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  • 1
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    Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke
    Springer Science and Business Media LLC ; 2023
    In:  European Radiology
    Details
    In: European Radiology, Springer Science and Business Media LLC
    Abstract: Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort. Methods A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics. Results Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration ( p   〈  0.001). Both ARWMC ( p   〈  0.05) and cortical atrophy ( p   〈  0.001) were independent predictors of clinical outcome at 90 days when controlled for confounders in both regression analyses and led to a minor improvement of prediction accuracy in machine learning models ( p   〈  0.001), with atrophy among the top-5 predictors. Conclusion NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT. Clinical relevance statement Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection. Key Points • Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning–based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-023-10107-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1472718-3
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  • 2
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    How Covid-19 changed the epidemiology of febrile urinary tract infections in children in the emergency department during the first outbreak
    Springer Science and Business Media LLC ; 2022
    In:  BMC Pediatrics Vol. 22, No. 1 ( 2022-09-15)
    Details
    In: BMC Pediatrics, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-09-15)
    Abstract: The first Covid-19 pandemic affected the epidemiology of several diseases. A general reduction in the emergency department (ED) accesses was observed during this period, both in adult and pediatric contexts. Methods This retrospective study was conducted on the behalf of the Italian Society of Pediatric Nephrology (SINePe) in 17 Italian pediatric EDs in March and April 2020, comparing them with data from the same periods in 2018 and 2019. The total number of pediatric (age 0–18 years) ED visits, the number of febrile urinary tract infection (UTI) diagnoses, and clinical and laboratory parameters were retrospectively collected. Results The total number of febrile UTI diagnoses was 339 (73 in 2020, 140 in 2019, and 126 in 2018). During the first Covid-19 pandemic, the total number of ED visits decreased by 75.1%, the total number of febrile UTI diagnoses by 45.1%, with an increase in the UTI diagnosis rate (+ 121.7%). The data collected revealed an increased rate of patients with two or more days of fever before admission ( p  = 0.02), a significant increase in hospitalization rate (+ 17.5%, p  = 0.008) and also in values of C reactive protein (CRP) ( p  = 0.006). In 2020, intravenous antibiotics use was significantly higher than in 2018 and 2019 (+ 15%, p  = 0.025). Urine cultures showed higher Pseudomonas aeruginosa and Enterococcus faecalis percentages and lower rates of Escherichia coli ( p  = 0.02). Conclusions The first wave of the Covid-19 pandemic had an essential impact on managing febrile UTIs in the ED, causing an absolute reduction of cases referring to the ED but with higher clinical severity. Children with febrile UTI were more severely ill than the previous two years, probably due to delayed access caused by the fear of potential hospital-acquired Sars-Cov-2 infection. The possible increase in consequent kidney scarring in this population should be considered.
    Type of Medium: Online Resource
    ISSN: 1471-2431
    URL: Article
    DOI: 10.1186/s12887-022-03516-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041342-7
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  • 3
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    NIMG-02. NON-INVASIVE DETECTION OF IDH MUTANT 1p19q NON-CODELETED GLIOMAS USING THE T2-FLAIR MISMATCH SIGN
    Oxford University Press (OUP) ; 2019
    In:  Neuro-Oncology Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi161-vi161
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi161-vi161
    Abstract: To assess the validity and pathophysiology of the T2/FLAIR mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted glioma. METHODS MRI scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade glioma and 295 glioblastoma) were evaluated for the presence of a T2/FLAIR-mismatch sign (defined as complete/near-complete hyperintense signal on T2w, simultaneous hypointense signal on FLAIR except for a hyperintense peripheral rim) by two independent reviewers. Sensitivity, specificity, accuracy, positive and negative predictive value (PPV, NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of spatial differences in ADC and rCBV values comparing the FLAIR-hypointense core vs. hyperintense rim in cases with presence of a T2/FLAIR-mismatch sign was performed. RESULTS There was substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s Kappa = 0.75 [95% CI 0.57–0.93]). The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them were IDH-mutant, 1p/19q non-codeleted tumors (sensitivity=10.9%, specificity=100%, PPV=100%, NPV=3.0%, accuracy=13.3%). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, especially not in any of the IDH-mutant glioblastoma cases (n=5). In tumors with T2/FLAIR-mismatch sign the ADC values were significantly lower in the rim as compared to the core (p=0.0005) whereas there was n o difference in rCBV values (p=0.4258). CONCLUSION This study confirms the high specificity of the T2/FLAIR-mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted gliomas, although sensitivity is low and applicability is limited to lower-grade gliomas. The identified spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflects differences in tumor cellularity and microenvironment.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noz175.674
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2094060-9
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  • 4
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    Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes
    Springer Science and Business Media LLC ; 2023
    In:  European Radiology
    Details
    In: European Radiology, Springer Science and Business Media LLC
    Abstract: Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. Methods Preoperative MRI-data from n  = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z -score normalization (N4/ z -score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF ( n  = 410) and TCGA ( n  = 160). Results Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/ z -score) in the internal test set. Both N4/WS and z -score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. Conclusion Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/ z -score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. Clinical relevance statement Intensity normalization such as N4/WS or N4/ z -score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. Key Points • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-023-10034-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1472718-3
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  • 5
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    Management of the congenital solitary kidney: consensus recommendations of the Italian Society of Pediatric Nephrology
    Springer Science and Business Media LLC ; 2022
    In:  Pediatric Nephrology Vol. 37, No. 9 ( 2022-09), p. 2185-2207
    Details
    In: Pediatric Nephrology, Springer Science and Business Media LLC, Vol. 37, No. 9 ( 2022-09), p. 2185-2207
    Abstract: In recent years, several studies have been published on the prognosis of children with congenital solitary kidney (CSK), with controversial results, and a worldwide consensus on management and follow-up is lacking. In this consensus statement, the Italian Society of Pediatric Nephrology summarizes the current knowledge on CSK and presents recommendations for its management, including diagnostic approach, nutritional and lifestyle habits, and follow-up. Summary of the recommendations We recommend that any antenatal suspicion/diagnosis of CSK be confirmed by neonatal ultrasound (US), avoiding the routine use of further imaging if no other anomalies of kidney/urinary tract are detected. A CSK without additional abnormalities is expected to undergo compensatory enlargement, which should be assessed by US. We recommend that urinalysis, but not blood tests or genetic analysis, be routinely performed at diagnosis in infants and children showing compensatory enlargement of the CSK. Extrarenal malformations should be searched for, particularly genital tract malformations in females. An excessive protein and salt intake should be avoided, while sport participation should not be restricted. We recommend a lifelong follow-up, which should be tailored on risk stratification, as follows: low risk: CSK with compensatory enlargement, medium risk: CSK without compensatory enlargement and/or additional CAKUT, and high risk: decreased GFR and/or proteinuria, and/or hypertension. We recommend that in children at low-risk periodic US, urinalysis and BP measurement be performed; in those at medium risk, we recommend that serum creatinine also be measured; in high-risk children, the schedule has to be tailored according to kidney function and clinical data.
