Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

Journal of the American College of Radiology, Journal Year: 2024, Volume and Issue: 21(5), P. 740 - 751

Published: Jan. 12, 2024

Language: Английский

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Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review

Published: June 11, 2024

Cancer is one of the leading causes death, making timely diagnosis and prognosis very important. Utilization AI (artificial intelligence) enables providers to organize process patient data in a way that can lead better overall outcomes. This review paper aims look at varying uses for clinical utility. PubMed EBSCO databases were utilized finding publications from January 1, 2013, December 22, 2023. Articles collected using key search terms such as “artificial intelligence” “machine learning.” Included collection studies application determining cancer multi-omics data, radiomics, pathomics, laboratory data. The resulting 89 categorized into eight sections based on type then further subdivided two subsections focusing prognosis, respectively. 8 integrated more than form omics, namely genomics, transcriptomics, epigenomics, proteomics. Incorporating alongside omics represents significant advancement. Given considerable potential this domain, ongoing prospective are essential enhance algorithm interpretability ensure safe integration.

Language: Английский

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A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: March 7, 2023

Objective To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs). Materials methods Retrospective analysis was performed records 198 patients with SCSNs that were surgically resected examined pathologically at two medical institutions between January 2020 June 2021. Patients from Center 1 included training cohort (n = 147), 2 external validation 52). Radiomic extracted chest CT images. The least absolute shrinkage selection operator (LASSO) regression model used radiomic feature extraction computation scores. Clinical features, subjective findings, scores to build multiple predictive models. Model performance by evaluating area under receiver operating characteristic curve (AUC). best selected efficacy evaluation cohort, column line plots created. Results Pulmonary malignant significantly associated vascular alterations both (p < 0.001) cohorts. Eleven after dimensionality reduction calculate Based these three prediction models constructed: (Model 1), score 2), comprehensive 3), AUCs 0.672, 0.888, 0.930, respectively. optimal an AUC 0.905 applied decision indicated plot clinically useful. Conclusion Predictive constructed CT-based can help clinicians diagnose pulmonary guide making.

Language: Английский

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Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

Clinical Oncology, Journal Year: 2023, Volume and Issue: 36(8), P. 498 - 513

Published: Sept. 27, 2023

Language: Английский

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Diagnostic performance of CT scan–based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: Oct. 23, 2023

Objective The purpose of this study was to evaluate the diagnostic performance computed tomography (CT) scan–based radiomics in prediction lymph node metastasis (LNM) gastric cancer (GC) patients. Methods PubMed, Embase, Web Science, and Cochrane Library databases were searched for original studies published until 10 November 2022, satisfying inclusion criteria included. Characteristics included approach data constructing 2 × tables extracted. quality score (RQS) Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) utilized assessment studies. Overall sensitivity, specificity, odds ratio (DOR), area under curve (AUC) calculated assess accuracy. subgroup analysis Spearman’s correlation coefficient done exploration heterogeneity sources. Results Fifteen with 7,010 GC patients We conducted analyses on both signature combined (based clinical features) models. pooled DOR, AUC models compared 0.75 (95% CI, 0.67–0.82) versus 0.81 0.75–0.86), 0.80 0.73–0.86) 0.85 0.79–0.89), 13 7–23) 23 13–42), 0.81–0.86) 0.90 0.87–0.92), respectively. meta-analysis indicated a significant among revealed that arterial phase CT scan, tumoral nodal regions interest (ROIs), automatic segmentation, two-dimensional (2D) ROI could improve accuracy venous tumoral-only ROI, manual 3D Overall, quite acceptable based QUADAS-2 RQS tools. Conclusion has promising potential LNM preoperatively as non-invasive tool. Methodological is main limitation Systematic review registration https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=287676 , identifier CRD42022287676.

Language: Английский

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The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee—a systematic review and meta-analysis

Irish Journal of Medical Science (1971 -), Journal Year: 2024, Volume and Issue: 193(5), P. 2525 - 2530

Published: June 1, 2024

Abstract The primary aim of this study was to systematically review current literature evaluating the use radiomics in establishing role magnetic resonance imaging (MRI) findings native knees predicting features osteoarthritis (OA). A systematic performed with respect PRISMA guidelines search studies reporting radiomic analysis analyse patients knee OA. Sensitivity and specificity analyses were included for meta-analysis. Following our initial 1271 studies, only 5 met inclusion criteria. This 1730 (71.5% females) a mean age 55.4 ± 15.6 years (range 24–66). RQS 16.6 (11–21). Meta-analysis demonstrated pooled sensitivity MRI OA 0.74 (95% CI 0.71, 0.78) 0.85 0.83, 0.87), respectively. results suggest that high sensitivities MRI-based may represent potential biomarker early identification classification Such inform surgeons facilitate earlier non-operative management select pre-symptomatic patients, prior clinical or radiological evidence degenerative change.

Language: Английский

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Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools

European Radiology, Journal Year: 2024, Volume and Issue: 35(1), P. 202 - 214

Published: July 16, 2024

To assess the methodological quality of radiomics-based models in endometrial cancer using radiomics score (RQS) and METhodological radiomICs (METRICS).

Language: Английский

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Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

Cancers, Journal Year: 2024, Volume and Issue: 16(20), P. 3511 - 3511

Published: Oct. 17, 2024

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature lymphoma makes challenging to definitively pinpoint valuable biomarkers for predicting tumor biology selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically

Language: Английский

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Insights into radiomics: impact of feature selection and classification

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Radiomics is an innovative discipline in medical imaging that uses advanced quantitative feature extraction from radiological images to provide a non-invasive method of interpreting the intricate biological panorama diseases. This takes advantage unique characteristics imaging, where radiation or ultrasound combines with tissues, reveal disease features and important biomarkers are invisible human eye. plays crucial role healthcare, spanning diagnosis, prognosis, recurrences, treatment response assessment, personalized medicine. systematic approach includes image preprocessing, segmentation, extraction, selection, classification, evaluation. survey attempts shed light on roles selection classification play discovering forecasting directions despite challenges posed by high dimensionality (i.e., when data contains huge number features). By analyzing 47 relevant research articles, this study has provided several insights into key techniques used across different stages radiology workflow. The findings indicate 27 articles utilized SVM classifier, while 23 surveyed studies LASSO approach. demonstrates how these particular methodologies have been widely research. assessment did, however, also point out areas require more research, such as evaluating stability algorithms adopting novel approaches like ensemble hybrid methods. Additionally, we examine some emerging subfields within field radiomics.

Language: Английский

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KI-unterstützter Ultraschall – Möglichkeiten und Grenzen

Die MKG-Chirurgie, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

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