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This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein–protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker. [ABSTRACT FROM AUTHOR]

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938., (© 2018 International Society for Magnetic Resonance in Medicine.)

Objectives: This study examines the role of tumor texture on computed tomography(CT) images as a complement to clinical prognostic factors in predicting survival in patients of non‑small cell lung carcinoma (NSCLC) treated with radical chemo‑radiation (CRT). Methods: A total of 93 patients with confirmed NSCLC treated with CRT accrued in a study approved by the institutional ethics committee were analyzed for CT‑based radiomic features. Pretreatment CT images were used to contour the primary tumor and texture features were computed by the image filtration method to differentially highlight fine to coarse textures. Texture parameters included mean intensity, entropy, kurtosis, standard deviation, and mean positive pixel and skewness. Optimal threshold cut‑off values of the above tumor texture features were analyzed. These features were explored as imaging biomarkers to predict survival using Kaplan–Meier and Cox proportional hazard model. Results: Median follow‑up of the entire cohort was 23.5 months [Interquartile range, IQR: 14–37] while for alive patients, median follow‑up was 31 months (IQR: 23–49), 47 (50.6%) patients had died at the last follow‑up. Univariate analysis revealed certain features like age, gender, response to therapy, and texture features like mean and kurtosis in CT images to be significant predictors of survival. In multivariate analysis, age (P = 0.006), gender (P = 0.004), treatment response (P < 0.0001), and two CT texture parameters: mean (P = 0.027) and kurtosis (P = 0.002) were independent prognostic factors of survival. Interpretation and Conclusion: CT‑derived tumor heterogeneity (mean and kurtosis) complements clinical factors for predicting survival in NSCLC patients treated with CRT. Tumor radiomics warrants further validation as potential prognostic biomarkers for these patients. [ABSTRACT FROM AUTHOR]

In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients. [ABSTRACT FROM AUTHOR]

Purpose: To explore the value of tissue quantitative diffusion analysis of ultrasound elastography in the diagnosis of early-stage chronic kidney disease (CKD). Methods: The observation group comprised 54 patients with early-stage CKD treated at Fuzhou No 7 Hospital, and the control group consisted of 40 healthy individuals who underwent physical examinations at the same hospital. The renal parenchyma of the participants were examined using ultrasonography, color Doppler ultrasonography, and tissue quantitative diffusion analysis of ultrasound elastography. Renal dimensions (diameter, thickness, and renal parenchyma thickness), interlobar artery blood flow parameters, and 11 elastic characteristic values were analyzed and compared between the two groups. The area under the receiver-operating characteristic (ROC) curve, cut-off values, sensitivity, and specificity were calculated using the ROC curve analysis. Results: There were no significant differences in the blood flow parameters of the interlobar artery and the dimensions of renal meridians between the two groups. In the observation group, the mean (MEAN) decreased, while the blue area ratio and skewness, increased, compared to the control group (p < 0.05). In addition, the ROC curve revealed that the blue area ratio, MEAN, and skewness had significant diagnostic value (the area under the curve > 0.7). Notably, the best cut-off value of the MEAN was found to be 106, indicating that a MEAN value less than 106 represented early-stage CKD. Also, this cutoff value had a sensitivity of 80% and a specificity of 81%. Conclusion: Tissue quantitative diffusion analysis of ultrasound elastography can quantitatively evaluate renal parenchymal damage in early-stage CKD using quantitative diffusion parameters, with the MEAN parameter, having a cutoff of 106, being particularly effective. This parameter and cutoff value offer a valuable tool for the early detection and diagnosis of CKD, potentially improving patient outcomes through earlier intervention. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye. Method: This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). Results: The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the “one-standard error” rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. Conclusion: The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker. [ABSTRACT FROM AUTHOR]

