UMR 1288 – Imagerie Translationnelle en Oncologie (LITO)

Publications de l’unité

Année de publication : 2021

Alexandre Leduc, Samia Chaouni, Frédéric Pouzoulet, Ludovic De Marzi, Frédérique Megnin-Chanet, Erwan Corre, Dinu Stefan, Jean-Louis Habrand, François Sichel, Carine Laurent (2021 Mar 13)

Differential normal skin transcriptomic response in total body irradiated mice exposed to scattered versus scanned proton beams.

Scientific reports : 11 : 5876 : DOI : 10.1038/s41598-021-85394-0 En savoir plus
Résumé

Proton therapy allows to avoid excess radiation dose on normal tissues. However, there are some limitations. Indeed, passive delivery of proton beams results in an increase in the lateral dose upstream of the tumor and active scanning leads to strong differences in dose delivery. This study aims to assess possible differences in the transcriptomic response of skin in C57BL/6 mice after TBI irradiation by active or passive proton beams at the dose of 6 Gy compared to unirradiated mice. In that purpose, total RNA was extracted from skin samples 3 months after irradiation and RNA-Seq was performed. Results showed that active and passive delivery lead to completely different transcription profiles. Indeed, 140 and 167 genes were differentially expressed after active and passive scanning compared to unirradiated, respectively, with only one common gene corresponding to RIKEN cDNA 9930021J03. Moreover, protein-protein interactions performed by STRING analysis showed that 31 and 25 genes are functionally related after active and passive delivery, respectively, with no common gene between both types of proton delivery. Analysis showed that active scanning led to the regulation of genes involved in skin development which was not the case with passive delivery. Moreover, 14 ncRNA were differentially regulated after active scanning against none for passive delivery. Active scanning led to 49 potential mRNA-ncRNA pairs with one ncRNA mainly involved, Gm44383 which is a miRNA. The 43 genes potentially regulated by the miRNA Gm44393 confirmed an important role of active scanning on skin keratin pathway. Our results demonstrated that there are differences in skin gene expression still 3 months after proton irradiation versus unirradiated mouse skin. And strong differences do exist in late skin gene expression between scattered or scanned proton beams. Further investigations are strongly needed to understand this discrepancy and to improve treatments by proton therapy.

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Année de publication : 2020

Wolfgang A Weber, Johannes Czernin, Carolyn J Anderson, Ramsey D Badawi, Henryk Barthel, Frank Bengel, Lisa Bodei, Irène Buvat, Marcelo DiCarli, Michael M Graham, Jan Grimm, Ken Herrmann, Lale Kostakoglu, Jason S Lewis, David A Mankoff, Todd E Peterson, Heinrich Schelbert, Heiko Schöder, Barry A Siegel, H William Strauss (2020 Dec 9)

The Future of Nuclear Medicine, Molecular Imaging, and Theranostics.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine : 263S-272S : DOI : 10.2967/jnumed.120.254532 En savoir plus
Résumé

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A-S Cottereau, M Meignan, C Nioche, N Capobianco, J Clerc, L Chartier, L Vercellino, O Casasnovas, C Thieblemont, I Buvat (2020 Dec 5)

Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT.

Annals of oncology : official journal of the European Society for Medical Oncology : 404-411 : DOI : S0923-7534(20)43174-6 En savoir plus
Résumé

We analyzed the prognostic value of a new baseline positron emission tomography (PET) parameter reflecting the spread of the disease, the largest distance between two lesions (Dmax). We tested its complementarity to metabolic tumor volume (MTV) in a large cohort of diffuse large B-cell lymphoma (DLBCL) patients from the REMARC trial (NCT01122472).

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Marie-Judith Saint Martin, Fanny Orlhac, Pia Akl, Fahad Khalid, Christophe Nioche, Irène Buvat, Caroline Malhaire, Frédérique Frouin (2020 Nov 12)

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study.

Magma (New York, N.Y.) : DOI : 10.1007/s10334-020-00892-y En savoir plus
Résumé

Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies.

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Lalith Kumar Shiyam Sundar, Otto Muzik, Irène Buvat, Luc Bidaut, Thomas Beyer (2020 Oct 17)

Potentials and caveats of AI in hybrid imaging.

