Christophe Nioche

Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data.

The use of texture indices to characterize tumor heterogeneity from PET images is being increasingly investigated in retrospective studies, yet the interpretation of PET-derived texture index values has not been thoroughly reported. Furthermore, the calculation of texture indices lacks a standardized methodology, making it difficult to compare published results. To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Methods: Ten sphere models mimicking different activity distribution patterns and the 18F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured. The values of texture indices and how they changed as a function of the activity distribution were assessed and compared with the visual assessment of tumor heterogeneity. Results: Using the sphere models and real tumors, we identified 2 sets of texture indices reflecting different types of uptake heterogeneity. Set 1 included homogeneity, entropy, short-run emphasis, and long-run emphasis, all of which were sensitive to the presence of uptake heterogeneity but did not distinguish between hyper- and hyposignal within an otherwise uniform activity distribution. Set 2 comprised high-gray-level-zone emphasis and low-gray-level-zone emphasis, which were mostly sensitive to the average uptake rather than to the uptake local heterogeneity. Four of 6 texture indices significantly differed between homogeneous and heterogeneous lesions as defined by 2 nuclear medicine physicians (P < 0.05). All texture index values were sensitive to voxel size (variations up to 85.8% for the most homogeneous sphere models) and edge effects (variations up to 29.1%). Conclusion: Unlike a previous report, our study found that variations in texture indices were intuitive in the sphere models and real tumors: the most homogeneous uptake distribution exhibited the highest homogeneity and lowest entropy. Two families of texture index reflecting different types of uptake patterns were identified. Variability in texture index values as a function of voxel size and inclusion of tumor edges was demonstrated, calling for a standardized calculation methodology. This study provides guidance for nuclear medicine physicians in interpreting texture indices in future studies and clinical practice.

Evaluation of quantitative criteria for glioma grading with static and dynamic 18F-FDopa PET/CT.

Purpose The aim of this study was to compare various acquisition and processing protocols for noninvasive glioma grading using either static or dynamic 18F-FDopa PET. Methods Dynamic studies were performed in 33 patients. Based on histopathological analysis, 18 patients had a high-grade (HG) tumor and 15 patients had a low-grade (LG) tumor. For static imaging, SUVmean and SUVmax were calculated for different acquisition time ranges after injection. For dynamic imaging, the transport rate constant k1 was calculated according to a compartmental kinetic analysis using an image-derived input function. Results With the use of a 5-minute static imaging protocol starting at 38 minutes after injection, newly diagnosed HG tumors could be distinguished from LG tumors with a sensitivity of 70% and a specificity of 90% with a threshold of SUVmean of 2.5. In recurrent tumors, a sensitivity of 100% and a specificity of 80% for identifying HG tumors were obtained with a threshold set to 1.8. Dynamic imaging only slightly, but nonsignificantly, improved differential diagnosis. Conclusions Static and dynamic imaging without blood sampling can discriminate between LG and HG for both newly diagnosed and recurrent gliomas. In dynamic imaging, excellent discrimination was obtained by considering the transport rate constant k1 of tumors. In static imaging, the best discrimination based on SUV was obtained for SUVmean calculated from a 5-minute acquisition started at 38 minutes after injection.

Prediction of cervical cancer recurrence using textural features extracted from 18 F-FDG PET images acquired with different scanners

Objectives To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. Methods 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values. Results Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p<0.001) and identified local recurrence more accurately than SUVmax (p=0.022). Four features were significantly different between G1 and G2 in the liver. Spatial resampling was not sufficient to explain the stratification effect. Conclusion This study showed that radiomic features could predict local recurrence of LACC better than SUVmax. Further investigation is needed before applying a model designed using data from one PET scanner to another.

A post-reconstruction harmonization method for multicenter radiomic studies in PET

Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols. Methods: Pretreatment 18F-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization. Results: In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S (P < 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments (P > 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department. Conclusion: The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT

PurposeImage texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.MethodsWe performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dosexa0<xa026xa0Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.ResultsSeventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (pxa0<xa00.001, R2:0.664).ConclusionsXerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.

Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma

Few methodological studies regarding widely used textural indices robustness in MRI have been reported. In this context, this study aims to propose some rules to compute reliable textural indices from multimodal 3D brain MRI. Diagnosis and post-biopsy MR scans including T1, post-contrast T1, T2 and FLAIR images from thirty children with diffuse intrinsic pontine glioma (DIPG) were considered. The hybrid white stripe method was adapted to standardize MR intensities. Sixty textural indices were then computed for each modality in different regions of interest (ROI), including tumor and white matter (WM). Three types of intensity binning were compared [Formula: see text]: constant bin width and relative bounds; [Formula: see text] constant number of bins and relative bounds; [Formula: see text] constant number of bins and absolute bounds. The impact of the volume of the region was also tested within the WM. First, the mean Hellinger distance between patient-based intensity distributions decreased by a factor greater than 10 in WM and greater than 2.5 in gray matter after standardization. Regarding the binning strategy, the ranking of patients was highly correlated for 188/240 features when comparing [Formula: see text] with [Formula: see text], but for only 20 when comparing [Formula: see text] with [Formula: see text], and nine when comparing [Formula: see text] with [Formula: see text]. Furthermore, when using [Formula: see text] or [Formula: see text] texture indices reflected tumor heterogeneity as assessed visually by experts. Last, 41 features presented statistically significant differences between contralateral WM regions when ROI size slightly varies across patients, and none when using ROI of the same size. For regions with similar size, 224 features were significantly different between WM and tumor. Valuable information from texture indices can be biased by methodological choices. Recommendations are to standardize intensities in MR brain volumes, to use intensity binning with constant bin width, and to define regions with the same volumes to get reliable textural indices.

LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786-9. ©2018 AACR.

New insights in radiation-induced leukoencephalopathy: a prospective cross-sectional study

BackgroundRadiation-induced leukoencephalopathy (RIL) is the most threatening delayed complication of cerebral radiotherapy (RT) and remains roughly defined by cognitive dysfunction associated with diffuse FLAIR MRI white matter hyperintensities after brain irradiation. We documented clinical, neuropsychological, and radiological aspects of RI in order to refine diagnostic criteria.MethodsPatients referred to our center for deterioration in cognitive complaint at least 6xa0months after completing a focal or whole brain RT underwent a systematic cross-sectional assessment including clinical examination, neuropsychological tests, and a standardized MRI protocol. Patients with progressive tumor were excluded.ResultsForty patients were prospectively enrolled. Of these, 26 had received a focal RT, median dose of 53xa0Gy (range 50 to 60), and 14 had received a whole brain RT, median dose of 30xa0Gy. Cognitive complaints, gait apraxia, and urinary troubles were reported in 100, 67, and 38% of cases, respectively. On neuropsychological examination, patients displayed a global and severe cognitive decline through a subcortical frontal mode. The cognitive changes observed were not hippocampic, but related to executive dysfunction. On MRI, 68% of the patients had extensive FLAIR hyperintensities with anterior predominance, 87% had brain atrophy, and 21% had intraparenchymal cysts. T2*-weighted MRI showed small asignal areas in 53% of the patients. These abnormalities are evocative of cerebral small vessel disease. Fractional anisotropy in the corpus callosum correlated with the cognitive evaluation. No differentiation in terms of cognitive and MRI features could be made between patients treated with focal brain RT (glioma) and patients treated with WBRT (for brain metastases or PCNSL).ConclusionsRIL can be defined by clinical symptoms (subcortical frontal decline, gait apraxia, urinary incontinence) and MRI criteria (cortico-subcortical atrophy, spread FLAIR HI, T2* asignals). This condition mimics a diffuse progressive cerebral small vessel disease triggered by RT, independent of RT protocol.

Influence of age on radiomic features in 18 F-FDG PET in normal breast tissue and in breast cancer tumors

Background To help interpret measurements in breast tissue and breast tumors from 18F-FDG PET scans, we studied the influence of age in measurements of PET parameters in normal breast tissue and in a breast cancer (BC) population. Results 522 women were included: 331 pts without history of BC (B-VOI) and 191 patients with BC (T-VOI). In B-VOI, there were significant differences between all age groups for Standardized Uptake Values (SUVs) and for 12 textural indices (TI) whereas histogram-based indices (HBI) did not vary between age groups. SUV values decreased over time whereas Homogeneity increased. We had a total of 210 T-VOI and no significant differences were found according to the histological type between 190 ductal carcinoma and 18 lobular carcinoma. Conversely, according to BC subtype most differences in PET parameters between age groups were found in Triple-Negative tumors (52) for 9 TI. On post-hoc Hochberg, most differences were found between the <45 year old (PRE) group and POST groups in NBT and in Triple-Negative tumors. Conclusion We found significant SUVs and TI differences as a function of age in normal breast tissue and in BC radiomic phenotype with Triple-Negative tumors being the most affected. Our findings suggest that age should be taken into account as a co-covariable in radiomic models. Methods Patients were classified in 3 age groups: <45 yo (PRE), ≥45 and <55 yo (PERI) and ≥55 and <85 yo (POST) and we compared PET parameters using Anova test with post-hoc Bonferroni/Hochberg analyses: SUV (max, mean and peak), HBI and TI in both breasts and in breast tumor regions.

Analyse quantitative et impact de la correction de l’effet de volume partiel en TEP à la 18F-Dopa pour le diagnostic de la maladie de Parkinson

Objectifs Nous avons evalue les performances diagnostiques de la quantification en TEP a la 18F-Dopa pour les syndromes parkinsoniens et l’impact de la correction de l’effet de volume partiel (EVP). Patients et methodes Nous avons mis en concordance spatiale les images TEP avec les IRM de 49xa0patients (50xa0examens) adresses de maniere prospective pour l’exploration de syndromes parkinsoniens. Nous avons separe les patients en deux groupesxa0: NS+ lorsqu’ils presentaient une pathologie avec atteinte de la voie nigro-striee et NS− sans atteinte de la voie nigro-striee. Les rapports de fixation entre les sous-regions striatales segmentees sur l’IRM et la fixation cerebrale non specifique ont ete compares. Les valeurs mesurees ont ensuite ete corrigees de l’effet de volume partiel en utilisant la methode de la matrice de contamination croisee. Resultats Il existe une difference significative de la fixation du striatum entre les patients NS+ et NS−, la sous-region possedant les meilleures performances diagnostiques est le putamen controlateral aux symptomes dominants avec une aire sous la courbe ROC de 0,948xa0et une sensibilite et une specificite de 92xa0%. La correction de l’EVP a permis d’augmenter l’estimation de la fixation du striatum de 29xa0a 41xa0% selon les sous-regions. Nous avons egalement observe qu’il etait possible de reduire le temps d’acquisition de l’examen sans impact sur la quantification. Conclusions L’analyse quantitative en TEP a la 18F-Dopa offre de bonnes performances diagnostiques pour les syndromes parkinsoniens. La correction de l’effet de volume partiel permet d’augmenter l’estimation de la fixation du striatum.