Introduction: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However,... Show moreIntroduction: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort.Methods: Patients who received first-line anti-PD1 +/- anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT, and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-out cross vali-dation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model.Results: A total of 620 patients were included, of which 59.2% experienced clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95% CI, 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95% CI, 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95% CI, 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). Discussion: The radiomics model achieved a moderate predictive value of clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT -derived radiomics, and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Boswinkel, V.; Sok, F.I.; Kruse-Ruijter, M.F.; Nijholt, I.M.; Jansen, F.A.R.; Haak, M.C.; ... ; Wezel-Meijler, G. van 2021
Background: Brain growth in moderate preterm (MP; gestational age (GA) 32(+0)-33(+6) weeks) and late preterm infants (LP; GA 34(+0)-36+6 weeks) may be impaired, even in the absence of brain injury... Show moreBackground: Brain growth in moderate preterm (MP; gestational age (GA) 32(+0)-33(+6) weeks) and late preterm infants (LP; GA 34(+0)-36+6 weeks) may be impaired, even in the absence of brain injury.Aims: The aims of this study were to assess brain measurements of MP and LP infants, and to compare these with full-term infants (GA > 37 weeks) using linear cranial ultrasound (cUS) at term equivalent age (TEA).Study design: cUS data from two prospective cohorts were combined. Two investigators performed offline measurements on standard cUS planes. Eleven brain structures were compared between MP, LP and full-term infants using uni-and multivariable linear regression.Results were adjusted for postmenstrual age at cUS and corrected for multiple testing. Results: Brain measurements of 44 MP, 54 LP and 52 full-term infants were determined on cUS scans at TEA. Biparietal diameter and basal ganglia-insula width were smaller in MP (-9.1 mm and -1.7 mm, p < 0.001) and LP infants (-7.0 mm and -1.7 mm, p < 0.001) compared to full-term infants. Corpus callosum - fastigium length was larger in MP (+2.2 mm, p < 0.001) than in full-term infants. No significant differences were found between MP and LP infants.Conclusions: These findings suggest that brain growth in MP and LP infants differs from full-term infants. Whether these differences have clinical implications remains to be investigated. Show less