Search results for "Radiomics"
showing 10 items of 47 documents
A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mou…
2022
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard te…
[Radiomics and artificial intelligence: new frontiers in medicine.]
2020
Radiomics is a new frontier of medicine based on the extraction of quantitative data from radiological images which can not be seen by radiologist's naked eye and on the use of these data for the creation of clinical decision support systems. The long-term goal of radiomics is to improve the non-invasive diagnosis of focal and diffuse diseases of different organs by understanding links between extracted quantitative imaging data and the underlying molecular and pathological characteristics of lesions. In the last decade, several studies have highlighted the enormous potential of radiomics in both tumoral and non-tumoral diseases of many organs and systems including brain, lung, breast, gast…
ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI
2022
Breast cancer is the most common malignancy that threatening women’s health. Although Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) for breast lesions characterization is widely used in the clinical practice, physician grading performance is still not optimal, showing a specificity of about 72%. In this work Radiomics was used to analyze a dataset acquired with two different protocols in order to train Machine-Learning algorithms for breast cancer classification. Original radiomic features were expanded considering Laplacian of Gaussian filtering and Wavelet Transform images to evaluate whether they can improve predictive performance. A Multi-Instant features selection invo…
Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT.
2020
Background: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. Methods: We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis…
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
2021
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-…
Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics
2021
In healthcare industry 4.0, a big role is played by radiomics. Radiomics concerns the extraction and analysis of quantitative information not visible to the naked eye, even by expert operators, from biomedical images. Radiomics involves the management of digital images as data matrices, with the aim of extracting a number of morphological and predictive variables, named features, using automatic or semi-automatic methods. Multidisciplinary methods as machine learning and deep learning are fully involved in this field. However, the large number of features requires efficient and effective core methods for their selection, in order to avoid bias or misinterpretations problems. In this work, t…
A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Onco…
2022
Background: In recent decades, the application of machine learning technologies to medical imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics field. Radiomics offer new insight into glioma, aiding in clinical decision-making and patients’ prognosis evaluation. Although meningiomas represent the most common primary CNS tumor and the majority of them are benign and slow-growing tumors, a minor part of them show a more aggressive behavior with an increased proliferation rate and a tendency to recur. Therefore, their treatment may represent a challenge. Methods: According to PRISMA guidelines, a systematic literature review was performed. We included selected a…
Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges
2021
Abstract The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature
2021
Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to…
A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma
2017
[EN] Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictiv…