Publications de l’unité

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.

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.

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.

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.

Résumé

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.