Evidence of pseudoprogression in patients treated with PD1/ PDL1 antibodies across tumor types

Background: PD(L)1 antibodies (anti-PD(L)-1) have been a major breakthrough in several types of cancer. Novel patterns of response and progression have been described with anti-PD(L)-1. We aimed at characterizing pseudoprogression (PSPD) among patients with various solid tumor types treated by anti-PD(L)-1. Methods: All consecutive patients (pts) enrolled in phase 1 trials with advanced solid tumors and lymphomas treated in phase I clinical trials evaluating monotherapy by anti-PD(L)-1 at Gustave Roussy were analyzed. We aimed to assess prevalence and outcome of PSPD across tumor types. We also intended to describe potential clinical and pathological factors associated with PSPD. Results: A total of 169 patients treated with anti-PD(L)-1 were included in the study. Most frequent tumor types included melanoma (n = 57) and non-small cell lung cancer (n = 19). At first tumor evaluation 77 patients (46%) presented with immune unconfirmed progressive disease. Six patients (8%) experienced PSPD: 2 patients with partial response; 4 patients with stable disease. Increase in target lesions in the first CT-scan was more frequently associated to PSPD (67% vs 33%; P = .04). Patients with a PSPD had a superior survival when compared to patients progressing (median OS: 10.7 months vs 8.7 months; P = .07). Conclusions: A small subset of PSPD patients may experience response after an initial progression. Assessment of the current strategy for immune-related response evaluations may require further attention

Outcome of Patients with Non-Small Cell Lung Cancer and Brain Metastases Treated with Checkpoint Inhibitors

Introduction: Although frequent in NSCLC, patients with brain metastases (BMs) are often excluded from immune checkpoint inhibitor (ICI) trials. We evaluated BM outcome in a less-selected NSCLC cohort. Methods: Data from consecutive patients with advanced ICI-treated NSCLC were collected. Active BMs were defined as new and/or growing lesions without any subsequent local treatment before the start of ICI treatment. Objective response rate (ORR), progression-free survival, and overall survival (OS) were evaluated. Multivariate analyses were performed by using a Cox proportional hazards model and logistic regression. Results: A total of 1025 patients were included; the median follow-up time from start of ICI treatment was 15.8 months. Of these patients, 255 (24.9%) had BMs (39.2% active, 14.3% symptomatic, and 27.4% being treated with steroids). Disease-specific Graded Prognostic Assessment (ds-GPA) score was known for 94.5% of patients (35.7% with a score of 0-1, 58.5% with a score of 1.5-2.5, and 5.8% with a score of 3). The ORRs with BM versus without BM were similar: 20.6% (with BM) versus 22.7% (without BM) (p = 0.484). The intracranial ORR (active BM with follow-up brain imaging [n = 73]) was 27.3%. The median progression-free survival times were 1.7 (95% confidence interval [CI]: 1.5-2.1) and 2.1 (95% CI: 1.9-2.5) months, respectively (p = 0.009). Of the patients with BMs, 12.7% had a dissociated cranial-extracranial response and two (0.8%) had brain pseudoprogression. Brain progression occurred more in active BM than in stable BM (54.2% versus 30% [p <0.001]). The median OS times were 8.6 months (95% CI: 6.8-12.0) with BM and 11.4 months (95% CI: 8.6-13.8) months with no BM (p = 0.035). In the BM subgroup multivariate analysis, corticosteroid use (hazard ratio [HR] = 2.37) was associated with poorer OS, whereas stable BMs (HR = 0.62) and higher ds-GPA classification (HR = 0.48-0.52) were associated with improved OS. Conclusion: In multivariate analysis BMs are not associated with a poorer survival in patients with ICI-treated NSCLC. Stable patients with BM without baseline corticosteroids and a good ds-GPA classification have the best prognosis. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved

