Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI DOI Creative Commons
Xinzhi Teng, Yongqiang Wang, Alexander James Nicol

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(16), P. 1835 - 1835

Published: Aug. 22, 2024

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in diagnosis and prognosis oncological conditions. However, repeatability reproducibility radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address paucity discussion regarding factors influence subsequent impact on application models. We provide a synthesis literature CT/MR-based features, examining sources variation, number reproducible availability individual feature indices. differentiate variation into random effects, challenging control but can be quantified through simulation methods such as perturbation, biases, arise from scanner variability inter-reader differences significantly affect generalizability model performance diverse settings. Four suggestions studies suggested: (1) detailed reporting sources, (2) transparent disclosure calculation parameters, (3) careful selection suitable reliability indices, (4) metrics. underscores importance effects harmonizing biases between development settings facilitate successful translation models research practice.

Language: Английский

Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics DOI Creative Commons
Dianning He, Xin Zhang, Zhihui Chang

et al.

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: March 12, 2024

The presence of heterogeneity is a significant attribute within the context ovarian cancer. This study aimed to assess predictive accuracy models utilizing quantitative

Language: Английский

Citations

3
Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade DOI
Maziar Khateri, Farshid Babapour Mofrad, Parham Geramifar

et al.

Physical and Engineering Sciences in Medicine, Journal Year: 2024, Volume and Issue: 47(2), P. 741 - 753

Published: March 25, 2024

Language: Английский

Citations

3
Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks DOI Creative Commons
Kevin Tran, Daniel Ginzburg, Wei Hong

et al.

European Radiology, Journal Year: 2024, Volume and Issue: 34(10), P. 6527 - 6543

Published: April 16, 2024

Lung cancer, the second most common presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts address them with quality scoring tools.

Language: Английский

Citations

3
Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement DOI
Fenglian Jing, Xinchao Zhang, Yunuan Liu

et al.

Clinical & Translational Oncology, Journal Year: 2024, Volume and Issue: unknown

Published: July 31, 2024

Language: Английский

Citations

3
Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI DOI Creative Commons
Xinzhi Teng, Yongqiang Wang, Alexander James Nicol

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(16), P. 1835 - 1835

Published: Aug. 22, 2024

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in diagnosis and prognosis oncological conditions. However, repeatability reproducibility radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address paucity discussion regarding factors influence subsequent impact on application models. We provide a synthesis literature CT/MR-based features, examining sources variation, number reproducible availability individual feature indices. differentiate variation into random effects, challenging control but can be quantified through simulation methods such as perturbation, biases, arise from scanner variability inter-reader differences significantly affect generalizability model performance diverse settings. Four suggestions studies suggested: (1) detailed reporting sources, (2) transparent disclosure calculation parameters, (3) careful selection suitable reliability indices, (4) metrics. underscores importance effects harmonizing biases between development settings facilitate successful translation models research practice.

Language: Английский

Citations

3