High Gray Level Research Articles

Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung

The rapidly evolving field of radiomics has shown that radiomic features are able to capture characteristics of both tumor and normal tissue that can be used to make accurate and clinically relevant predictions. In the present study we sought to determine if radiomic features can characterize the adverse effects caused by normal tissue injury as well as identify if human embryonic stem cell (hESC) derived extracellular vesicle (EV) treatment can resolve certain adverse complications. A cohort of 72 mice (n = 12 per treatment group) were exposed to X-ray radiation to the whole lung (3 × 8 Gy) or to the apex of the right lung (3 × 12 Gy), immediately followed by retro-orbital injection of EVs. Cone-Beam Computed Tomography images were acquired before and 2 weeks after treatment. In total, 851 radiomic features were extracted from the whole lungs and < 20 features were selected to train and validate a series of random forest classification models trained to predict radiation status, EV status and treatment group. It was found that all three classification models achieved significantly high prediction accuracies on a validation subset of the dataset (AUCs of 0.91, 0.86 and 0.80 respectively). In the locally irradiated lung, a significant difference between irradiated and unirradiated groups as well as an EV sparing effect were observed in several radiomic features that were not seen in the unirradiated lung (including wavelet-LLH Kurtosis, wavelet HLL Large Area High Gray Level Emphasis, and Gray Level Non-Uniformity). Additionally, a radiation difference was not observed in a secondary comparison cohort, but there was no impact of imaging machine parameters on the radiomic signature of unirradiated mice. Our data demonstrate that radiomics has the potential to identify radiation-induced lung injury and could be applied to predict therapeutic efficacy at early timepoints.

Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma.

The adrenocortical carcinoma (ACC) is a rare and highly aggressive malignancy originating from the adrenal cortex. These patients usually undergo chemotherapy with etoposide, doxorubicin, cisplatin and mitotane (EDP-M) in case of locally advanced or metastatic ACC. Computed tomography (CT) radiomics showed to be useful in adrenal pathologies. The study aimed to analyze the association between response to EDP-M treatment and CT textural features at diagnosis in patients with locally advanced or metastatic ACCs. We enrolled 17 patients with advanced or metastatic ACC who underwent CT before and after EDP-M therapy. The response to treatment was evaluated according to RECIST 1.1, Choi, and volumetric criteria. Based on the aforementioned criteria, the patients were classified as responders and not responders. Textural features were extracted from the biggest lesion in contrast-enhanced CT images with LifeX software. ROC curves were drawn for the variables that were significantly different (p < 0.05) between the two groups. Long-run high grey level emphasis (LRHGLE_GLRLM) and histogram kurtosis were significantly different between responder and not responder groups (p = 0.04) and the multivariate ROC curve combining the two features showed a very good AUC (0.900; 95%IC: 0.724-1.000) in discriminating responders from not responders. More heterogeneous tissue texture of initial staging CT in locally advanced or metastatic ACC could predict the positive response to EDP-M treatment. Adrenal texture is able to predict the response to EDP-M therapy in patients with advanced ACC.

Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: Data from the osteoarthritis initiative.

The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit using intermediate-weighted fat-suppressed MR images. One reader manually segmented each participant's anterior and posterior horn of the medial menisci at the OAI baseline visit. Next, 61 different radiomic features were extracted from each medial meniscus horn. We performed a classification and regression tree (CART) analysis to determine the classification rules and important variables that predict incident destabilizing meniscal tear. The CART correctly classified 24 of the 34 cases and 172 out of 181 controls with a sensitivity of 70.6% and a specificity of 95.0%. The CART identified large zone high gray level emphasis (i.e., more coarse texture) from the posterior horn as the most important variable to classify who would develop an incident destabilizing medial meniscal tear. The use of radiomic features provides sensitive and quantitative measures of meniscal alterations, allowing us to intervene and prevent destabilizing meniscal tears.

