Publications by authors named "Wiro J Niessen"

287 Publications

Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics.

Cancers (Basel) 2021 Dec 21;14(1). Epub 2021 Dec 21.

Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands.

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center ( = 371 patients), and two external sets of which one was a publicly available patient cohort ( = 195 patients) and the other contained data from patients from two hospitals ( = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts ( = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts ( = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.
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http://dx.doi.org/10.3390/cancers14010012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749796PMC
December 2021

The Effect of Hearing Aid Use on the Association Between Hearing Loss and Brain Structure in Older Adults.

Ear Hear 2021 Oct 27. Epub 2021 Oct 27.

Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.

Objectives: Recent studies have shown an association between poorer hearing thresholds and smaller brain tissue volumes in older adults. Several underlying causal mechanisms have been opted, with a sensory deprivation hypothesis as one of the most prominent. If hearing deprivation would lead to less brain volume, hearing aids could be hypothesized to moderate this pathway by restoration of hearing. This study aims to investigate whether such a moderating effect of hearing aids exists.

Design: The authors conducted a cross-sectional study involving aging participants of the population-based Rotterdam Study. Hearing aid use was assessed by interview and hearing loss was quantified using pure-tone audiometry. Total brain volume, gray matter and white matter volume and white matter integrity [fractional anisotropy (FA) and mean diffusivity] were measured using magnetic resonance imaging. Only participants with a pure tone average at 1, 2, and 4 kHz (PTA1,2,4) of ≥35 dB HL were included. Associations of hearing loss with brain volume and global measures of white matter integrity were analyzed using linear regression, with hearing aid use and interaction between hearing aid use and PTA1,2,4 included as independent variables. Models were adjusted for age, sex, time between audiometry and magnetic resonance imaging, level of education, and cardiovascular risk factors.

Results: Out of 459 included participants with mean age (range) 70.4 (52 to 92) 41% were female. Distributions of age and sex among hearing aid users (n = 172) did not significantly differ from those without hearing aids. PTA1,2,4 was associated with lower FA, but not with a difference in total brain volume, gray matter volume, white matter volume, or mean diffusivity. Interaction between hearing aid use and PTA1,2,4 was not associated with FA or any of the other outcome measures. Additional analysis revealed that interaction between hearing aid use and age was associated with lower FA.

Conclusions: We found no evidence for a moderating effect of hearing aids on the relationship between hearing loss and brain structure in a population of older adults. However, use of hearing aids did appear as an effect modifier in the association between age and white matter integrity. Future longitudinal research is needed to clarify these results.
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http://dx.doi.org/10.1097/AUD.0000000000001148DOI Listing
October 2021

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia.

Brain 2021 Oct 11. Epub 2021 Oct 11.

Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, OX3 9DU Oxford, UK.

Several CSF and blood biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), astrogliosis (glial fibrillary acidic protein (GFAP)), and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage FTD, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. 275 presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialised DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on prior diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF NfL, blood pNfH, blood GFAP, and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve (AUC) of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The AUC to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic FTD revealed that NPTX2 and NfL are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.
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http://dx.doi.org/10.1093/brain/awab382DOI Listing
October 2021

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Commun Biol 2021 09 17;4(1):1094. Epub 2021 Sep 17.

Department of Radiology and Nuclear Medicine, Erasmus MC, Medical Center, Rotterdam, the Netherlands.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.
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http://dx.doi.org/10.1038/s42003-021-02622-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448759PMC
September 2021

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.

Clin Exp Metastasis 2021 10 17;38(5):483-494. Epub 2021 Sep 17.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003-2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician's and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.
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http://dx.doi.org/10.1007/s10585-021-10119-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510954PMC
October 2021

Associations of thrombus perviousness derived from entire thrombus segmentation with functional outcome in patients with acute ischemic stroke.

J Biomech 2021 11 28;128:110700. Epub 2021 Aug 28.