Methods (San Diego, Calif.) : DOI : S1046-2023(20)30218-8 En savoir plus
Résumé

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

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Nicolò Capobianco, Michel A Meignan, Anne-Segolene Cottereau, Laetitia Vercellino, Ludovic Sibille, Bruce Spottiswoode, Sven Zuehlsdorff, Olivier Casasnovas, Catherine Thieblemont, Irene Buvat (2020 Jun 14)

Deep learning FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine : DOI : jnumed.120.242412 En savoir plus
Résumé

Total metabolic tumor volume (TMTV), calculated from F-labeled fluoro-2-deoxyglucose (F-FDG) positron-emission tomography-computed tomography (PET/CT) baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus currently exists regarding the most accurate approach for such segmentation. Further, all methods still require extensive manual input from an experienced reader. We examined whether an artificial intelligence (AI)-based method could estimate TMTV with a comparable prognostic value to TMTV measured by experts. Baseline F-FDG PET/CT scans of 301 DLBCL patients from the REMARC trial (NCT01122472) were retrospectively analyzed. An automated whole-body high-uptake segmentation algorithm identified all three-dimensional regions of interest (ROI) with increased tracer uptake. The resulting ROIs were processed using a convolutional neural network trained on an independent cohort and classified as nonsuspicious or suspicious uptake. The AI-based TMTV was estimated as the sum of the volumes of ROIs classified as suspicious uptake. The reference TMTV was measured by two experienced readers using independent semiautomatic software. The AI-based TMTV was compared to the reference TMTV in terms of prognostic value for progression-free survival (PFS) and overall survival (OS). The AI-based TMTV was significantly correlated with the reference TMTV (ρ=0.76; p<0.001). Using the AI-based approach, an average of 24 regions per subject with increased tracer uptake were identified, and an average of 20 regions per subject were correctly identified as nonsuspicious or suspicious, yielding 85% classification accuracy, 80% sensitivity, 88% specificity, compared to the reference TMTV region. Both TMTV results were predictive of PFS (hazard ratio: 2.4 and 2.6 for AI-based and reference TMTVs, respectively; p<0.001) and OS (hazard ratio: 2.8 and 3.7 for AI-based and reference TMTVs, respectively; p<0.001). TMTV estimated fully automatically using an AI-based approach was consistent with that obtained by experts and displayed a significant prognostic value for PFS and OS in DLBCL patients. Classification of high uptake regions using deep learning for rapidly discarding physiological uptake may considerably simplify TMTV estimation, reduce observer variability and facilitate the use of TMTV as a predictive factor in DLBCL patients.

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Catriona Wimberley, Duc Loc Nguyen, Charles Truillet, Marie-Anne Peyronneau, Zuhal Gulhan, Matteo Tonietto, Fawzi Boumezbeur, Raphael Boisgard, Sylvie Chalon, Viviane Bouilleret, Irène Buvat (2020 Mar 27)

Longitudinal mouse-PET imaging: a reliable method for estimating binding parameters without a reference region or blood sampling.

European journal of nuclear medicine and molecular imaging : DOI : 10.1007/s00259-020-04755-5 En savoir plus
Résumé

Longitudinal mouse PET imaging is becoming increasingly popular due to the large number of transgenic and disease models available but faces challenges. These challenges are related to the small size of the mouse brain and the limited spatial resolution of microPET scanners, along with the small blood volume making arterial blood sampling challenging and impossible for longitudinal studies. The ability to extract an input function directly from the image would be useful for quantification in longitudinal small animal studies where there is no true reference region available such as TSPO imaging.

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Alex Zwanenburg, Martin Vallières, Mahmoud A Abdalah, Hugo J W L Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Irène Buvat, Gary J R Cook, Christos Davatzikos, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Y Fedorov, Roberto Gatta, Robert J Gillies, Vicky Goh, Michael Götz, Matthias Guckenberger, Sung Min Ha, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T H Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Elisabeth A G Pfaehler, Arman Rahmim, Arvind U K Rao, Jonas Scherer, Muhammad Musib Siddique, Nanna M Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J H M Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G C Troost, Taman Upadhaya, Vincenzo Valentini, Lisanne V van Dijk, Joost van Griethuysen, Floris H P van Velden, Philip Whybra, Christian Richter, Steffen Löck (2020 Mar 11)

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Radiology : 328-338 : DOI : 10.1148/radiol.2020191145 En savoir plus
Résumé

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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Année de publication : 2019

Anne-Sophie Dirand, Frédérique Frouin, Irène Buvat (2019 Nov 30)

A downsampling strategy to assess the predictive value of radiomic features.

