Can Some Viral Respiratory Infections Observed Before the Pandemic Announcement Be Related to SARS-CoV-2?

OBJECTIVE: There have been doubts that SARS-CoV-2 has been circulating before the first case was announced. The aim of this study was to evaluate the possibility of COVID-19 in some cases diagnosed to be viral respiratory tract infection in the pre-pandemic period in our center. MATERIAL AND METHODS: Patients who were admitted to our hospital's pulmonary diseases, infectious diseases, and intensive care clinics with the diagnosis of viral respiratory system infection within a 6-month period between October 2019 and March 12, 2020, were screened. Around 248 archived respiratory samples from these patients were analyzed for SARS-CoV-2 ribonucleic acid by real-timequantitative polymerase chain reaction. The clinical, laboratory, and radiological data of the patients were evaluated. RESULTS: The mean age of the study group was 47.5 (18-89 years); 103 (41.5%) were female and 145 (58.4%) were male. The most common presenting symptoms were cough in 51.6% (n = 128), fever in 42.7% (n = 106), and sputum in 27.0% (n = 67). Sixty-nine percent (n = 172) of the patients were pre-diagnosed to have upper respiratory tract infection and 22.0% (n = 55) had pneumonia, one-third of the patients (n = 84, 33.8%) were followed in the service. Respiratory viruses other than SARS-CoV-2 were detected in 123 (49.6%) patients. Influenza virus (31.9%), rhinovirus (10.5%), and human metapneumovirus (6.5%) were the most common pathogens, while none of the samples were positive for SARS-CoV-2 RNA. Findings that could be significant for COVID-19 pneumonia were detected in the thorax computed tomography of 7 cases. CONCLUSION: The negative SARS-CoV-2 real-time-quantitative polymerase chain reaction results in the respiratory samples of the cases followed up in our hospital for viral pneumonia during the pre-pandemic period support that there was no COVID-19 among our cases during the period in question. However, if clinical suspicion arises, both SARS and non-SARS respiratory viral pathogens should be considered for differential diagnosis.

Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented. The arteries were then partitioned into several parts based on size (radius). Adaptive thresholding and constrained morphological operations were used to identify suspicious PE regions within each part. The confidence of a suspicious region to be PE was scored based on its contrast in the arteries. This approach was applied to the publicly available RSNA Pulmonary Embolism CT Dataset (RSNA-PE) to identify three-dimensional (3-D) PE negative and positive image patches, which were used to train a 3-D Recurrent Residual U-Net (R2-Unet) to automatically segment PE. The feasibility of this computer algorithm was validated on an independent test set consisting of 91 CTPA scans acquired from a different medical institute, where the PE regions were manually located and outlined by a thoracic radiologist (>18 years' experience). An R2-Unet model was also trained and validated on the manual outlines using a 5-fold cross-validation method. The CNN model trained on the high-confident PE regions showed a Dice coefficient of 0.676±0.168 and a false positive rate of 1.86 per CT scan, while the CNN model trained on the manual outlines demonstrated a Dice coefficient of 0.647±0.192 and a false positive rate of 4.20 per CT scan. The former model performed significantly better than the latter model (p<0.01). The promising performance of the developed PE detection and segmentation algorithm suggests the feasibility of training a deep learning network without dedicating significant efforts to manual annotations of the PE regions on CTPA scans.

Transbronchial lung cr onchial lung cryobiopsy for the diagnosis of diffuse obiopsy for the diagnosis of diffuse parenchymal lung disease: Pitfalls and challenges, a single center experience.