The physiological loss of muscle mass and strength with aging is referred to as "sarcopenia", whose combined effect with osteoporosis is a serious threat to the elderly, accounting for decreased mobility and increased risk of falls with consequent fractures. In previous studies, we observed a high degree of inter-individual variability in paraspinal muscle fatty infiltration, one of the most relevant indices of muscle wasting. This aspect led us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different ages (age range of 23–85 years) and physio-pathological status (healthy young, pre-menopause, menopause, and osteoporosis) were used to set up a method based on fractal-derived texture analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from the GBA (gliding box algorithm) curvilinear plot fitted by our hyperbola model function. Succolarity was estimated by parameter µ, for the four main directions through an algorithm implemented with this purpose. The results show that lacunarity, by quantifying muscle fatty infiltration, can discriminate between osteoporosis and healthy aging, while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal-derived features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease. [ABSTRACT FROM AUTHOR]

Objective: Thyroid nodules are a common occurrence in adults. Although the majority of thyroid nodules are benign, a small percentage are cancerous. The combined phosphorylated histone H2AX (γ-H2AX) and p53-binding protein 1 (53BP1) assay was utilized to detect deoxyribonucleic acid (DNA) damage and DNA repair as biomarkers of the cellular stress response. Using the combined γ-H2AX and 53BP1 assay, we evaluated DNA damage, DNA repair capacity, and malignancy risk in peripheral blood mononuclear cells (PBMCs) of euthyroid individuals with nodular goiter. Materials and Methods: Peripheral blood samples were collected from 33 euthyroid patients with newly diagnosed nodular goiter and 30 healthy control participants. A fully automatic image analysis system was used for analyzing DNA damage (γ-H2AX), including DNA double-strand breaks (DSBs), and DNA repair (53BP1). Results: Euthyroid patients with nodular goiter exhibited a higher mean number of γ-H2AX foci per cell and a higher percentage of apoptotic cells compared to the control subjects (p=0.022 and p=0.005, respectively). Conclusion: This study found considerably higher DNA damage in euthyroid patients with nodular goiter than in control individuals. The increase in DNA damage occurs in the early stages of carcinogenesis. These patients were expected to exhibit compromised DNA repair along with enhanced DNA damage, increasing the risk of carcinogenesis. However, euthyroid patients with nodular goiter might be at a high risk of thyroid malignancy due to the high level of DNA damage. A long-term follow-up of these patients would provide better evidence of the relationship between DNA damage and the malignancy risk of thyroid. [ABSTRACT FROM AUTHOR]

Imaging biomarkers permit improved approaches to identify the most at-risk patients encountering knee osteoarthritis (KOA) progression. This study aimed to investigate the utility of trabecular bone texture (TBT) extracted from plain radiographs, associated with a set of clinical, biochemical, and radiographic data, as a predictor of long-term radiographic KOA progression. We used data from the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium dataset. The reference model made use of baseline TBT parameters adjusted for clinical covariates and radiological scores. Several models based on a combination of baseline and 24-month TBT variations (TBT∆TBT) were developed using logistic regression and compared to those based on baseline-only TBT parameters. All models were adjusted for baseline clinical covariates, radiological scores, and biochemical descriptors. The best overall performances for the prediction of radio-symptomatic, radiographic, and symptomatic progression were achieved using TBT∆TBT parameters solely, with area under the ROC curve values of 0.658 (95% CI: 0.612–0.705), 0.752 (95% CI: 0.700–0.804), and 0.698 (95% CI: 0.641–0.756), respectively. Adding biochemical markers did not significantly improve the performance of the TBT∆TBT-based model. Additionally, when TBT values were taken from the entire subchondral bone rather than just the medial, lateral, or central compartments, better results were obtained. [ABSTRACT FROM AUTHOR]

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1. [ABSTRACT FROM AUTHOR]