Scientific reports : 17869 : DOI : 10.1038/s41598-019-54190-2 En savoir plus
Résumé

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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Claire Provost, Hamid Mammar, Anne Belly-Poinsignon, Olivier Madar, Laurence Champion (2019 Nov 7)

Pharmacokinetic Analysis of [18F]FAZA Dynamic PET Imaging Acquisitions for Highlighting Sacrum Tumor Profiles.

Clinical nuclear medicine : e36-e38 : DOI : 10.1097/RLU.0000000000002813 En savoir plus
Résumé

A patient enrolled in a clinical trial (NCT02802969) with suspicion of chordoma underwent an [F]FAZA PET/CT, a radiolabeled nitroimidazole analog of hypoxia PET imaging. The patient’s images showed a different tumor profile compared to those observed in other hypoxic or nonhypoxic chordoma patients. The motivation for using [F]FAZA pharmacokinetic imaging was to compare this profile with histologically confirmed cases of chordoma. Through visual imaging and quantification of blood and tumor time-activity curves, we excluded the hypothesis that it was a chordoma, diagnosing a paraganglioma.

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Irène Buvat, Fanny Orlhac (2019 Sep 22)

The Dark Side of Radiomics: On the Paramount Importance of Publishing Negative Results.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine : 1543-1544 : DOI : 10.2967/jnumed.119.235325 En savoir plus
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Charlotte Laurent, Laure Ricard, Olivier Fain, Irene Buvat, Amir Adedjouma, Michael Soussan, Arsène Mekinian (2019 Aug 29)

PET/MRI in large-vessel vasculitis: clinical value for diagnosis and assessment of disease activity.

Scientific reports : 12388 : DOI : 10.1038/s41598-019-48709-w En savoir plus
Résumé

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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Patrick Veit-Haibach, Irène Buvat, Ken Herrmann (2019 Jun 27)

EJNMMI supplement: bringing AI and radiomics to nuclear medicine.

European journal of nuclear medicine and molecular imaging : 2627-2629 : DOI : 10.1007/s00259-019-04395-4 En savoir plus
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Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérôme Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan, Irène Buvat (2019 Jun 16)

F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine : 40-45 : DOI : 10.2967/jnumed.119.229450 En savoir plus
Résumé

We assessed the predictive value of new radiomic features characterizing lesion dissemination in baseline F-FDG PET and tested whether combining them with baseline metabolic tumor volume (MTV) could improve prediction of progression-free survival (PFS) and overall survival (OS) in diffuse large B-cell lymphoma (DLBCL) patients. From the LNH073B trial (NCT00498043), patients with advanced-stage DLCBL and F-FDG PET/CT images available for review were selected. MTV and several radiomic features, including the distance between the 2 lesions that were farthest apart (Dmax), were calculated. Receiver-operating-characteristic analysis was used to determine the optimal cutoff for quantitative variables, and Kaplan-Meier survival analyses were performed. With a median age of 46 y, 95 patients were enrolled, half of them treated with R-CHOP biweekly (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and the other half with R-ACVBP (rituximab, doxorubicin, cyclophosphamide, vindesine, bleomycin, and prednisone), with no significant impact on outcome. Median MTV and Dmax were 375 cm and 45 cm, respectively. The median follow-up was 44 mo. High MTV and Dmax were adverse factors for PFS ( = 0.027 and = 0.0003, respectively) and for OS ( = 0.0007 and = 0.0095, respectively). In multivariate analysis, only Dmax was significantly associated with PFS ( = 0.0014) whereas both factors remained significant for OS ( = 0.037 and = 0.0029, respectively). Combining MTV (>384 cm) and Dmax (>58 cm) yielded 3 risk groups for PFS ( = 0.0003) and OS ( = 0.0011): high with 2 adverse factors (4-y PFS and OS of 50% and 53%, respectively, = 18), low with no adverse factor (94% and 97%, = 36), and an intermediate category with 1 adverse factor (73% and 88%, = 41). Combining MTV with a parameter reflecting the tumor burden dissemination further improves DLBCL patient risk stratification at staging.

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Sylvain Auvity, Sébastien Goutal, Benoît Thézé, Catarina Chaves, Benoît Hosten, Bertrand Kuhnast, Wadad Saba, Raphaël Boisgard, Irène Buvat, Salvatore Cisternino, Nicolas Tournier (2019 Jun 10)

Corrigendum to « Evaluation of TSPO PET imaging, a marker of glial activation, to study the neuroimmune footprints of morphine exposure and withdrawal » [Drug Alcohol Depend. 170 (2017) 43-50].

Drug and alcohol dependence : 266-268 : DOI : S0376-8716(19)30162-0 En savoir plus
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