Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

Crombé, Amandine; Kind, Michèle; Fadli, David; Loarer, François Le; Italiano, Antoîne; Buy, Xavier; Saut, Olivier

doi:10.1038/s41598-020-72535-0

Cited by 41 publications

(40 citation statements)

References 36 publications

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“…The intraclass correlation coefficient (ICC) was the statistical method used in most of the papers reporting a reproducibility analysis [ 13 , 15 – 18 , 20 , 22 – 25 , 27 – 29 , 31 ]. ICC threshold ranged between 0.6 [ 13 ] and 0.9 [ 22 ] for reproducible features.…”

Section: Resultsmentioning

confidence: 99%

“…In 16 (33%) investigations [13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the reproducibility of radiomic features was assessed on the basis of repeated segmentations performed by different readers and/or the same reader at different time points. Two (4%) studies presented an analysis to assess the reproducibility based on different acquisition [30] or post-processing [31] techniques. Of note, segmentations were validated by a second experienced reader in 15 studies [12,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] without, however, addressing the issue of radiomic feature reproducibility.…”

Section: Reproducibility Strategiesmentioning

confidence: 99%

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CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

et al. 2021

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Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

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Section: Resultsmentioning

confidence: 99%

Section: Reproducibility Strategiesmentioning

confidence: 99%

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

et al. 2021

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“…However, the subsequent downstream effects of standardization may be substantial when human eyes no longer become the driving force of analysis. For instance, Crombé et al demonstrated marked differences in radiomic outcome prediction performance using different intensity standardization methods 42 . We have performed a preliminary radiomic feature analysis comparing intensity standardization methods in Supplementary Data S6 which generally follows the trends highlighted in our study, but further work is needed to determine effects on HNC-specific outcome prediction tasks.…”

Section: Discussionmentioning

confidence: 99%

MRI Intensity Standardization Evaluation Design for Head and Neck Quantitative Imaging Applications

McDonald

et al. 2021

Preprint

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Background: Conventional MRI poses unique challenges in quantitative analysis due to a lack of specific physical meaning for voxel intensity values. In recent years, intensity standardization methods to optimize MRI signal consistency have been developed to address this problem. However, the effects of standardization methods on the head and neck region have not been previously investigated. Purpose: This study proposes a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluate different intensity standardization methods for T2-weighted MRI of the head and neck region. Methods: Two image cohorts of five head and neck cancer patients, one with heterogeneous acquisition parameters (median age 59 years [range, 53-61]), and another with homogeneous acquisition parameters from a clinical trial (NCT04265430) (median age 61 years [range, 54-77]) were retrospectively analyzed. The standard deviation of cohort-level normalized mean intensity (SD NMIc), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, the Friedman test with a subsequent post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMIc among methods. Results: Consistency (SD NMIc across ROIs) between T2-weighted images is substantially more impaired in the cohort with heterogeneous acquisition parameters (0.28 +- 0.04) than in the cohort with homogeneous acquisition parameters (0.15 +- 0.05). Consequently, intensity standardization methods more significantly improve consistency in the cohort with heterogeneous acquisition parameters (corrected p < 0.005 for all methods compared to no standardization) than in the cohort with homogeneous acquisition parameters (corrected p > 0.05 for all methods compared to no standardization). Conclusions: Our findings stress the importance of image acquisition parameter standardization, together with the need for testing intensity consistency before performing quantitative analysis of MRI.

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“…Nevertheless, the robust features often resulted to have low diagnostic performance [ 16 ]. In addition, applying a post-reconstruction batch harmonization has been also proposed to reduce the variability among centers by using global scaling, in which signal intensities are harmonized by eliminating the mean and the unit variance is downsized; z-standardization, where each feature is normalized considering the mean and standard deviation by providing some comparable results; and histogram-matching, by transforming intensity histogram in order to combine them and find the reference histogram [ 17 ]. However, each method has different advantages and drawbacks, and they should be weighted in different clinical scenarios.…”

Section: Technical Principlesmentioning

confidence: 99%

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

Caruso

Polici

Zerunian

et al. 2021

Cancers

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Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

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Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

Cited by 41 publications

References 36 publications

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

MRI Intensity Standardization Evaluation Design for Head and Neck Quantitative Imaging Applications

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

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