Las frágiles y peligrosas medusas

International audienceIntroduction: Our aim was to explore the prognostic value of anthropometric parameters in patients treated with nivolumab for stage IV non-small cell lung cancer (NSCLC). Methods: We retrospectively included 55 patients with NSCLC treated by nivolumab with a pretreatment 18FDG positron emission tomography coupled with computed tomography (PET/CT). Anthropometric parameters were measured on the CT of PET/CT by in-house software (Anthropometer3D) allowing an automatic multi-slice measurement of Lean Body Mass (LBM), Fat Body Mass (FBM), Muscle Body Mass (MBM), Visceral Fat Mass (VFM) and Sub-cutaneous Fat Mass (SCFM). Clinical and tumor parameters were also retrieved. Receiver operator characteristics (ROC) analysis was performed and overall survival at 1 year was studied using Kaplan-Meier and Cox analysis. Results: FBM and SCFM were highly correlated (ρ = 0.99). In ROC analysis, only FBM, SCFM, VFM, body mass index (BMI) and metabolic tumor volume (MTV) had an area under the curve (AUC) significantly higher than 0.5. In Kaplan-Meier analysis using medians as cut-offs, prognosis was worse for patients with low SCFM (<5.69 kg/m2; p = 0.04, survivors 41% vs 75%). In Cox univariate analysis using continuous values, BMI (HR = 0.84, p= 0.007), SCFM (HR = 0.75, p = 0.003) and FBM (HR = 0.80, p= 0.004) were significant prognostic factors. In multivariate analysis using clinical parameters (age, gender, WHO performance status, number prior regimens) and SCFM, only SCFM was significantly associated with poor survival (HR = 0.75, p = 0.006). Conclusions: SCFM is a significant prognosis factor of stage IV NSCLC treated by nivolumab

Radiomic and anthropometric analyses in multimodal imaging for exploring predictive and prognostic factors in oncology

Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients.Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients

Analyses radiomique et anthropométrique en imagerie multimodale pour l'exploration de facteurs prédictifs et pronostiques en oncologie.

Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients.Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients

Analyses radiomique et anthropométrique en imagerie multimodale pour l'exploration de facteurs prédictifs et pronostiques en oncologie.

Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients.Personalized medicine refers to the adaptation of medical treatment to the individual characteristics of each diseaseand each patient. In oncology, this adaptation depends in particular on the prognosis of the disease in order to adapt a treatmentto the severity of the cancer. Medical imaging, represented in particular by the CT scan and the PET scan, allows the extraction andanalysis of morphological and functional characteristics of the cancer, called "radiomics", but also of the patient's characteristics,called "anthropometrics". The objective of this work was to explore the use of radiomic and anthropometric parameters inmultimodal imaging for the exploration of prognostic factors in oncology.For the anthropometric side, we developed and evaluated a software, named "Anthropometer3D", allowing themeasurement of muscle, lean, fat, visceral adipose tissue and subcutaneous adipose tissue masses in a multi-slice and automaticway from PET/CT scans and showed the prognostic value of subcutaneous adipose tissue for stage IV lung cancers treated withimmunotherapy and of muscle mass for lung cancers treated with radio-chemotherapyFor the radiomic side, we have developed and evaluated algorithms for the automatic segmentation and classification ofoncological PET/CT scans and developed a software, named "Oncometer3D", for the extraction of tumor activity, fragmentation,dispersion and massiveness characteristics from PET scans. We showed that one fragmentation parameter, the volume to totaltumor area ratio, was an independent prognostic factor in diffuse large-cell B-cell lymphoma and that several morphological tumorparameters correlated with circulating tumor DNA in B-cell hemopathies.In conclusion, medical imaging participates in the global evaluation of cancer from a macroscopic point of view byallowing a radiomic analysis centered on the tumor but also anthropometrically centered on the patient. Improved prognosticationusing these two approaches could lead to better therapeutic management of patients

Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications

International audienceImmunotherapy by using immune checkpoint inhibitors is a revolutionary development in oncology. Medical imaging is also impacted by this new therapy, particularly nuclear medicine imaging (also called radionuclide imaging), which uses radioactive tracers to visualize metabolic functions. Our aim was to review the current applications of nuclear medicine imaging in immunotherapy, along with their limitations, and the perspectives offered by this imaging modality. Method: Articles describing the use of radionuclide imaging in immunotherapy were researched using PubMed by April 2019 and analyzed. Results: More than 5000 articles were analyzed, and nearly 100 of them were retained. Radionuclide imaging, notably 18F-FDG PET/CT, already has a major role in many cancers for pre-therapeutic and therapeutic evaluation, diagnoses of adverse effects, called immune-related adverse events (IrAE), and end-of-treatment evaluations. However, these current applications can be hindered by immunotherapy, notably due to atypical response patterns such as pseudoprogression, which is defined as an increase in the size of lesions, or the visualization of new lesions, followed by a response, and hyperprogression, which is an accelerated tumor growth rate after starting treatment. To overcome these difficulties, new opportunities are offered, particularly therapeutic evaluation criteria adapted to immunotherapy and immuno-PET allowing us to predict responses to immunotherapy. Moreover, some new technological solutions are also promising, such as radiomic analyses and body composition on associated anatomical images. However, more research has to be done, notably for the diagnosis of hyperprogression and pseudoprogression. Conclusion: Immunotherapy, by its major impact on cancer and by the new patterns generated on images, is revolutionary in the field of medical images. Nuclear medicine imaging is already established and will be able to help meet new challenges through its plasticity

How to use PET/CT in the evaluation of response to radiotherapy.

International audienceRadiotherapy is a major treatment modality for many cancers. Tumor response after radiotherapy determines the subsequent steps of the patient's management (surveillance, adjuvant or salvage treatment and palliative care). Tumor response assessed during radiotherapy offers a promising opportunity to adapt the treatment plan to reduced or increased target volume, to specifically target sub-volumes with relevant biological characteristics (metabolism, hypoxia, proliferation, etc.) and to further spare the organs at risk. In addition to its role in the diagnosis and the initial staging, Positron Emission Tomography combined with a Computed Tomography (PET/CT) provides functional information and is therefore attractive to evaluate tumor response. The aim of this paper is to review the published data addressing PET/CT as an evaluation tool in irradiated tumors. Reports on PET/CT acquired at various times (during radiotherapy, after initial (chemo-) radiotherapy, after definitive radiotherapy and during posttreatment follow-up) in solid tumors (lung, head-and-neck, cervix, esophagus, prostate and rectum) were collected and reviewed. Various tracers and technical aspects are also discussed. 18F-FDG PET/CT has a well-established role in clinical routine after definitive chemo-radiotherapy for locally advanced head-and-neck cancers. 18F-choline PET/CT is indicated in prostate cancer patients with biochemical failure. 18F-FDG PET/CT is optional in many other circumstances and the clinical benefits of assessing tumor response with PET/CT remain a field of very active research. The combination of PET with Magnetic Resonance Imaging (PET/MRI) may prove to be valuable in irradiated rectal and cervix cancers. Tumor response can be evaluated by PET/CT with clinical consequences in multiple situations, notably in head and neck and prostate cancers, after radiotherapy. Further clinical evaluation for most cancers is still needed, possibly in association to MRI

Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT

International audienceAnthropometric parameters like muscle body mass (MBM), fat body mass (FBM), lean body mass (LBM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) are used in oncology. Our aim was to develop and evaluate the software Anthropometer3D measuring these anthropometric parameters on the CT of PET/CT. This software performs a multi-atlas segmentation of CT of PET/CT with extrapolation coefficients for the body parts beyond the usual acquisition range (from the ischia to the eyes). The multi-atlas database is composed of 30 truncated CTs manually segmented to isolate three types of voxels (muscle, fat, and visceral fat). To evaluate Anthropomer3D, a leave-one-out cross-validation was performed to measure MBM, FBM, LBM, VAT, and SAT. The reference standard was based on the manual segmentation of the corresponding whole-body CT. A manual segmentation of one CT slice at level L3 was also used. Correlations were analyzed using Dice coefficient, intra-class coefficient correlation (ICC), and Bland-Altman plot. The population was heterogeneous (sex ratio 1:1; mean age 57 years old [min 23; max 74]; mean BMI 27 kg/m2 [min 18; max 40]). Dice coefficients between reference standard and Anthropometer3D were excellent (mean+/-SD): muscle 0.95 ± 0.02, fat 1.00 ± 0.01, and visceral fat 0.97 ± 0.02. The ICC was almost perfect (minimal value of 95% CI of 0.97). All Bland-Altman plot values (mean difference, 95% CI and slopes) were better for Anthropometer3D compared to L3 level segmentation. Anthropometer3D allows multiple anthropometric measurements based on an automatic multi-slice segmentation. It is more precise than estimates using L3 level segmentation

Detection and Segmentation of Lymphomas in 3D PET Images via Clustering with Entropy-based OptimizationStrategy.

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RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images

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Radiogan : réseaux de neurones profonds génératifs conditionnels pour la synthétisation d’images TEP au FDG

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