Contribution of tissue textural pattern and conventional index to glioma staging in FDopa-PET/CT

National audienceAim: We studied whether the characterization of tumor texture in FDopa-PET/CT could assist in the identification of tumor grades in both primitive and recurrent gliomas. Materials and Methods: Eighty one patients with gliomas were studied, including 52 newly diagnosed tumors and 29 recurrent tumors. For each tumor, the SUVpeak and metabolic volume (MV) were measured, as well as 32 textural indices (TI). The ability of SUVpeak, MV and TI was investigated by using each index alone first (with ROC analyses), and then by using couples consisting of one TI with SUVpeak in a binomial model (with ROC analyses and a reclassification method). The pathological examination was assumed to provide the gold standard grade. Results: Neither SUVpeak nor MV could discriminate low-grade tumors (LG) from high-grade tumors (HG) in newly-diagnosed tumors, while SUVpeak alone could discriminate LG from HG in recurrent tumors (p=0.02). Combining a TI with SUVpeak led to a significant LG / HG discrimination for newly-diagnosed tumors (p = 0.01). Among all TI, entropy led to the best reclassification performance. Conclusion: The co-analysis of FDopa-PET/CT SUVpeak and well-selected TI (such as entropy) made it possible to improve the classification of newly-diagnosed gliomas

Validation of a method to compensate multicenter effects affecting CT radiomic features

International audienc

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

International audienc

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

Characterization of glial tumors in PET/CT 18F-dopa and in perfusion MRI

L’IRM apporte des informations morphologiques concernant la tumeur, mais également des informations concernant sa micro-vascularisation. En TEP/TDM, l’accumulation de la 18F-FDopa dans les cellules tumorales résulte de l’activité métabolique plus importante que celle des tissus sains. Nous avons étudié 28 gliomes pour lesquels nous avons analysé les données provenant d’IRM et de TEP/TDM. Une méthode de recalage a été développée afin de combiner les informations issues des deux modalités TEP et IRM et d’extraire des volumes d’intérêt sur la base des données conjointes TEP et IRM. L’analyse du contenu de ces volumes d’intérêt par un modèle de mélange gaussien a permis de différencier, dans ces volumes, les tissus tumoraux et les tissus sains, et d’obtenir ainsi des volumes tumoraux et de référence communs pour les modalités TEP et IRM. Des paramètres issus de la TEP ou de l’IRM ont ensuite été calculés dans ces volumes communs aux deux modalités, pour caractériser les tumeurs et les tissus sains. L’analyse discriminante linéaire (ADL) des données TEP/TDM et d’IRM combinées permet de discriminer les différentes classes tissulaires. Les courbes Receiver Operating Characteristic ROC combinées à l’ADL permettent d’évaluer les critères multiples [SUVmax , rCBV] et [rk1 , rCBV] et conduisent à des AUC respectives de 0,88 et 0,92. En considérant les informations combinées [SUVmax , rCBV], nous avons obtenu une sensibilité de détection des tumeurs de haut grade de 95% pour une spécificité correspondante de 60% ainsi qu’une valeur prédictive négative de 52% pour une valeur prédictive positive de 95%. De même, avec le critère [rk1 , rCBV], nous avons obtenu une spécificité de 60% pour 95% de sensibilité de détection des tumeurs de haut grade ainsi qu’une valeur prédictive négative de 60% pour une valeur prédictive positive de 95%. Nos travaux montrent que la fusion des informations microvasculaires et métaboliques est possible. Dans le cas du diagnostic différentiel des gliomes, l’information microvasculaire n’apporte cependant pas d’information plus discriminante que l’information métabolique seule.MRI provides morphological information about tumour, but also provides information regarding the micro-vascularization of the tumour. In PET/CT, the accumulation of 18F-FDopa in tumour cells results from the metabolic activity greater than that of healthy tissues. We studied 28 gliomas for which we analysed data from MRI and PET/CT. A registration method has been developed to combine information from both PET and MRI and to extract volumes of interest consistent with the information included in the two modalities. In these volumes, the tumour compartment and normal tissue compartment were identified using a Gaussian mixture model. Parameters from PET or MRI data were then calculated in these compartments. ROC analyses combined with linear discriminant analyses were used to assess whether joint observation of standardized uptake value (SUVmax ) and relative Cerebral Blood Volume (rCBV) or of relative rk1 and rCBV could distinguish between low grade and high grade tumours. We found that using this joint analysis, 82.4% of high-grade tumors and 70.0% of low-grade tumors were correctly classified (AUC of 0.88 for [SUVmax , rCBV] and of 0.92 for [rk1 , rCBV]). Considering the [SUVmax , rCBV] combined information, the sensitivity for detecting high-grade tumors was 95% with a specificity of 60%. The negative predictive value was 52% for a positive predictive value of 95%. Similarly, considering the [rk1 , rCBV] combined information, we also a specificity of 60% associated with a 95% sensitivity for detecting high-grade tumors, with a negative predictive value of 60% and positive predictive value of 95%. Our work shows that joint analysis of microvascular and metabolic information is possible by combining PET and MR imaging data. However, we found that, in our patient population, the microvascular information given by MR did not bring information more discriminating than the metabolic information derived from PET only

Caractérisation des tumeurs gliales en TEP/TDM à la 18F-Dopa et en IRM de perfusion