Comparative Analysis of LSTM and BiLSTM in Image Detection Processing

Tuberculosis is an infectious disease and requires serious treatment. Extrapulmonary Tuberculosis is detected using a microscope. Currently it will take a long time because the fluid preparations are viewed in a microscope one by one carefully and in the fluid preparations there are 150 fields of vision. Examination for Extra Pulmonary Tuberculosis by culture takes between 1-2 weeks or even more. Examination by biopsy will take a long time because the fluid preparations are looked at carefully under the microscope one by one. The image of Tuberculosis is expressed if in the image there is a bacillus object in red, and it turns out that apart from the bacillus object there are other objects also in red. So that examinations for tuberculosis can be more efficient, examinations using computer technology are needed. This research aims to compare the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) classification methods in the detection of extra-pulmonary tuberculosis disease to obtain better accuracy results. This research carried out HSI color space transformation, segmentation using global thresholding, feature extraction using 13 features based on shape and texture using the Correlation Based Feature Selection (CFS) feature selection method. The results show that BiLSTM has the best accuracy with a value of 88.40% at the number of features = 3, namely Short Run High Gray-Level Emphasis, Run Length Nonuniformity, Minor axis length, while LSTM produces an accuracy of 63.19% at the number of features = 5. BiLSTM is capable of detecting opposite features, meaning that BiLSTM can detect opposite features in data sequences and BiLSTM's ability to understand multiple contexts, so it tends to provide more accurate results in some data classification tasks.

Clinical significance of signal shadowing during intraoperative optical coherence tomography-assisted vitreoretinal surgery

This study aimed to analyze the clinical significance of signal shadowing during intraoperative optical coherence tomography (iOCT)-assisted vitreoretinal surgery caused by vitreoretinal instruments, tissue dyes, and vitreous substitutes, and to objectively quantify its impact on iOCT imaging. This is a retrospective observational study of postoperative image analysis from one hundred seventeen (117) patients who underwent iOCT-assisted vitrectomy. The image data were divided into three groups: vitreoretinal instruments, tissue dyes, and vitreous substitutes. The data was then processed using graphic software to measure the grade of picture quality distortion and compared to paired image controls without clinically perceptive interference, then analyzed statistically. The intraocular portion of all studied vitreoretinal instruments caused a high average gray level interference compared to controls ranging from 32 to 68% reduction, obscuring the area of interest significantly. The tips of the instruments produced low-grade shadowing, allowing the underlying tissue to be distinguished. The analyzed dyes demonstrated a wide interference range: ICG (− 75.12%), and triamcinolone (− 26.13%) showed dose-dependent high shadowing, while VITREODYNE™ (49.3%) and brilliant blue G (14.06%) exhibited no perceived distortions whilst increasing average gray levels. All analyzed vitreous substitutes (air, SF6, C3F8, PFCL, and silicone oil) showed an insignificant shadowing effect on iOCT. Certain dyes and vitreous substitutes produce a negligible shadowing effect compared to controls and other dyes, providing an advantage during real-time iOCT imaging. All analyzed vitreoretinal instruments showed a significant interference that should prompt the development of new imaging techniques or the implementation of materials with low-grade interference to overcome a clinically relevant shadowing effect on iOCT, maximizing the technology’s visual accuracy and surgical diagnostic aid proficiency.

A Fast Gap Detection Algorithm Based on Machine Vision

In order to be able to quickly locate the position of the gap of an industrial device in the industrial inspection process, a fast gap detection algorithm based on machine vision is proposed by using the feature of a high gray level difference between the image gap and the background in machine vision. First, nonlinear equalization preprocessing is adopted, and then multiple one-dimensional functions are composed based on the sum of the gray values of the horizontal axis as well as the differentiation, and then the proximity position averaging operation is done after obtaining the positional information through the extreme value and the most value operation. The algorithm transforms the two-dimensional image into a combination of multiple one-dimensional functions with related features, and finally accurate position information is obtained by analysing and fusing the one-dimensional functions. Multiple discriminative bases are used for the same data source to ensure the accuracy of the results. Experiments show that the method proposed is faster and less error-prone, has higher accuracy and detection efficiency, and meets the needs of industrial real-time detection, compared with the traditional Hough transform algorithm and the LSD detection algorithm.

Assessment of short-term effect of platelet-rich plasma treatment of tendinosis using texture analysis of ultrasound images.

