Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.

DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution

Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section data and the difference in section thickness bring considerable difficulties in the study based on the image big data. In this article, we introduce DeepVolume, a two-step deep learning architecture to address the challenge of accurate thin-section MR image reconstruction. The first stage is the brain structure-aware network, in which the thick-section MR images in axial and sagittal planes are fused by a multitask 3-D U-net with prior knowledge of brain volume segmentation, which encourages the reconstruction result to have correct brain structure. The second stage is the spatial connection-aware network, in which the preliminary reconstruction results are adjusted slice-by-slice by a recurrent convolutional network embedding convolutional long short-term memory (LSTM) block, which enhances the precision of the reconstruction by utilizing the previously unassessed sagittal information. We used 305 paired brain MRI samples with thickness of 1.0 mm and 6.5 mm in this article. Extensive experiments illustrate that DeepVolume can produce the state-of-the-art reconstruction results by embedding more anatomical knowledge. Furthermore, considering DeepVolume as an intermediate step, the practical and clinical value of our method is validated by applying the brain volume estimation and voxel-based morphometry. The results show that DeepVolume can provide much more reliable brain volume estimation in the normalized space based on the thick-section MR images compared with the traditional solutions.

MIL normalization -- prerequisites for accurate MRI radiomics analysis

The quality of magnetic resonance (MR) images obtained with different instruments and imaging parameters varies greatly. A large number of heterogeneous images are collected, and they suffer from acquisition variation. Such imaging quality differences will have a great impact on the radiomics analysis. The main differences in MR images include modality mismatch (M), intensity distribution variance (I), and layer-spacing differences (L), which are referred to as MIL differences in this paper for convenience. An MIL normalization system is proposed to reconstruct uneven MR images into high-quality data with complete modality, a uniform intensity distribution and consistent layer spacing. Three radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis of glioma, were used to verify the effect of MIL normalization on radiomics analysis. Three retrospective glioma datasets were analyzed in this study: BraTs (285 cases), TCGA (112 cases) and HuaShan (403 cases). They were used to test the effect of MIL on three different radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis. MIL normalization included three components: multimodal synthesis based on an encoder-decoder network, intensity normalization based on CycleGAN, and layer-spacing unification based on Statistical Parametric Mapping (SPM). The Dice similarity coefficient, areas under the curve (AUC) and six other indicators were calculated and compared after different normalization steps. The MIL normalization system can improved the Dice coefficient of segmentation by 9% (P < .001), the AUC of pathological grading by 32% (P < .001), and IDH1 status prediction by 25% (P < .001) when compared to non-normalization. The proposed MIL normalization system provides high-quality standardized data, which is a prerequisite for accurate radiomics analysis.

Changes of Brain Structure in Patients With Metastatic Non-Small Cell Lung Cancer After Long-Term Target Therapy With EGFR-TKI

Purpose

Epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) therapy is the routine treatment for patients with metastatic non-small cell lung cancer (NSCLC) harboring positive EGFR mutations. Patients who undergo such treatment have reported cognitive decline during follow-up. This study, therefore, aimed to evaluate brain structural changes in patients receiving EGFR-TKI to increase understanding of this potential symptom.

Method

The medical records of 75 patients with metastatic NSCLC (without brain metastasis or other co-morbidities) who received EGFR-TKI therapy from 2010 to 2017 were reviewed. The modified Scheltens Visual Scale and voxel-based morphometry were used to evaluate changes in white matter lesions (WML) and gray matter volume (GMV), respectively.

Results

The WML scores were higher at the 12-month [8.65 ± 3.86; 95% confidence interval (CI), 1.60–2.35; p < 0.001] and 24-month follow-ups (10.11 ± 3.85; 95% CI, 2.98–3.87; p < 0.001) compared to baseline (6.68 ± 3.64). At the 24-month follow-up, the visual scores were also significantly higher in younger patients (3.89 ± 2.04) than in older patients (3.00 ± 1.78; p = 0.047) and higher in female patients (3.80 ± 2.04) than in male patients (2.73 ± 1.56; p = 0.023). Additionally, significant GMV loss was observed in sub-regions of the right occipital lobe (76.71 voxels; 95% CI, 40.740–112.69 voxels), left occipital lobe (93.48 voxels; 95% CI, 37.48–149.47 voxels), and left basal ganglia (37.57 voxels; 95% CI, 21.58–53.57 voxels) (all p < 0.005; cluster-level false discovery rate < 0.05).

Conclusions

An increase in WMLs and loss of GMV were observed in patients with metastatic NSCLC undergoing long-term EGFR-TKI treatment. This might reflect an unknown side-effect of EGFR-TKI treatment. Further prospective studies are necessary to confirm our findings.

Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non-responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter-hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non-responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.

Voxel-based morphometry results in first-episode schizophrenia: a comparison of publicly available software packages

Investigations of brain structure in schizophrenia using magnetic resonance imaging (MRI) have identified variations in regional grey matter (GM) volume throughout the brain but the results are mixed. This study aims to investigate whether the inconsistent voxel-based morphometry (VBM) findings in schizophrenia are due to the use of different software packages. T1 MRI images were obtained from 86 first-episode schizophrenia (FESZ) patients and 86 age- and gender-matched Healthy controls (HCs). VBM analysis was carried out using FMRIB software library (FSL) 5.0 and statistical parametric mapping 8 (SPM8). All images were processed using the default parameter settings as provided by these software packages. FSL-VBM revealed widespread GM volume reductions in FESZ patients compared with HCs, however, for SPM-VBM, only increased and circumscribed GM volume changes were found, both software revealed increased GM volume within cerebellum. Significant correlations between Positive and Negative Syndrome Scale (PANSS) and GM volume were mainly found in frontal regions. Algorithms of GM tissue segmentation, image registration and statistical strategies might contribute to these disparate results.