Background and Objective: Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra‐motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and neuropsychological assessments of the hippocampal and parahippocampal regions in patients with ALS. Methods: A systematic review was conducted in the Scopus and PubMed databases for studies published between January 2000 and July 2023. The inclusion criteria were (1) MRI studies to assess hippocampal and parahippocampal regions in ALS patients, and (2) studies reporting neuropsychological data in patients with ALS. Results: A total of 46 studies were included. Structural MRI revealed hippocampal atrophy, especially in ALS‐FTD, involving specific subregions (CA1, dentate gyrus). Disease progression and genetic factors impacted atrophy patterns. Diffusion tensor imaging (DTI) showed increased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in the hippocampal tracts and adjacent regions, indicating loss of neuronal and white matter integrity. Functional MRI (fMRI) revealed reduced functional connectivity (FC) between the hippocampus, parahippocampus, and other regions, suggesting disrupted networks. Perfusion MRI showed hypoperfusion in parahippocampal gyri. Magnetic resonance spectroscopy (MRS) found changes in the hippocampus, indicating neuronal loss. Neuropsychological tests showed associations between poorer memory and hippocampal atrophy or connectivity changes. CA1‐2, dentate gyrus, and fimbria atrophy were correlated with worse memory. Conclusions: The hippocampus and the connected regions are involved in ALS. Hippocampal atrophy disrupted connectivity and metabolite changes correlate with cognitive and functional decline. Specific subregions can be particularly affected. The hippocampus is a potential biomarker for disease monitoring and prognosis. [ABSTRACT FROM AUTHOR]

Background: Antiangiogenic therapies for glioblastoma (GBM) such as bevacizumab (BVZ), have been unable to extend survival in large patient cohorts. However, a subset of patients having angiogenesis-dependent tumors might benefit from these therapies. Currently, there are no biomarkers allowing to discriminate responders from non-responders before the start of the therapy. Methods: 40 patients from the randomized GENOM009 study complied the inclusion criteria (quality of images, clinical data available). Of those, 23 patients received first line temozolomide (TMZ) for eight weeks and then concomitant radiotherapy and TMZ. 17 patients received BVZ+TMZ for seven weeks and then added radiotherapy to the treatment. Clinical variables were collected, tumors segmented and several geometrical measures computed including: Contrast enhancing (CE), necrotic, and total volumes; equivalent spherical CE width; several geometric measures of the CE ‘rim’ geometry and a set of image texture measures. The significance of the results was studied using Kaplan-Meier and Cox proportional hazards analysis. Correlations were assessed using Spearman correlation coefficients. Results: Kaplan-Meier and Cox proportional hazards analysis showed that total, CE and inner volume (p = 0.019, HR = 4.258) and geometric heterogeneity of the CE areas (p = 0.011, HR = 3.931) were significant parameters identifying response to BVZ. The group of patients with either regular CE areas (small geometric heterogeneity, median difference survival 15.88 months, p = 0.011) or those with small necrotic volume (median survival difference 14.50 months, p = 0.047) benefited substantially from BVZ. Conclusion: Imaging biomarkers related to the irregularity of contrast enhancing areas and the necrotic volume were able to discriminate GBM patients with a substantial survival benefit from BVZ. A prospective study is needed to validate our results. [ABSTRACT FROM AUTHOR]

Background: Image texture analysis is a noninvasive technique for quantifying intratumoral heterogeneity, with derived texture features reported to be closely related to the treatment outcome of tumors. Gastric cancer is one of the most common tumors and the third leading cause of cancer-related deaths worldwide. Although trastuzumab is associated with a survival gain among patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer, optimal patient selection is challenging. The purpose of this study was to determine whether CT texture features of HER2-positive gastric cancer were related to the survival rate after trastuzumab treatment. Methods and Findings: Patients diagnosed with HER2-positive advanced gastric cancer from February 2007 to August 2014 were retrospectively selected. Using in-house built software, histogram features (kurtosis and skewness) and gray-level co-occurrence matrices (GLCM) features (angular second moment [ASM], contrast, entropy, variance, and correlation) were derived from the CT images of HER2-positive advanced gastric cancer in 26 patients. All the patients were followed up for more than 6 months, with no confirmed deaths. The patients were dichotomized into a good and poor survival group based on cutoff points of overall survival of 12 months. A receiver-operating characteristics (ROC) analysis was performed to test the ability of each texture parameter to identify the good survival group. Kaplan–Meier curves for patients above and below each threshold were constructed. Using a threshold of >265.8480 for contrast, >488.3150 for variance, and ≤0.1319×10−3. for correlation, all of the area under the ROC curves showed fair accuracy (>0.7). Kaplan–Meier analysis showed statistically significant survival difference between two groups according to optimal cutoff values of contrast, variance, correlation and ASM. However, as this study had a small number of patients, a further study with a larger population will be needed to validate the results. Conclusions: Heterogeneous texture features on CT images were associated with better survival in patients with HER2-positive advanced gastric cancer who received trastuzumab-based treatment. Therefore, texture analysis shows potential to be a clinically useful imaging biomarker providing additional prognostic information for patient selection. [ABSTRACT FROM AUTHOR]

