Journal of Computer Assisted Tomography最新文献

Objective: Displaced fragments in meniscal flap tears may be challenging to detect radiologically but are clinically relevant for treatment planning. This study aimed to characterize fragment migration patterns on MRI and evaluate associated intra-articular pathologies.

Methods: In this retrospective analysis of 89 knee MRIs performed between January 2018 and May 2022, patients with confirmed meniscal flap tears were assessed for tear location, fragment displacement direction, and associated findings, including cartilage defects, ligament injuries, bone marrow edema, osteophytes, osteochondral lesions, and joint effusion. Statistical associations between tear features and accompanying pathologies were evaluated.

Results: Inferior coronary recess was the most frequent displacement site, especially in medial tears (68.8%, P=0.007). Medial tears more often had cartilage defects (66.3%, P=0.024) and osteochondral lesions (55.0%, P=0.015). Posterior horn involvement predominated, and ACL tears were strongly associated with intercondylar notch displacement (77.8%, P=0.001).

Conclusion: Meniscal flap tears are most commonly located in the posterior horn of the medial meniscus and tend to displace into the inferior coronary recess. Their frequent association with cartilage damage, osteochondral lesions, and ACL injuries underscores the importance of careful MRI evaluation to support surgical decision-making.

Purpose: To evaluate MRI features for differentiating pulmonary hamartomas from other nodule pathologies, including carcinoid tumors and non-small cell lung cancer (NSCLC).

Materials and methods: This retrospective study analyzed chest MRIs of 48 patients with pulmonary nodules from 2017 to 2025. Patients had either pathologic diagnosis or stable follow-up confirming hamartoma. Two blinded radiologists reviewed images and recorded enhancement pattern (solid, rim, or speckled/mesh). The signal intensity index (SII) was calculated using chemical shift imaging.

Results: Hamartomas (n=19, 40%), carcinoids (n=17, 35%), and adenocarcinomas (n=9, 19%) were the most common diagnoses. Rim and speckled/mesh enhancement patterns were nearly exclusive to hamartomas (22% and 39% vs. 0% and 4%, respectively, P<0.001). An SII >10% showed 61% sensitivity and 100% specificity for hamartomas. Combining rim or speckled/mesh enhancement patterns and SII >10% yielded 79% sensitivity and 96.6% specificity for hamartomas.

Conclusions: Although chemical shift imaging is diagnostic for hamartomas, signal dropout is present in just over half of cases. Rim or speckled/mesh enhancement patterns are also highly suggestive. Combining these MRI features significantly improves diagnostic accuracy for pulmonary hamartomas, potentially reducing further follow-up or invasive procedures for indeterminate nodules.

Carotid blowout is the rupture of a carotid artery or branch vessel, a dangerous complication in post-treatment head and neck cancer patients. The purpose of this retrospective case review is to identify imaging features suggestive of eminent carotid blowout on routine surveillance post-treatment neck imaging, so that a prompt neuroendovascular consult is obtained and the patient is preemptively treated, thus improving patient outcomes. It is important to carefully interrogate the carotid vessels with appropriate window/leveling for irregularity, adjacent tissue ulceration, exposed vessel to either air or fluid, surrounding tumor, and pseudoaneurysm.

Objective: Low keV virtual monoenergetic (VME) images are effective in enhancing vessel opacification but require dual-energy CT (DECT), limiting widespread clinical use. Recent advancements in deep learning (DL) enable the generation of VME images from single-energy CT (SECT). However, the performance of the methods has not been evaluated in any clinical use case. The purpose of this work was to assess both objective and subjective image quality of deep learning-based VME images derived from heterogeneous SECT data for pulmonary angiography.

Methods: In this retrospective study, 52 sets of SECT pulmonary angiography images were processed using a deep learning method to estimate material basis images. 40 keV VME images were generated from heterogeneous SECT data using a pretrained physics-constrained Deep-En-Chroma DL model. Two thoracic radiologists, blinded to the image reconstruction method, evaluated pulmonary vessel opacification and overall image quality on DL-VME and SECT images using 5-point Likert scales. Objective image quality was assessed by measuring enhanced vessel contrast and contrast-to-noise ratio (CNR). Statistical analysis was performed using paired t tests and Mann-Whitney U tests.