BACKGROUND: : Transbronchial lung cryobiopsy (TBLC) is a minimally invasive technique of the diagnosis of diffuse parenchymal lung diseases (DPLD). The aim of this study is to determine the clinical-radiological and histopathological characteristics of patients in whom cryobiopsy contributes to the diagnosis. METHODS: : In this retrospective study, we searched for the medical records of patients who underwent TBLC from July 2015 to March 2020 at the pulmonology department of our university hospital clinic. Radiological images were evaluated by a chest radiologist experienced in DPLD. Prediagnosis was indicated by clinical-radiological findings. The final diagnosis was determined by the contribution of histopathological diagnosis. The agreement of pretest/posttest diagnosis and the diagnostic yield of TBLC were calculated. RESULTS: Sixty-one patients with female predominance (59.0%) and current or ex-smoker (49.2%) made up the study population. We found the diagnostic yield of TBLC 88.5%. The most common radiological and clinical-radiological diagnosis was idiopathic pulmonary fibrosis (IPF) (n = 12, 19.6%) while the most common multidisciplinary final diagnosis was cryptogenic organizing pneumonia (COP) (n = 14, %22.9). The concordance of pre/posttests was significant (p < 0.001) with a kappa agreement = 0.485. The usual interstitial pneumonia (UIP) diagnosis was detected in six patients among 12 who were prediagnosed as IPF having also a suspicion of other DPLD by clinical-radiological evaluation (p < 0.001). After the contribution of TBLC, the multidisciplinary final diagnosis of 22(36.1) patients changed. The histopathological diagnosis in which the clinical-radiological diagnosis changed the most was nonspecific interstitial pneumonia (NSIP). DISCUSSION: We found the overall diagnostic yield of TBLC high. The pretest clinical-radiological diagnosis was often compatible with the multidisciplinary final diagnosis. However, TBLC is useful for the confirmation of clinical radiological diagnosis as well as clinical entities such as NSIP which is difficult to diagnose clinical-radiological. We also suggest that TBLC should be considered in patients whose clinicopathological IPF diagnosis is not precise.

Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes.

BACKGROUND: Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues. PURPOSE: To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes. MATERIAL AND METHODS: Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined. RESULTS: The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT. CONCLUSION: Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.

Prevalence of Gastrointestinal Symptoms and Clinical Outcomes in Hospitalized Coronavirus Disease 2019 Patients: A Single-Center Study from Turkey.

BACKGROUND: In patients with coronavirus disease 2019, the gastrointestinal symptoms have been reported increasingly in addition to the respiratory system symptoms. The studies show that the prevalence of gastrointestinal system symptoms and how the gastrointestinal system contributes to the severity and prognosis of the disease is still not clear. This study aims to find the prevalence of gastrointestinal symptoms and the correlation between the gastrointestinal symptoms and the clinical results in hospitalized patients diagnosed with coronavirus disease 2019. METHODS: This study retrospectively analyzes patients diagnosed with coronavirus disease 2019 and hospitalized in the pandemic unit between March 2020 and August 2020 and compares their demographic and clinical characteristics, laboratory and radiologic findings, coronavirus disease 2019 treatments received, the clinical course of the disease, and the gastrointestinal symptoms. RESULTS: In our study, we included 322 patients diagnosed with coronavirus disease 2019 and hospitalized; 39 patients (12.1%) were admitted to the hospital with at least one gastrointestinal symptom (nausea and vomiting, diarrhea, abdominal pain, and the loss of taste). Nausea and vomiting are the most common gastrointestinal symptoms with a prevalence of 7.1%, followed by diarrhea with 2.8%, the loss of taste with 2.2%, and abdominal pain with 1.5%. The mean age and D-dimer levels of the patients showing gastrointestinal symptoms were lower than those who did not have any gastrointestinal symptoms. We did not find a significant correlation between the presence of the gastrointestinal symptoms and the severity of the disease, treatment received, risk of acute respiratory distress syndrome and septic shock, admission to the intensive care unit, the need for mechanical ventilation, the mortality rate or the length of hospitalization in the medical floor or the intensive care unit. CONCLUSION: In this study, we observed that 12.1% of coronavirus disease 2019 patients apply to the hospital due to gastrointestinal symptoms. Furthermore, the gastrointestinal symptoms do not seem to affect the severity and the course of the disease, it is important to identify coronavirus disease 2019 patients showing unusual symptoms such as the gastrointestinal symptoms at an early stage to protect healthcare professionals from infection risk.