Purpose: Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD., Methods: Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features., Results: The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation., Conclusion: The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

Objectives: Because size-based imaging criteria poorly capture biologic response in desmoid-type fibromatosis (DF), changes in MRI T2 signal intensity are frequently used as a response surrogate, but remain qualitative. We hypothesized that absolute quantification of DF T2 relaxation time derived from parametric T2 maps would be a feasible and effective imaging biomarker of disease activity. Methods: This IRB-approved retrospective study included 11 patients with DF, managed by observation or systemic therapy, assessed by 3T MRI. Tumor maximum diameter, volume, and T2-weighted signal intensity were derived from manual tumor segmentations. Tumor:muscle T2 signal ratios were recorded. Two readers measured tumor T2 relaxation times using a commercial T2 scanning sequence, manual ROI delineation and commercial calculation software enabling estimation of reader reliability. Objective response rates based on RECIST1.1 and best responses were compared between sizebased and signal-based parameters. Results: Median patient age was 52.6 years; 8 subjects were female (73%). Nine patients with longitudinal assessments were followed for an average of 314 days. Median baseline tumor diameter was 7.2 cm (range 4.4 - 18.2 cm). Median baseline T2 was 65.1 ms (range 40.4 - 94.8 ms, n=11); median at last follow-up was 44.3 ms (-32% from baseline; range 29.3 - 94.7 ms, n=9). T2 relaxation times correlated with tumor:muscle T2 signal ratios, Spearman p=0.78 (p<0.001). T2 mapping showed high inter-reader reliability, ICC=0.84. The best response as a percentage change in T2 values was statistically significant (mean -17.9%, p=0.05, paired t-test) while change in diameter was not (mean -8.9%, p=0.12). Conclusions: Analysis of T2 relaxation time maps of DF may offer a feasible quantitative biomarker for assessing the extent of response to treatment. This approach may have high inter-reader reliability. [ABSTRACT FROM AUTHOR]

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled 'radiogenomics', a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research. [ABSTRACT FROM AUTHOR]

Nephron number (Nglom) and size (Vglom) are correlated with risk for chronic cardiovascular and kidney disease and may be predictive of renal allograft viability. Unfortunately, there are no techniques to assess Nglom and Vglom in intact kidneys. This work demonstrates the use of cationized ferritin (CF) as a magnetic resonance imaging (MRI) contrast agent to measure Nglom and Vglom in viable human kidneys donated to science. The kidneys were obtained from patients with varying levels of cardiovascular and renal disease. CF was intravenously injected into three viable human kidneys. A fourth control kidney was perfused with saline. After fixation, immunofluorescence and electron microscopy confirmed binding of CF to the glomerulus. The intact kidneys were imaged with three-dimensional MRI and CF-labeled glomeruli appeared as punctate spots. Custom software identified, counted, and measured the apparent volumes of CF-labeled glomeruli, with an ~6% false positive rate. These measurements were comparable to stereological estimates. The MRI-based technique yielded a novel whole kidney distribution of glomerular volumes. Histopathology demonstrated that the distribution of CF-labeled glomeruli may be predictive of glomerular and vascular disease. Variations in CF distribution were quantified using image texture analyses, which be a useful marker of glomerular sclerosis. This is the first report of direct measurement of glomerular number and volume in intact human kidneys. [ABSTRACT FROM AUTHOR]

