Radiomics Characterization of Tissues in an Animal Brain Tumor Model Imaged Using Dynamic Contrast Enhanced (DCE) MRI DOI Creative Commons
Hassan Bagher‐Ebadian,

Stephen L. Brown,

Mohammad M. Ghassemi

et al.

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 14, 2022

Abstract Purpose: Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain model measured using dynamic-contrast-enhanced (DCE) MRI. Methods: Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis a nested (NM) selection technique classify regions according vasculature considered as the source truth. A two-dimensional convolutional-based radiomics performed on raw-DCE-MRI brains generate dynamic maps. respective maps used build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). Silhouette-Coefficient (SC) feature engineering analyses spaces K-SOMs quantify distinction power different features compared for classification models. Results: Results showed that eight outperformed prediction three average percent difference SCs between was: 29.875%±12.922%, p<0.001. Conclusions: This work establishes important first step toward spatiotemporal signatures, which is fundamental staging tumors evaluation response treatments.

Language: Английский

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine DOI Creative Commons
Maurizio Cè, Giovanni Irmici,

Chiara Foschini

et al.

Current Oncology, Journal Year: 2023, Volume and Issue: 30(3), P. 2673 - 2701

Published: Feb. 22, 2023

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages diagnostic and therapeutic process. Although histological investigation will remain difficult to replace, in near future radiomic approach allow a complementary, repeatable non-invasive characterization lesion, assisting oncologists neurosurgeons selecting best option correct molecular target chemotherapy. AI-driven tools are already playing an important role surgical planning, delimiting extent lesion (segmentation) its relationships with structures, thus allowing precision surgery as radical reasonably acceptable preserve quality life. Finally, AI-assisted prediction complications, recurrences response, suggesting most appropriate follow-up. Looking future, AI-powered promise integrate biochemical clinical data stratify risk direct patients personalized screening protocols.

Language: Английский

Citations

52
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
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
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
Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study DOI Creative Commons

Sudeepta Maiti,

Shailesh Nayak,

Karthikeya D. Hebbar

et al.

F1000Research, Journal Year: 2024, Volume and Issue: 13, P. 91 - 91

Published: Feb. 1, 2024

Background Breast cancer (BC) is one of the main causes cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early precise identification differentiation breast lesions are crucial. To investigate accuracy radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) lobular (ILC). Methods This a retrospective study. The IDC 30 ILC 28 Dukes MRI data set Cancer Archive (TCIA), were included. RF DCE-MRI sequence using 3D slicer. relevance was evaluated maximum minimum redundancy (mRMR) Mann-Whitney test. Receiver Operating Characteristic (ROC) curve analysis performed ascertain in distinguishing between ILC. Results Ten DCE MRI-based RFs used our study showed significant difference (p <0.001) We noticed that RF, such as Gray level run length matrix (GLRLM) gray variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under (AUC) 0.998), co-occurrence (GLCM) average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range 95.24%, 97.31%, 0.968), had strongest ability differentiate Conclusions derived sequences can be settings malignant breast, ILC, without requiring intrusive procedures.

Language: Английский

Citations

4
Machine learning and new insights for breast cancer diagnosis DOI Creative Commons

Guo Ya,

Heng Zhang,

Leilei Yuan

et al.

Journal of International Medical Research, Journal Year: 2024, Volume and Issue: 52(4)

Published: April 1, 2024

Breast cancer (BC) is the most prominent form of among females all over world. The current methods BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency intervention. subsequent imaging features mathematical analyses can then be used to generate ML models, which stratify, differentiate detect benign malignant lesions. Given marked advantages, radiomics a frequently tool recent research clinics. Artificial neural networks deep (DL) are novel forms that evaluate data using computer simulation human brain. DL directly processes unstructured information, such as images, sounds language, performs precise clinical image stratification, medical record tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on application images intervention radiomics, namely ML. aim was provide guidance scientists regarding use artificial intelligence clinic.

Language: Английский

Citations

3
An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis DOI
Reza Elahi, Mahdis Nazari

Radiological Physics and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Language: Английский

Citations

3
radMLBench: A dataset collection for benchmarking in radiomics DOI Creative Commons
Aydın Demircioğlu

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 182, P. 109140 - 109140

Published: Sept. 12, 2024

Language: Английский

Citations

2