The Predictive Values of Respiratory Rate Oxygenation Index and Chest Computed Tomography Severity Score for High-Flow Nasal Oxygen Failure in Critically Ill Patients with Coronavirus Disease-2019.

Background: The prediction of high-flow nasal oxygen (HFNO) failure in patients with coronavirus disease-2019 (COVID-19) having acute respiratory failure (ARF) may prevent delayed intubation and decrease mortality. Aims: To define the related risk factors to HFNO failure and hospital mortality. Study Design: Retrospective cohort study. Methods: To this study, 85 critically ill patients (≥18 years) with COVID-19 related acute kidney injury who were treated with HFNO were enrolled. Treatment success was defined as the de-escalation of the oxygenation support to the conventional oxygen therapies. HFNO therapy failure was determined as the need for invasive mechanical ventilation or death. The patients were divided into HFNO-failure (HFNO-F) and HFNO-success (HFNO-S) groups. Electronic medical records and laboratory data were screened for all patients. Respiratory rate oxygenation (ROX) index on the first hour and chest computed tomography (CT) severity score were calculated. Factors related to HFNO therapy failure and mortality were defined. Results: This study assessed 85 patients (median age 67 years, 69.4% male) who were divided into two groups as HFNO success (n = 33) and HFNO failure (n = 52). The respiratory rate oxygenation (ROX) was measured at 1 hour and the computed tomography (CT) score indicated HFNO failure and intubation, with an area under the receiver operating characteristic of 0.695 for the ROX index and 0.628 for the CT score. A ROX index of <3.81 and a CT score of >15 in the first hour of therapy were the predictors of HFNO failure and intubation. Age, Acute Physiology and Chronic Health Evaluation II score, arterial blood gas findings "(i.e., partial pressure of oxygen [PaO2], PaO2 [fraction of inspired oxygen]/SO2 [oxygen saturation] ratio)", and D-dimer levels were also associated with HFNO failure; however, based on logistic regression analysis, a calculated ROX on the first hour of therapy of <3.81 (odds ratio [OR] = 4.78, 95% confidence interval [CI] = 1.75-13.02, P = 0.001) and a chest CT score of >15 (OR = 2.83, 95% CI = 1.01-7.88, P = <0.001) were the only independent risk factors. In logistic regression analysis, a ROX calculated on the first hour of therapy of <3.81 (OR = 4.78, [95% CI = 1.75-13.02], P = 0.001) and a chest CT score of >15 (OR 2.83, 95% CI = 1.01-7.88, P = <0.001) were the independent risk factors for the HFNO failure. The intensive care unit and hospital mortality rates were 80.2% and 82.7%, respectively, in the HFNO failure group. Conclusion: The early prediction of HFNO therapy failure is essential considering the high mortality rate in patients with HFNO therapy failure. Using the ROX index and the chest CT severity score combined with the other clinical parameters may reduce mortality. Additionally, multi-centre observational studies are needed to define the predictive value of ROX and chest CT score not only for COVID-19 but also other causes of ARF.

Evaluation of COVID-19 antibody response with using three different tests.