Glioma is the primary tumor in the central nervous system, and poses one of the greatest challenges in clinical treatment. MIB-1 and p53 are the most useful biomarkers for gliomas and could help neurosurgeons establish a therapeutic schedule. However, these biomarkers are commonly detected with the help of immunohistochemistry (IHC), which wastes time and energy and is often influenced by subjective factors. To reduce the subjective factors and improve the efficiency in the judgment of IHC, a novel magnetic resonance image (MRI) analysis method is proposed in the present study to detect the expression status of MIB-1 and p53 in IHC. The proposed method includes two kinds of MRI acquisition (FLAIR and T1 FLAIR images), regions of interest (ROIs) selection, texture features (i.e. the gray level gradient cooccurrence matrix (GLGCM), Minkowski functions (MFs), etc) extraction in ROIs, and classification with a support vector machine in a leave-oneout cross validation strategy. By classifying the ROIs, the performance of the method was evaluated by accuracy, area under ROC curve (AUC), etc. A high accuracy (0.7640 ± 0.0225) and AUC (0.7873 ± 0.0377) for MIB-I detection were achieved. In terms of the texture features, 0.7621 ± 0.0199, 0.7666±0.0365 and 0.7426±0.0451AUCcan be obtained using only GLCM, RLM or GLGCM for MIB-1 detection, respectively. In all, the experimental results demonstrated that MR image texture features are associated with the expression status of MIB-1 and p53. The proposed method has the potential to realize high accuracy and robust detection for MIB-I expression status, which makes it promising for clinical glioma diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

To evaluate the association between radiomic features (RFs) extracted from 18F‐FDG PET/CT (18F‐FDG‐PET) with progression‐free survival (PFS) and overall survival (OS) in diffuse large‐B‐cell lymphoma (DLBCL) patients eligible to first‐line chemotherapy. DLBCL patients who underwent 18F‐FDG‐PET prior to first‐line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical‐radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow‐up was 34.7 months (Inter‐Quartile Range (IQR) 11.3–66.3 months) for PFS and 41.1 (IQR 18.4–68.9) for OS. Radiomic score resulted associated with PFS and OS (p < 0.001), outperforming conventional PET parameters. C‐index (95% CI) for PFS prediction were 0.67 (0.58–0.76), 0.81 (0.75–0.88) and 0.84 (0.77–0.91) for clinical, radiomic and combined clinical‐radiomic model, respectively. C‐index for OS were 0.77 (0.66–0.89), 0.84 (0.76–0.91) and 0.90 (0.81–0.98). In the Kaplan‐Meier analysis (low‐IPI vs. high‐IPI), the radiomic score was significant predictor of PFS (p < 0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18F‐FDG‐PET might be proposed in DLBCL to stratify high‐risk versus low‐risk patients of relapse after first‐line therapy, especially in low‐IPI patients. [ABSTRACT FROM AUTHOR]

Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood‐based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour‐based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non‐recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non‐invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood‐based liquid biopsies obtained primarily from tumour‐educated blood platelets (TEP) is utilised. This RNA‐seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF‐MAPK, P53, PRC2‐EZH2, YAP conserved, MEK‐MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well‐known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it. [ABSTRACT FROM AUTHOR]

Eye diseases that are common and many diseases that result in visual ailments, such as diabetes and vascular disease, can be diagnosed through retinal imaging. The enhancement of retinal images often helps in diagnosing diseases related to retinal organ failure. However, today's image enhancement methods may lead to artificial boundaries, sudden color gradation, and the loss of image details. Therefore, to prevent these side effects, a new method of retinal image enhancement is proposed. In this work, we propose a new method for enhancing the overall contrast of colored retinal images. That is, we propose low-light image enhancement using a new retinex method based on a powerful semidecoupled retinex method. In particular, illumination layer I gradually approximates the S input image according to the file. This leads to a complete Gaussian transformation model, while the R-layer reflectance is estimated jointly by S and intermediary by I to suppress image noise simultaneously during R estimation on the publicly available Messidor database. From our assessment measurements (PSNR and SSIM), we show that this proposed method is effective in comparison with the relevant and recently proposed retinal imaging methods; moreover, the color, which is determined by the data, does not change the image structure. Finally, a technique is presented to improve the pronounced color of a retinal image, which is useful for ophthalmologists to screen for retinal disease more effectively. Moreover, this technique can be used in the development of robotics for imaging tests to search for clinical markers. [ABSTRACT FROM AUTHOR]