Computational Medical Imaging in Neuro Oncology

L'imagerie en neuro-oncologie joue un rôle majeur dans la démarche diagnostique et dans la prise en charge des patients atteints de tumeurs cérébrales. Elle permet non seulement de préciser le siège de la lésion, de la caractériser, mais aussi d’établir son retentissement sur les structures cérébrales. Elle fait le bilan de son extension aboutissant à un diagnostic probabiliste mais non de certitude. Le développement de techniques d'imagerie ainsi que d'analyses bio-informatiques et biostatistiques poussées (analyses computationnelles) permettent aujourd’hui une approche plus fine de la caractérisation tumorale. Leur application intervient aux différents temps du diagnostic : temps initial pour la caractérisation lésionnelle, et lors du suivi pour la réponse au traitement. Dans ce travail de thèse, nous nous intéresserons aux étapes de validation de différentes techniques d'imagerie utilisées à visée de caractérisation tumorale, telles que l'analyse des caractéristiques tumorales en IRM de diffusion, de perfusion quantitative T2* puis à la technique de radiomique utilisée sur les séquences morphologiques. L'objectif premier de cette thèse est l'utilisation de méthodes computationnelles pour classifier les lésions tumorales (tâche de classification) pour distinguer différents types de tumeurs cérébrales, telles que les glioblastomes, métastases, puis pour classifier et évaluer la capacité de l'analyse computationnelle IRM et faire la différence entre des lésions cérébrales des effets post thérapeutiques avec une approche radiomique et fonctionnelle ; après avoir réglé des aspects techniques sur l'image tels que les harmonisations des examens et des features radiomics utilisant des fantômes et en in VIVO. Cette méthode de classification assistée par ordinateur combinant l'imagerie par résonance magnétique (IRM) conventionnelle et l'IRM de perfusion (avec de nouvelles techniques T2*Hyper bande, ASL) et de diffusion (classique et IVIM) sera développée. Le schéma proposé comprend plusieurs étapes, notamment la définition de la meilleure méthode de segmentation précédée par une méthode d’harmonisation suivie par l'extraction des caractéristiques intrinsèques des lésions segmentées et de l’oedème péri-lésionnel (forme et intensité de la tumeur, caractéristiques de texture). Nous essayerons aussi de prédire le devenir des patients porteurs de glioblastome qui est une tâche plus compliquée pour les algorithmes de machine learning. Le deuxième objectif est de développer un modèle de ‘deep learning’ pour la diminution des quantités de chélates de gadolinium injectés aux patients sur les séquences 3DT1, et également l’augmentation de la sensibilité de détection des lésions sur les séquences 3DT1. Les indicateurs de texture sur les séquences conventionnelles seront post-traités. L’association de l'utilisation de paramètres vasculaires reflétant l'angiogenèse tumorale, en perfusion T2* DSC et la quantification de la cellularité tumorale par de diffusion avec calcul de l'ADC permettront de déterminer des biomarqueurs robustes et reproductibles. Les limites de ces techniques avancées ainsi que les limites de l’étude de type radiomique doivent être prises en compte pour toute analyse quantitative de l'image, dont la valeur du paramètre mesuré ne sera plus alors le reflet des seules caractéristiques biologiques et histologiques des tissus mais aussi de contingences techniques. Cela est particulièrement important lors de l'obtention de données quantitatives qui pourront être utilisées comme biomarqueurs en pratique clinique, pour le diagnostic, pronostic ou suivi des patients, ou lors de projets de recherche. Ces techniques d'imagerie fonctionnelle et de texture semblent prometteuses pour la caractérisation des lésions cérébrales et permettront d'élaborer des algorithmes décisionnels afin d'améliorer leur diagnostic, de diminuer le recours à des prélèvements invasifs, d'avoir un suivi précis et régulier et d'adapter les thérapeutiques.Imaging in neuro-oncology has a major role in the diagnostic process and in the management of cerebral tumors. It allows not only to specify the location of the lesion and to characterize it, but also to establish its impact on the brain structures. It assesses lesion staging, leading to a probabilistic diagnosis, but not a definitive one. The development of imaging techniques as well as advanced bioinformatics and biostatistics (computational analysis) now allows a more refined approach to tumor characterization. The applications will be present at different times of diagnosis: initial time for lesion characterization, during follow-up for the response to treatment. In this thesis, we will focus on the validation steps of different imaging techniques used for tumor characterization, such as the analysis of tumor characteristics in diffusion MRI, quantitative T2* perfusion MRI and the radiomics technique used on morphological sequences. The primary objective of this thesis is the use of computational methods by studying classification methods to distinguish different types of brain tumors, such as glioblastomas, metastases and then a genomic classification of glioblastomas. and to evaluate the ability of computational MRI analysis to differentiate between brain lesions and post-therapeutic effects with a radiomic and functional approach; after adjusting technical aspects on the image such as harmonizations of the examinations and radiomics features using phantoms and in VIVO. This computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI (with new T2*Hyperband, ASL) and diffusion MRI (conventional and IVIM) will be developed. The proposed scheme includes several steps, including the definition of the best segmentation method preceded by a harmonization method followed by the extraction of the intrinsic characteristics of the segmented lesions and the peri-lesional edema (tumor shape and intensity, textural characteristics). Objective 2 is to develop a deep learning model to either decrease the amount of gadolinium chelates injected to patients on 3DT1 sequences or increase the sensitivity of lesion detection on 3DT1 sequences. The texture indexes on conventional sequences will be post-processed. The use of vascular parameters reflecting tumor angiogenesis, in T2* DSC perfusion and the quantification of tumor cellularity by diffusion with calculation of ADC will allow the determination of robust and reproducible biomarkers. The limitations of these advanced techniques as well as the limitations of the radiomics study must be taken into account for any quantitative image analysis, where the value of the measured parameter will no longer reflect only biological and histological characteristics of the tissue but also technical contingencies. This is particularly important when obtaining quantitative data that can be used as biomarkers in clinical practice, for diagnosis, prognosis or patient follow-up, or in research projects. These functional and textural imaging techniques seem promising for the characterization of brain lesions and will allow to elaborate decisional algorithms to improve their diagnosis, to decrease the recourse to invasive sampling, to have a precise and regular follow-up and to adapt the therapeutics