BACKGROUND AND OBJECTIVES: In this study, the performance of three different commercial antibody assays for COVID-19 was examined and parameters affecting the antibody response were investigated. The correlation of patients' chest CT results, procalcitonin, CRP, and D-dimer levels with the antibody response were retrospectively evaluated. MATERIALS AND METHODS: COVID-19 antibodies were detected by three commercially available assays in each patient. Two of the assays were rapid immunochromatographic tests and - one was an ELISA-based IgG assay. SARS-CoV-2 RNA was tested by "COVID-19 RT-qPCR Detection Kit" using nasopharyngeal swab samples. The results of antibody tests were compared with each other, RT-qPCR, Biochemical parameters and chest CT findings. RESULTS: RT-qPCR was positive in 46.6% (41/88) of the evaluated patients among which 77.3% (68/88) were healthcare workers. Seventeen (41.4%) of viral RNA positive patients had a positive antibody result with at least two assays. Both of the rapid immunochromatographic tests had identical sensitivity of 36.6% and specificity of 100%, compared to RT-qPCR assay; while the sensitivity of the ELISA based Euroimmune test was 43.9%, and the specificity was 95.7%. The sensitivity and specificity of the immunochromatographic tests were 75% and 100% respectively, compared to ELISA test result. There was a correlation between antibody positivity and old age and male gender. The presence of typical chest CT findings increased the antibody positivity 13.62 times. Antibody positivity was also increased with the decrease in Ct value of the PCR assay. There was no significant relationship between the biochemical parameters (CRP, D-dimer and procalcitonin values) and the antibody or RT-qPCR results. CONCLUSION: There was a correlation between antibody response and male gender, older age, presence of symptoms, typical chest CT findings and low PCR-Ct value.

Clinical characteristics and risk factors for 28-day mortality in critically ill patients with COVID-19: a retrospective cohort study

Background: To date, the coronavirus disease 2019 (COVID-19) caused more than 2.6 million deaths all around the world. Risk factors for mortality remain unclear. The primary aim was to determine the independent risk factors for 28-day mortality. Materials and methods: In this retrospective cohort study, critically ill patients (≥ 18 years) who were admitted to the intensive care unit due to COVID-19 were included. Patient characteristics, laboratory data, radiologic findings, treatments, and complications were analyzed in the study. Results: A total of 249 patients (median age 71, 69.1% male) were included in the study. 28-day mortality was 67.9% (n = 169). The median age of deceased patients was 75 (66­81). Of them, 68.6% were male. Cerebrovascular disease, dementia, chronic kidney disease, and malignancy were significantly higher in the deceased group. In the multivariate analysis, sepsis/septic shock (OR, 15.16, 95% CI, 3.96­58.11, p < 0.001), acute kidney injury (OR, 4.73, 95% CI, 1.55­14.46, p = 0.006), acute cardiac injury (OR, 9.76, 95% CI, 1.84­51.83, p = 0.007), and chest CT score higher than 15 (OR, 4.49, 95% CI, 1.51-13.38, p = 0.007) were independent risk factors for 28-day mortality. Conclusion: Early detection of the risk factors and the use of chest CT score might improve the outcomes in patients with COVID-19.

Three cases of immunoglobulin G4-related respiratory disease with uncommon imaging findings.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-RD) is a rare multisystemic idiopathic fibroinflammatory disorder. The rare form of IgG4-RD with isolated thorax involvement is called immunoglobulin G4-related respiratory disease (IgG4-RRD). IgG4-RRD, which is reported in a limited number of cases in the literature, can be categorized into four types on the prevalent chest computed tomography (CCT) findings: solid nodular, round-shaped ground-glass opacity, alveolar interstitial, and bronchovascular. Solid nodular form of IgG4-RRD with mass-like lesions is sporadic and described in the literature with a small number of case reports. OBJECTIVES/METHODS: We aim to present the radiologic, pathologic, and clinical findings of three cases of IgG4-RRD mimicking lung cancer. RESULTS: In all three patients, IgG4-RRD occurred with mass-like lesions in the thorax. In case-1 and 2, CCT showed multiple, nodular lesions and multiple mediastinal lymph nodes. On positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT), the masses showed increased 18F-FDG uptake in case-2 and 3. The gold standard histopathological verification for IgG4-RRD was provided for all cases. CONCLUSIONS: IgG4-RD is an immune-mediated condition comprised of a collection of disorders that share particular pathologic, radiologic, serologic, and clinical features. Isolated IgG4-RRD is rarely seen and is available in the literature as case reports. IgG4-RRD, which can make lung involvement in different patterns, rarely appears with mass-like lesions. Still, IgG4-RRD must be considered in the differential diagnosis of mass lesions detected in CCT. Laboratory, radiological, and histopathological findings of the disease should be evaluated together for an accurate diagnosis.