Department of Radiology and Nuclear Medicine, Erasmus Universtiy Medical Center, Rotterdam, the Netherlands; Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands.

Thrombus perviousness is strongly associated with functional outcome and intravenous alteplase treatment success in patients with acute ischemic stroke. Accuracy of thrombus attenuation increase (TAI) assessment may be compromised by a heterogeneous thrombus composition and interobserver variations of currently used manual measurements. We hypothesized that TAI is more strongly associated with clinical outcomes when evaluated on the entire thrombus. In 195 patients, five TAI measures were performed: one manual by placing three regions of interest (TAI) and four automated ones assessing densities from the entire thrombus. The automated TAI measures were calculated by comparing quartiles; Q1, Q2, and Q3 of the non-contrast and contrast enhanced thrombus density distribution and using the lag of the maximum of the cross correlations (MCC). Associations with functional outcome (mRS at 90 days) were assessed with univariate and multivariable analyses. All entire TAI measures were significantly associated with functional outcome with odd ratios (OR) of 1.63(95 %CI:1.19-2.25, p = 0.003) for Q1, 1.56(95 %CI:1.16-2.10, p = 0.003) for Q2, 1.24(95 %CI:1.00-1.54, p = 0.045) for Q3, and 1.70(95 %CI:1.24-2.34, p = 0.001) for MCC per 10 HU increase in univariate models. TAI was not significantly associated with functional outcome (p = 0.055). In the multivariable logistic regression models including age, NIHSS, and recanalization, only TAI measures derived from the entire thrombus were independently associated with favorable outcome; OR of 1.64(95 %CI:1.01-2.66, p = 0.048) for Q2 and 1.82(1.13-2.95, p = 0.014) for MCC per 10 HU increase of thrombus attenuation. The novel perviousness measures of the entire thrombus are more strongly associated with functional outcome than the traditional manual perviousness assessments.
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http://dx.doi.org/10.1016/j.jbiomech.2021.110700DOI Listing
November 2021

Virtual extensions improve perception-based instrument alignment using optical see-through devices.

IEEE Trans Vis Comput Graph 2021 11 2;27(11):4332-4341. Epub 2021 Nov 2.

Instrument alignment is a common task in various surgical interventions using navigation. The goal of the task is to position and orient an instrument as it has been planned preoperatively. To this end, surgeons rely on patient-specific data visualized on screens alongside preplanned trajectories. The purpose of this manuscript is to investigate the effect of instrument visualization/non visualization on alignment tasks, and to compare it with virtual extensions approach which augments the realistic representation of the instrument with simple 3D objects. 18 volunteers performed six alignment tasks under each of the following conditions: no visualization on the instrument; realistic visualization of the instrument; realistic visualization extended with virtual elements (Virtual extensions). The first condition represents an egocentric-based alignment while the two other conditions additionally make use of exocentric depth estimation to perform the alignment. The device used was a see-through device (Microsoft HoloLens 2). The positions of the head and the instrument were acquired during the experiment. Additionally, the users were asked to fill NASA-TLX and SUS forms for each condition. The results show that instrument visualization is essential for a good alignment using see-through devices. Moreover, virtual extensions helped achieve the best performance compared to the other conditions with medians of 2 mm and 2° positional and angular error respectively. Furthermore, the virtual extensions decreased the average head velocity while similarly reducing the frustration levels. Therefore, making use of virtual extensions could facilitate alignment tasks in augmented and virtual reality (AR/VR) environments, specifically in AR navigated surgical procedures when using optical see-through devices.
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http://dx.doi.org/10.1109/TVCG.2021.3106506DOI Listing
November 2021

Resistance to developing brain pathology due to vascular risk factors: the role of educational attainment.

Neurobiol Aging 2021 10 24;106:197-206. Epub 2021 Jun 24.

Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands. Electronic address:

Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.06.006DOI Listing
October 2021

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease.

Neuroimage Clin 2021 4;31:102712. Epub 2021 Jun 4.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
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http://dx.doi.org/10.1016/j.nicl.2021.102712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203808PMC
September 2021

Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort.

