The widening gap between radiomics research and clinical translation: rethinking current practices and shared responsibilities

Опубликована: Янв. 1, 2025

Язык: Английский

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Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Abstract Objectives Conduct a systematic review and meta-analysis on the application of Radiomics Quality Score (RQS). Materials methods A search was conducted from January 1, 2022, to December 31, 2023, for reviews which implemented RQS. Identification articles prior 2022 via previously published review. scores individual radiomics papers, their associated criteria scores, these all readers were extracted. Errors in RQS noted corrected. The papers matched with publication date, imaging modality, country, where available. Results total 130 included, quality 117/130 (90.0%), 98/130 (75.4%), multiple reader data 24/130 (18.5%) 3258 correlated study date publication. Criteria scoring errors discovered 39/98 (39.8%) articles. Overall mean 9.4 ± 6.4 (95% CI, 9.1–9.6) (26.1% 17.8% (25.3%–26.7%)). positively year (Pearson R = 0.32, p < 0.01) significantly higher after (year 2018, 5.6 6.1 (5.1–6.1); ≥ 10.1 (9.9–10.4); 0.01). Only 233/3258 (7.2%) 50% maximum different across modalities ( Ten year, one negatively correlated. Conclusion adherence is increasing time, although vast majority studies are developmental rarely provide high level evidence justify clinical translation proposed models. Key Points Question What have achieved has it increased sufficient? Findings extracted resulted score 6.4. time. Clinical relevance Although many not demonstrated sufficient translation. As new appraisal tools emerge, current role may change.

Язык: Английский

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Diagnostic performance of microRNAs for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis

Frontiers in Oncology, Год журнала: 2025, Номер 14

Опубликована: Янв. 14, 2025

Purpose To provide a detailed pooled analysis of the diagnostic accuracy microRNAs (miRNAs) in predicting response to transarterial chemoembolization (TACE) hepatocellular carcinoma (HCC). Methods A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, and Web Science identify studies assessing performance miRNAs TACE HCC. Two independent reviewers performed quality assessment data extraction using Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative (NLR), odds (DOR), area under summary receiver operating characteristic (SROC) curve were calculated bivariate random-effects model. Subgroup analyses meta-regression explore potential sources heterogeneity, including sample size, criteria, specimen source, evaluation methods, efficacy interval window, geographical location. Results Seven studies, comprising 320 HCC responders 187 non-responders, included this meta-analysis. The studied miR-373, miR-210, miR-4492, miR-1271, miR-214, miR-133b, miR-335. sensitivity recurrence after 0.79 [95% CI: 0.72-0.84], specificity 0.82 0.74-0.88]. DOR 17 9-33], SROC (AUC) 0.85 0.81-0.88], indicating excellent accuracy. revealed significant differences based on criteria Meta-regression did not any interstudy heterogeneity. Conclusion MiRNAs show promise as tools for patients. However, their clinical application requires further validation larger cohorts. Future research should focus standardizing RNA selecting consistent endogenous controls, adopting uniform improve reliability reduce variability.

Язык: Английский

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Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence

Deleted Journal, Год журнала: 2024, Номер 1(1)

Опубликована: Янв. 1, 2024

Abstract The advent of radiomics has revolutionized medical image analysis, affording the extraction high dimensional quantitative data for detailed examination normal and abnormal tissues. Artificial intelligence (AI) can be used enhancement a series steps in pipeline, from acquisition preprocessing, to segmentation, feature extraction, selection, model development. aim this review is present most AI methods explaining advantages limitations methods. Some prominent architectures mentioned include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural transformers. Employing these models process analysis significantly enhance quality effectiveness while addressing several that reduce predictions. Addressing enable clinical decisions wider adoption. Importantly, will highlight how assist overcoming major bottlenecks implementation, ultimately improving translation potential method.

Язык: Английский

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The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer

Cancers, Год журнала: 2024, Номер 16(17), С. 3111 - 3111

Опубликована: Сен. 9, 2024

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, line with more personalized treatment approaches. Total neoadjuvant (TNT) plays a pivotal shift from traditional surgical approach to non-surgical approaches such as ‘watch-and-wait’. MRI central this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted (DWI), regression grade (mrTRG), have proven valuable for staging, response assessment, patient prognosis. Functional techniques, dynamic contrast-enhanced (DCE-MRI), alongside emerging biomarkers derived radiomics artificial intelligence (AI) potential transform offering enhance T N histopathological characterization, prediction response, recurrence detection, identification genomic features. This review outlines validated MRI-derived both prognostic predictive significance, while also exploring management. Furthermore, we discuss ‘watch-and-wait’ approach, highlighting important practical aspects selecting patients

Язык: Английский

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Artificial Intelligence in Gastrointestinal Imaging

Radiologic Clinics of North America, Год журнала: 2025, Номер 63(3), С. 477 - 490

Опубликована: Янв. 4, 2025

Язык: Английский

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Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI

Breast Cancer Targets and Therapy, Год журнала: 2025, Номер Volume 17, С. 103 - 113

Опубликована: Янв. 1, 2025

Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) essential customizing treatment plans enhancing patient results. This research sought to create verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors improve the accuracy of predicting ALNM young cancer patients. We performed retrospective analysis at one facility, involving creation validation two stages.At first, medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, MRI-determined ALN status.A more detailed subsequently by integrating characteristics derived DCE-MRI images.These models were created using logistic regression analyses on training dataset, their effectiveness assessed measuring area receiver operating characteristic curve (AUC) separate dataset. The surpassed clinical-only model, recording an AUC 0.892 dataset 0.877 dataset.Significant included MRI-reported status select features, which markedly enhanced model's predictive capacity. Integrating significantly improves prediction providing valuable tool personalized planning. study underscores potential merging advanced imaging data insights refine oncological models. Future should expand multicentric studies include genomic boost nomogram's generalizability precision.

Язык: Английский

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Rethinking radio-genomics: pitfalls in multi-omics integration involving radiomics

Опубликована: Фев. 1, 2025

Язык: Английский

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Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study

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

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Reliability of rectal MRI radiomic features: Comparing rectal MRI radiomic features across reader expertise levels, image segmentation technique, and timing of rectal MRI in patients with locally advanced rectal cancer

European Journal of Radiology, Год журнала: 2025, Номер 185, С. 112019 - 112019

Опубликована: Фев. 26, 2025

Язык: Английский

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