Anatomical factors in the development of medial-sided osteochondral lesion of the talus: a comparative analysis.

PURPOSE: The aim of this study was to determine anatomical predictors for the occurrence of medial osteochondral lesions of the talus (OLT), by analyzing morphometric variables obtained from magnetic resonance imaging (MRI). METHODS: A total of 430 ankles with 215 ankle MRIs from patients with nontraumatic OLTs on the medial side of the talar dome and an equal number of age, sex, and side-matched healthy controls were analyzed in this retrospective study. The specific MRI parameters that were measured include the anterior opening angle of the talus (AOT), the angle between the tibial axis and medial malleolus (TMM), the angle of the tibial plafond to the malleoli (PMA), the angle between the anterior and posterior talofibular ligaments (ATFL-PTFL angle), length of the trochlea tali arc (TAL), sagittal length of distal tibial articular surface (TAS), the ratio of the sagittal length of distal tibial articular surface to the length of the trochlea tali arc (TAS/TAL), and the depth of the incisura fibularis (IncDep). RESULTS: AOT, IncDep, ATFL-PTFL angle, PMA, TMM, TAL, and TAS/TAL exhibited significant differences between the two groups. The established cut-off values were 13° (AUC 0.875) for AOT, 3.7 mm (AUC 0.565) for IncDep, 78° (AUC 0.729) for ATFL-PTFL angle, 14° (AUC 0.581) for PMA, 15° (AUC 0.907) for TMM, 34.3 mm (AUC 0.599) for TAL, and 0.81 (AUC 0.719) for TAS/TAL. Multivariate logistic regression analysis revealed Odds Ratio (OR) = 22.22 for AOT > 13°, OR = 4.23 for ATFL-PTFL angle > 78°, OR = 1.99 for PMA ≤ 14°, OR = 31.598 for TMM > 15°, OR = 3.79 for TAS/TAL ≤ 0.81. CONCLUSION: This study highlights the significance of anatomical parameters, particularly the TMM and AOT, as key predictors of OLT.

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images. METHODS: Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG. RESULTS: In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen's kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen's kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response. CONCLUSION: The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches.

Purpose: Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV. Methods and materials: Ninety-three LARC patients who underwent CRT in our institution between 2012 and 2019 were retrospectively scanned. Patients were divided into CR and non-CR groups. Tumor, tumor+mesorectum and CTV were segmented on T2 preCRT MRI images. Extracted features were compared for best area under the curve (AUC) of CR prediction with 15 machine-learning models. Results: CR was observed in 25 patients (26.8%), of whom 13 had pathological, and 12 had clinical complete response. For tumor, tumor+mesorectum and CTV segmentations, the best AUC were 0.84, 0.81, 0.77 in the training set and 0.85, 0.83 and 0.72 in the test set, respectively; sensitivity and specificity for the test set were 76%, 90%, 76% and 71%, 67% and 62%, respectively. Conclusion: Although the highest AUC result is obtained from the tumor segmentation, the highest accuracy and sensitivity are detected with tumor+mesorectum segmentation and these findings align with previous studies, suggesting that the mesorectum contains valuable insights for CR. The lowest result is obtained with CTV segmentation. More studies with mesorectum and pelvic nodal regions included in segmentation are needed.

Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID-19.

Several data sets have been collected and various artificial intelligence models have been developed for COVID-19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground-glass opacities by conventional CXR has limited the use of this modality in the diagnostic work-up of COVID-19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well-established deep CXR models for this new data set and develop a machine learning-based non-maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID-19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID-19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID-19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID-19 on chest CTX examination. COVID-Net, a specially designed convolutional neural network, was used to detect cases of COVID-19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID-Net variants (COVIDNet-CXR3-A, -B, -C/COVIDNet-CXR4-A, -B, -C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID-Net's six variants, the area under the curve (AUC) using the ensemble COVID-Net CXR 4A-4B-3C was .78, sensitivity 67%, specificity 95%; COVID-Net CXR 4a-3b-3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID-Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature.

Percutaneous CT-Guided Microwave Ablation for the Treatment of Osteoid Osteomas: A Single Center Experience.

RATIONALE AND OBJECTIVES: The aim of the current study was to evaluate the feasibility and effectiveness of CT-guided microwave ablation (MWA) in the treatment of osteoid osteomas (OO). MATERIALS AND METHODS: Data from 59 consecutive patients who underwent percutaneous CT-MWA for OO treatment were examined in the current retrospective study. The period of this study spanned from January 2021 to May 2023 at a single institution. The study involved an evaluation of clinical and radiological characteristics, procedural data, Visual Analog Scale (VAS) pain scores, complication incidences, as well as clinical and technical success rates. Statistical analyses were performed by using the Wilcoxon test with Bonferroni correction, Friedman, Spearman, Mann-Whitney U test. RESULTS: 59 patients with an average age of 17.31 ± 8.53 years underwent CT-guided MWA for the treatment of OO. The procedure demonstrated a high success rate, with 96.6% of cases achieving both technical and clinical success. However, recurrence was observed in two patients (3.4%) at the three-month follow-up. These cases were successfully managed with a second MWA procedure. The median VAS pain scores reported by the patients was significantly improved post-procedure: from 8.64 ± 1.14 before treatment to 0.63 ± 0.98 in the first month, 0.41 ± 1.02 in the third month, and 0.15 ± 0.45 in the sixth month. Only one patient (1.7%) experienced a minor complication; no major complications were recorded in this study. CONCLUSION: CT-guided percutaneous MWA is a minimally invasive and a highly effective and safe approach for the treatment of OO.

Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study.

OBJECTIVE: To evaluate the association of body composition parameters with outcomes in Covid-19. METHODS: 173 patients hospitalized for Covid-19 infection in 6 European centers were included in this retrospective study. Measurements were performed at L3-level and comprised skeletal muscle index (SMI), muscle density (MD), and adipose tissue measurements [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral-to-subcutaneous-adipose-tissue-area-ratio (VSR)]. The association with mortality, the need for intubation (MV), and the need for admission to ICU within 30 days were evaluated. RESULTS: Higher SAT density was associated with a greater risk of MV (OR = 1.071, 95%CI=(1.034;1.110), p < 0.001). Higher VAT density was associated with admission to ICU (OR = 1.068, 95%CI=(1.029;1.109), p < 0.001). Higher MD was a protective factor for MV and ICU admission (OR = 0.914, 95%CI=(0.870;0.960), p < 0.001; OR = 0.882, 95%CI=(0.832;0.934), p = 0.028). Higher VSR was associated with mortality (OR = 2.147, 95%CI=(1.022;4.512), p = 0.044). Male sex showed the strongest influence on the risk of ICU admission and MV. SMI was not associated with either parameter. CONCLUSION: In patients hospitalized for Covid-19 infection, higher VSR seems to be a strong prognostic factor of short-term mortality. Weak associations with clinical course were found for MD and adipose tissue measurements. Male sex was the strongest prognostic factor of adverse clinical course. ADVANCES IN KNOWLEDGE: VSR is a prognostic biomarker for 30-day mortality in patients hospitalized for Covid-19 disease.

Prognostic Role of the Pectoralis Musculature in Patients with COVID-19. A Multicenter Study.

RATIONALE AND OBJECTIVES: To evaluate the impact of low skeletal muscle mass in patients with COVID-19 on relevant outcomes like 30-day mortality, need for intubation and need for intensive care unit admission. MATERIALS AND METHODS: For this study, data from six centers were acquired. The acquired sample comprises 1138 patients. There were 547 women (48.1%) and 591 men (51.9%) with a mean age of 54.5 ± 18.8 years; median age, 55 years; range, 18-84 years). In every case, thoracic CT without intravenous application of contrast medium was performed. The following parameters of the pectoralis muscles were estimated: muscle area as a sum of the bilateral areas of the pectoralis major and minor muscles, muscle density, muscle index (PMI) (pectoralis muscle area divided by the patient's body height square) as a ratio pectoralis major and minor muscles divided by the patient's body height2, and muscle gauge as PMI x muscle density. RESULTS: Overall, 220 patients (19.33%) were admitted to the intensive care unit. In 171 patients (15.03%), mechanical lung ventilation was performed. Finally, 154 patients (13.53%) died within the observation time of 30-day. All investigated parameters of pectoralis muscle were lower in the patients with unfavorable courses of Covid-19. All pectoralis muscle parameters were associated with 30-day mortality in multivariate analyses adjusted for age and sex: pectoralis muscle area, HR = 0.93 CI 95% (0.91-0.95) p < 0.001; pectoralis muscle density, HR = 0.94 CI 95% (0.93-0.96) p < 0.001; pectoralis muscle index, HR = 0.79 CI 95% (0.75-0.85) p < 0.001, pectoralis muscle gauge, HR = 0.995 CI 95% (0.99-0.996) p < 0.001. CONCLUSION: in COVID-19, survivors have larger areas and higher index, gauge and density of the pectoralis muscles in comparison to nonsurvivors. However, the analyzed muscle parameters cannot be used for prediction of disease courses.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study.

Background: Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims: We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods: Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results: RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (ß = 0.097, p < 0.05) and pleural effusion (ß = 10.631, p < 0.05) on COVID-19 pneumonia patients. Conclusion: The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Correlation of laboratory parameters and Chest CT findings in young adults with COVID-19 and comparison of imaging findings with children.

PURPOSE: We aimed to compare COVID-19 imaging findings of young adults (19-35 years of age) with those of children (0-18 years) and to correlate imaging findings of young adults with their laboratory tests. MATERIALS AND METHODS: This retrospective study included Real Time-Polymerase Chain Reaction (RT-PCR) confirmed 130 young adults (mean age: 28.39 ± 4.77; 65 male, 65 female) and 36 children (mean age: 12.41 ± 4.51; 17 male, 19 female), between March and June 2020. COVID-19 related imaging findings on chest CT were examined in young adults and compared with children by the Mann-Whitney U, and Chi-square or Fisher's exact test. Laboratory examinations of young adults were assessed in terms of correlation with radiological findings by the Spearman's correlation analysis. RESULTS: Bilateral multiple distributions (p = 0.014), subpleural involvement, and pleural thickening (p = 0.004), GGOs with internal consolidations were more frequent in adults (p = 0.009). Infiltrations were significantly larger than 20 mm in young adults (p = 0.011). The rates of feeding vessel sign, vascular enlargement, and halo sign were significantly higher in young adults (p < 0.003). Highly significant positive correlations were found between radiological and biochemical parameters. CONCLUSION: Distribution, size, and pattern of COVID-19 related imaging findings differed in children and young adults. Radiological findings were correlated with biochemical parameters but not with blood count results of young adults.