CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

Feasibility and Diagnostic Performance of Functional SYNTAX Score Derived From Dynamic CT Myocardial Perfusion Imaging.

BACKGROUND: Computed tomography (CT) fractional flow reserve (FFR)-derived functional SYNTAX score (FSSCT-FFR) is a valuable method for guiding treatment strategy in patients with multivessel coronary artery disease. Dynamic CT myocardial perfusion imaging (CT-MPI) demonstrates higher diagnostic accuracy than CT-FFR in identifying hemodynamically significant coronary artery disease. We aimed to evaluate the feasibility of CT-MPI-derived FSS (FSSCT-MPI) with reference to invasive FSS. METHODS: In this retrospective study, patients with multivessel coronary artery disease who underwent dynamic CT-MPI+ coronary CT angiography and invasive coronary angiography or FFR within 4 weeks were consecutively included. Invasive (FSSinvasive) and noninvasive FSS (FSSCT-MPI and FSSCT-FFR) were calculated by an online calculator, which assigned points to lesions with hemodynamic significance (defined as FFRinvasive ≤0.80, invasive coronary angiography diameter stenosis ≥90%, CT-FFR ≤0.80, and myocardial ischemia on CT-MPI). Weighted κ value and net reclassification index were calculated to determine the consistency and incremental discriminatory power of FSSCT-MPI. Receiver operating characteristic curve analysis was used for the comparison of FSSCT-MPI and FSSCT-FFR in detecting intermediate- to high-risk patients. RESULTS: A total of 119 patients (96 men; 64.6±10.6 years) with 305 obstructive lesions were included. The average FSSCT-MPI, FSSCT-FFR, and FSSinvasive were 15.58±13.03, 16.18±13.30, and 13.11±12.22, respectively. The agreement on risk classification based on the FSSCT-MPI tertiles was good (weighted κ, 0.808). With reference to FSSinvasive, FSSCT-MPI correctly reclassified 27 (22.7%) patients from the intermediate- to high SYNTAX score group to the low-score group (net reclassification index, 0.30; P<0.001). In patients with severe calcification, FSSCT-MPI had better diagnostic value than FSSCT-FFR in detecting intermediate- to high-risk patients when compared with FSSinvasive (area under the curve, 0.976 versus 0.884; P<0.001). CONCLUSIONS: Noninvasive FSS derived from CT-MPI is feasible and has strong concordance with FSSinvasive. It allows accurate categorization of FSS in patients with multivessel coronary artery disease, in particular with severe calcification.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

[Features of detection and interpretation of intravital and postmortem changes according to the results of traditional X-ray and X-ray computed tomography of objects from historical graves and artefacts]. / Osobennosti vyyavleniya i interpretatsii prizhiznennykh i posmertnykh izmenenii po rezul'tatam traditsionnoi rentgenografii i rentgenovskoi komp'yuternoi tomografii ob"ektov iz istoricheskikh zakhoronenii i artefaktov.

OBJECTIVE: To study emergence mechanism, physical nature, pattern of intravital and postmortem changes of biological and non-biological objects originated in the period from 1550 to 1918 yr. using traditional X-ray and X-ray computed tomography. MATERIAL AND METHODS: The relics of Saint Macarius the Roman of Novgorod, the remains of the First Reverend of the Resurrection Novodevichy Convent in Saint Petersburg Mother Superior Theophania, damages on the chair leg on which Tsesarevich Alexey sat during the shooting of Russian Emperor Nicholas II, his family and entourage in 1918 in Yekaterinburg were stidued. RESULTS AND CONCLUSION: The application of highly informative methods of traditional X-ray and X-ray computed tomography of biological and non-biological objects showed their high informativity and allowed to correctly interpret the emergence mechanism, physical nature, pattern of intravital and postmortem changes of skeleton bones and historical artefact (chair legs) originated long ago. The necessity of special professional training and advanced training of experts in forensic radiology to prevent possible diagnostic and expert errors has been substantiated.

Application of radiomics in ischemic stroke.

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions.

PURPOSE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Right main pulmonary artery distensibility on dynamic ventilation CT and its association with respiratory function.

BACKGROUND: Heartbeat-based cross-sectional area (CSA) changes in the right main pulmonary artery (MPA), which reflects its distensibility associated with pulmonary hypertension, can be measured using dynamic ventilation computed tomography (DVCT) in patients with and without chronic obstructive pulmonary disease (COPD) during respiratory dynamics. We investigated the relationship between MPA distensibility (MPAD) and respiratory function and how heartbeat-based CSA is related to spirometry, mean lung density (MLD), and patient characteristics. METHODS: We retrospectively analyzed DVCT performed preoperatively in 37 patients (20 female and 17 males) with lung cancer aged 70.6 ± 7.9 years (mean ± standard deviation), 18 with COPD and 19 without. MPA-CSA was separated into respiratory and heartbeat waves by discrete Fourier transformation. For the cardiac pulse-derived waves, CSA change (CSAC) and CSA change ratio (CSACR) were calculated separately during inhalation and exhalation. Spearman rank correlation was computed. RESULT: In the group without COPD as well as all cases, CSACR exhalation was inversely correlated with percent residual lung volume (%RV) and RV/total lung capacity (r = -0.68, p = 0.003 and r = -0.58, p = 0.014). In contrast, in the group with COPD, CSAC inhalation was correlated with MLDmax and MLD change rate (MLDmax/MLDmin) (r = 0.54, p = 0.020 and r = 0.64, p = 0.004) as well as CSAC exhalation and CSACR exhalation. CONCLUSION: In patients with insufficient exhalation, right MPAD during exhalation was decreased. Also, in COPD patients with insufficient exhalation, right MPAD was reduced during inhalation as well as exhalation, which implied that exhalation impairment is a contributing factor to pulmonary hypertension complicated with COPD. RELEVANCE STATEMENT: Assessment of MPAD in different respiratory phases on DVCT has the potential to be utilized as a non-invasive assessment for pulmonary hypertension due to lung disease and/or hypoxia and elucidation of its pathogenesis. KEY POINTS: • There are no previous studies analyzing all respiratory phases of right main pulmonary artery distensibility (MPAD). • Patients with exhalation impairment decreased their right MPAD. • Analysis of MPAD on dynamic ventilation computed tomography contributes to understanding the pathogenesis of pulmonary hypertension due to lung disease and/or hypoxia in patients with expiratory impairment.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma.

