UMR 1288 – Translational Imaging in Oncology

The laboratory of Translational Imaging in Oncology (LITO) is a mixed research unit (UMR 1288) created on 1/1/2020 and supported by Inserm (=French NIH) and Institut Curie, the 1st cancer center in France.  LITO is the continuation of the In Vivo Molecular Imaging lab (IMIV, UMR 1023) created in 2015 for 5 years (2015-2019).

LITO includes about 30 researchers, physicists, engineers, MDs, PharmDs, and technologists and is organized in two research groups with complementary activities:

  • The first group is the Biomarkers for Phenotype Imaging group and is dedicated to the design and evaluation of innovative radiotracers for molecular imaging using Positron Emission Tomography (PET) to target molecular mechanisms key to cancer development. The goal of this group is twofold: first, better understand the mechanisms underlying cancer development and response or resistance to treatment, second optimize patient management through the identification of imaging phenotype assisting personalized medicine.
  • The second group is the Integrated Radiomics for Precision Medicine group. This group takes davantage of PET biomarkers, but also biomarkers from Magnetic Resonance Imaging (MRI) and computer tomography (CT) and combines these biomarkers with other types of biomarkers (clinical, pathological, omics, from liquid biopsies) to profile the cancer disease for prognostic or predictive purpose. The models are also developed in such a way that they contribute to a better understanding of cancer-related processes.

 

 

Key publications

Year of publication 2020

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
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

Year of publication 2019

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
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
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
Fanny Orlhac, Frédérique Frouin, Christophe Nioche, Nicholas Ayache, Irène Buvat (2019 Jan 30)

Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics.

Radiology : 53-59 : DOI : 10.1148/radiol.2019182023