Publications de l’unité

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Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 See also the editorial by Kuhl and Truhn in this issue.

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Many studies are devoted to the design of radiomic models for a prediction task. When no effective model is found, it is often difficult to know whether the radiomic features do not include information relevant to the task or because of insufficient data. We propose a downsampling method to answer that question when considering a classification task into two groups. Using two large patient cohorts, several experimental configurations involving different numbers of patients were created. Univariate or multivariate radiomic models were designed from each configuration. Their performance as reflected by the Youden index (YI) and Area Under the receiver operating characteristic Curve (AUC) was compared to the stable performance obtained with the highest number of patients. A downsampling method is described to predict the YI and AUC achievable with a large number of patients. Using the multivariate models involving machine learning, YI and AUC increased with the number of patients while they decreased for univariate models. The downsampling method better estimated YI and AUC obtained with the largest number of patients than the YI and AUC obtained using the number of available patients and identifies the lack of information relevant to the classification task when no such information exists.

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Diagnosis of large vessel vasculitis (LVV) and evaluation of its inflammatory activity can be challenging. Our aim was to investigate the value of hybrid positron-emission tomography/magnetic resonance imaging (PET/MRI) in LVV. All consecutive patients with LVV from the Department of Internal Medicine who underwent PET/MRI were included. Three PET/MRI patterns were defined: (i) « inflammatory, » with positive PET (>liver uptake) and abnormal MRI (stenosis and/or wall thickening); (ii) « fibrous », negative PET (≤liver uptake) and abnormal MRI; and (iii) « normal ». Thirteen patients (10 female; median age: 67-years [range: 23-87]) underwent 18 PET/MRI scans. PET/MRI was performed at diagnosis (n = 4), at relapse (n = 7), or during remission (n = 7). Among the 18 scans, eight (44%) showed an inflammatory pattern and three (17%) a fibrous pattern; the other seven were normal. The distribution of the three patterns did not differ between patients with Takayasu arteritis (TA, n = 10 scans) and those with giant cell arteritis (GCA, n = 8 scans). PET/MRI findings were normal in 2/10 (20%) TA scans vs. 5/8 (62%) GCA scans (p = 0.3). Median SUV was 4.7 [2.1-8.6] vs. 2 [1.8-2.6] in patients with active disease vs. remission, respectively (p = 0.003). PET/MRI is a new hybrid imaging modality allowing comprehensive and multimodal analysis of vascular wall inflammation and the vascular lumen. This technique offers promising perspectives for the diagnosis and monitoring of LVV.

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Overcoming the efflux mediated by ATP-binding cassette (ABC) transporters at the blood-brain barrier (BBB) remains a challenge for the delivery of small molecule tyrosine kinase inhibitors (TKIs) such as erlotinib to the brain. Inhibition of ABCB1 and ABCG2 at the mouse BBB improved the BBB permeation of erlotinib but could not be achieved in humans. BBB disruption induced by focused ultrasound (FUS) was investigated as a strategy to overcome the efflux transport of erlotinib in vivo. In rats, FUS combined with microbubbles allowed for a large and spatially controlled disruption of the BBB in the left hemisphere. ABCB1/ABCG2 inhibition was performed using elacridar (10 mg/kg i.v). The brain kinetics of erlotinib was studied using C-erlotinib Positron Emission Tomography (PET) imaging in 5 groups (n = 4-5 rats per group) including a baseline group, immediately after sonication (FUS), 48 h after FUS (FUS + 48 h), elacridar (ELA) and their combination (FUS + ELA). BBB integrity was assessed using the Evan’s Blue (EB) extravasation test. Brain exposure to C-erlotinib was measured as the area under the curve (AUC) of the brain kinetics (% injected dose (%ID) versus time (min)) in volumes corresponding to the disrupted (left) and the intact (right) hemispheres, respectively. EB extravasation highlighted BBB disruption in the left hemisphere of animals of the FUS and FUS + ELA groups but not in the control and ELA groups. EB extravasation was not observed 48 h after FUS suggesting recovery of BBB integrity. Compared with the control group (AUC = 1.4 ± 0.5%ID.min), physical BBB disruption did not impact the brain kinetics of C-erlotinib in the left hemisphere (p > .05) either immediately (AUC = 1.2 ± 0.1%ID.min) or 48 h after FUS (AUC = 1.1 ± 0.3%ID.min). Elacridar similarly increased C-erlotinib brain exposure to the left hemisphere in the absence (AUC = 2.2 ± 0.5%ID.min, p < .001) and in the presence of BBB disruption (AUC = 2.1 ± 0.5%ID.min, p < .001). AUC was never significantly different from AUC (p > .05), in any of the tested conditions. BBB integrity is not the rate limiting step for erlotinib delivery to the brain which is mainly governed by ABC-mediated efflux. Efflux transport of erlotinib persisted despite BBB disruption.

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To help interpret measurements in breast tissue and breast tumors from F-FDG PET scans, we studied the influence of age in measurements of PET parameters in normal breast tissue and in a breast cancer (BC) population.

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Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

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Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy. This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. .