Intensity 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 metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-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 histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: < 0.0001–0.02). Unsupervised clustering performed on each radiomics dataset showed that only clusters from the No-IHT, IHTstd, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.

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BackgroundHeterogeneity on pretreatment dynamic contrast‐enhanced (DCE)‐MRI of sarcomas may be prognostic, but the best technique to capture this characteristic remains unknown.PurposeTo investigate the best method to extract prognostic data from baseline DCE‐MRI.Study TypeRetrospective, single‐center.PopulationFifty consecutive uniformly‐treated adults with nonmetastatic high‐grade sarcomas.Field Strength/Sequence1.5T; T2‐weighted‐imaging, fat‐suppressed fast spoiled gradient echo DCE‐MRI.AssessmentNinety‐two radiomics features (RFs) were extracted at each DCE‐MRI phase (11, from t = 0–88 sec). Relative changes in RFs (rRFs) since the acquisition baseline were calculated (11 × 92 rRFs). Curves of rRF as function of time postinjection were integrated (92 integrated‐rRFs [irRFs]). Ktrans and area under the time–intensity curve at 88‐sec parametric maps were computed and 2 × 92 parametric‐RFs (pRFs) were extracted. Five DCE‐MRI‐based radiomics models were built on: an RFs subset (32 sec, 64 sec, 88 sec); all rRFs; all irRFs; and all pRFs. Two models were elaborated as reference, on: conventional radiological features; and T2‐WI RFs.Statistical TestsA common machine‐learning approach was applied to radiomics models. Features with P < 0.05 at univariate analysis were entered in a LASSO‐penalized Cox regression including bootstrapped 10‐fold cross‐validation. The resulting radiomics scores (RScores) were dichotomized per their median and entered in multivariate Cox models for predicting metastatic relapse‐free survival. Models were compared with integrative area under the curve (AUC) and concordance index.ResultsOnly dichotomized RScores from models based on rRFs subset, all rRFS and irRFS correlated with prognostic (P = 0.0107–0.0377). The models including all rRFs and irRFs had the highest c‐index (0.83), followed by the radiological model. The radiological model had the highest integrative AUC (0.87), followed by models including all rRFs and irRFs. The radiological and full rRFs models were significantly better than the T2‐based radiomics model (P = 0.02).Data ConclusionThe initial DCE‐MRI of STS contains prognostic information. It seems more relevant to make predictions on rRFs instead of pRFs.Evidence Level: 3Technical Efficacy: 3J. Magn. Reson. Imaging 2020;52:282–297.

Background Because of long diagnostic intervals, soft‐tissue sarcoma (STS) patients can undergo several MRIs before treatments. However, only the latest pre‐treatment MRI is used in clinical practice and the natural changes in MRI presentations of STS occurring before any medical procedure remain unknown. Purpose To qualitatively and quantitatively depict the natural history of MRI presentations of STS prior to medical intervention, to investigate their prognostic value, and to compare methods to calculate the changes in radiomics features (named delta‐radiomics features). Study Type Retrospective. Subjects Sixty‐eight patients with locally advanced histologically proven STS and two pre‐treatment contrast‐enhanced (CE) MRIs (median age: 64 years, median delay between MRIs: 77 days). Field Strength/Sequence Two‐dimensional (2D) turbo spin echo (TSE) T1‐weighted‐imaging (WI) and T2‐WI; 2D TSE or 3D gradient echo CE‐T1‐WI at 1.5 T. Radiomics analysis was performed on 2D TSE CE‐T1‐WI. Assessment Three radiologists independently reported morphological features, evaluating changes in STS dimensions, intra‐tumoral necrotic and hemorrhagic signals and heterogeneity, and changes in the tumor peritumoral enhancement, edema, and tail sign. After homogenizing the MRIs to account for differences in acquisition parameters, STS were 3D‐segmented on both CE‐T1‐WI MRIs and radiomic features (RFs) were extracted. Changes in RFs between the two MRIs were calculated according to five methods: absolute, absolute/time between MRIs, relative, relative/time between MRIs, and log ratio. Histopathological samples were reviewed to count mitosis and Ki67 immunostaining. Survival data regarding local relapse, metastatic relapse, and disease‐related deaths were collected. Statistical Tests Reproducibility analysis (using intra‐class correlation coefficient and [weighted] kappa), hierarchical clusterings based on changes in RFs, survival analyses (using Cox regressions), and association with histopathology (using Student's t‐test, Wilcoxon, or Chi‐squared test). A P‐value of <0.05 was considered to be statistically significant. Results There were 15 and 26 local and metastatic progressions, respectively. Average tumor size increase between scans was +39.8%. Metastatic relapse‐free survival (MFS) was associated with: increases in size, tumor heterogeneity on T1‐WI, T2‐WI, and CE‐T1‐WI, necrotic signal, peritumoral enhancement, and tail sign. Local relapse‐free survival (LFS) was associated with: increase in tumor heterogeneity on T1‐WI, necrotic signal, hemorrhagic signal and peritumoral edema, and clusters based on the logarithmic changes in RFs (Log‐RF). Increase in heterogeneity on CE‐T1‐WI and Log‐RF clusters were independent predictors for MFS and LFS, respectively, in stepwise multivariate Cox regression (hazard ratio [HR] = 2.78 and HR = +∞ respectively). Associations were found between changes in necrotic signal, heterogeneity on CE‐T1‐WI and peritumoral enhancement, and histological markers of proliferation. Data Conclusion Changes in ...

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant has a higher infection rate than previous variants but results in less severe disease. However, the impacts of omicron and vaccination on chest CT findings are difficult to evaluate. Purpose To investigate the impact of vaccination status and predominant variant on chest CT findings, diagnostic and severity scores in multicenter sample of consecutive patients referred to emergency departments for proven COVID-19. Materials and Methods This retrospective, multicenter study included adults referred to 93 emergency departments with SARS-CoV-2 infection according to RT-PCR and known vaccination status between July 2021 and March 2022. Clinical data and structured chest CT reports including semiquantitative diagnostic and severity scores following the French Society of Radiology-Thoracic Imaging Society guidelines were extracted from a teleradiology database. Observations were divided into ‘delta-predominant’, ‘transition’, and ‘omicron-predominant’ periods. Associations between scores and variant and vaccination status were investigated with Chi-square tests and ordinal regressions. Multivariable analyses evaluated the influence of omicron variant and vaccination status on the diagnostic and severity scores. Results Overall, 3876 patients were included (median age: 68 years [Q1-Q3: 54-80], 1695 females). Diagnostic and severity scores were associated with the predominant variant (delta- versus omicron-predominant, Chi-square=112.4 and 33.7, both P <.001) and vaccination (Chi-square=243.6 and 210, both P <.001) and their interaction (Chi-square=4.3, P =.04 and Chi-square=28.7, P <.001, respectively). In multivariable analyses, omicron variant was associated with lower odds of typical CT findings than delta variant (OR=0.46, P <.001). Two and three vaccine doses were associated with lower odds of demonstrating typical CT findings (OR=0.32 and OR=0.20, both P <.001), and of having high severity score (OR=0.47 and OR=0.33, both P <.001), compared with unvaccinated patients. Conclusion Both the omicron variant and vaccination were associated with less typical chest CT manifestations for COVID-19 and lesser extent of disease. See also the editorial by Yoon and Goo in this issue.