Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence DOI Creative Commons
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

Biology, Journal Year: 2023, Volume and Issue: 12(2), P. 213 - 213

Published: Jan. 29, 2023

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains curative therapy; however, at first detection, one-third patients are an early enough stage for this approach to be effective, thus rendering diagnosis as efficient improving survival. Therefore, identification higher-risk patients, whose risk correlated genetic and pre-cancerous conditions, employment non-invasive-screening modalities would appropriate. For several at-risk such those suffering from sclerosing cholangitis or fibropolycystic disease, use periodic (6–12 months) imaging by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), computed tomography (CT) in association serum CA19-9 measurement has been proposed. cirrhosis it proposed that iCCA monitored similar fashion HCC patients. The possibility using Artificial Intelligence models evaluate could favor these entities, although more data needed support practical utility applications field screening. reasons, appropriate develop screening programs research protocols setting. In fact, success reauires patient compliance multidisciplinary cooperation.

Language: Английский

The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review DOI
Roberta Fusco, Vincenza Granata, Sergio Venanzio Setola

et al.

Physica Medica, Journal Year: 2025, Volume and Issue: 130, P. 104891 - 104891

Published: Jan. 8, 2025

Language: Английский

Citations

2
An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies DOI Creative Commons
Roberta Fusco, Vincenza Granata,

Igino Simonetti

et al.

Current Oncology, Journal Year: 2024, Volume and Issue: 31(1), P. 403 - 424

Published: Jan. 10, 2024

The aim of this informative review was to investigate the application radiomics in cancer imaging and summarize results recent studies support oncological with particular attention breast cancer, rectal primitive secondary liver cancer. This also aims provide main findings, challenges limitations current methodologies. Clinical published last four years (2019–2022) were included review. Among 19 analyzed, none assessed differences between scanners vendor-dependent characteristics, collected images individuals at additional points time, performed calibration statistics, represented a prospective study registered database, conducted cost-effectiveness analysis, reported on clinical application, or multivariable analysis non-radiomics features. Seven reached high radiomic quality score (RQS), seventeen earned by using validation steps considering two datasets from distinct institutes open science data domains (radiomics features calculated set representative ROIs are source). potential is increasingly establishing itself, even if there still several aspects be evaluated before passage into routine practice. There challenges, including need for standardization across all stages workflow cross-site real-world heterogeneous datasets. Moreover, multiple centers more samples that add inter-scanner characteristics will needed future, as well collecting time points, reporting statistics performing database.

Language: Английский

Citations

11
Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography DOI Creative Commons
Ketki Kinkar, Brandon K.K. Fields, Mary Yamashita

et al.

Frontiers in Radiology, Journal Year: 2024, Volume and Issue: 3

Published: Jan. 5, 2024

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to lack consensus on data quality, benchmarked robust implementation, consensus-based guidelines ensure standardization generalization. Contrast-enhanced mammography (CEM) improved sensitivity specificity compared current standards cancer diagnostic i.e., (MG) and/or conventional ultrasound (US), with comparable accuracy MRI (current benchmark), but at much lower cost higher throughput. This makes CEM an excellent tool for widespread lesion characterization all women, underserved minority women. Underlining critical need early detection accurate diagnosis cancer, this review examines limitations approaches reveals how AI can help overcome them. The Methodical approaches, such as processing, feature extraction, quantitative analysis, classification, segmentation, data, detection, screening support have carefully analysed recent studies addressing diagnosis. Recent described by Checklist Intelligence Medical Imaging (CLAIM) establish framework rigorous evaluation surveying inspired criteria.

Language: Английский

Citations

9
State-of-the-art for contrast-enhanced mammography DOI Creative Commons
Matthew F. Covington, Samantha Salmon,

B. D. Weaver

et al.

British Journal of Radiology, Journal Year: 2024, Volume and Issue: 97(1156), P. 695 - 704

Published: Jan. 20, 2024

Abstract Contrast-enhanced mammography (CEM) is an emerging breast imaging technology with promise for cancer screening, diagnosis, and procedural guidance. However, best uses of CEM in comparison other modalities such as tomosynthesis, ultrasound, MRI remain inconclusive many clinical settings. This review article summarizes recent peer-reviewed literature, emphasizing retrospective reviews, prospective trials, meta-analyses published from 2020 to 2023. The intent this supplement prior comprehensive reviews summarize the current state-of-the-art CEM.