Neuroimage 2021 09 4;238:118233. Epub 2021 Jun 4.

Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands. Electronic address:

Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118233DOI Listing
September 2021

Predicting symptomatic mesenteric mass in small intestinal neuroendocrine tumors using radiomics.

Endocr Relat Cancer 2021 Jun 21;28(8):529-539. Epub 2021 Jun 21.

Department of Internal Medicine, section Endocrinology, Erasmus MC, Rotterdam,the Netherlands.

Metastatic mesenteric masses of small intestinal neuroendocrine tumors (SI-NETs) are known to often cause intestinal complications. The aim of this study was to identify patients at risk to develop these complications based on routinely acquired CT scans using a standardized set of clinical criteria and radiomics. Retrospectively, CT scans of SI-NET patients with a mesenteric mass were included and systematically evaluated by five clinicians. For the radiomics approach, 1128 features were extracted from segmentations of the mesenteric mass and mesentery, after which radiomics models were created using a combination of machine learning approaches. The performances were compared to a multidisciplinary tumor board (MTB). The dataset included 68 patients (32 asymptomatic, 36 symptomatic). The clinicians had AUCs between 0.62 and 0.85 and showed poor agreement. The best radiomics model had a mean AUC of 0.77. The MTB had a sensitivity of 0.64 and specificity of 0.68. We conclude that systematic clinical evaluation of SI-NETs to predict intestinal complications had a similar performance than an expert MTB, but poor inter-observer agreement. Radiomics showed a similar performance and is objective, and thus is a promising tool to correctly identify these patients. However, further validation is needed before the transition to clinical practice.
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http://dx.doi.org/10.1530/ERC-21-0064DOI Listing
June 2021

autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients.

IEEE Trans Med Imaging 2021 09 31;40(9):2380-2391. Epub 2021 Aug 31.

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.
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http://dx.doi.org/10.1109/TMI.2021.3077113DOI Listing
September 2021

The P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning.

J Pers Med 2021 Apr 1;11(4). Epub 2021 Apr 1.

Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands.

Patients with mutated (-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the status in a non-invasive manner. Patients with melanoma lung metastases, known status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for mutation status. A combination of various machine learning methods was used to develop decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 -mt; 52 wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.
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http://dx.doi.org/10.3390/jpm11040257DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066683PMC
April 2021

Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration.

Neuroimage 2021 07 29;235:118004. Epub 2021 Mar 29.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.

This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility compared with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118004DOI Listing
July 2021

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade.

Diagnostics (Basel) 2021 Feb 22;11(2). Epub 2021 Feb 22.

Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands.

Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to evaluate the generalizability of radiomics models for prostate cancer classification and to compare the performance of these models to the performance of radiologists. Multiparametric MRI, photographs and histology of radical prostatectomy specimens, and pathology reports of 107 patients were obtained from three healthcare centers in the Netherlands. By spatially correlating the MRI with histology, 204 lesions were identified. For each lesion, radiomics features were extracted from the MRI data. Radiomics models for discriminating high-grade (Gleason score ≥ 7) versus low-grade lesions were automatically generated using open-source machine learning software. The performance was tested both in a single-center setting through cross-validation and in a multi-center setting using the two unseen datasets as external validation. For comparison with clinical practice, a multi-center classifier was tested and compared with the Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scoring performed by two expert radiologists. The three single-center models obtained a mean AUC of 0.75, which decreased to 0.54 when the model was applied to the external data, the radiologists obtained a mean AUC of 0.46. In the multi-center setting, the radiomics model obtained a mean AUC of 0.75 while the radiologists obtained a mean AUC of 0.47 on the same subset. While radiomics models have a decent performance when tested on data from the same center(s), they may show a significant drop in performance when applied to external data. On a multi-center dataset our radiomics model outperformed the radiologists, and thus, may represent a more accurate alternative for malignancy prediction.
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http://dx.doi.org/10.3390/diagnostics11020369DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926758PMC
February 2021

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases.

