Considerable Predictor for Malignancy in Clinical T1 Lung Adenocarcinoma

2021 ◽  
Vol 16 (3) ◽  
pp. S135-S136
Author(s):  
Z. Qiu ◽  
C. Zhang ◽  
H. Wang ◽  
R. Fu ◽  
F. Cai ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Attenuation Value

scholarly journals Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-16
Author(s):  
Xiaowe Xu ◽  
Jiawei Zhang ◽  
Jinglan Liu ◽  
Yukun Ding ◽  
Tianchen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Denoising ◽  
Ct Images ◽  
High Dose ◽  
Ct Image ◽  
Paired Data ◽  
High Quality ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Global Cycle

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

Air-Containing Space in Lung Adenocarcinoma: High-Resolution Computed Tomography Findings

2002 ◽  
Vol 26 (6) ◽  
pp. 1026-1031 ◽  
Author(s):  
Atsushi Nambu ◽  
Kazuyuki Miyata ◽  
Katsura Ozawa ◽  
Masahisa Miyazawa ◽  
Yuuko Taguchi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Adenocarcinoma ◽  
High Resolution Computed Tomography

COMPUTED TOMOGRAPHY (CT) IMAGE IN REPAIRING TENDON INJURY IN ATHLETES

2021 ◽  
Vol 27 (7) ◽  
pp. 699-702
Author(s):  
Hongliang Li
Keyword(s):  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Tendon Injury ◽  
Fracture Dislocation ◽  
Ct Image ◽  
Diagnostic Efficiency ◽  
Level Of Evidence ◽  
Anatomical Basis ◽  
Hands And Feet ◽  
Ct And Mri

ABSTRACT Introduction: endon injury can usually be divided into the following types: fracture, dislocation, compression, bone insert, tendon injury, among which dislocation and compression are more common. Objective: To evaluate the application value of Computed Tomography (CT) image in tendon display. Methods: CT scan of the hands and feet was performed in our hospital for suspected tendon lesions. The CT and MRI data of 61 patients with tendon injury were retrospectively analyzed, and the diagnostic efficiency of CT and MRI were compared and analyzed. Results: The diagnostic accuracy of 61 patients was 89.71% (61/68). Except for chronic tendon injury (12/19), the diagnostic accuracy of other lesions was 100%. The sensitivity of CT and MRI in the diagnosis of hand tendon injury was 94.7% and 90.7%, the specificity was 99.3% and 98.6%, and the coincidence rate was 97.7% and 96.3%. Conclusions: CT images are accurate in localization and characterization of tendon injury, with high sensitivity and specificity, and can provide accurate anatomical basis for surgery. Level of evidence II; Therapeutic studies - investigation of treatment results.

Features Based on Thin-section Computed Tomography Identify the Typeof lung Adenocarcinoma

2020 ◽  
Author(s):  
Zhiqiang Li ◽  
Hongwei Zheng ◽  
Shanshan Liu ◽  
Xinhua Wang ◽  
Lei Xiao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Thin Section ◽  
Ground Glass Opacity ◽  
Smoking History ◽  
Ground Glass ◽  
Quantitative Aspect ◽  
Adenocarcinoma In Situ ◽  
Invasive Adenocarcinoma ◽  
Pleural Invasion

Abstract Background: To investigate whether thin-section computed tomography (TSCT) features may efficiently guide the invasiveness basedclassification of lung adenocarcinoma. Methods: Totally, 316 lung adenocarcinoma patients (from 2011-2015) were divided into three groups: 56 adenocarcinoma in situ (AIS), 98 minimally invasive adenocarcinoma (MIA), and 162 invasive adenocarcinoma (IAC) according their pathological results. Their TSCT features, including nodule pattern, shape, pleural invasion, solid proportion, border, margin, vascular convergence, air bronchograms, vacuole sign, pleural indentation, diameter, solid diameter, and CT values of ground-glass nodules (GGN) were analyzed. Pearson’s chi-square test, Fisher’s exact test and One-way ANOVA were adopted tocomparebetweengroups. Receiver operating characteristic (ROC) analysis wereperformedto assess its value for prediction and diagnosis. Results: Patients with IAC were significantly elder than those in AIS or MIA group,and more MIA patients had a smoking history than AIS and IAC. No recurrence happened in the AIS and MIA groups, while 4.3% recurrences were confirmed in the IAC group. As for TSCT variables, we found AIS group showed dominantly higher 91.07%PGGN pattern and 87.50% round/oval nodules than that in MIA and IAC group. In contrast, MIA group showed more cases with undefined border and vascular convergence than AIS and IAC group. Importantly, IAC group uniquely showed higher frequency of pleural invasion compared with MIA and AIS group. The majority of patients (82.1%) in IAC group showed ≥ 50% solid proportion. We found diameter and solid diameter of the lesions were notably larger in the IAC group compared with AIS and MIA groupin quantitative aspect. In addition, for MGGNs, the CT values of ground-glass opacity (GGO) and ground-glass opacity solid portion (GGO-solid) were both higher in the IAC group than AIS and MIA. Finally, we also observed that smooth margin took a dominant proportion in the AIS group while most cases in the IAC group had a lobulate margin. Patients in MIA and IAC group shared higher level of air bronchograms and vacuole signs than AIS group. Conclusions: The unique features in different groups identified by TSCT had diagnosis value for lung adenocarcinoma.

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