L IRM apporte des informations morphologiques concernant la tumeur, mais également des informations concernant sa micro-vascularisation. En TEP/TDM, l accumulation de la 18F-FDopa dans les cellules tumorales résulte de l activité métabolique plus importante que celle des tissus sains. Nous avons étudié 28 gliomes pour lesquels nous avons analysé les données provenant d IRM et de TEP/TDM. Une méthode de recalage a été développée afin de combiner les informations issues des deux modalités TEP et IRM et d extraire des volumes d intérêt sur la base des données conjointes TEP et IRM. L analyse du contenu de ces volumes d intérêt par un modèle de mélange gaussien a permis de différencier, dans ces volumes, les tissus tumoraux et les tissus sains, et d obtenir ainsi des volumes tumoraux et de référence communs pour les modalités TEP et IRM. Des paramètres issus de la TEP ou de l IRM ont ensuite été calculés dans ces volumes communs aux deux modalités, pour caractériser les tumeurs et les tissus sains. L analyse discriminante linéaire (ADL) des données TEP/TDM et d IRM combinées permet de discriminer les différentes classes tissulaires. Les courbes Receiver Operating Characteristic ROC combinées à l ADL permettent d évaluer les critères multiples [SUVmax , rCBV] et [rk1 , rCBV] et conduisent à des AUC respectives de 0,88 et 0,92. En considérant les informations combinées [SUVmax , rCBV], nous avons obtenu une sensibilité de détection des tumeurs de haut grade de 95% pour une spéci cité correspondante de 60% ainsi qu une valeur prédictive négative de 52% pour une valeur prédictive positive de 95%. De même, avec le critère [rk1 , rCBV], nous avons obtenu une spécificité de 60% pour 95% de sensibilité de détection des tumeurs de haut grade ainsi qu une valeur prédictive négative de 60% pour une valeur prédictive positive de 95%. Nos travaux montrent que la fusion des informations microvasculaires et métaboliques est possible. Dans le cas du diagnostic différentiel des gliomes, l information microvasculaire n apporte cependant pas d information plus discriminante que l information métabolique seule.MRI provides morphological information about tumour, but also provides information regarding the micro-vascularization of the tumour. In PET/CT, the accumulation of 18F-FDopa in tumour cells results from the metabolic activity greater than that of healthy tissues. We studied 28 gliomas for which we analysed data from MRI and PET/CT. A registration method has been developed to combine information from both PET and MRI and to extract volumes of interest consistent with the information included in the two modalities. In these volumes, the tumour compartment and normal tissue compartment were identi ed using a Gaussian mixture model. Parameters from PET or MRI data were then calculated in these compartments. ROC analyses combined with linear discriminant analyses were used to assess whether joint observation of standardized uptake value (SUVmax ) and relative Cerebral Blood Volume (rCBV) or of relative rk1 and rCBV could distinguish between low grade and high grade tumours. We found that using this joint analysis, 82.4% of high-grade tumors and 70.0% of low-grade tumors were correctly classi ed (AUC of 0.88 for [SUVmax , rCBV] and of 0.92 for [rk1 , rCBV]). Considering the [SUVmax , rCBV] combined information, the sensitivity for detecting high-grade tumors was 95% with a speci city of 60%. The negative predictive value was 52% for a positive predictive value of 95%. Similarly, considering the [rk1 , rCBV] combined information, we also a specificity of 60% associated with a 95% sensitivity for detecting high-grade tumors, with a negative predictive value of 60% and positive predictive value of 95%. Our work shows that joint analysis of microvascular and metabolic information is possible by combining PET and MR imaging data. However, we found that, in our patient population, the microvascular information given by MR did not bring information more discriminating than the metabolic information derived from PET only.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data

International audienc

Reorganization of large-scale cognitive networks during automation of imagination of a complex sequential movement

0info:eu-repo/semantics/publishe

Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics

International audienceAbstract Nonbiological differences related to CT scanner type can be removed from radiomic feature values, allowing radiomics features to be combined in multicenter or multivendor studies.Background Radiomics extracts features from medical images more precisely and more accurately than visual assessment. However, radiomics features are affected by CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying biologically important texture features.PurposeTo investigate whether a compensation method could correct for the variations of radiomic feature values caused by using different CT protocols.Materials and MethodsPhantom data involving 10 texture patterns and 74 patients in cohorts 1 (19 men; 42 patients; mean age, 60.4 years; September–October 2013) and 2 (16 men; 32 patients; mean age, 62.1 years; January–September 2007) scanned by using different CT protocols were retrospectively included. For any radiomic feature, the compensation approach identified a protocol-specific transformation to express all data in a common space that were devoid of protocol effects. The differences in statistical distributions between protocols were assessed by using Friedman tests before and after compensation. Principal component analyses were performed on the phantom data to evaluate the ability to distinguish between texture patterns after compensation.ResultsIn the phantom data, the statistical distributions of features were different between protocols for all radiomic features and texture patterns (P .05). Principal component analysis demonstrated that each texture pattern was no longer displayed as different clusters corresponding to different imaging protocols, unlike what was observed before compensation. The correction for scanner effect was confirmed in patient data with 100% (10 of 10 features for cohort 1) and 98% (87 of 89 features for cohort 2) of P values less than .05 before compensation, compared with 30% (three of 10) and 15% (13 of 89) after compensation.ConclusionImage compensation successfully realigned feature distributions computed from different CT imaging protocols and should facilitate multicenter radiomic studies

Validation of a method to compensate multicenter effects affecting CT radiomic features

International audienc