Posterior Reversible Encephalopathy Syndrome Following Chemotherapy and Immune Checkpoint Inhibitor Combination in a Patient with Small-Cell Lung Cancer

Posterior reversible encephalopathy syndrome (PRES) is a rare neurological complication that occurs following a sudden blood pressure increase. We report the case of a 64-year-old patient presenting PRES several hours after the administration of a combination of chemotherapy and a checkpoint inhibitor (carboplatin-etoposide-atezolizumab) for small-cell lung cancer. He presented consciousness disorders associated with partial epileptic seizure secondarily generalized. His arterial blood pressure was elevated and brain imaging showed multiple bilateral subcortical parietal, temporal, occipital and cerebellar T2 high signals, predominantly in the posterior region. There were no abnormal T1 signals nor bleeding but a left apparent diffusion coefficient restriction was noted. On arterial spin labelling perfusion sequences, there was an increased perfusion within the left temporo-parieto-occipital, left thalamic and right cerebellar regions. Finally, the neurological symptoms completely regressed after several days of optimal antihypertensive and antiepileptic treatment. The clinical context and radiological features, as well as the progressive resolution of the neurological symptoms, were all in favor of PRES. PRES can occur after the administration of chemotherapy and/or immunotherapy. Prompt diagnosis is crucial through a spectrum of suspicious clinical and radiological characteristics that must be rapidly recognized to quickly anticipate the optimal therapeutic strategy and avoid unnecessary complications

Machine-learning based guided diagnosis of parotid tumours from MRI

International audienceDiagnosis of parotid gland tumours rely on magnetic resonance examination, fine-needle aspiration biopsy, or in some cases, invasive surgery that can damage facial nerves. In this work, we propose a machine learning model that can discriminate parotid tumours into histopathological subtypes from magnetic resonance imaging (MRI) scans, and further evaluate its impact on the diagnostic decisions of radiologists. We aim at improving the diagnosis of parotid neoplasms while avoiding any physical harm to patients. The radiologists improved their performance after observing the predictions of the algorithm. We conclude that machine-learning-based radiomics classification can assist radiologists

Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

International audienceIn brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction andimage resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations

Machine-learning based guided diagnosis of parotid tumours from MRI

International audienceDiagnosis of parotid gland tumours rely on magnetic resonance examination, fine-needle aspiration biopsy, or in some cases, invasive surgery that can damage facial nerves. In this work, we propose a machine learning model that can discriminate parotid tumours into histopathological subtypes from magnetic resonance imaging (MRI) scans, and further evaluate its impact on the diagnostic decisions of radiologists. We aim at improving the diagnosis of parotid neoplasms while avoiding any physical harm to patients. The radiologists improved their performance after observing the predictions of the algorithm. We conclude that machine-learning-based radiomics classification can assist radiologists

Iodine footprint: Moving towards environmental responsibility

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