Editorial Comment on “Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully-Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1,204 Heathy Adults Using Unenhanced CT as Reference Standard”

2020 ◽  
Author(s):  
Amir A. Borhani
Keyword(s):  
Deep Learning ◽  
Editorial Comment ◽  
Liver Steatosis ◽  
Reference Standard ◽  
Unenhanced Ct ◽  
Tool Evaluation ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Volumetric Segmentation

scholarly journals Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

2021 ◽  
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Parenchyma ◽  
Ct Images ◽  
Fibrosis Stage ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Liver Surface ◽  
Fibrosis Staging

Abstract Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

Comparison of liver SUV using unenhanced CT versus contrast-enhanced CT for attenuation correction in 18F-FDG PET/CT

2014 ◽  
Vol 35 (5) ◽  
pp. 472-477 ◽  
Author(s):  
Edwin E.G.W. ter Voert ◽  
Hanneke W.M. van Laarhoven ◽  
Peter J.M. Kok ◽  
Wim J.G. Oyen ◽  
Eric P. Visser ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Fdg Pet ◽  
Unenhanced Ct ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Pet Ct ◽  
Enhanced Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning

2020 ◽  
Vol 214 (3) ◽  
pp. 605-612 ◽  
Author(s):  
Takashi Tanaka ◽  
Yong Huang ◽  
Yohei Marukawa ◽  
Yuka Tsuboi ◽  
Yoshihisa Masaoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

2019 ◽  
Vol 212 (3) ◽  
pp. 554-561 ◽  
Author(s):  
Lisa M. Ho ◽  
Ehsan Samei ◽  
Maciej A. Mazurowski ◽  
Yuese Zheng ◽  
Brian C. Allen ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Unenhanced Ct ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Ct Contrast

Automatic detection of brain metastases on contrast-enhanced CT with deep-learning feature-fused single-shot detectors

2021 ◽  
Vol 136 ◽  
pp. 109577
Author(s):  
Shiori Amemiya ◽  
Hidemasa Takao ◽  
Shimpei Kato ◽  
Hiroshi Yamashita ◽  
Naoya Sakamoto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
Automatic Detection ◽  
Single Shot ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Se Woo Kim ◽  
Jung Hoon Kim ◽  
Suha Kwak ◽  
Minkyo Seo ◽  
Changhyun Ryoo ◽  
...  
Keyword(s):  
Emergency Department ◽  
Abdominal Pain ◽  
Deep Learning ◽  
Acute Abdominal Pain ◽  
Point Scale ◽  
Contrast Enhanced Ct ◽  
Underlying Malignancy ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Emergency Department Patients

AbstractOur objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorithm generating DL-SCE-CT using NECT with paired precontrast/postcontrast images. For clinical application, 353 patients from three institutions who visited the ED with AAP were included. Six reviewers (experienced radiologists, ER1-3; training radiologists, TR1-3) made diagnostic and disposition decisions using NECT alone and then with NECT and DL-SCE-CT together. The radiologists’ confidence in decisions was graded using a 5-point scale. The diagnostic accuracy using DL-SCE-CT improved in three radiologists (50%, P = 0.023, 0.012, < 0.001, especially in 2/3 of TRs). The confidence of diagnosis and disposition improved significantly in five radiologists (83.3%, P < 0.001). Particularly, in subgroups with underlying malignancy and miscellaneous medical conditions (MMCs) and in CT-negative cases, more radiologists reported increased confidence in diagnosis (83.3% [5/6], 100.0% [6/6], and 83.3% [5/6], respectively) and disposition (66.7% [4/6], 83.3% [5/6] and 100% [6/6], respectively). In conclusion, DL-SCE-CT enhances the accuracy and confidence of diagnosis and disposition regarding patients with AAP in the ED, especially for less experienced radiologists, in CT-negative cases, and in certain disease subgroups with underlying malignancy and MMCs.

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