PURPOSE: Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS: Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS: In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION: Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images.

BACKGROUND: This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. METHODS: Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations. RESULTS: Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR. CONCLUSIONS: Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs.

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study.

BACKGROUND: Diagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical planning. This indicates the need for a more efficient and patient-centred approach, suggesting a gap in the existing diagnostic methods and the potential for artificial intelligence technologies to address this gap. This study aimed to create a multimodal hybrid transformer model using the Vision Transformer that leverages LDCT features and clinical data to improve surgical decision-making for patients with incidentally detected mediastinal tumours. METHODS: This retrospective study analysed patients with mediastinal tumours between 2010 and 2021. Patients eligible for surgery (n=30) were considered 'positive,' whereas those without tumour enlargement (n=32) were considered 'negative.' We developed a hybrid model combining a convolutional neural network with a transformer to integrate imaging and clinical data. The dataset was split in a 5:3:2 ratio for training, validation and testing. The model's efficacy was evaluated using a receiver operating characteristic (ROC) analysis across 25 iterations of random assignments and compared against conventional radiomics models and models excluding clinical data. RESULTS: The multimodal hybrid model demonstrated a mean area under the curve (AUC) of 0.90, significantly outperforming the non-clinical data model (AUC=0.86, p=0.04) and radiomics models (random forest AUC=0.81, p=0.008; logistic regression AUC=0.77, p=0.004). CONCLUSION: Integrating clinical and LDCT data using a hybrid transformer model can improve surgical decision-making for mediastinal tumours, showing superiority over models lacking clinical data integration.

Take CT, get PET free: AI-powered breakthrough in lung cancer diagnosis and prognosis.

PET scans provide additional clinical value but are costly and not universally accessible. Salehjahromi et al.1 developed an AI-based pipeline to synthesize PET images from diagnostic CT scans, demonstrating its potential clinical utility across various clinical tasks for lung cancer.

[Estimation of acetabular cup prosthesis coverage rate in total hip arthroplasty based on X-ray films].

Objective: To explore the method of accurately estimating the acetabular cup prosthesis coverage rate (hereinafter referred to as "cup coverage rate") in total hip arthroplasty (THA) based on X-ray films, and to determine the effective parameters that can be used to estimate the cup coverage rate. Methods: The three-dimensional printed pelvic models were established based on CT data of 16 healthy pelvis, and the acetabular prosthesis were implanted according to conventional THA procedure. The length and width of the uncovered area of the acetabular cup prosthesis were measured by a modified X-ray recording method with a rotating C-arm X-ray machine, and the cup coverage rate was calculated. Then the differences among the traditional anteroposterior X-ray recording method, the modified method, and actual measurement on pelvic model were statistically analyzed. The correlation between the area of the uncovered area of the prosthesis and its width and length was analyzed by using multiple linear regression analysis. Results: The cup coverage rates of traditional method, modified method, and actual measurement were 78.22%±3.36%, 86.74%±3.61%, and 89.62%±2.62%, respectively, with significant differences ( P<0.05). The results of multiple linear regression analysis showed that the width and length were positively linear with the uncovered area of the prosthesis, and the regression equation was as follows: uncovered area of the prosthesis=-21.192+0.248×width+0.140×length, and the coefficient of determination R 2=0.857, P<0.001. Conclusion: Compared with the traditional method, the modified method can more accurately evaluate the cup coverage rate during THA, and the width of the uncovered area of the prosthesis can be used as an effective reference for the cup coverage rate.

[The mechanical ventilation guided by electrical impedance tomography in acute respiratory distress syndrome].

Acute respiratory distress syndrome (ARDS) is a common and critical clinical condition characterized by diffuse damage to the lung interstitium, alveoli, and increased permeability of pulmonary blood vessels. CT can be used to assess the imaging features, severity, and prediction of ARDS, but it requires patient transportation to the CT room and is only a static examination. Electrical impedance tomography (EIT) is an increasingly widely used monitoring tool in clinical applications in recent years. It enables continuous real-time assessment of lung ventilation distribution at the bedside and has high clinical value in optimizing mechanical ventilation parameters for critically ill patients. This article introduces the basic principles of EIT and how to better utilize EIT technology to guide mechanical ventilation treatment for ARDS patients.