Results: Compared with SECT, DL-VME images demonstrated significantly higher subjective image quality score and vessel opacification score (P≤0.008). DL-VME yielded a higher average contrast for emboli (1085 vs. 331 HU, P<0.001) and improved CNR (17.8 vs. 11.1, P<0.001). Results of subgroup analysis indicate no significant variation in VME performance across patient sex, scanner model, radiation dose, and tube potential. The vessel opacification scores of both VME and SECT demonstrate dependence on patient weight, with VME providing better vessel opacity for both lighter and heavier patients.

Conclusions: A measure of 40 keV DL-VME derived from SECT effectively enhances both vessel opacification and image quality in CT pulmonary angiography. The image quality advantage of DL-VME over SECT remains robust across variations in data acquisition and patient variables.

Objective: To identify risk factors predicting visceral pleural invasion (VPI) in part-solid and solid nodules through meta-analysis, and to develop a predictive model in an independent cohort.

Methods: The PubMed, Embase, and Web of Science databases were systematically searched to identify studies on pleural-related semantic, nodule semantic, and quantitative computed tomography (CT) features to predict VPI. The pooled odds ratios (ORs) for semantic features and standardized mean differences (SMDs) for quantitative features were calculated to develop a predictive model. A total of 203 patients (147 VPI-negative and 56 VPI-positive) were enrolled in the validation cohort between January and December 2024. The diagnostic performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).

Results: Thirteen studies with 3999 patients were included in this meta-analysis. Several key risk factors were identified to construct the predictive model, including pleural indentation (OR: 3.428, 95% CI: 2.559-4.593), nodule type (OR: 4.867, 95% CI: 3.915-6.051), spiculation (OR: 2.581, 95% CI: 1.640-4.062), lobulation (OR: 1.855, 95% CI: 1.148-2.997), vessel convergence sign (OR: 3.606, 95% CI: 1.698-7.656), and the maximum solid diameter (SMD: 0.894, 95% CI: 0.600-1.188). This model yielded an AUC of 0.892 (95% CI: 0.840-0.931) in the validation cohort.

Conclusions: This meta-analysis involved the construction of an effective model for predicting VPI by integrating pleural indentation, nodule type, spiculation, lobulation, vessel convergence sign, and maximum solid diameter, which could inform preoperative clinical decision-making for subpleural part-solid and solid nodules.

Objective: This study investigated the associations of radiomics based on baseline MRI with recurrence and disease-free survival (DFS) after neoadjuvant chemotherapy (NAC) in young women with breast cancer.

Materials and methods: In total, 181 women aged 40 years or younger with breast cancer who underwent MRI before NAC were allocated into the training (n=126) and test cohorts (n=55). Three radiomics signatures were built using the intratumoral, peritumoral, and combined regions of MR images. Univariate and multivariate logistic regression were performed to select independent risk factors to construct a clinical model. A nomogram model was developed by integrating the clinical model and the combined radiomics signature. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Multivariate Cox regression and Kaplan-Meier analyses were used to determine the associations of various models with DFS.

Results: Among the radiomics signatures, the combined signature best predicted recurrence, with AUCs of 0.899 and 0.849 in the training and test cohorts, respectively. The nomogram model displayed the best performance in predicting recurrence in the training (AUC=0.925) and test cohorts (AUC=0.880). The nomogram model most accurately predicted DFS in the training (C-index=0.872) and test cohorts (C-index=0.846).

Conclusions: The nomogram model based on pretreatment breast MRI could effectively predict breast cancer recurrence in young women undergoing NAC and serve as a potential biomarker for the risk stratification of DFS.

Objective: Minimally invasive parathyroidectomy (MIP) requires high-fidelity localization of parathyroid adenomas through preoperative imaging, commonly 4-dimensional computed tomography (4D-CT). Texture analysis extracts high-order mathematical features from an image and may be applied to 4D-CT for quantitative differentiation of lymph nodes and thyroid nodules from parathyroid adenomas.

Methods: This is a retrospective cohort study of 51 patients diagnosed with PHPT and known parathyroid adenoma and/or thyroid nodule who have undergone preoperative 4D-CT imaging before parathyroidectomy. Three anatomic structures (parathyroid adenoma, lymph node, and thyroid nodule) were manually segmented on 25-second arterial phase axial sections of the 4D-CT scans. Radiomic data were extracted for shape, first-order, and second-order classes (107 total features) for each of the structures in each patient. A series of t tests were conducted to assess for radiomic features with statistically significant differences in lymph nodes or thyroid nodules when compared with parathyroid adenomas. A multivariable logistic regression model for discrimination of parathyroid adenomas was trained on a subset of the data set and assessed on a hold-out test subset.