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Additionally to the discriminator which enforces the model to create realistic organ delineations, it embeds cascaded partially pre-trained convolutional encoder-decoders as generator. Encoder fine-tuning from a large amount of non-medical images alleviates data scarcity limitations. The network is trained end-to-end to benefit from simultaneous multi-level segmentation refinements using auto-context. Employed for healthy liver, kidneys and spleen segmentation, our pipeline provides promising results by outperforming state-of-the-art encoder-decoder schemes. Followed for the Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge organized in conjunction with the IEEE International Symposium on Biomedical Imaging 2019, it gave us the first rank for three competition categories: liver CT, liver MR and multi-organ MR segmentation. Combining cascaded convolutional and adversarial networks strengthens the ability of deep learning pipelines to automatically delineate multiple abdominal organs, with good generalization capability. The comprehensive evaluation provided suggests that better guidance could be achieved to help clinicians in abdominal image interpretation and clinical decision making.

Diaphragmatic Ultrasound in Non-Cystic Fibrosis Bronchiectasis: Relationship to Clinical Parameters.

The aim of this study was to assess diaphragm thickness (DT) and mobility (DM) and to investigate their relationship to clinical parameters in patients with non-cystic fibrosis (non-CF) bronchiectasis. Thirty-eight patients with non-CF bronchiectasis were enrolled in this cross-sectional study. DT was measured using ultrasound at different lung volumes (at residual volume [DTRV], functional residual capacity [DTFRC] and total lung capacity [DTTLC]). DM was measured using ultrasound during quiet breathing (DMQB) and deep breathing (DMDB). Disease severity, pulmonary function, respiratory muscle strength, exercise capacity and physical activity were assessed. DTRV correlated with disease severity (ρ = 0.332, p = 0.042), FEV1% (r = 0.387, p = 0.016) and FVC% (r = 0.405, p = 0.012). DTFRC correlated with FVC% (r = 0.331, p = 0.042). DTTLC correlated with disease severity (r = 0.430, p = 0.007) and total physical activity time (r = 0.379, p = 0.019). DMDB correlated with disease severity (ρ = -0.380, p = 0.019), FEV1% (r = 0.369, p = 0.023) and FVC% (r = 0.405, p = 0.012). DT is related to disease severity, pulmonary function and physical activity, while DM is related to disease severity and pulmonary function in patients with non-CF bronchiectasis.

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging.

PURPOSE: Because of the widespread use of CT in the diagnosis of COVID 19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way. METHODS: Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID 19. The patients' history, physical examination, CT findings, RT PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID 19 or non-COVID 19. Chest CT scans were classified according to the COVID 19 S CT diagnosis criteria. Cohen's kappa analysis was used. RESULTS: Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID 19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID 19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV. CONCLUSION: COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.

The effect of bone mineral density on development of Schmorl's nodes in young patients.

OBJECTIVE: The aim of this study was to detect the relationship between the development of Schmorl's nodes (SNs) and bone mineral density (BMD) in young patients. METHODS: Computerized tomography (CT) images of the thoracolumbar vertebral column were retrospectively examined by two experienced radiologists for SNs. The diagnostic criterion for SN was defined as a node size larger than one-third but not more than two-thirds of the relevant vertebral endplate. Considering the eligibility criteria, a total of 74 individuals (60 males and 14 females; mean age: 24.3 years; age range: 18-40 years) with SN at the thoracolumbar vertebrae were included in the patient group, and a total of 38 age- and gender-matched individuals (30 males and 8 females; mean age: 25 years) with no evidence of SN were included in the control group. All these individuals were younger than 40 years. In the patient group, SNs were assessed in terms of the distribution of the thoracolumbar vertebrae, the location of the upper and lower endplates, and the total number of lesions. In all individuals included in the study, BMD was measured from the axial CT sections by quantitative CT and then compared between the two groups. RESULTS: The distribution of age and gender was comparable between the two groups (p=0.438). A total of 208 SNs were identified in the patient group. Of these, 92 (44%) were located at the thoracic vertebrae and 116 (56%) at the lumbar vertebrae. The mean BMD was 131.6 g/cm3 in the patient group and 140.7 g/cm3 in the control group (p=0.03). There was no significant relationship between the total number of SNs per patient and the mean BMD (p=0.156). CONCLUSION: Evidence from this study revealed that low BMD may be a predisposing factor for the development of SNs in patients younger than 40 years. LEVEL OF EVIDENCE: Level III, Diagnostic Study.