Computer-aided diagnosis (i.e., texture analyses) tools are becoming increasingly beneficial methods to monitor subtle tissue changes. The aim of this pilot study was to investigate short-term effect of platelet rich plasma (PRP) treatment in supraspinatus and common extensor of the forearm tendinosis by using texture analysis of ultrasound (US) images as well as by clinical questionnaires. Thirteen patients (7 male and 6 female, age 36-60 years, mean age 51.2 ± 5.2) were followed after US guided PRP treatment for tendinosis of two tendons (9 patients with lateral epicondylitis and 4 with supraspinatus tendinosis). Clinical and US assessment was performed prior to as well as 3 months after PRP treatment with validated clinical questionnaires. Tissue response in tendons was assessed by using gray level run length matrix method (GLRLM) of US images. All patients improved of tendinosis symptoms after PRP treatment according to clinical questionnaires. Almost all GLRLM features were statistically improved 3 months after PRP treatment. GLRLM-long run high gray level emphasis (LRLGLE) revealed the best moderate positive and statistically significant correlation after PRP (r = 0.4373, p = 0.0255), followed by GLRLM-low gray level run emphasis (LGLRE) (r = 0.3877, p = 0.05). Texture analysis of tendinosis US images was a useful quantitative method for the assessment of tendon remodeling after minimally invasive PRP treatment. GLRLM features have the potential to become useful imaging biomarkers to monitor spatial and time limited tissue response after PRP, however larger studies with similar protocols are needed.

Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics.

We aimed to investigate the feasibility of metabolomics to explain the underlying biological implications of radiomics features obtained from magnetic resonance imaging (MRI) preceding carbon ion radiotherapy (CIRT) in patients with prostate cancer and to further explore the clinical significance of radiomics features on the prognosis of patients, based on their biochemical recurrence (BCR) status. Metabolomic results obtained using high-performance liquid chromatography coupled with tandem mass spectrometry of urine samples, combined with pre-RT radiomic features extracted from MRI images, were evaluated to investigate their biological significance. Receiver operating characteristic (ROC) curve analysis was subsequently conducted to examine the correlation between these biological implications and clinical BCR status. Statistical and metabolic pathway analyses were performed using MetaboAnalyst and R software. Correlation analysis revealed that methionine alteration extent was significantly related to four radiomic features (Contrast, Difference Variance, Small Dependence High Gray Level Emphasis, and Mean Absolute Deviation), which were significantly correlated with BCR status. The area under the curve (AUC) for BCR prediction of these four radiomic features ranged from 0.704 to 0.769, suggesting that the higher the value of these four radiomic features, the greater the decrease in methionine levels after CIRT and the lower the probability of BCR. Pre-CIRT MRI radiomic features were associated with CIRT-suppressed metabolites. These radiomic features can be used to predict the alteration in the amplitude of methionine after CIRT and the BCR status, which may contribute to the optimization of the CIRT strategy and deepen the understanding of PCa.

Development of CT-based radiomic model to predict 5-year progression-free survival (PFS) in locally advanced head and neck squamous cell carcinoma (LAHNSCC) treated with definitive chemoradiation.

6076 Background: Definitive chemoradiation is the current standard treatment in LAHNSCC. Prognostic biomarkers that enable to reliably predict LAHNSCC disease progression are needed to stratify the risk of progression to tailor treatment intensity in future clinical trials. Imaging biomarkers have emerged as promising alternatives to non-invasively predict patient outcomes. Aim: to identify an imaging signature to predict progression-free survival (PFS) in LAHNSCC. Methods: A single-center, retrospective, observational study was designed. Clinical data and pre-treatment CT images from LAHNSCC patients treated with definitive chemoradiation from 2014 to 2022 were collected. Manual tumor segmentation was performed slice by slice by a qualified image technician and supervised by a radiologist with 20+ years in HNSCC using the DICOM viewer of Quibim platform. A total of 108 radiomic features (shape and textures) were extracted from each region of interest. Feature reduction techniques were applied to select the characteristics used in the classification model. The primary endpoint of this study was 5-year PFS. Random Forest, Support Vector Classifier (SVC), Logistic Regression, Gradient Boosting (GB) and Extreme Gradient Boosting classification models were used. A 5-fold cross validation strategy was followed to evaluate the performance of the models in terms of AUROC, sensitivity, specificity, and accuracy. The importance of each feature in the model was measured by applying the Shapley Additive exPlanations (SHAP) method. Results: Baseline CT exams from 102 LAHNSCC patients were included; 50% and 23% were stage IVA and IVB, respectively. The remaining 27% (stage II [3%] and stage III [24%]) were treated in an organ-preservation intention. 67% of patients presented locoregional or distant progression at 5 years. All radiomic features (108) and 6 clinical variables were used in the predictive model, for which a cross-validation was performed. GB as the feature selection technique and SVC as the classification model provided the best results. The final model was able to predict 5-year PFS with an average AUC of 0.82 (95% CI: [0.73, 0.91]), a sensitivity of 0.69 (95% CI: [0.57, 0.81]), a specificity of 0.82 (95% CI: [0.7, 0.94]) and an accuracy of 0.73 (95% CI: [0.62, 0.84]). From most to least important, features contributing to the predictive model were: TNM stage (SHAP: 0.038), glszm Small Area Emphasis (SHAP: 0.026), glcm Idmn (SHAP: 0.023), glszm Large Area High Gray Level Emphasis (SHAP: 0.018) and glrlm Run Entropy (SHAP: 0.015). Conclusions: Radiomic biomarkers from pre-treatment CT images in combination with TNM stage were predictive of 5-year PFS to chemoradiation in LAHNSCC patients and might be helpful for patient risk stratification. Further validation of these imaging biomarkers is ongoing.

Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.

In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT.

This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.

Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

The texture analysis of cancer cells leads to a procedure to distinguish spatial differences within an image and extract essential information. This study used two test tumours images to determine cancer type, location, and geometric characteristics (area, size, dimensions, radius, etc.). The suggested algorithm was designed to detect and distinguish breast cancer using the segmentation-based threshold technique. The method of texture analysis Grey Level Size Zone method was used to extract 11 features: Small Zone Emphasis, Large Zone Emphasis, Low Grey Level Zone Emphasis, High Grey Level Zone Emphasis, Small Zone Low Grey Level Emphasis, Small Zone High Grey Level Emphasis, Large Zone Low Grey Level Emphasis, Large Zone High Grey Level Emphasis, Grey Level Non-Uniformity Normalized, Grey Level Non-Uniformity, and Zone Percentage. The results show that the tumuor's location is highly accurate depending on the extracting properties. The results of the suggested method give the decision to identify the type of tumours. The geometry of the tumour helps describe the tumour.

Spin‐Selective Full and Subtle Light Intensity Manipulation with Diatomic Metasurfaces

AbstractMetasurfaces are considered to be ideal candidates for precise and continuous manipulation of optical wave intensity at nanoscale pixels, which is the main basis of display and imaging devices. Malus's law has been widely used as the basic design principle for the realization of linearly polarized optical wave intensity manipulation. However, there is no such straightforward and concise law like Malus's law that can be used to design metasurfaces for full and subtle intensity manipulation of circularly polarized optical waves. Here, a new design strategy based on the collective interference effect in diatomic metasurfaces is presented to fill this gap, which can create a simple and complete mapping from a single structural variable to the reflection intensity of circularly polarized waves. The full and subtle intensity manipulation of circularly polarized optical waves can be conveniently realized by changing one structural parameter in the diatomic metasurface. Spin‐selective high‐level grayscale images are numerically and experimentally implemented based on the proposed diatomic metasurfaces. The proposed grayscale imaging scheme with subwavelength spatial resolution and high gray level is promising for advanced display and information encryption applications.

Signal shadowing during intraoperative optical coherence tomography assisted vitreoretinal surgery: A systemic analysis

AbstractPurpose: To analyse the clinical significance of signal shadowing during intraoperative optical coherence tomography (iOCT) assisted vitreoretinal surgery (PPV) caused by vitreoretinal instruments (VRI), dyes, and vitreous substitutes and to quantify its impact on iOCT imaging.Methods: In this retrospective observational study, imaging data from thirty‐five (35) patients undergoing an iOCT‐assisted PPV with Rescan® 700 (Zeiss, Oberkochen, Germany) were analysed. The iOCT image data were divided into three groups: VRI (17 cases), tissue dyes (8 cases), and vitreous substitutes (10 cases). The images were then post‐processed using a graphic software (histogram, grey level (GL)) and compared with paired image controls without clinically perceptive interference to measure the grade of picture quality distortion. Data were statistically analysed.Results: The intraocular portion of all VRI caused a high average GL interference compared to controls (26.9 ± 2.3 vs. 35.9 ± 0.83; p &lt; 0.05), obscuring the area of interest. The instruments' tips produced a low‐grade shadowing, allowing the underlying tissue to be distinguished. The stains analysed demonstrated a wide interference range, indocyanine green (9.2 ± 1.4) and triamcinolon acetonide (13.9 ± 10.0) showed a high shadowing, while lutein‐based dye (38.4 ± 6.4) and Brilliant Blue (39.9 ± 3.3) exhibited no significant distortion. All vitreous substitutes analysed showed an insignificant shadowing effect on iOCT (Air 42.9 ± 5.1, PFCL 46.0 ± 5.1 vs. 44.5 ± 0.32; p &lt; 0.05).Conclusions: All intraocular instruments showed a significant image interference during iOCT assisted surgery. All dyes except Brilliant Blue produced additional shadowing effect compared to controls. Implementation of new imaging techniques or/and materials with low‐grade interference for surgical instruments could overcome a clinically relevant shadow effect on iOCT and maximize the technology's visual accuracy.

