Incremental value of radiomics-based heterogeneity to the existing risk criteria in predicting recurrence of hepatocellular carcinoma after liver transplantation DOI
Pei Nie, Juntao Zhang,

Wenjie Miao

et al.

European Radiology, Journal Year: 2023, Volume and Issue: 33(9), P. 6608 - 6618

Published: April 4, 2023

Language: Английский

Impact of [18F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role DOI Creative Commons

Alireza Safarian,

Seyed Ali Mirshahvalad,

Hadi Nasrollahi

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Lung cancer remains one of the most prevalent cancers globally and leading cause cancer-related deaths, accounting for nearly one-fifth all fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) plays a vital role in assessing lung managing disease progression. While traditional PET/CT imaging relies on qualitative analysis basic quantitative parameters, radiomics offers more advanced approach to analyzing tumor phenotypes. Recently, has gained attention its potential enhance prognostic diagnostic capabilities [18F]FDG various cancers. This review explores expanding PET/CT-based radiomics, particularly when integrated with artificial intelligence (AI), cancer, especially non-small cell (NSCLC). We how AI improve diagnostics, staging, subtype identification, molecular marker detection, which influence treatment decisions. Additionally, we address challenges clinical integration, such as protocol standardization, feature reproducibility, need extensive prospective studies. Ultimately, hold great promise enabling personalized effective treatments, potentially transforming management.

Language: Английский

Citations

5
[18F]FDG-PET/CT Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications DOI Creative Commons
Reyhaneh Manafi‐Farid, Emran Askari, Isaac Shiri

et al.

Seminars in Nuclear Medicine, Journal Year: 2022, Volume and Issue: 52(6), P. 759 - 780

Published: June 15, 2022

Lung cancer is the second most common and leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed ([18F]FDG-PET/CT) plays an essential role in diagnosis, evaluation response to treatment, prediction outcomes. The images are evaluated qualitative conventional quantitative indices. However, there far more information embedded images, which can be extracted by sophisticated algorithms. Recently, concept uncovering analyzing invisible data from medical called radiomics, gaining attention. Currently, [18F]FDG-PET/CT radiomics growingly lung discover if it enhances diagnostic performance or implication management cancer. In this review, we provide a short overview technical aspects, as they discussed different articles special issue. We mainly focus on [18F]FDG-PET/CT‐based artificial intelligence non-small cell cancer, impacting early detection, staging, tumor subtypes, biomarkers, patient's

Language: Английский

Citations

56
Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma DOI
Junjie Zhang, Guanghui Wang, Jialiang Ren

et al.

European Radiology, Journal Year: 2022, Volume and Issue: 32(6), P. 4079 - 4089

Published: Jan. 20, 2022

Language: Английский

Citations

45
Advances of PET/CT in Target Delineation of Lung Cancer Before Radiation Therapy DOI

Cedric Richlitzki,

Farkhad Manapov,

Adrien Holzgreve

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: 55(2), P. 190 - 201

Published: March 1, 2025

Language: Английский

Citations

1
Clinical application of AI-based PET images in oncological patients DOI

Jiaona Dai,

Hui Wang,

Yuchao Xu

et al.

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 91, P. 124 - 142

Published: March 10, 2023

Language: Английский

Citations

19
Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features DOI
Hao Zhang, Genji Yuan, Chao Wang

et al.

European Radiology, Journal Year: 2023, Volume and Issue: 33(7), P. 5069 - 5076

Published: April 26, 2023

Language: Английский

Citations

18
A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study DOI
Pei Nie, Guangjie Yang,

Yanmei Wang

et al.

European Radiology, Journal Year: 2023, Volume and Issue: 33(12), P. 8858 - 8868

Published: June 30, 2023

Language: Английский

Citations

17
Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI DOI
Yuxuan Han, Tianzuo Wang, Peng Wu

et al.

Magnetic Resonance Imaging, Journal Year: 2020, Volume and Issue: 77, P. 36 - 43

Published: Nov. 18, 2020

Language: Английский

Citations

42
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma DOI
Xun Zhao,

Yu-Jing Liang,

Xu Zhang

et al.

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2022, Volume and Issue: 49(8), P. 2972 - 2982

Published: April 26, 2022

Language: Английский

Citations

27
The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer DOI

Jie Ma,

Dong Guo,

Wenjie Miao

et al.

Abdominal Radiology, Journal Year: 2022, Volume and Issue: 47(4), P. 1244 - 1254

Published: Feb. 26, 2022

Language: Английский

Citations

24