Intensity Harmonization Techniques Influence Radiomics Features and Radiomics-based Predictions in Sarcoma Patients

International audienceIntensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype with machine-learning to improve predictive models, such as metastasticrelapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogra

Intérêts et limites de l'IRM pour évaluer la nécrose dans les sarcomes des tissus mous (2 études rétrospectives de 42 cas)

BORDEAUX2-BU Santé (330632101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Impact of CT-based body composition parameters at baseline, their early changes and response in metastatic cancer patients treated with immune checkpoint inhibitors

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Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications?

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Influence of Temporal Parameters of DCE-MRI on the Quantification of Heterogeneity in Tumor Vascularization

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Precision of manual two-dimensional segmentations of lung and liver metastases and its impact on tumour response assessment using RECIST 1.1

Background: Response evaluation criteria in solid tumours (RECIST) has significant limitations in terms of variability and reproducibility, which may not be independent. The aim of the study was to evaluate the precision of manual bi-dimensional segmentation of lung, liver metastases, and to quantify the uncertainty in tumour response assessment. Methods: A total of 520 segmentations of metastases from six livers and seven lungs were independently performed by ten physicians and ten scientists on CT images, reflecting the variability encountered in clinical practice. Operators manually contoured the tumours, firstly independently according to the RECIST and secondly on a preselected slice. Diameters and areas were extracted from the segmentations. Mean standard deviations were used to build regression models and 95% confidence intervals (95% CI) were calculated for each tumour size and for limits of progressive disease (PD) and partial response (PR) derived from RECIST 1.1. Results: Thirteen aberrant segmentations (2.5%) were observed without significant differences between the physicians and scientists; only the mean area of liver tumours (p = 0.034) and mean diameter of lung tumours (p = 0.021) differed significantly. No difference was observed between the methods. Inter-observer agreement was excellent (intra-class correlation >0.90) for all variables. In liver, overlaps of the 95% CI with the 95% CI of limits of PD or PR were observed for diameters above 22.7 and 37.9 mm, respectively. An overlap of 95% CIs was systematically observed for area. No overlaps were observed in lung. Conclusions: Although the experience of readers might not affect the precision of segmentation in lung and liver, the results of manual segmentation performed for tumour response assessment remain uncertain for large liver metastases

Imaging features of SMARCA4-deficient thoracic sarcomas: a multi-centric study of 21 patients

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