Texture analysis is a quantitative imaging analysis that provides novel biomarkers beyond conventional image reading. Our aim was to use texture analysis of pulmonary emboli derived from thoracic computed tomography for prediction of mortality and prognosis of acute pulmonary embolism (PE). Overall, 216 patients (116 female, 53.7%) were included in the analysis. Texture analysis was calculated on axial slices of the contrast enhanced pulmonary angiography of the proximal embolus. Clinical scores, serological parameters, need for intubation, intensive care unit (ICU) admission and mortality was assessed and correlated with the texture features. In the correlation analysis, there were several associations with mortality in days, the highest for the parameter S(0,5)SumVarnc (r = −0.43, P < 0.001). Another parameter, S(3,−3)AngScMom correlated with sepsis-related organ failure assessment score (SOFA)-score (r = 0.31, P < 0.001). Several texture features correlated with venous lactate and glucose levels. In discrimination analysis, there were significant differences in regard to texture features between survivors and non-survivors and between patients with and without the need for ICU admission (P = 0.02, respectively). These results highlight the potential clinical benefit of texture features in patients with acute PE as novel imaging biomarkers. Further studies are needed to validate these results. [ABSTRACT FROM AUTHOR]

Background: Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods: We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results: Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data. Conclusions: QI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform. [ABSTRACT FROM AUTHOR]

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes. [ABSTRACT FROM AUTHOR]

Featured Application: The present work contributes to the investigation on the robustness and reproducibility of radiomic studies, a fundamental step for bringing radiomics towards clinical implementation. Radiomic analysis of 18F[FDG] PET/CT images might identify predictive imaging biomarkers, however, the reproducibility of this quantitative approach might depend on the methodology adopted for image analysis. This retrospective study investigates the impact of PET segmentation method and the selection of different target lesions on the radiomic analysis of baseline 18F[FDG] PET/CT images in a population of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. The whole tumor burden was segmented on PET images applying six methods: (1) 2.5 standardized uptake value (SUV) threshold; (2) 25% maximum SUV (SUVmax) threshold; (3) 42% SUVmax threshold; (4) 1.3∙liver uptake threshold; (5) intersection among 1, 2, 4; and (6) intersection among 1, 3, 4. For each method, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG) were assessed, and their association with survival outcomes (progression-free survival PFS and overall survival OS) was investigated. Methods 1 and 2 provided stronger associations and were selected for the next steps. Radiomic analysis was then performed on two target lesions for each patient: the one with the highest SUV and the largest one. Fifty-three radiomic features were extracted, and radiomic scores to predict PFS and OS were obtained. Two proportional-hazard regression Cox models for PFS and OS were developed: (1) univariate radiomic models based on radiomic score; and (2) multivariable clinical–radiomic model including radiomic score and clinical/diagnostic parameters (IPI score, SUVmax, TMTV, WTLG, lesion volume). The models were created in the four scenarios obtained by varying the segmentation method and/or the target lesion; the models' performances were compared (C-index). In all scenarios, the radiomic score was significantly associated with PFS and OS both at univariate and multivariable analysis (p < 0.001), in the latter case in association with the IPI score. When comparing the models' performances in the four scenarios, the C-indexes agreed within the confidence interval. C-index ranges were 0.79–0.81 and 0.80–0.83 for PFS radiomic and clinical–radiomic models; 0.82–0.87 and 0.83–0.90 for OS radiomic and clinical–radiomic models. In conclusion, the selection of either between two PET segmentation methods and two target lesions for radiomic analysis did not significantly affect the performance of the prognostic models built on radiomic and clinical data of DLBCL patients. These results prompt further investigation of the proposed methodology on a validation dataset. [ABSTRACT FROM AUTHOR]

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics. [ABSTRACT FROM AUTHOR]

