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

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.

Delayed CT to Evaluate Renal Masses Incidentally Discovered at Contrast-enhanced CT: Demonstration of Vascularity with Deenhancement

Radiology ◽  
1999 ◽  
Vol 213 (3) ◽  
pp. 674-680 ◽  
Author(s):  
Michael Macari ◽  
Morton A. Bosniak
Keyword(s):  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Renal Masses — Evaluation by Amplitude Coded Colour Doppler Sonography and Multiphasic Contrast — Enhanced CT

1999 ◽  
Vol 40 (4) ◽  
pp. 457-461 ◽  
Author(s):  
Michael Riccabona ◽  
D. Szolar ◽  
K. Preidler ◽  
M. Uggowitzer ◽  
C. Kugler ◽  
...  
Keyword(s):  
Doppler Sonography ◽  
Colour Doppler Sonography ◽  
Colour Doppler ◽  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Small (< 4 cm) Renal Masses: Differentiation of Angiomyolipoma Without Visible Fat From Renal Cell Carcinoma Using Unenhanced and Contrast-Enhanced CT

2015 ◽  
Vol 205 (6) ◽  
pp. 1194-1202 ◽  
Author(s):  
Naoki Takahashi ◽  
Shuai Leng ◽  
Kazuhiro Kitajima ◽  
Daniel Gomez-Cardona ◽  
Prabin Thapa ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Small renal masses (≤ 4 cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta–lesion attenuation difference (ALAD) on contrast-enhanced CT

2020 ◽  
Vol 125 (12) ◽  
pp. 1280-1287 ◽  
Author(s):  
Francesco Gentili ◽  
Ilaria Bronico ◽  
Umberto Maestroni ◽  
Francesco Ziglioli ◽  
Enrico Maria Silini ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Small Renal Masses ◽  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

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

Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT

2016 ◽  
Vol 42 (5) ◽  
pp. 1485-1492 ◽  
Author(s):  
Shuai Leng ◽  
Naoki Takahashi ◽  
Daniel Gomez Cardona ◽  
Kazuhiro Kitajima ◽  
Brian McCollough ◽  
...  
Keyword(s):  
Small Renal Masses ◽  
Renal Masses ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct
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