Single Region Of Interest Research Articles

Role of Unenhanced Computed Tomography of the Chest in the Prediction of Anaemia: A Cross-sectional Study

Introduction: Anaemia is a common clinical condition. The detection of incidental findings on unenhanced Computed Tomography (CT) that predict the presence of anaemia may play a pivotal role in early diagnosis, treatment and prognosis. Aim: To assess the role of unenhanced CT thorax in predicting anaemia by determining the sensitivity and specificity of the Interventricular Septum Sign (IVS) and Aortic Rim Sign (ARS) in the prediction of anaemia. Materials and Methods: This was a cross-sectional study conducted on 300 patients who underwent High-resolution Computed Tomography (HRCT) of the thorax using a 128-slice CT scanner in the Department of Radiology, Nizam’s Institute of Medical Sciences, Hyderabad, Telangana, India, Nizams Institute of Medical Sciences, Hyderabad, from July 2022 to September 2022. An estimation of haemoglobin levels was performed from a peripheral blood sample within 24 hours of the scan and the diagnosis of anaemia was made based on World Health Organisation (WHO) guidelines of haemoglobin values according to age and gender. On imaging, the following parameters were analysed: the variables considered for each patient included age and gender, the presence or absence of ARS, the presence or absence of an IVS and the Hounsfield Units (HU) values of seven selected Regions of Interest (ROI) on each examination. Three ROIs were placed in the Left Ventricular (LV) cavity, three on the IVS and a single ROI was placed in the aorta at the same level. Mean, percentage, frequency and standard deviation were calculated. The Chi-square test was applied for statistical analysis and a p-value of <0.05 was considered significant. Results: Out of the total sample size of 300, 188 were males and 112 were females with the mean age of 47.16±19.77 years. The mean haemoglobin percentage in the study was 11.62±2.25 gm%. Approximately 195 (65%) out of 300 patients were found to be anaemic based on blood reports. Among them, the majority were diagnosed with moderate anaemia 109 (36.3%), followed by mild anaemia 76 (25.3%) and severe anaemia 10 (3.3%), respectively. The sensitivity of the dense IVS was 100% for severe anaemia, 75.6% for moderate anaemia and 55.3% for mild anaemia. In comparison, the aortic rim sign showed a sensitivity of 100% for severe anaemia, 86.5% for moderate anaemia and 67.69% for mild anaemia, with a specificity of 88.57%. Conclusion: The presence of an IVS on an unenhanced CT predicts the likelihood of underlying anaemia and suggests the need for further evaluation and timely management.

Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?

To measure hepatic steatosis (HS) in hospitalized COVID-19 patients using unenhanced chest computed tomography (CT) imaging and to evaluate the relationship between disease severity and prognosis in adult patients.This retrospective study included 152 consecutive hospitalized COVID-19 patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test. The COVID-19 Reporting and Data System (CO-RADS) and the chest CT score were evaluated. HS measurements were performed based on CT images using a single region of interest placed on the right liver lobe (segments V-VII). HS was defined as a liver attenuation value <40 Hounsfield units. Data were collected and compared with the patients' prognostic parameters.Of the 152 inpatients, 137 patients (90.1%) had a CT score ≥3 and 109 patients (71.7%) had a CO-RADS score ≥4, 43 (28.2%) had HS. All patients with HS (100%) and 94/109 (86.2%) patients without HS had a CT score ≥3. There was a statistically significant difference between the two groups in terms of chest CT score (p=0.006). There was no statistically significant difference between the two groups in terms of CO-RADS score (p=0.291). The median CRP levels were significantly increased in patients with HS compared to patients without HS (p=0.023). There was no significant difference in ICU hospitalization and mortality due to the presence of HS (p>0.05).The current study revealed significantly higher chest CT scores in COVID-19 patients with HS measured on CT compared to those without HS. Opportunistic use of CT images for the detection of HS can be considered as an adjunctive tool in the risk analysis of COVID-19 patients hospitalized due to COVID-19 pneumonia.The severity of COVID-19 disease is increased in hospitalized patients with hepatosteatosis compared to patients with a normal liver. Density measurements for the evaluation of HS using opportunistic CT applications can be considered as an adjunctive tool in the prognostic evaluation of hospitalized patients with COVID-19 pneumonia. · Parlak AE, Erdem Toslak İ, Turkoglu Selcuk N. Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8377.