Simple Summary: Few significant advances have been made over recent decades in predicting lung cancer progression risk after complete surgical removal of tumor in stage IA non-small-cell lung cancers (NSCLCs). Although several biomarkers have shown some predictive value, it is unclear whether these markers add value to traditional TNM staging. We developed an integrated deep learning evaluation (IDLE) score to combine patient's preoperative lung CT image findings and postoperative pathologic assessment and found that this score can better predict cancer progression risk than TNM staging and tumor grade. Improved predictive value of the IDLE score was primarily due to the complementary use of tumor measurements in CT images from an entire lung as well as microscopic tissue characteristics. Our findings suggest that integrating measurements from different aspects of tumor morphology is more robust for increasing prediction accuracy than building on the measurements of similar aspects of tumor morphology. Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist's readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis. [ABSTRACT FROM AUTHOR]

Using a novel variant of polarized light microscopy for high-contrast imaging and quantification of unstained histology slides, the current study assesses the prognostic potential of peri-tumoral collagenous stroma architecture in 32 human stage III colorectal cancer (CRC) patient samples. We analyze three distinct polarimetrically-derived images and their associated texture features, explore different unsupervised clustering algorithm models to group the data, and compare the resultant groupings with patient survival. The results demonstrate an appreciable total accuracy of ~ 78% with significant separation (p < 0.05) across all approaches for the binary classification of 5-year patient survival outcomes. Surviving patients preferentially belonged to Cluster 1 irrespective of model approach, suggesting similar stromal microstructural characteristics in this sub-population. The results suggest that polarimetrically-derived stromal biomarkers may possess prognostic value that could improve clinical management/treatment stratification in CRC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Colon cancer is one of the most common cancers in the world, and the therapeutic workflow is dependent on the TNM staging system and the presence of clinical risk factors. However, in the case of patients with non-metastatic disease, evaluating the benefit of adjuvant chemotherapy is a clinical challenge. Radiomics could be seen as a non-invasive novel imaging biomarker able to outline tumor phenotype and to predict patient prognosis by analyzing preoperative medical images. Radiomics might provide decisional support for oncologists with the goal to reduce the number of arbitrary decisions in the emerging era of personalized medicine. To date, much evidence highlights the strengths of radiomics in cancer workup, but several aspects limit the use of radiomics methods as routine. The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann–Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC <  0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease. [ABSTRACT FROM AUTHOR]

Background: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. Method: FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. Results: The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. Conclusion: The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD. [ABSTRACT FROM AUTHOR]

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastomas carry a poor prognosis and usually presents with heterogeneous regions in the brain tumor. Multi-parametric MR images can show morphological characteristics. Radiomics features refer to the extraction of a large number of quantitative measurements that describe the geometry, intensity, and texture which were extracted from contrast-enhanced T1-weighted images from anatomical MRI and metabolic features from PET. It also provides a qualitative image interpretation as well as cellular, molecular, and tumor properties. Thus, it derives additional information about the entire tumor volume which is generally of irregular shape and size from routinely evaluated "non-invasive" imaging biomarkers techniques. We demonstrated volumetric habitats and signatures in necrosis, solid tumor, peritumoral tissue, and edema with key biological processes and phenotype features. This provides physicians with key information on how the disease is progressing in the brain and can also give an indication of how well treatment is working. Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to construct a radiomics-based MRI sequence from high-resolution magnetic resonance imaging (HRMRI), combined with clinical high-risk factors for non-invasive differentiation of the plaque of symptomatic patients from asyptomatic patients. Methods: A total of 115 patients were retrospectively recruited. HRMRI was performed, and patients were diagnosed with symptomatic plaques (SPs) and asymptomatic plaques (ASPs). Patients were randomly divided into training and test groups in the ratio of 7:3. T2WI was used for segmentation and extraction of the texture features. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were employed for the optimized model. Radscore was applied to construct a diagnostic model considering the T2WI texture features and patient demography to assess the power in differentiating SPs and ASPs. Results: SPs and ASPs were seen in 75 and 40 patients, respectively. Thirty texture features were selected by mRMR, and LASSO identified a radscore of 16 radiomics features as being related to plaque vulnerability. The radscore, consisting of eight texture features, showed a better diagnostic performance than clinical information, both in the training (area under the curve [AUC], 0.923 vs. 0.713) and test groups (AUC, 0.989 vs. 0.735). The combination model of texture and clinical information had the best performance in assessing lesion vulnerability in both the training (AUC, 0.926) and test groups (AUC, 0.898). Conclusion: This study demonstrated that HRMRI texture features provide incremental value for carotid atherosclerotic risk assessment. [ABSTRACT FROM AUTHOR]

