Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance ...of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies.
A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.
Objective
Thalamic volume is a candidate magnetic resonance imaging (MRI)‐based marker associated with neurodegeneration to hasten development of neuroprotective treatments. Our objective is to ...describe the longitudinal evolution of thalamic atrophy in MS and normal aging, and to estimate sample sizes for study design.
Methods
Six hundred one subjects (2,632 MRI scans) were analyzed. Five hundred twenty subjects with relapse‐onset MS (clinically isolated syndrome, n = 90; relapsing–remitting MS, n = 392; secondary progressive MS, n = 38) underwent annual standardized 3T MRI scans for an average of 4.1 years, including a 1mm3 3‐dimensional T1‐weighted sequence (3DT1; 2,485 MRI scans). Eighty‐one healthy controls (HC) were scanned longitudinally on the same scanner using the same protocol (147 MRI scans). 3DT1s were processed using FreeSurfer's longitudinal pipeline after lesion inpainting. Rates of normalized thalamic volume loss in MS and HC were compared in linear mixed effects models. Simulation‐based sample size calculations were performed incorporating the rate of atrophy in HC.
Results
Thalamic volume declined significantly faster in MS subjects compared to HC, with an estimated decline of −0.71% per year (95% confidence interval CI = −0.77% to −0.64%) in MS subjects and −0.28% per year (95% CI = −0.58% to 0.02%) in HC (p for difference = 0.007). The rate of decline was consistent throughout the MS disease duration and across MS clinical subtypes. Eighty or 100 subjects per arm (α = 0.1 or 0.05, respectively) would be needed to detect the maximal effect size with 80% power in a 24‐month study.
Interpretation
Thalamic atrophy occurs early and consistently throughout MS. Preliminary sample size calculations appear feasible, adding to its appeal as an MRI marker associated with neurodegeneration. Ann Neurol 2018;83:223–234
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses.
In a ...retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC.
Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses.
The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.
When people with stroke recover gait speed, they report improved function and reduced disability. However, the minimal amount of change in gait speed that is clinically meaningful and associated with ...an important difference in function for people poststroke has not been determined.
The purpose of this study was to determine the minimal clinically important difference (MCID) for comfortable gait speed (CGS) associated with an improvement in the modified Rankin Scale (mRS) score for people between 20 to 60 days poststroke.
This was a prospective, longitudinal, cohort study.
The participants in this study were 283 people with first-time stroke prospectively enrolled in the ongoing Locomotor Experience Applied Post Stroke (LEAPS) multi-site randomized clinical trial. Comfortable gait speed was measured and mRS scores were obtained at 20 and 60 days poststroke. Improvement of >or=1 on the mRS was used to detect meaningful change in disability level.
Mean (SD) CGS was 0.18 (0.16) m/s at 20 days and 0.39 (0.22) m/s at 60 days poststroke. Among all participants, 47.3% experienced an improvement in disability level >or=1. The MCID was estimated as an improvement in CGS of 0.16 m/s anchored to the mRS.
Because the mRS is not a gait-specific measure of disability, the estimated MCID for CGS was only 73.9% sensitive and 57.0% specific for detecting improvement in mRS scores.
We estimate that the MCID for gait speed among patients with subacute stroke and severe gait speed impairments is 0.16 m/s. Patients with subacute stroke who increase gait speed >or=0.16 m/s are more likely to experience a meaningful improvement in disability level than those who do not. Clinicians can use this reference value to develop goals and interpret progress in patients with subacute stroke.
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DOBA, FSPLJ, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
OBJECTIVEExcessive dissatisfaction and stress among physicians can precipitate burnout, which results in diminished productivity, quality of care, and patient satisfaction and treatment adherence. ...Given the multiplicity of its harms and detriments to workforce retention and in light of the growing physician shortage, burnout has garnered much attention in recent years. Using a national survey, the authors formally evaluated burnout among neurosurgery trainees.METHODSAn 86-item questionnaire was disseminated to residents in the American Association of Neurological Surgeons database between June and November 2015. Questions evaluated personal and workplace stressors, mentorship, career satisfaction, and burnout. Burnout was assessed using the previously validated Maslach Burnout Inventory. Factors associated with burnout were determined using univariate and multivariate logistic regression.RESULTSThe response rate with completed surveys was 21% (346/1643). The majority of residents were male (78%), 26-35 years old (92%), in a stable relationship (70%), and without children (73%). Respondents were equally distributed across all residency years. Eighty-one percent of residents were satisfied with their career choice, although 41% had at some point given serious thought to quitting. The overall burnout rate was 67%. In the multivariate analysis, notable factors associated with burnout included inadequate operating room exposure (OR 7.57, p = 0.011), hostile faculty (OR 4.07, p = 0.008), and social stressors outside of work (OR 4.52, p = 0.008). Meaningful mentorship was protective against burnout in the multivariate regression models (OR 0.338, p = 0.031).CONCLUSIONSRates of burnout and career satisfaction are paradoxically high among neurosurgery trainees. While several factors were predictive of burnout, including inadequate operative exposure and social stressors, meaningful mentorship proved to be protective against burnout. The documented negative effects of burnout on patient care and health care economics necessitate further studies for potential solutions to curb its rise.
Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the ...feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Contribution of normal aging to brain atrophy in MS Azevedo, Christina J; Cen, Steven Y; Jaberzadeh, Amir ...
Neurology : neuroimmunology & neuroinflammation,
2019-November, Volume:
6, Issue:
6
Journal Article
Peer reviewed
Open access
OBJECTIVETo identify the top brain regions affected by MS-specific atrophy (i.e., atrophy in excess of normal aging) and to test whether normal aging and MS-specific atrophy increase or decrease in ...these regions with age.
METHODSSix hundred fifty subjects (2,790 MRI time points) were analyzed520 subjects with relapse-onset MS from a 5-year prospective cohort with annual standardized 1-mm 3D T1-weighted images (3DT1s; 2,483 MRIs) and 130 healthy controls with longitudinal 3DT1s (307 MRIs). Rates of change in all FreeSurfer regions (v5.3) and Structural Image Evaluation Using Normalization of Atrophy (SIENA) were estimated with mixed-effects models. All FreeSurfer regions were ranked by the MS-specific atrophy slope/standard error ratio (βMS × time/SEβMS × time). In the top regions, age was added as an effect modifier to test whether MS-specific atrophy varied by age.
RESULTSThe top-ranked regions were all gray matter structures. For SIENA, normal aging increased from 0.01%/y at age 30 years to −0.31%/y at age 60 years (−0.11% ± 0.032%/decade, p < 0.01), whereas MS-specific atrophy decreased from −0.38%/y at age 30 years to −0.12%/y at age 60 years (0.09% ± 0.035%/decade, p = 0.01). Similarly, in the thalamus, normal aging increased from −0.15%/y at age 30 years to −0.62%/y at age 60 years (−0.16% ± 0.079%/decade, p < 0.05), and MS-specific atrophy decreased from −0.59%/y at age 30 years to −0.05%/y at age 60 years (0.18% ± 0.08%/decade, p < 0.05). In the putamen and caudate, normal aging and MS-specific atrophy did not vary by age.
CONCLUSIONSFor SIENA and thalamic atrophy, the contribution of normal aging increases with age, but does not change in the putamen and caudate. This may have substantial implications to understand the biology of brain atrophy in MS.
The objective of this study is to compare forward-projected model-based iterative reconstruction solution (FIRST), a newer fully iterative CT reconstruction method, with adaptive iterative dose ...reduction 3D (AIDR 3D) in low-dose screening CT for lung cancer. Differences in image noise, image quality, and pulmonary nodule detection, size, and characterization were specifically evaluated.
Low-dose chest CT images obtained for 50 consecutive patients between December 2015 and January 2016 were retrospectively reviewed. Images were reconstructed using FIRST and AIDR 3D for both lung and soft-tissue reconstruction. Images were independently reviewed to assess image noise, subjective image quality (with use of a 5-point Likert scale, with 1 denoting far superior image quality; 2, superior quality; 3, equivalent quality; 4, inferior quality; and 5, far inferior quality), pulmonary nodule count, size of the largest pulmonary nodule, and characterization of the largest pulmonary nodule (i.e., solid, part solid, or ground glass).
Across all 50 cases, measured image noise was lower with FIRST than with AIDR 3D (lung window, 44% reduction, 41 ± 7 vs 74 ± 8 HU, respectively; soft-tissue window, 32% reduction, 11 ± 2 vs 16 ± 2 HU, respectively). Readers subjectively rated images obtained with FIRST as comparable to images obtained with AIDR 3D (mean ± SD Likert score for FIRST vs AIDR 3D, 3.2 ± 0.3 for soft-tissue reconstructions and 3.0 ± 0.3 for lung reconstructions). For each reader, very good agreement regarding nodule count was noted between FIRST and AIDR 3D (interclass correlation coefficient ICC, 0.83 for reader 1 and 0.78 for reader 2). Excellent agreement regarding nodule size (ICC, 0.99 for reader 1 and 0.99 for reader 2) and characterization of the largest nodule (kappa value, 0.92 for reader 1 and 0.82 for reader 2) also existed.
Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.
Objective
To determine the intra‐, inter‐ and test‐retest variability of CT‐based texture analysis (CTTA) metrics.
Materials and methods
In this study, we conducted a series of CT imaging experiments ...using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra‐scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post‐reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test‐retest) and robustness (intra‐scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter‐scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust.
Results
As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform‐based texture metrics was overall most reliable across the two scanners and scanning conditions. Post‐processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used.
Conclusion
Following large‐scale validation, identification of reliable CTTA metrics can aid in conducting large‐scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Objectives
To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1–2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3–4) and low TNM stage ...(stages I–II) ccRCC from high TNM stage (stages III–IV).
Methods
A total of 587 subjects (mean age 60.2 years ± 12.2; range 22–88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC).
Results
The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62–0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74–0.86). Comparable AUCs of 0.73 (95% CI 0.65–0.8) and 0.77 (95% CI 0.7–0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation–based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification.
Conclusion
Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models.
Summary statement
Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC.
Key Points
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Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62–0.78).
•
Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74–0.86).
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Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65–0.80) and 0.77 (95% CI 0.70–0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
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