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate. Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Background Shuffled datasets, individual region pixels were randomly shuffled. For Lesion, Rim, and Background Mask datasets, masks of the regions were used. A Convex Hull dataset was generated to evaluate the importance of lesion size. Squared Pearson correlation (r2) was used to compare the predictive performance of ablated datasets relative to the benchmark. The Rim region influenced r2 more than the other two regions in all experiments, indicating the most relevant contribution of this region to the performance of the algorithms. In addition, similar performance was observed for all regions when pixels were shuffled or only a mask was used, indicating intensity information was not independently informative without textural context. These ablation experiments enabled topographic clinical insights on FAF images from a DL-based GA progression prediction algorithm. Results from this study may lead to new insights on GA progression prediction.

An automatic radiomic-based approach for disease localization: A pilot study on COVID-19

Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a nxn grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted.The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics.

Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy

BackgroundThere is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.MethodsSeventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.ResultsADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.ConclusionsWhole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

Leveraging bone age assessment via a novel joint decomposition teacher–student learning paradigm from X-ray images

ABSTRACT Advancements in technology has accelerated the evolution of bone age assessment (BAA) methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each region of interest (ROI). In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model. Empirical results on digital hand atlas report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy

BackgroundTo integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT).MethodsTotal of 143 EC patients in the authors’ hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated.ResultsModels with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively.ConclusionCT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Role of magnetic resonance imaging (MRI) in liver iron quantification in thalassemic (thalassemia major) patients

BackgroundIron overload is a major problem in beta thalassemia patients due to repeated blood transfusions. The liver is the first organ to be loaded with iron. An accurate assessment of iron overload is necessary for managing iron chelation therapy in such patients. Iron quantification by MRI scores over liver biopsy due to its non-invasive nature.MethodsFifty-one patients with thalassemia major were subjected to 3.0-T MRI. Multiecho T2* sequence was used to cover the entire liver. Region of interest (ROI) was placed in three areas with maximum signal change, and an average T2* value was obtained. Similarly, a single ROI was placed at the mid-interventricular septum in the heart, and T2* value was obtained. T2* values so obtained were converted to iron concentration with the help of a T2* iron concentration calculator. The liver iron values were correlated with serum ferritin value.ResultsThere was a significant negative correlation between liver iron concentration (LIC) and T2* value of the liver (r = − 0.895, p < 0.01) and between cardiac iron concentration (CIC) and T2* value of the heart (r = − 0.959, p < 0.01). There was a slight positive correlation between LIC and serum ferritin (r = 0.642, p < 0.01) and no correlation between CIC and serum ferritin (r = − 0.137, p = 0.354).ConclusionsMRI is a useful tool to titrate the doses of chelating agents as it is accurate and non-invasive, does not involve radiation hazards and hence can be repeated as and when needed. Simultaneous assessment of cardiac iron overload is an added advantage of MRI.

Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI.