    Type of Medium: Online Resource
    ISSN: 0931-041X , 1432-198X
    URL: Article
    DOI: 10.1007/s00467-022-05528-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1463004-7
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  • 6
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    Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT
    BMJ ; 2022
    In:  Journal of NeuroInterventional Surgery
    Details
    In: Journal of NeuroInterventional Surgery, BMJ
    Abstract: Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort. Methods A total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman’s coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong’s test. Results Patients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7–21.7) vs 18.8 mL (IQR 9.4–33.9), p 〈 0.001). AIV was highly correlated with ASPECTS (rho=0.78, p 〈 0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p 〈 0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machine-learning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p 〉 0.05). Conclusion AIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.
    Type of Medium: Online Resource
    ISSN: 1759-8478 , 1759-8486
    URL: Article
    DOI: 10.1136/jnis-2022-019400
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 2506028-4
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  • 7
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    Author Correction: Federated learning enables big data for rare cancer boundary detection
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-01-26)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-01-26)
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-36188-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 8
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    Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma
    Oxford University Press (OUP) ; 2023
    In:  Neuro-Oncology Advances Vol. 5, No. 1 ( 2023-01-01)
    Details
    In: Neuro-Oncology Advances, Oxford University Press (OUP), Vol. 5, No. 1 ( 2023-01-01)
    Abstract: Pseudoprogression (PsPD) is a major diagnostic challenge in the follow-up of patients with glioblastoma (GB) after chemoradiotherapy (CRT). Conventional imaging signs and parameters derived from diffusion and perfusion-MRI have yet to prove their reliability in clinical practice for an accurate differential diagnosis. Here, we tested these parameters and combined them with radiomic features (RFs), clinical data, and MGMT promoter methylation status using machine- and deep-learning (DL) models to distinguish PsPD from Progressive disease. Methods In a single-center analysis, 105 patients with GB who developed a suspected imaging PsPD in the first 7 months after standard CRT were identified retrospectively. Imaging data included standard MRI anatomical sequences, apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) maps. Median values (ADC, nrCBV) and RFs (all sequences) were calculated from DL-based tumor segmentations. Generalized linear models with LASSO feature-selection and DL models were built integrating clinical data, MGMT methylation status, median ADC and nrCBV values and RFs. Results A model based on clinical data and MGMT methylation status yielded an areas under the receiver operating characteristic curve (AUC) = 0.69 (95% CI 0.55–0.83) for detecting PsPD, and the addition of median ADC and nrCBV values resulted in a nonsignificant increase in performance (AUC = 0.71, 95% CI 0.57–0.85, P = .416). Combining clinical/MGMT information with RFs derived from ADC, nrCBV, and from all available sequences both resulted in significantly (both P & lt; .005) lower model performances, with AUC = 0.52 (0.38–0.66) and AUC = 0.54 (0.40–0.68), respectively. DL imaging models resulted in AUCs ≤ 0.56. Conclusion Currently available imaging biomarkers could not reliably differentiate PsPD from true tumor progression in patients with glioblastoma; larger collaborative efforts are needed to build more reliable models.
    Type of Medium: Online Resource
    ISSN: 2632-2498
    URL: Article
    DOI: 10.1093/noajnl/vdad016
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3009682-0
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  • 9
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    Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Advances Vol. 4, No. 1 ( 2022-01-01)
    Details
    In: Neuro-Oncology Advances, Oxford University Press (OUP), Vol. 4, No. 1 ( 2022-01-01)
    Abstract: Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset. Conclusion The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
    Type of Medium: Online Resource
    ISSN: 2632-2498
    URL: Article
    DOI: 10.1093/noajnl/vdac138
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 3009682-0
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  • 10
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    Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study
    Elsevier BV ; 2024
    In:  The Lancet Oncology Vol. 25, No. 3 ( 2024-03), p. 400-410
    Details
    In: The Lancet Oncology, Elsevier BV, Vol. 25, No. 3 ( 2024-03), p. 400-410
    Type of Medium: Online Resource
    ISSN: 1470-2045
    URL: Article
    DOI: 10.1016/S1470-2045(23)00641-1
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2049730-1
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