An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

Current Oncology, Год журнала: 2024, Номер 31(1), С. 403 - 424

Опубликована: Янв. 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.

Язык: Английский

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Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

La radiologia medica, Год журнала: 2024, Номер 129(7), С. 957 - 966

Опубликована: Май 18, 2024

Язык: Английский

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Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging

La radiologia medica, Год журнала: 2024, Номер 129(3), С. 420 - 428

Опубликована: Фев. 2, 2024

Язык: Английский

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Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features

Breast Cancer Research and Treatment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

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Application of Magnetic Resonance Imaging in Breast Cancer Patients

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

The advantage of the multi-parametric method for breast cancer is different contributions diverse parameters in magnetic resonance image (MRI). T1-weighted imaging (T1WI) detects signal intensity differences tissue according to longitudinal relaxation times. Dynamic contrast-enhanced (DCE-MRI) can estimate vascularity and permeability lesion by semiquantitative quantitative parameters. ultrafast DCE-MRI presents new kinetic Diffusion-weighted (DWI) provides information related tumor cell density, advanced diffusion-weighted techniques, such as diffusion kurtosis imaging, intravoxel incoherent motion, time-dependent MRI, exhibit perspectives microscale assessment. Moreover, T2-weighted important measurement water content tissue. Magnetic spectroscopy (MRS) detect choline levels metabolites elastography (MRE) provide mechanical properties tissue, including stiffness, elasticity, viscosity, improve specificity characterization. In this chapter, we a technical theoretical background these reveal application multi-parameter cancer.

Язык: Английский

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Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Diagnostics, Год журнала: 2024, Номер 14(2), С. 152 - 152

Опубликована: Янв. 9, 2024

Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in pre-surgical setting, predict tumor budding liver metastases. Methods: Patients MRI setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, 851 features extracted as median values PyRadiomics Python package. Balancing performed inter- intraclass correlation coefficients calculated between observer within reproducibility all features. A Wilcoxon–Mann–Whitney nonparametric test receiver operating characteristics (ROC) carried out. feature selection procedures performed. Linear non-logistic regression models (LRM NLRM) different learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) support vector (SVM) considered. Results: The internal training set included 49 patients 119 validation cohort consisted total 28 single lesion patients. best predictor classify original_glcm_Idn obtained T1-W VIBE sequence arterial phase an accuracy 84%; wavelet_LLH_firstorder_10Percentile portal 92%; wavelet_HHL_glcm_MaximumProbability hepatobiliary excretion 88%; wavelet_LLH_glcm_Imc1 T2-W SPACE sequences 88%. Considering linear analysis, statistically significant increase 96% weighted combination 13 radiomic from phase. Moreover, classifier KNN trained sequence, obtaining 95% AUC 0.96. reached 94%, sensitivity 86% specificity 95%. Conclusions: Machine are promising tools predicting budding. there compared feature.

Язык: Английский

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The application of 3D printing technology in tumor radiotherapy in the era of precision medicine

Applied Materials Today, Год журнала: 2024, Номер 40, С. 102368 - 102368

Опубликована: Авг. 1, 2024

Язык: Английский

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Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

La radiologia medica, Год журнала: 2024, Номер unknown

Опубликована: Окт. 30, 2024

Язык: Английский

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Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Journal of Clinical Medicine, Год журнала: 2024, Номер 13(2), С. 547 - 547

Опубликована: Янв. 18, 2024

Background: Small renal masses (SRMs) are defined as contrast-enhanced lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a cell carcinomas (RCCs). Currently, 50–61% of all tumors found incidentally. Methods: The characteristics the lesion influence choice type management, include several methods SRM including nephrectomy, partial ablation, observation, and also stereotactic body radiotherapy. Typical imaging available for differentiating benign from malignant ultrasound (US), (CEUS), computed tomography (CT), magnetic resonance (MRI). Results: Although is first technique used detect small lesions, it has limitations. CT main most widely characterization. advantages MRI compared better contrast resolution tissue characterization, use functional sequences, possibility performing examination patients allergic iodine-containing medium, absence exposure ionizing radiation. For a correct evaluation during follow-up, necessary reliable method assessment represented by Bosniak classification system. This was initially developed based on findings, 2019 revision proposed inclusion features; however, latest not yet received widespread validation. Conclusions: radiomics an emerging increasingly central field applications such characterizing masses, distinguishing RCC subtypes, monitoring response targeted therapeutic agents, prognosis metastatic context.

Язык: Английский

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DCE-MRI Radiomic analysis in triple negative ductal invasive breast cancer. Comparison between BRCA and not BRCA mutated patients: Preliminary results

Magnetic Resonance Imaging, Год журнала: 2024, Номер 113, С. 110214 - 110214

Опубликована: Июль 22, 2024

The research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative cancer (TNBC). This retrospective study included consecutive histologically diagnosed TNBC who underwent DCE-MRI 2010–2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps wash-in wash-out computed lesions manually segmented, drawing a 5 mm-Region Interest (ROI) inside tumor another mm-ROI contralateral healthy gland. Features for each map ROI extracted Pyradiomics-3D Slicer considered first separately (tumor gland) then together. In analysis more important status classification selected Maximum Relevance Minimum Redundancy algorithm used fit four classifiers. population 67 86 (21 BRCA1-mutated, 65 non BRCA-carriers). best classifiers BRCA Support Vector Classifier Logistic Regression models fitted both gland features, reaching an Area Under ROC Curve (AUC) 0.80 (SD 0.21) 0.79 0.20), respectively. Three higher BRCA1-mutated compared BRCA-mutated: Total Energy Correlation gray level cooccurrence matrix, measured maps, Root Mean Squared, tumor. showed feasibility potential radiomics predicting mutational status.

Язык: Английский

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