Objective To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone ( p < 0.001), 0.847 when based on clinical variables alone ( p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables ( p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Background Although the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group has made recommendations for response assessment in patients with medulloblastoma (MBL) and leptomeningeal seeding tumors, these criteria have yet to be evaluated. Methods We examined MR imaging and clinical data in a multicenter retrospective cohort of 269 patients with MBL diagnoses, high grade glioma, embryonal tumor, germ cell tumor, or choroid plexus papilloma. Interobserver agreement, objective response (OR) rates, and progression-free survival (PFS) were calculated. Landmark analyses were performed for OR and progression status at 0.5, 1.0, and 1.5 years after treatment initiation. Cox proportional hazards models were used to determine the associations between OR and progression with overall survival (OS). Subgroup analyses based on tumor subgroup and treatment modality were performed. Results The median follow-up time was 4.0 years. In all patients, the OR rate was .0.565 (95% CI: 0.505–0.625) by RAPNO. The interobserver agreement of OR determination between 2 raters (a neuroradiologist and a neuro-oncologist) for the RAPNO criteria in all patients was 83.8% (k statistic = 0.815; P &lt; 0.001). At 0.5-, 1.0-, and 1.5-year landmarks, both OR status and PFS determined by RAPNO were predictive of OS (hazard ratios [HRs] for 1-year landmark: OR HR = 0.079, P &lt; 0.001; PFS HR = 10.192, P &lt; 0.001). In subgroup analysis, OR status and PFS were predictive of OS for all tumor subtypes and treatment modalities. Conclusion RAPNO criteria showed excellent consistency in the treatment response evaluation of MBL and other leptomeningeal seeding tumors. OR and PFS determined by RAPNO criteria correlated with OS.

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