Results: When comparing parathyroid adenomas and lymph nodes, 14/18 first-order features and 44/75 second-order features were statistically significantly different (P<0.05), of which 13/18 first-order features and 16/75 second-order features were potent discriminators (P<0.0001). No features were significantly different between parathyroid adenomas and thyroid nodules. A multivariable logistic regression model for discrimination of parathyroid adenomas from lymph nodes achieved strong predictive performance (AUC: 0.95, 95% CI: 0.86-1).

Conclusions: Parathyroid adenomas and lymph nodes have statistically distinct radiomic textural signatures on arterial phase 4D-CT, with the most significant differences found in first-order textural features. These findings may facilitate the development of future machine learning models for automated differentiation of parathyroid adenomas, further enhancing uptake of MIP and improving clinical outcomes.

Objective: We aimed to improve preoperative accuracy of tumor staging by evaluating the ability of qualitative and quantitative CT imaging features to predict perirenal fat invasion (PFI) in renal cell carcinoma (RCC).

Methods: This retrospective case-control study included 86 patients with pathologically proven PFI and 169 controls matched for tumor size without PFI who were treated by nephrectomy between January 2016 and December 2020. Two radiologists independently evaluated the qualitative imaging features of tumor complexity, shape, margin, tumor-fascia contact, perirenal vascularity, fascial thickening, septation, stranding, and nodules. We also compared tumor contact length and protruding distance between the groups. Multivariate logistic regression analyses identified significant predictors of PFI, and diagnostic performance metrics of all predictors were assessed to create a combined model that included all significant predictors.

Results: Lobulated shapes and irregular margins were more prevalent in the group with than without PFI (P<0.05). Perirenal increased vascularity, fascial thickening, septation, stranding, and nodularity were also significantly more prevalent in the PFI group (P<0.05 for all). Tumor contact length and protruding distance were significantly greater in the PFI group (P=0.002). Multivariate analysis identified the following independent predictors of PFI: lobulated tumors [odds ratio (OR): 2.03; P=0.042], irregular margin (OR: 3.40; P=0.007), perirenal fascial thickening (OR: 4.20; P<0.001), and contact length >154.2 mm (OR: 3.82; P=0.019). The diagnostic performance of these combined predictors was moderate, with 61.6% sensitivity, 79.3% specificity, and 73.3% accuracy.

Conclusions: Qualitative CT features (lobulated tumors, irregular margins, perirenal thickened fascia) and an objective quantitative parameter (threshold 154.2 mm tumor contact length) were significant independent predictors of perirenal fat invasion in RCC. These findings emphasize the complementary value of combining subjective and objective imaging features to enhance preoperative staging accuracy.

Objective: To evaluate whether our new protocol that uses a saline test injection and a leak detection sensor (LDS) reduces the frequency and amount of contrast media (CM) extravasation during the intravenous CM administration for CT.

Methods: This retrospective study included 20,342 patients who underwent CECT at our hospital from March 2021 to November 2021 (old protocol, direct patient observation, and CM injection pressure monitoring, n=10,529) and from March 2024 to November 2024 (new protocol, old protocol plus saline test injection, and the LDS attachment, n=9813). We compared the frequency and the volume of extravasation between the 2 protocols using the Fisher exact test and the Mann-Whitney U test. We also evaluated the accuracy of the LDS.

Results: Extravasation occurred in 51 patients (age 72.1±12.2 y, 33 men) under the old protocol and in 26 patients (age 73.6±9.0 y, 17 men) with the new protocol. The overall frequency of extravasation and the number of patients with an extravasation volume of at least 20 mL were significantly lower with the new protocol than the old protocol (0.48% vs. 0.26%; 0.16% vs. 0.03% all, P<0.01). The extravasation volume was significantly reduced with the new protocol (14 vs. 6 mL, P<0.01). The sensitivity of the LDS to detect extravasation of 3, 5, 10, and 15 mL was 50%, 88%, 93%, and 100%, respectively; specificity was 99% for all.

Conclusions: Our new protocol reduced the frequency and dose of CM extravasation.