J Craniofac Surg 2021 May;32(3):956-963

Department of Radiology and Nuclear Medicine.

Abstract: Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.
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http://dx.doi.org/10.1097/SCS.0000000000007379DOI Listing
May 2021

Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations.

Neuroimage 2021 02 16;227:117646. Epub 2020 Dec 16.

Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands.

Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117646DOI Listing
February 2021

Cerebral small vessel disease genomics and its implications across the lifespan.

Nat Commun 2020 12 8;11(1):6285. Epub 2020 Dec 8.

University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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http://dx.doi.org/10.1038/s41467-020-19111-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722866PMC
December 2020

Cerebral Blood Flow in Patients with Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation.

J Am Geriatr Soc 2021 02 17;69(2):494-499. Epub 2020 Oct 17.

Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Heart Center, Amsterdam, the Netherlands.

Background: Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life-saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an increase in cerebral blood flow.

Design: Prospective cohort study.

Setting: European high-volume tertiary multidisciplinary cardiac care.

Participants: Thirty-one patients (78.3 ± 4.6 years; 61% female) with severe symptomatic aortic valve stenosis.

Measurements: Noninvasive prospective assessment of cardiac output (L/min) by inert gas rebreathing and cerebral blood flow of the total gray matter (mL/100 g per min) using arterial spin labeling magnetic resonance imaging in resting state less than 24 hours before TAVI and at 3-month follow-up. Cerebral blood flow change was defined as the difference relative to baseline.

Results: On average, cardiac output in patients with severe aortic valve stenosis increased from 4.0 ± 1.1 to 5.4 ± 2.4 L/min after TAVI (P = .003). The increase in cerebral blood flow after TAVI strongly varied between patients (7% ± 24%; P = .41) and related to the increase in cardiac output, with an 8.2% (standard error = 2.3%; P = .003) increase in cerebral blood flow per every additional liter of cardiac output following the TAVI procedure.

Conclusion: Following TAVI, there was an association of increase in cardiac output with increase in cerebral blood flow. These findings encourage future larger studies to determine the influence of TAVI on cerebral blood flow and cognitive function.
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http://dx.doi.org/10.1111/jgs.16882DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894507PMC
February 2021

Hypertensive Exposure Markers by MRI in Relation to Cerebral Small Vessel Disease and Cognitive Impairment.

JACC Cardiovasc Imaging 2021 01 30;14(1):176-185. Epub 2020 Sep 30.

Department of Cardiology, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Science, Amsterdam, the Netherlands. Electronic address:

Objectives: This study sought to investigate the extent of hypertensive exposure as assessed by cardiovascular magnetic resonance imaging (MRI) in relation to cerebral small vessel disease (CSVD) and cognitive impairment, with the aim of understanding the role of hypertension in the early stages of deteriorating brain health.

Background: Preserving brain health into advanced age is one of the great challenges of modern medicine. Hypertension is thought to induce vascular brain injury through exposure of the cerebral microcirculation to increased pressure/pulsatility. Cardiovascular MRI provides markers of (subclinical) hypertensive exposure, such as aortic stiffness by pulse wave velocity (PWV), left ventricular (LV) mass index (LVMi), and concentricity by mass-to-volume ratio.

Methods: A total of 559 participants from the Heart-Brain Connection Study (431 patients with manifest cardiovascular disease and 128 control participants), age 67.8 ± 8.8 years, underwent 3.0-T heart-brain MRI and extensive neuropsychological testing. Aortic PWV, LVMi, and LV mass-to-volume ratio were evaluated in relation to presence of CSVD and cognitive impairment. Effect modification by patient group was investigated by interaction terms; results are reported pooled or stratified accordingly.