Anal squamous cell carcinoma: Impact of radiochemotherapy evolution over years and an explorative analysis of MRI prediction of tumor response in a mono-institutional series of 131 patients.

Radiochemotherapy (RCHT) for the treatment of anal squamous cell carcinoma (ASCC) has evolved dramatically, also thanks to intensity-modulated RT (IMRT) and 3D image guidance (3D IGRT). Despite most patients presenting fair outcomes, unmet needs still exist. Predictors of poor tumor response are lacking; acute toxicity remains challenging; and local relapse remains the main pattern of failure. Between 2010 and 2020, ASCC stages I-III treated with 3D conformal radiotherapy or IMRT and CDDP-5FU or Mytomicine-5FU CHT were identified. Image guidance accepted included 2D IGRT or 3D IGRT. The study endpoints included freedom from locoregional recurrence (FFLR), colostomy free survival (CFS), freedom from distant metastasis (FFDM), overall survival (OS), and acute and late toxicity as measured by common terminology criteria for adverse events (CTCAE) version 5.0. An exploratory analysis was performed to identify possible radiomic predictors of tumor response. Feature extraction and data analysis were performed in Python™, while other statistics were performed using SPSS® v.26.0 software (IBM®). A total of 131 patients were identified. After a median FU of 52 months, 83 patients (63.4%) were alive. A total of 35 patients (26.7%) experienced locoregional failure, while 31 patients (23.7%) relapsed with distant metastasis. Five year FFLR, CFS, DMFS and PS resulted 72.3%, 80.1%, 74.5% and 64.6%. In multivariate analysis, 2D IGRT was associated with poorer FFLR, OS, and CFS (HR 4.5, 4.1, and 5.6, respectively); 3DcRT was associated with poorer OS and CFS (HR 3.1 and 6.6, respectively). IMRT reduced severe acute gastro-intestinal (GI) and severe skin acute toxicity in comparison with 3DcRT. In the exploratory analysis, the risk of relapse depended on a combination of three parameters: Total Energy, Gray Level Size Zone Matrix's Large Area High Gray Level Emphasis (GLSZM's LAHGLE), and GTV volume. Advances in radiotherapy have independently improved the prognosis of ASCC patients over years while decreasing acute GI and skin toxicity. IMRT and daily 3D image guidance may be considered standard of care in the management of ASCC. A combination of three pre-treatment MRI parameters such as low signal intensity (SI), high GLSZM's LAHGLE, and GTV volume could be integrated in risk stratification to identify candidates for RT dose-escalation to be enrolled in clinical trials.

Radiomics-based analysis by machine learning techniques improves characterization of functionally significant coronary lesions