While breast ultrasound (US) is a useful tool for diagnosing breast masses, it can entail false-positive biopsy results because of some overlapping features between benign and malignant breast masses and subjective interpretation. To evaluate the performance of conductivity imaging for reducing false-positive biopsy results related to breast US, as compared to diffusion-weighted imaging (DWI) and abbreviated MRI consisting of one pre- and one post-contrast T1-weighted imaging. Prospective. Seventy-nine women (median age, 44 years) with 86 Breast Imaging Reporting and Data System (BI-RADS) category 4 masses as detected by breast US. 3-T, T2-weighted turbo spin echo sequence, DWI, and abbreviated contrast-enhanced MRI (T1-weighted gradient echo sequence). US-guided biopsy (reference standard) was obtained on the same day as MRI. The maximum and mean conductivity parameters from whole and single regions of interest (ROIs) were measured. Apparent diffusion coefficient (ADC) values were obtained from an area with the lowest signal within a lesion on the ADC map. The performance of conductivity, ADC, and abbreviated MRI for reducing false-positive biopsies was evaluated using the following criteria: lowest conductivity and highest ADC values among malignant breast lesions and BI-RADS categories 2 or 3 on abbreviated MRI. One conductivity parameter with the maximum area under the curve (AUC) from receiver operating characteristics was selected. A P-value <0.05 was considered statistically significant. US-guided biopsy revealed 65 benign lesions and 21 malignant lesions. The mean conductivity parameter of the single ROI method was selected (AUC = 0.74). Considering conductivity (≤0.10 S/m), ADC (≥1.60 × 10-3 mm2 /sec), and BI-RADS categories 2 or 3 reduced false-positive biopsies by 23% (15 of 65), 38% (25 of 65), and 43% (28 of 65), respectively, without missing malignant lesions. Conductivity imaging may show lower performance than DWI and abbreviated MRI in reducing unnecessary biopsies. 2 TECHNICAL EFFICACY: Stage 2.

Diagnostic value of mammography density of breast masses by using deep learning.

In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density. This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity. In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC= 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5mm ROI model, we obtained the highest AUC (AUC = 0.877P < 0.001). Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

3D open-top light-sheet microscopy and 3D microdissection of neuoadjuvant-treated primary prostate cancer reveals latent subclonal mutations.

e17109 Background: Intratumoral molecular heterogeneity is a well described phenomenon in primary prostate cancer. Increased genomic heterogeneity is associated with more aggressive clinical behavior, including recurrence and metastasis. However, current methods to characterize heterogeneity suffer from tradeoffs between generating high quality morphologic information, correlating morphology with genomic alterations, and workflow considerations. We describe a technology to image prostate samples in 3D, perform 3D microdissection of regions of interest (ROIs), and preserve nucleic acids for DNA sequencing. Methods: Samples (N = 18) were obtained from patients (N = 12) enrolled in an open-label, single-site, Phase II neoadjuvant trial in men with NCCN high to very-high risk PC. Patients received 12 weeks of apalutamide, abiraterone acetate plus prednisone, degarelix, and indomethacin followed by radical prostatectomy (RP). Fresh frozen prostate samples (~2x2x0.4cm) were obtained from RP specimens. Frozen samples were fixed in an ethanol-based fixative, stained with nuclear (TOPRO-3) and general protein (eosin) fluorescent dyes, cleared with ethyl cinnamate, and imaged using 3D open-top light-sheet (OTLS) fluorescence microscopy. Image-guided 3D microdissection was performed on ROIs with distinct morphologies. Microdissected ROIs measured 0.5x0.5x2mm. Microdissected samples were sent for DNA extraction and sequencing using the comprehensive clinically-validated UW-OncoPlex NGS assay. Results: The tissue from neoadjuvant-treated patients contained sparse carcinoma, with cancer volumes ranging from 0.01-0.3 cm2 and cellularity ranging from 2%-30%. Molecular alterations were detected in a sample with only 0.07 cm2 volume of carcinoma with 5% cellularity. The read depth of 3D microdissected samples was 3.9 times greater than the read depth of FFPE samples from the same patients. At least two morphologically distinct ROIs were 3D microdissected from 11 of 12 patients. Four of the 18 samples had molecular alterations that differed between ROIs, i.e. intra-sample molecular heterogeneity. For example, one sample with three microdissected ROIs had a PTEN alteration exclusive to a single ROI. Conclusions: The findings show the potential for 3D microdissection of morphologically distinct subclones in samples from neoadjuvant-treated patient, to enable sequencing of specific cellular populations. The sparsity of carcinoma necessitated a spatially targeted approach for extracting material for sequencing. The increased yield from avoiding FFPE processing and carcinoma enrichment from targeted 3D microdissection of carcinoma ROIs enabled the detection of spatially distinct molecular subclones. Future work will include the analysis of additional samples and implementation of AI-guided ROI detection to streamline workflows.