Results: Aortic PWV (odds ratio [OR]: 1.17; 95% confidence interval [CI]: 1.05 to 1.30 in patient groups only), LVMi (in carotid occlusive disease, OR: 5.69; 95% CI: 1.63 to 19.87; in other groups, OR: 1.30; 95% CI: 1.05 to 1.62]) and LV mass-to-volume ratio (OR: 1.81; 95% CI: 1.46 to 2.24) were associated with CSVD. Aortic PWV (OR: 1.07; 95% CI: 1.02 to 1.13) and LV mass-to-volume ratio (OR: 1.27; 95% CI: 1.07 to 1.51) were also associated with cognitive impairment. Relations were independent of sociodemographic and cardiac index and mostly persisted after correction for systolic blood pressure or medical history of hypertension. Causal mediation analysis showed significant mediation by presence of CSVD in the relation between hypertensive exposure markers and cognitive impairment.

Conclusions: The extent of hypertensive exposure is associated with CSVD and cognitive impairment beyond clinical blood pressure or medical history. The mediating role of CSVD suggests that hypertension may lead to cognitive impairment through the occurrence of CSVD.
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http://dx.doi.org/10.1016/j.jcmg.2020.06.040DOI Listing
January 2021

Cerebral blood flow and cognitive functioning in patients with disorders along the heart-brain axis: Cerebral blood flow and the heart-brain axis.

Alzheimers Dement (N Y) 2020 12;6(1):e12034. Epub 2020 May 12.

Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Amsterdam UMC VU University Medical Center Amsterdam the Netherlands.

Introduction: We examined the role of hemodynamic dysfunction in cognition by relating cerebral blood flow (CBF), measured with arterial spin labeling (ASL), to cognitive functioning, in patients with heart failure (HF), carotid occlusive disease (COD), and patients with cognitive complaints and vascular brain injury on magnetic resonance imaging (MRI; ie, possible vascular cognitive impairment [VCI]).

Methods: We included 439 participants (124 HF; 75 COD; 127 possible VCI; 113 reference participants) from the Dutch multi-center Heart-Brain Study. We used pseudo-continuous ASL to estimate whole-brain and regional partial volume-corrected CBF. Neuropsychological tests covered global cognition and four cognitive domains.

Results: CBF values were lowest in COD, followed by VCI and HF, compared to reference participants. This did not explain cognitive impairment, as we did not find an association between CBF and cognitive functioning.

Discussion: We found that reduced CBF is not the major explanatory factor underlying cognitive impairment in patients with hemodynamic dysfunction along the heart-brain axis.
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http://dx.doi.org/10.1002/trc2.12034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507476PMC
May 2020

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics.

Eur J Radiol 2020 Oct 8;131:109266. Epub 2020 Sep 8.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands. Electronic address:

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types.

Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset.

Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74.

Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.
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http://dx.doi.org/10.1016/j.ejrad.2020.109266DOI Listing
October 2020

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Nat Commun 2020 09 22;11(1):4796. Epub 2020 Sep 22.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
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http://dx.doi.org/10.1038/s41467-020-18367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508833PMC
September 2020

Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare.

Med Image Anal 2020 12 19;66:101800. Epub 2020 Aug 19.

DIKU, Dept. of Computer Science, Univ of Copenhagen, Denmark.

In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.
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http://dx.doi.org/10.1016/j.media.2020.101800DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437567PMC
December 2020

Structural disconnectivity and the risk of dementia in the general population.

Neurology 2020 09 8;95(11):e1528-e1537. Epub 2020 Jul 8.

From the Departments of Radiology and Nuclear Medicine (L.G.M.C., F.J.W., M.d.G., A.v.d.L., W.J.N., M.W.V.), Epidemiology (L.G.M.C., F.J.W., M.d.G., M.K.I., M.W.V., M.A.I.), Neurology (F.J.W., M.K.I., M.A.I.), and Medical Informatics (M.d.G., W.J.N.), Erasmus MC, Rotterdam; and Department of Imaging Physics, Faculty of Applied Sciences (W.J.N.), Delft University of Technology, Delft, the Netherlands.