Objectives: To explore the effectiveness of magnetic resonance image (MRI)-based biomarkers for identifying benign and malignant parotid tumors via diagnostic model analysis. Methods: This retrospective study included 109 patients (development cohort and validation cohort) who underwent MRI preoperatively, including T1- and T2-weighted images. Parameters based on 2D or 3D texture analysis were extracted from tumor lesions by MaZda software, fisher discriminant and bootstrap method were used to perform parameter reduction, diagnostic models with the selected biomarkers were established along with clinical data, model performance (discrimination and calibration) was furtherly evaluated by internal and external validation, decision curve analysis was applied to measure the improvement of clinical benefits. Results: S(5,5) Entrop, S(0,1) ASM, WavEnHH (s-4), S(1,1,0) Entropy and Perc.10% were significantly associated with the pathological diagnosis of parotid tumor (benign versus malignancy), when adding these biomarkers to the regression analysis, model performance significantly improved in the development cohort (likelihood-ratio-test; p < 0.05, with an increase of AUC from 0.72 (reference model) to 0.85), and these results were maintained in a small external validation cohort. Decision curve analysis indicated that clinical benefit was greater with the application of MRI-based biomarkers. Conclusions: MRI-based texture analysis is proven to be an effective tool in differentiating benign and malignant parotid tumors, preoperative diagnosis was improved with the selected biomarkers compared to the reference model. [ABSTRACT FROM AUTHOR]

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction. [ABSTRACT FROM AUTHOR]

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials. [ABSTRACT FROM AUTHOR]

Autosomal dominant polycystic kidney disease (ADPKD) and autosomal recessive polycystic kidney disease (ARPKD) are characterized by bilateral cystic kidney disease leading to progressive kidney function decline. These diseases also have distinct liver manifestations. The range of clinical presentation and severity of both ADPKD and ARPKD is much wider than was once recognized. Pediatric and adult nephrologists are likely to care for individuals with both diseases in their lifetimes. This article will review genetic, clinical, and imaging predictors of kidney and liver disease progression in ADPKD and ARPKD and will briefly summarize pharmacologic therapies to prevent progression. [ABSTRACT FROM AUTHOR]

Purpose: Recent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel. Method: A cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer‐aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well‐known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA. Results: All included radiomic features lacked intercondition reproducibility (OCCC < 0.9) among all the 36 conditions. Out of 226 radiomic features analyzed, only 17 and 18 features were considered reproducible (OCCC ≥ 0.9) to dose and kernel variation, respectively, within the corresponding condition subsets. Slice thickness demonstrated the largest impact on radiomic feature values where only one to five features were reproducible at a few condition subsets. ANOVA revealed significant interactions (P < 0.05) between CT parameters affecting the variability of >50% of radiomic features. Conclusion: We systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies. [ABSTRACT FROM AUTHOR]

Background: Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. Purpose: To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. Study Type: Systematic review and meta‐analysis. Population: In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. Field Strength/Sequence: 1.0T, 1.5T, or 3.0T/fluid‐attenuated inversion recovery (FLAIR) and/or proton density/T2‐weighted fast spin echo sequences or gradient echo T1‐weighted sequences. Assessment: After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. Statistical Tests: The pooled WMH volume with 95% confidence interval (CI) was calculated using the random‐effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta‐regression analysis of WMH volume on age was performed. Results: Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta‐analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88–5.53 cm3). At meta‐regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27). Data Conclusion: The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. Level of Evidence: 1 Technical Efficacy Stage: 1 [ABSTRACT FROM AUTHOR]

This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules. [ABSTRACT FROM AUTHOR]

The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis. [ABSTRACT FROM AUTHOR]