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time-of-flight camera.

Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body. Fifty-three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1-2 s), medium (2-15 s) and long (15-300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10-runs 10-fold cross-validation scheme on the task of differentiating people with iRBD from people without RBD. The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses. Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping

BackgroundMicrostructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases. ObjectivesWe aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei. MethodsMagnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10–70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei. ResultsMean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R2 = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R2 = 0.335, p < 0.001), globus pallidus (GP; R2 = 0.062, p = 0.001), and dentate nucleus (DN; R2 = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10–70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10–70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R2 = 0.586). ConclusionsQSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.

Simplification of 2D shear wave elastography by enlarged SWE box and multiple regions of interest in one acquisition

Two-dimensional (2D) Shear Wave Elastography (SWE) is an easy to perform technique to evaluate the liver stiffness. To simplify the procedure and reduce the acquisition time we enlarged the size of the SWE-box and set ten regions of interest (ROI) in one acquisition. We compare the accuracy of this method to ten separate acquisitions in a small box each with a single ROI measurement. Sixty-nine volunteers with diffuse chronic liver disease were studied with 2D-SWE using a Canon Aplio i800 ultrasound system. The shear-wave-speed was measured in the right lobe in ten separate acquisitions and compared to one acquisition with increased size of the SWE-box and ten different ROI measurements. A Bland-Altmann plot was drawn and the interclass correlation coefficient (ICC) was calculated to compare both methods. Finally, 2D-SWE was successfully performed thru both methods in sixty-six participants. Between both methods the ICC is 0.82. The results of this study show a good reliability between ten separate measures and one grouped measure with ten ROI if the mean is below 1.6m/s (7.7kPa). For higher degrees of fibrosis (≥F2) further investigations are needed.

0247 Sleep Disturbance is Associated with Dysregulation of Positive and Negative Affect Systems

Abstract Introduction Sleep disturbance is a risk factor for the development of mood disorders and up to 90% of mood disorder patients report sleep problems. However, the neural mechanisms by which poor sleep contributes to mood disorders are not well understood. We investigated whether sleep disturbance was associated with dysregulation of positive and negative affect systems, including passive reactivity and active emotion regulation. Methods Participants (n=55, Mage=24.4 years, 53% female) selected for high, medium, and low scores on the PROMIS Sleep Disturbance scale completed a cognitive reappraisal task in an fMRI scanner. Participants were presented with International Affective Picture Stimuli (30 positive, 30 negative, 15 neutral) and were instructed to either passively view or actively up- or down-regulate their emotional experience. We tested five conditions: view-positive, upregulate-positive, view-negative, downregulate-negative, view-neutral. Participants also completed objective (i.e., 7-day actigraphy) and self-report (i.e., Insomnia Severity Index [ISI]) measures of sleep prior to the scan. Analyses in AFNI were constrained within an emotion regulation network, identified using a Neurosynth mask, and treated as a single region of interest. Voxelwise (puncorr&amp;lt;.005) and clusterwise thresholds (p&amp;lt;.05) were used to correct for multiple comparisons. Results Actigraphy-assessed sleep duration was associated with supplementary motor area (SMA) activity when upregulating positive affect relative to passively viewing positive images (k=44 voxels, clusterwise p=.04); participants who slept less showed greater SMA activity. ISI score was marginally associated with dorsolateral prefrontal cortex (dlPFC) activity when downregulating negative affect relative to an implicit baseline (k=30 voxels, clusterwise p=.10); individuals with greater insomnia severity showed more dlPFC activity. PROMIS Sleep Disturbance showed no significant associations. Conclusion Markers of poor sleep (i.e., lower sleep duration, greater insomnia severity) were associated with heightened SMA and dlPFC activity during cognitive reappraisal. This may suggest inefficiency in modulating positive affect via verbal and motor processes (i.e., SMA) and negative affect via cognitive control (i.e., dlPFC). Alternatively, individuals with poor sleep may have greater emotional reactivity to modulate. Mood disorders are commonly associated with increased negative affect and blunted positive affect. Our findings suggest a plausible neural substrate for how sleep disturbance contributes to dysregulation of these systems. Support (If Any) NIMH R21 MH102412.

Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features

Objective. Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance. Approach. We retrospectively assembled a dataset involving 1535 lesions (740 malignant and 795 benign). Regions of interest (ROI) surrounding suspicious lesions are extracted and two types of features are computed from each ROI. The first one includes 40 radiomic features and the second one includes automated features computed from a VGG16 network using a transfer learning method. A single channel ROI image is converted to three channel pseudo-ROI images by stacking the original image, a bilateral filtered image, and a histogram equalized image. Two VGG16 models using pseudo-ROIs and 3 stacked original ROIs without pre-processing are used to extract automated features. Five linear support vector machines (SVM) are built using the optimally selected feature vectors from the handcrafted features, two sets of VGG16 model-generated automated features, and the fusion of handcrafted and each set of automated features, respectively. Main Results. Using a 10-fold cross-validation, the fusion SVM using pseudo-ROIs yields the highest lesion classification performance with area under ROC curve (AUC = 0.756 ± 0.042), which is significantly higher than those yielded by other SVMs trained using handcrafted or automated features only (p < 0.05). Significance. This study demonstrates that both handcrafted and automated futures contain useful information to classify breast lesions. Fusion of these two types of features can further increase CAD performance.

Quantitative determination of pulmonary emphysema in follow-up LD-CTs of patients with COVID-19 infection.

PurposeTo evaluate the association between the coronavirus disease 2019 (COVID-19) and post-inflammatory emphysematous lung alterations on follow-up low-dose CT scans.MethodsConsecutive patients with proven COVID-19 infection and a follow-up CT were retrospectively reviewed. The severity of pulmonary involvement was classified as mild, moderate and severe. Total lung volume, emphysema volume and the ratio of emphysema/-to-lung volume were quantified semi-automatically and compared inter-individually between initial and follow-up CT and to a control group of healthy, age- and sex-matched patients. Lung density was further assessed by drawing circular regions of interest (ROIs) into non-affected regions of the upper lobes.ResultsA total of 32 individuals (mean age: 64 ± 13 years, 12 females) with at least one follow-up CT (mean: 52 ± 66 days, range: 5–259) were included. In the overall cohort, total lung volume, emphysema volume and the ratio of lung-to-emphysema volume did not differ significantly between the initial and follow-up scans. In the subgroup of COVID-19 patients with > 30 days of follow-up, the emphysema volume was significantly larger as compared to the subgroup with a follow-up < 30 days (p = 0.045). Manually measured single ROIs generally yielded lower attenuation values prior to COVID-19 pneumonia, but the difference was not significant between groups (all p > 0.05).ConclusionCOVID-19 patients with a follow-up CT >30 days showed significant emphysematous lung alterations. These findings may help to explain the long-term effect of COVID-19 on pulmonary function and warrant validation by further studies.

Pros and cons in tinnitus brain: Enhancement of global connectivity for alpha and delta waves

Interactions among cortical areas of tinnitus brain remained unclear. Weaker alpha and stronger delta activities in tinnitus have been noted over auditory cortices. However, the interplay between a single substrate with whole brain within alpha/delta band remained unknown. Thirty-one patients with chronic tinnitus were recruited. Thirty-four healthy volunteers served as controls. Magnetoencephalographic measurements of spontaneous activities were performed. The strength of alpha/delta activities was analyzed. By dividing cortices into 38 regions of interest (ROIs), measurements of connectivity were performed using amplitude envelope correlation (AEC). Global connectivity was calculated by adding and averaging connectivity of single ROI with every other region. There were no significant differences in mean power of alpha and delta band between groups, despite the trend of stronger alpha and weaker delta band in controls. The global connectivity of alpha wave was significantly stronger in tinnitus for left frontal pole, and of delta wave for bilateral pars orbitalis, bilateral superior temporal, bilateral middle temporal, right pars triangularis, right transverse temporal, right inferior temporal, and right supra-marginal. The global connectivity of alpha/delta waves was enhanced for tinnitus in designated ROIs of frontal/temporal/parietal lobes. The underlying mechanism(s) might be associated with augmentation/modulation of tinnitus perception. Our results corroborated the evolving consensus about neural correlates inside frontal/temporal/parietal lobes as essential elements of hubs for central processing of tinnitus. Further study to explore the resolution of effective connectivity between those ROIs and respective substrates by using AEC will be necessary for the evaluation of pathogenetic scenario for tinnitus.