Treatment response prediction of rehabilitation program in children with cerebral palsy using radiomics strategy: protocol for a multicenter prospective cohort study in west China

Background

Cerebral palsy (CP) is a major cause of chronic childhood disability worldwide, causing activity limitation as well as impairments in sensation, cognition, and communication. Leveraging biomarkers to establish individualized predictions of future treatment responses will be of great value. We aim to develop and validate a model that can be used to predict the individualized treatment response in Children with CP.

Methods

A multicenter prospective cohort study will be conducted in 4 hospitals in west China. One hundred and thirty children with CP will be recruited and undergo clinical assessment using the Peabody Developmental Motor Scales, Manual Ability Classification System (MACS), Hand Assessment for Infants (HAI), Assisting Hand Assessment (AHA), and Gross Motor Function Classification System (GMFCS). The data collected will include MRI image, clinical status, and socioeconomic status. The clinical information and MRI features extracted using radiomics strategy will be combined for exploratory analysis. The accuracy, sensitivity, and specificity of the model will be assessed using multiple modeling methodologies. Internal and external validation will be used to evaluate the performance of the radiomics model.

Discussion

We hypothesized that the findings from this study could provide a critical step towards the prediction of treatment response in children with CP, which could also complement other biomarkers in the development of precision medicine approaches for this severe disorder.

Trial registration

The study was registered with clinicaltrials.gov (NCT02979743).

Shape-related characteristics of age-related differences in subcortical structures

OBJECTIVES

With an increasing aging population, it is important to understand biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences. Fractal dimensionality is more sensitive to age-related differences, but is borne out of mathematical principles, rather than neurobiological relevance. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture.

METHODS

Structural MRIs from a combined sample of over 600 healthy adults were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each, volume and fractal dimensionality were calculated, as well as four distinct shape measures. These measures were examined for their utility in explaining age-related variability in brain structure.

RESULTS

The four shape measures were able to account for 80%-90% of the variance in fractal dimensionality. Of the distinct shape measures, surface-to-volume ratio was the most sensitive biomarker.

CONCLUSION

Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements. Our results indicate that shape characteristics are useful biological markers of aging.

Prediction of adverse motor outcome for neonates with punctate white matter lesions by MRI images using radiomics strategy: protocol for a prospective cohort multicentre study

INTRODUCTION

Punctate white matter lesions (PWML) are prevalent white matter disease in preterm neonates, and may cause motor disorders and even cerebral palsy. However, precise individual-based diagnosis of lesions that result in an adverse motor outcome remains unclear, and an effective method is urgently needed to guide clinical diagnosis and treatment. Advanced radiomics for multiple modalities data can provide a possible look for biomarkers and determine prognosis quantitatively. The study aims to develop and validate a model for prediction of adverse motor outcomes at a corrected age (CA) of 24 months in neonates with PWML.

METHODS AND ANALYSIS

A prospective cohort multicentre study will be conducted in 11 Chinese hospitals. A total of 394 neonates with PWML confirmed by MRI will undergo a clinical assessment (modified Neonatal Behavioural Assessment Scale). At a CA of 18 months, the motor function will be assessed by Bayley Scales of Infant and Toddler Development-III (Bayley-III). Mild-to-severe motor impairments will be confirmed using the Bayley-III and Gross Motor Function Classification System at a CA of 24 months. During the data collection, the perinatal and clinical information will also be recorded. According to the radiomics strategy, the extracted imaging features and clinical information will be combined for exploratory analysis. After using multiple-modelling methodology, the accuracy, sensitivity and specificity will be computed. Internal and external validations will be used to evaluate the performance of the radiomics model.

ETHICS AND DISSEMINATION

This study has been approved by the institutional review board of The First Affiliated Hospital of Xi'an Jiaotong University (XJTU1AF2015LSK-172). All parents of eligible participants will be provided with a detailed explanation of the study and written consent will be obtained. The results of this study will be published in peer-reviewed journals and presented at local, national and international conferences.

TRIAL REGISTRATION NUMBER

NCT02637817; Pre-results.

Postprandial Hyperglycemia Is Associated With White Matter Hyperintensity and Brain Atrophy in Older Patients With Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is associated with neurodegeneration and cerebrovascular disease. However, the precise mechanism underlying the effects of glucose management on brain abnormalities is not fully understood. The differential impacts of glucose alteration on brain changes in patients with and without cognitive impairment are also unclear. This cross-sectional study included 57 older type 2 diabetes patients with a diagnosis of Alzheimer's disease (AD) or normal cognition (NC). We examined the effects of hypoglycemia, postprandial hyperglycemia and glucose fluctuations on regional white matter hyperintensity (WMH) and brain atrophy among these patients. In a multiple regression analysis, postprandial hyperglycemia was independently associated with frontal WMH in the AD patients. In addition, postprandial hyperglycemia was significantly associated with brain atrophy, regardless of the presence of cognitive decline. Altogether, our findings indicate that postprandial hyperglycemia is associated with WMH in AD patients but not NC patients, which suggests that AD patients are more susceptible to postprandial hyperglycemia associated with WMH.