Abstract Background Computed Tomography Coronary Angiography (CTCA) is an effective non-invasive imaging modality for anatomo-functional assessment of coronary artery disease (CAD). Radiomics features have been used for diagnosis or outcome prediction, however, their potential value for characterizing flow limiting coronary lesions has not been explored. Purpose To assess whether application of novel radiomics and machine learning (ML) techniques on CTCA derived datasets improves characterization of functionally significant coronary lesions. Methods Consecutive patients with stable chest pain and intermediate pre-test likelihood for CAD, who underwent CTCA and PET-or SPECT-Myocardial Perfusion Imaging (MPI) respectively, were prospectively evaluated and included in the analysis. PET-MPI was considered abnormal when &amp;gt;1 contiguous segments showed both stress Myocardial Blood Flow ≤2.3mL/g/min and Myocardial Flow Reserve (MFR) ≤2.5 for 15O-water or &amp;lt;1.79 mL/g/min and ≤2.0 for 13N-ammonia respectively. Defect reversibility (DR) was defined as a summed difference score (SDS) between stress and rest images ≥2. CTCA and functional images were fused to assign each myocardial segment to the pertinent coronary territory. Stenosis severity, plaque characteristics and radiomic plaque features were assessed in the total length of the 3 main coronary vessels. In total, 1765 features were extracted from each vessel and a feature reduction and model creation pipeline was constructed [Figure 1]. Two separate datasets: a) coronary stenosis (≥50%) + plaque characteristics and b) coronary stenosis (≥50%) + plaque characteristics + radiomics were formulated and compared in terms of AUCs accordingly. Results A total of 292 coronary vessels (140 with corresponding PET-MPI data and 152 with SPECT MPI data) were analysed. Plaque burden and stenosis severity were the only independent predictors of impaired myocardial perfusion on PET-MPI, with an AUC = 0.749, (95% CI: 0.658–0.826). Stenosis severity, kurtosis, contrast, interquartile range and entropy were predictors of an abnormal PET-MPI result and their combination resulted in an AUC = 0.854, (95% CI: 0.775–0.914). The difference between the 2 models was statistically significant (p-diff: 0.02, 95% CI: 0.0165–0.194). Stenosis severity was the only predictor of a DR on SPECT-MPI, AUC = 0.624 (95% CI: 0.542–0.702). Small Dependence High Gray Level Emphasis, Cluster Prominence, Region Length, wavelet Median and square Median were predictors of a positive SPECT result, with AUC = 0.816, (95% CI: 0.745–0.875). The difference between the two models was statistically significant (p-diff: 0.006, 95% CI: 0.152–0.329) Conclusion Radiomic futures can be combined with anatomical and morphological characteristics of coronary lesions in CTCA imaging and provide valuable complementary information for characterizing functionally significant coronary lesions. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported from European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship and Innovation 2014-2022 (EPAnEK)”, titled: The Greek Research Infrastructure for Personalized Medicine (pMED-GR)

Extraction of Contact Erosion Region and Evaluation of 3-D Surface Roughness Based on Improved Region Growth Method

Noncontact measurement is a kind of efficient and nondestructive roughness measurement method. Machine vision and image processing technology are effective means for machining quality detection and surface roughness assessment. Aiming at the characteristics of high gray level and low brightness in the erosion area of contact, a method for identifying the erosion area of contact and evaluating surface roughness based on the improved region growing method is proposed. First, the image was preprocessed to improve the contrast, and the pixel with the largest mean value in the contact erosion image was taken as the seed point to realize automatic selection of seed points. On the basis of mask treatment, the real erosion area of the contact was extracted and the erosion area was calculated. The experimental results showed that this method can not only locate and extract erosion area quickly and effectively but also quantify the surface roughness with erosion area and determine the condition of erosion.

Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography.

The aim of the study is to investigate the diagnostic accuracy of radiomics on iodine maps from dual-energy computed tomography (DECT) in distinguishing lung cancer from benign pulmonary nodules. This retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 109 patients with 55 malignant nodules and 62 benign nodules underwent contrast-enhanced DECT. Eight iodine uptake parameters on iodine maps generated by DECT were calculated and established a predictive model. Eighty-seven radiomics features of entire tumor were extracted from iodine maps and established a radiomics model. The iodine uptake model and radiomics model were independently built based on the highly reproducible features using the least absolute shrinkage and selection operator method. The diagnostic accuracy of 2 models were assessed using receiver operating curve analysis. For external validation, 47 patients (25 benign and 22 malignant) from another hospital were assigned to testing data set. All iodine uptake features showed significant association with malignancy ( P < 0.01) and 2 selected features (mean value of virtual noncontrast images and mean value of vital part on contrast-enhanced image) constituted the iodine model. The radiomics model comprised 2 features (original shape sphericity and original glszm small area high gray level emphasis), which showed good discrimination both in the training cohort (area under the curve, 0.957) and validation cohort (area under the curve, 0.800). Radiomics model showed superior performance than iodine uptake model (accuracy, 89.7% vs 80.6%). Radiomics model extracted from iodine maps provided a robust diagnostic tool for discriminating pulmonary malignant nodules and had high potential in clinical application.

Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease

The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis. The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively). Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.