Language: Английский

Citations

9
Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study DOI Creative Commons
Vincenzo Venerito, Andreina Manfredi, Giuseppe Lopalco

et al.

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 9

Published: Jan. 9, 2023

Objectives Patients with rheumatoid arthritis (RA) and interstitial lung disease (ILD) have increased mortality compared to the general population factors capable of predicting RA-ILD long-term clinical outcomes are lacking. In oncology, radiomics allows quantification tumour phenotype by analysing characteristics medical images. Using specific software, it is possible segment organs on high-resolution computed tomography (HRCT) images extract many features that may uncover not detected naked eye. We aimed investigate whether from whole radiomic analysis HRCT alone predict in patients. Methods High-resolution tomographies RA patients January 2012 March 2022 were analyzed. The time between first available last follow-up visit or ILD-related death was recorded. performed a volumetric 3D Slicer, automatically segmenting lungs trachea via Lung CT Analyzer. A LASSO-Cox model carried out considering as outcome variable extracting exposure variables. Results retrieved HRCTs 30 median survival (interquartile range) 48 months (36–120 months). Thirteen (43.33%) died during observation period. Whole line segmentation fast reliable. included either grey level intensity within [high-resolution (HR) 9.35, 95% CI 1.56–55.86] positive predictor 10th percentile number voxels (HR 0.20, 0.05–0.84), voxel-based pre-processing information 0.23, 0.06–0.82) flatness 0.42, 0.18–0.98), negatively correlating mortality. correlation values their respective 1.52 0.82–2.83) also retained confounder. Conclusion Radiomic patients’ promote digital biomarker regardless disease.

Language: Английский

Citations

17
Artificial intelligence in breast cancer: application and future perspectives DOI

Shuixin Yan,

Jiadi Li,

Weizhu Wu

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(17), P. 16179 - 16190

Published: Sept. 1, 2023

Language: Английский

Citations

14
Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome DOI
Antonella Petrillo, Roberta Fusco,

Maria Luisa Barretta

et al.

La radiologia medica, Journal Year: 2023, Volume and Issue: 128(11), P. 1347 - 1371

Published: Oct. 6, 2023

Language: Английский

Citations

14
Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence DOI Open Access
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(2), P. 351 - 351

Published: Jan. 5, 2023

Pancreatic cancer (PC) is one of the deadliest cancers, and it responsible for a number deaths almost equal to its incidence. The high mortality rate correlated with several explanations; main late disease stage at which majority patients are diagnosed. Since surgical resection has been recognised as only curative treatment, PC diagnosis initial believed tool improve survival. Therefore, patient stratification according familial genetic risk creation screening protocol by using minimally invasive diagnostic tools would be appropriate. cystic neoplasms (PCNs) subsets lesions deserve special management avoid overtreatment. current programs based on annual employment magnetic resonance imaging cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For unfit MRI, computed tomography (CT) could proposed, although CT results in lower detection rates, compared small lesions. actual major limit incapacity detect characterize pancreatic intraepithelial neoplasia (PanIN) EUS MR/MRCP. possibility utilizing artificial intelligence models evaluate higher-risk favour these entities, more data needed support real utility applications field screening. motives, appropriate realize research settings.

Language: Английский

Citations

13
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment DOI
Vincenza Granata, Roberta Fusco, Federica De Muzio

et al.

La radiologia medica, Journal Year: 2023, Volume and Issue: 128(11), P. 1310 - 1332

Published: Sept. 11, 2023

Language: Английский

Citations

13
Detection and classification of breast cancer in mammogram images using entropy-based Fuzzy C-Means Clustering and RMCNN DOI

Rehna Kalam,

Ciza Thomas

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(24), P. 64853 - 64878

Published: Jan. 18, 2024

Language: Английский

Citations

4