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage.

BACKGROUND: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. PURPOSE: To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. MATERIAL AND METHODS: A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. RESULTS: Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. CONCLUSION: Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. METHODS: A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. RESULTS: The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). CONCLUSION: Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Cross-sectional areas of rectus abdominis and psoas muscles reduces following surgery in rectal cancer patients.

PURPOSE: To evaluate the trophic changes in rectus abdominis and psoas muscles in patients who underwent open or laparoscopic rectum resection for rectal cancer. METHODS: We retrospectively analyzed preoperative staging computerized tomographies (CT) and postoperative first oncological follow-up CTs of the patients who underwent low anterior resection (LAR) for rectal cancer from 2010 through 2015. We measured cross-sectional area of left and right rectus abdominis muscles from two levels (above and below umbilicus) where they are widest and psoas muscle at mid-level of the fourth lumbar vertebral body in axial CT images and compared preoperative and postoperative measurements. We investigated the effects of age, sex, administration of preoperative chemoradiotherapy (CRT), type of surgery (open or laparoscopic), or construction of a diverting ileostomy on cross-sectional muscle area changes. RESULTS: After applying inclusion and exclusion criteria 60 patients found to be eligible for the study. Muscle areas of all measurement sites were reduced postoperatively compared to paired preoperative values. There was no significant effect of age, sex, administration of preoperative CRT, type of surgery (open or laparoscopic), or construction of a diverting ileostomy to muscle cross-sectional area reductions. CONCLUSION: Cross-sectional areas of the rectus abdominis and the psoas muscles of rectal cancer patients reduces following rectum resection which indicates atrophy of these muscles. Clinicians should be aware of this problem and focus on prevention of muscle atrophy during the treatment of rectal cancer patients.

Vertebral body bone mineral density in patients with lumbar spondylolysis: a quantitative CT study.

PURPOSE: Spondylolysis is known to be a part of a disease process, which describes a defect in the pars interarticularis of vertebra. We aimed to use quantitative computed tomography (QCT) to measure vertebral body bone mineral density (BMD) in patients with lumbar spondylolysis and compare it with readings in controls. METHODS: Forty symptomatic patients with lumbar spondylolysis aged 18-52 years and 40 matched controls of same sex and approximate age (±2 years) were included in the study. Measurements of BMD were performed by QCT analysis for each vertebral body from T12 to L5 and mean BMD was calculated for each case. RESULTS: Of 40 patients, 22 (55%) demonstrated L5 spondylolysis, 14 (35%) L4 spondylolysis, three (7.5%) L3 spondylolysis, and one (2.5%) L2 spondylolysis. Spondylolisthesis was found in 29 patients (73%). Patients with spondylolisthesis were significantly older than patients without spondylolisthesis (42±6.9 vs. 37.2±5.4, P = 0.024). Mean BMD value of the patient group was significantly lower than that of the controls (105±24 mg/cm³ vs. 118.7±25.6 mg/cm³, P = 0.015). Subgroup analysis of 19 patients and 19 controls under the age of 40 revealed that the mean BMD value of the patients was significantly lower than that of the controls in the younger age group as well (108.7±23.5 mg/cm³ vs. 130±25.8 mg/cm³, P = 0.009). CONCLUSION: This study demonstrated that patients with spondylolysis had significantly lower mean vertebral body BMD compared with controls.