Objective: The disconnectivity hypothesis postulates that partial loss of connecting white matter fibers between brain regions contributes to the development of dementia. Using diffusion MRI to quantify global and tract-specific white matter microstructural integrity, we tested this hypothesis in a longitudinal population-based study.

Methods: Global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were obtained in 4,415 people without dementia (mean age 63.9 years, 55.0% women) from the prospective population-based Rotterdam Study with brain MRI between 2005 and 2011. We modeled the association of these diffusion measures with risk of dementia (follow-up until 2016) and with changes on repeated cognitive assessment after on average 5.4 years, adjusting for age, sex, education, macrostructural MRI markers, depressive symptoms, cardiovascular risk factors, and genotype.

Results: During a median follow-up of 6.8 years, 101 participants had incident dementia, of whom 83 had clinical Alzheimer disease (AD). Lower global values of FA and higher values of MD were associated with an increased risk of dementia (adjusted hazard ratio [95% confidence interval (CI)] per SD increase for MD 1.79 [1.44-2.23] and FA 0.65 [0.52-0.80]). Similarly, lower global values of FA and higher values of MD related to more cognitive decline in people without dementia (difference in global cognition per SD increase in MD [95% CI] was -0.04 [-0.07 to -0.01]). Associations were most profound in the projection, association, and limbic system tracts.

Conclusions: Structural disconnectivity is associated with an increased risk of dementia and more pronounced cognitive decline in the general population.
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http://dx.doi.org/10.1212/WNL.0000000000010231DOI Listing
September 2020

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications.

Cancers (Basel) 2020 Jun 17;12(6). Epub 2020 Jun 17.

Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands.

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making.

Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range.

Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77-0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial.

Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.
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http://dx.doi.org/10.3390/cancers12061606DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352160PMC
June 2020

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

Neuroimage 2020 09 31;218:116993. Epub 2020 May 31.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N ​= ​9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, κ=0.72-0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: ε=1%-5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N ​= ​58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116993DOI Listing
September 2020

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study.

Neuroimage 2020 10 24;220:116842. Epub 2020 Apr 24.

UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands. Electronic address:

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116842DOI Listing
October 2020

Predicting Global Cognitive Decline in the General Population Using the Disease State Index.

Front Aging Neurosci 2019 23;11:379. Epub 2020 Jan 23.

Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.

Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia.

Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI).

Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.9 ± 9.1 years). Participants with significant global cognitive decline were defined as the 5% of participants with the largest cognitive decline per year. We trained DSI to predict occurrence of significant global cognitive decline using a large variety of baseline features, including magnetic resonance imaging (MRI) features, cardiovascular risk factors, APOE-ε4 allele carriership, gait features, education, and baseline cognitive function as predictors. The prediction performance was assessed as area under the receiver operating characteristic curve (AUC), using 500 repetitions of 2-fold cross-validation experiments, in which (a randomly selected) half of the data was used for training and the other half for testing.

Results: A mean AUC (95% confidence interval) for DSI prediction was 0.78 (0.77-0.79) using only age as input feature. When using all available features, a mean AUC of 0.77 (0.75-0.78) was obtained. Without age, and with age-corrected features and feature selection on MRI features, a mean AUC of 0.70 (0.63-0.76) was obtained, showing the potential of other features besides age.

Conclusion: The best performance in the prediction of global cognitive decline in the general population by DSI was obtained using only age as input feature. Other features showed potential, but did not improve prediction. Future studies should evaluate whether the performance could be improved by new features, e.g., longitudinal features, and other prediction methods.
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http://dx.doi.org/10.3389/fnagi.2019.00379DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989484PMC
January 2020

Automatic Collateral Scoring From 3D CTA Images.

IEEE Trans Med Imaging 2020 06 15;39(6):2190-2200. Epub 2020 Jan 15.

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.
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http://dx.doi.org/10.1109/TMI.2020.2966921DOI Listing
June 2020
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