Journal Article10.1007/s00330-022-08553-5
An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions
Lijun Wang,Lufan Chang,Ran Luo,Xue'e Cui,Huanhuan Liu,Haoting Wu,Yanhong Chen,Yuzhen Zhang,Chenqing Wu,Fang-Liang Li,Hao Liu,Wenbin Guan,Dengbin Wang +12 more
19
TL;DR: An artificial intelligence system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.
read more
About: This article is published in European Radiology. The article was published on 08 Mar 2022. The article focuses on the topics: Medicine & Medicine.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Artificial intelligence in breast imaging: potentials and challenges.
Jiawei Li,Danli Sheng,Jiangang Chen,Chao You,Shuai Liu,Huixiong Xu,Cai Chang +6 more
TL;DR: The necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994 are introduced.
6
The role of epicardial and pericoronary adipose tissue radiomics in identifying patients with non-ST-segment elevation myocardial infarction from unstable angina
Jianhua Zhang,Yu Sun,Mengwei Su,Hongrui You,Rongrong Zhang,Jinglong Shi,Jing Ma,Li-bo Zhang,Ben-qiang Yang +8 more
TL;DR: In this paper , the radiomics features of EAT and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA).
4
Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models.
Yasemin Kayadibi,Mehmet Sakıpcan Saracoglu,S. Aladag Kurt,Enes Deger,F Boy,Nese Ucar,G E Icten +6 more
TL;DR: It is shown that the machine learning-based clinical-radiomics model might have the potential to accurately discriminate IGM and malignant lesions in evaluating NME areas.
3
Precision (personalized) medicine
Andreas Beckert
- 01 Jan 2023
TL;DR: Personalized medicine as discussed by the authors is a broad definition that spans a number of medical fields, however, the succinct definition could be stated as “the tailoring of a treatment to an individual based on their unique characteristics.
3
Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions
TL;DR: Meta-analysis of DCE and DWI-MRI for differentiating benign from malignant non-mass enhancement breast lesions found that the combination model outperformed DCE or DWI alone. The DCE-CRE feature was the most specific test for ruling in NME cancers.
2
References
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
•Journal Article
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Michael S. Bernstein,Li Fei-Fei,Alexander C. Berg,Aditya Khosla +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been running annually for five years (since 2010) and has become the standard benchmark for large-scale object recognition.
23.9K
Abbreviated Breast Magnetic Resonance Imaging (MRI): First Postcontrast Subtracted Images and Maximum-Intensity Projection—A Novel Approach to Breast Cancer Screening With MRI
Christiane K. Kuhl,Simone Schrading,Kevin Strobel,Hans H. Schild,Ralf-Dieter Hilgers,Heribert Bieling +5 more
TL;DR: An MRI acquisitionTime of 3 minutes and an expert radiologist MIP image reading time of 3 seconds are sufficient to establish the absence of breast cancer, with an NPV of 99.8%.
615
Background parenchymal enhancement on breast MRI: A comprehensive review.
Geraldine J. Liao,Geraldine J. Liao,Leah C. Henze Bancroft,Roberta M. Strigel,Rhea Chitalia,Despina Kontos,Linda Moy,Savannah C. Partridge,Habib Rahbar +8 more
TL;DR: BPE is reviewed with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment and areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of Bpe, and the standardization of B PE characterization.
103
Breast MRI using the VIBE sequence: clustered ring enhancement in the differential diagnosis of lesions showing non-masslike enhancement.
TL;DR: Clustered ring enhancement is thought to be a useful sign to differentiate between benign and malignant lesions, in addition to the BI-RADS MRI descriptors.
90