Complex Rearrangement of the Entire Retinal Posterior Pole in Patients with Relapsing Remitting Multiple Sclerosis.

There are consolidated data about multiple sclerosis (MS)–dependent retinal neurodegeneration occurring in the optic disk and the macula, although it is unclear whether other retinal regions are affected. Our objective is to evaluate, for the first time, the involvement of the entire retinal posterior pole in patients diagnosed with relapsing remitting multiple sclerosis (RRMS) unaffected by optic neuritis using Spectral Domain–Optical Coherence Tomography (SD–OCT). The study protocol was approved by Tor Vergata Hospital Institutional Ethics Committee (Approval number 107/16), and conforms to the tenets of the Declaration of Helsinki. After a comprehensive neurological and ophthalmological examination, 53 untreated RRMS patients (aged 37.4 ± 10) and 53 matched controls (aged 36.11 ± 12.94) were enrolled. In addition, each patient underwent an examination of the posterior pole using the SD-OCT built-in Spectralis posterior pole scanning protocol. After segmentation, the mean thickness, as well as the thickness of the 64 single regions of interest, were calculated for each retinal layer. No statistically significant difference in terms of average retinal thickness was found between the groups. However, MS patients showed both a significantly thinner ganglion cell layer (p < 0.001), and, although not statistically significant, a thinner inner nuclear layer (p = 0.072) and retinal nerve fiber layer (p = 0.074). In contrast, the retinal pigment epithelium (p = 0.014) and photoreceptor layers p < 0.001) resulted significantly thicker in these patients. Interestingly, the analysis of the region of interest showed that neurodegeneration was non-homogeneously distributed across each layer. This is the first report that suggests a complex rearrangement that affects, layer by layer, the entire retinal posterior pole of RRMS retinas in response to the underlying neurotoxic insult.

Morphological changes after cranial fractionated photon radiotherapy: Localized loss of white matter and grey matter volume with increasing dose

PurposeNumerous brain MR imaging studies have been performed to understand radiation-induced cognitive decline. However, many of them focus on a single region of interest, e.g. cerebral cortex or hippocampus. In this study, we use deformation-based morphometry (DBM) and voxel-based morphometry (VBM) to measure the morphological changes in patients receiving fractionated photon RT, and relate these to the dose. Additionally, we study tissue specific volume changes in white matter (WM), grey matter (GM), cerebrospinal fluid and total intracranial volume (TIV). Methods and materialsFrom our database, we selected 28 patients with MRI of high quality available at baseline and 1 year after RT. Scans were rigidly registered to each other, and to the planning CT and dose file. We used DBM to study non-tissue-specific volumetric changes, and VBM to study volume loss in grey matter. Observed changes were then related to the applied radiation dose (in EQD2). Additionally, brain tissue was segmented into WM, GM and cerebrospinal fluid, and changes in these volumes and TIV were tested. ResultsPerforming DBM resulted in clusters of dose-dependent volume loss 1 year after RT seen throughout the brain. Both WM and GM were affected; within the latter both cerebral cortex and subcortical nuclei show volume loss. Volume loss rates ranging from 5.3 to 15.3%/30 Gy were seen in the cerebral cortical regions in which more than 40% of voxels were affected. In VBM, similar loss rates were seen in the cortex and nuclei. The total volume of WM and GM significantly decreased with rates of 5.8% and 2.1%, while TIV remained unchanged as expected. ConclusionsRadiotherapy is associated with dose-dependent intracranial morphological changes throughout the entire brain. Therefore, we will consider to revise sparing of organs at risk based on future cognitive and neurofunctional data.