Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study DOI Open Access
Yousif Widaatalla, Tom Wolswijk, Muhammad Danial Khan

и другие.

Cancers, Год журнала: 2025, Номер 17(5), С. 768 - 768

Опубликована: Фев. 24, 2025

Radiomics has seen substantial growth in medical imaging; however, its potential optical coherence tomography (OCT) not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans benign nevi examine impact bin width (BW) selection on HRF stability. The effect using stable a classification model was also assessed. In this prospective study, 20 volunteers underwent test-retest imaging 40 nevi, resulting 80 scans. HRFs extracted manually delineated regions interest (ROIs) were assessed concordance correlation coefficients (CCCs) across BWs ranging 5 to 50. A unique set identified at each BW after removing highly correlated eliminate redundancy. These robust incorporated into multiclass classifier trained distinguish basal cell carcinoma (BCC), Bowen's disease. Six all BWs, with 25 emerging as optimal choice, balancing ability capture meaningful textural details. Additionally, intermediate (20-25) yielded 53 reproducible features. six achieved 90% accuracy AUCs 0.96 0.94 for BCC disease, respectively, compared 76% 0.86 0.80 conventional feature approach. This study highlights critical role enhancing stability provides methodological framework optimizing preprocessing radiomics. By demonstrating integration diagnostic models, we establish promising tool aid non-invasive diagnosis dermatology.

Язык: Английский

Multiparametric MRI for characterization of the tumour microenvironment DOI
Emily Hoffmann, Max Masthoff, Wolfgang G. Kunz

и другие.

Nature Reviews Clinical Oncology, Год журнала: 2024, Номер 21(6), С. 428 - 448

Опубликована: Апрель 19, 2024

Язык: Английский

Процитировано

21

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases DOI
Vincenza Granata, Roberta Fusco, Federica De Muzio

и другие.

La radiologia medica, Год журнала: 2022, Номер 127(7), С. 763 - 772

Опубликована: Июнь 2, 2022

Язык: Английский

Процитировано

49

Improving radiology workflow using ChatGPT and artificial intelligence DOI
İsmail Meşe, Ceylan Altıntaş Taşlıçay, Ali Kemal Sivrioğlu

и другие.

Clinical Imaging, Год журнала: 2023, Номер 103, С. 109993 - 109993

Опубликована: Окт. 6, 2023

Язык: Английский

Процитировано

29

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative DOI Creative Commons
Burak Koçak, Alessandra Borgheresi, Andrea Ponsiglione

и другие.

European Radiology Experimental, Год журнала: 2024, Номер 8(1)

Опубликована: Май 14, 2024

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, can be accomplished with systematic use guidelines. The CheckList for EvaluAtion Radiomics (CLEAR) was previously developed assist authors their radiomic reviewers evaluation. To take full advantage CLEAR, further explanation elaboration each item, well literature examples, may useful. main goal this work, Explanation Elaboration Examples CLEAR (CLEAR-E3), improve CLEAR's usability dissemination. In international collaborative effort, members the European Society Medical Imaging Informatics-Radiomics Auditing Group searched identify representative examples item. At least two demonstrating optimal reporting, were presented All selected from open-access articles, allowing users easily consult corresponding full-text articles. addition these, item's expanded elaborated. For easier access, resulting document available at https://radiomic.github.io/CLEAR-E3/ . As complementary effort we anticipate that initiative will greater ease transparency, editors reviewing manuscripts.Relevance statement Along original checklist, CLEAR-E3 expected provide more in-depth understanding items, concrete evaluating research.Key points• aims research, manuscripts.• Based on positive by EuSoMII Group, item elaborated CLEAR-E3.• accessed

Язык: Английский

Процитировано

17

Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes DOI Creative Commons
Jingyu Zhong,

Xian‐Wei Liu,

Junjie Lu

и другие.

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Abstract Objectives To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether meets estimates obtained by using established criteria. Methods We identified that were published from 01 January 2023 to 31 December in seven leading peer-reviewed radiological journals. reviewed justification methods, actual used. calculated compared used three criteria proposed Riley et al. investigated which characteristics factors associated with sufficient Results included 116 studies. Eleven out of one hundred sixteen justified size, 6/11 performed a priori calculation. The median (first third quartile, Q1, Q3) total is 223 (130, 463), those training are 150 (90, 288). (Q1, difference between minimum according −100 (−216, 183), differences more restrictive approach based on −268 (−427, −157). presence external testing specialty topic size. Conclusion Radiomics often designed without justification, whose may be too small avoid overfitting. Sample encouraged model. Key Points Question critical help minimize overfitting model, but overlooked underpowered research . Findings Few justified, calculated, or reported their most them did not meet recent formal Clinical relevance justification. Consequently, many It should justify, perform, report considerations

Язык: Английский

Процитировано

2

Clinical applications of artificial intelligence in liver imaging DOI
Akira Yamada, Koji Kamagata, Kenji Hirata

и другие.

La radiologia medica, Год журнала: 2023, Номер 128(6), С. 655 - 667

Опубликована: Май 10, 2023

Язык: Английский

Процитировано

20

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging DOI Creative Commons
Noriyuki Fujima, Koji Kamagata, Daiju Ueda

и другие.

Magnetic Resonance in Medical Sciences, Год журнала: 2023, Номер 22(4), С. 401 - 414

Опубликована: Янв. 1, 2023

Due primarily to the excellent soft tissue contrast depictions provided by MRI, widespread application of head and neck MRI in clinical practice serves assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition been extensively investigated research for their applicability across a range categories within medical imaging, including MRI. Analytical approaches AI shown potential addressing limitations associated with In this review, we focus on technical advancements deep-learning-based methodologies utility field encompassing aspects such as image acquisition reconstruction, lesion segmentation, disease classification diagnosis, prognostic prediction patients presenting We then discuss current offer insights regarding future challenges field.

Язык: Английский

Процитировано

18

Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews DOI
Burak Koçak, Ali Keleş, Fadime Köse

и другие.

European Radiology, Год журнала: 2024, Номер unknown

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

8

The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study DOI

Chuanghui Zhou,

Jianwei Zhou,

Y. Lv

и другие.

European Journal of Radiology, Год журнала: 2025, Номер 184, С. 111956 - 111956

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

1

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma DOI
Dandan Wang, Linhan Zhang,

Zhongqi Sun

и другие.

European Journal of Radiology, Год журнала: 2023, Номер 167, С. 111086 - 111086

Опубликована: Сен. 6, 2023

Язык: Английский

Процитировано

17