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Fulltext A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Anan N, Zainon R, Tamal M

Insights Imaging, 2022 Feb 05;13(1):22.
PMID: 35124733 DOI: 10.1186/s13244-021-01153-9

Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
Fulltext A review on optimization of Wilms tumour management using radiomics

Alhashim M, Anan N, Tamal M, Altarrah H, Alshaibani S, Hill R

BJR Open, 2024 Jan;6(1):tzae034.
PMID: 39483333 DOI: 10.1093/bjro/tzae034

BACKGROUND: Wilms tumour, a common paediatric cancer, is difficult to treat in low- and middle-income countries due to limited access to imaging. Artificial intelligence (AI) has been introduced for staging, detecting, and classifying tumours, aiding physicians in decision-making. However, challenges include algorithm accuracy, translation into conventional diagnosis, reproducibility, and reliability. As AI technology advances, radiomics, an AI tool, emerges to extract tumour morphology and stage information.
OBJECTIVES: This review explores the application of radiomics in Wilms tumour management, including its potential in diagnosis, prognosis, and treatment. Additionally, it discusses the future prospects of AI in this field and potential directions for automation-aided Wilms tumour treatment.
METHODS: The review analyses various research studies and articles on the use of radiomics in Wilms tumour management. This includes studies on automated deep learning-based classification, interobserver variability in histopathological analysis, and the application of AI in staging, detecting, and classifying Wilms tumours.
RESULTS: The review finds that radiomics offers several promising applications in Wilms tumour management, including improved diagnosis: it helps in classifying Wilms tumours from other paediatric kidney tumours, prognosis prediction: radiomic features can be used to predict both staging and response to preoperative chemotherapy, Treatment response assessment: Radiomics can be used to monitor the response of Wilms and to predict the feasibility of nephron-sparing surgery.
CONCLUSIONS: This review concludes that radiomics has the potential to significantly improve the diagnosis, prognosis, and treatment of Wilms tumours. Despite some challenges, such as the need for further research and validation, AI integration in Wilms tumour management offers promising opportunities for improved patient care.
ADVANCES IN KNOWLEDGE: This review provides a comprehensive overview of the potential applications of radiomics in Wilms tumour management and highlights the significant role AI can play in improving patient outcomes. It contributes to the growing body of knowledge on AI-assisted diagnosis and treatment of paediatric cancers.

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