Academic Radiology最新文献

Rationale and objectives: Fluid-attenuated inversion recovery vessel hyperintensities (FVHs) reflect the haemodynamic state and may aid in predicting the prognosis of border zone (BZ) infarct patients. This study was to explore the relationship between FVHs and functional outcomes for different BZ infarct subtypes following medical therapy administration.

Materials and methods: Consecutive patients with ischemic stroke were retrospectively enrolled and classified into internal BZ (IBZ) infarct, cortical BZ (CBZ) infarct and mixed-type infarct patients. FVHs were quantified using the FVH-Alberta Stroke Program Early CT Score (ASPECTS) system, and the scores were used to divide the patients into low-FVH (0-3) and high-FVH (4-7) groups. The FVH location and the cerebrovascular stenotic degree were recorded. Logistic regression was performed to identify risk factors for poor outcomes (modified Rankin scale score ≥3).

Results: A total of 207 BZ infarct patients (IBZ, n = 130; CBZ, n = 52; mixed-type, n = 25) were included. The FVH score was positively correlated with cerebrovascular stenosis (r = 0.332, P < 0.001) in all patients. A high FVH score was associated with poor outcomes in all (OR 2.568, 95% CI (1.147 to 5.753), P = 0.022) and in CBZ infarct patients (OR 9.258, 95% CI 1.113 to 77.035), P = 0.040). FVH-diffusion-weighted imaging (DWI) mismatch was not significantly associated with outcomes in the entire patient group or in any subgroup.

Conclusions: A high FVH score is associated with poor long-term outcomes in patients with CBZ infarcts but not in those with IBZ or mixed-type infarcts.

Rationale and objectives: To develop a radiomics model with enhanced diagnostic performance, reduced unnecessary fine needle aspiration biopsy (FNA) rate, and improved clinical net benefit for thyroid nodules.

Methods: We conducted a retrospective study of 217 thyroid nodules. Lesions were divided into training (n = 152) and verification (n = 65) cohorts. Three radiomics scores were derived from B-mode ultrasound (B-US) and strain elastography (SE) images, alone and in combination. A radiomics nomogram was constructed by combining high-frequency ultrasonic features and the best-performing radiomics score. The area under the receiver operating characteristic curve (AUC), unnecessary FNA rate, and decision curve analysis (DCA) results for the nomogram were compared to those obtained with the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and the combined TI-RADS+SE+ contrast-enhanced ultrasound (CEUS) advanced clinical score.

Results: The three radiomics scores (B-US, SE, B-US+SE) achieved training AUCs of 0.753 (0.668-0.825), 0.761 (0.674-0.838), and 0.795 (0.715-0.871), and validation AUCs of 0.732 (0.579-0.867), 0.753 (0.609-0.892), and 0.752 (0.592-0.899) respectively. The AUC of the nomogram for the entire patient cohort was 0.909 (0.864-0.954), which was higher than that of the ACR TI-RADS score (P < 0.001) and equivalent to the TI-RADS+SE+CEUS score (P = 0.753). Similarly, the unnecessary FNA rate of the radiomics nomogram was significantly lower than that of the ACR TI-RADS score (P = 0.007) and equivalent to the TI-RADS+SE+CEUS score (P = 0.457). DCA also showed that the radiomics nomogram brought more net clinical benefit than the ACR TI-RADS score but was similar to that of the TI-RADS+SE+CEUS score.

Conclusion: The radiomics nomogram developed in this study can be used as an objective, accurate, cost-effective, and noninvasive method for the characterization of thyroid nodules.

Rationale and objectives: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Patient Care Follow-up System (APCFS) for high-risk ICTFs. This study aimed to analyze the ICTFs detected by this system and the factors associated with them.

Materials and methods: This retrospective study was approved by the institutional review board. We analyzed 52,331 CT examinations conducted between 2019 and 2021. In cases where high-risk ICTFs were identified, radiologists utilized APCFS to prompt ordering clinicians for further patient care. We assessed the frequency, affected body organs, presence or absence of therapeutic interventions, and diagnoses of high-risk ICTFs. An automated machine learning platform was employed to analyze the factors associated with high-risk ICTFs.

Results: Among the 52,331 CT examinations, 507 (0.96%) revealed high-risk ICTFs, primarily affecting the lung (18.0%). Of these 507 high-risk ICTFs, 117 (23.1%) underwent therapeutic interventions, while 362 (71.4%) required only follow-up. Of the 117 cases undergoing interventions, 61 (52.1%) required surgery. Of the 219 high-risk ICTFs leading to a confirmed diagnosis, 146 (66.7%) were neoplastic lesions, including 88 (60.3%) malignancies, and 73 (33.3%) were non-neoplastic lesions. The top three risk factors associated with high-risk ICTFs in the regularized logistic regression model were the imaging protocol (especially aortic valve implantation planning protocol), imaging area (especially whole-body imaging), and clinical department (especially cardiology).

Conclusion: Utilizing APCFS, high-risk ICTFs were detected in approximately 1% of all CT examinations, likely associated with specific imaging protocols, areas, and clinical departments.

Rationale and objectives: Placenta accreta spectrum (PAS) is associated with significant morbidity and mortality. Current radiomic analysis of PAS magnetic resonance (MR) images is often performed on a single imaging plane. However, depending on the chosen imaging plane, radiomic features extracted from the same patient may vary due to the differing orientations and anatomical contexts, potentially leading to inconsistent results. In this study, we applied region of interest (ROI)-based radiomic analysis on axial and sagittal MR images in pregnant patients at high risk for PAS. Our objective was to compare MR textural features extracted from these imaging planes and to correlate these findings with surgical outcomes, aiming to enhance the accuracy of PAS diagnosis and treatment planning.

Materials and methods: This is a retrospective review of MR images of pregnancies with prenatally suspected PAS. Volumetric placental, uterus, and internal os of the cervix regions of interest (ROI) were manually segmented on axial and sagittal MR images for each patient. Radiomic features were extracted following the image biomarker standardization initiative guideline. Agreement in features extracted from axial and sagittal images were assessed using Spearman's rank correlation coefficient.

Results: Of the 101 pregnant patients that met the study inclusion criteria, 65 underwent cesarean hysterectomy for PAS. 77 percent of the radiomics features had strong Spearman rank correlations (>0.8) between axial and sagittal images, indicating that these imaging planes provide similar radiomics information. The diagnostic performance of features extracted from axial and sagittal planes was quantified under the receiver operating characteristics curve (AUC). We found that axial and sagittal planes have similar performance for the prediction of hysterectomy. Shape elongation, Placental Location within the Uterus (PLU), and heterogeneity features were significant predictors for hysterectomy regardless of the imaging plane.

Conclusion: Our study found that radiomics features extracted from axial and sagittal MR image plane in the same patient have excellent agreement and strong correlation. We identified several features present in both axial and sagittal images that were predictive in detecting PAS-suspected patient who required hysterectomy. These features may represent the underlying placental pathophysiology.

Purpose: To quantitatively and qualitatively evaluate and compare the performance of leading large language models (LLMs), including proprietary models (GPT-4, GPT-3.5 Turbo, Claude-3-Opus, and Gemini Ultra) and open-source models (Mistral-7b and Mistral-8×7b), in simplifying 109 interventional radiology reports.

Methods: Qualitative performance was assessed using a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates, including trust-breaking and post-therapy misconduct errors. Quantitative readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), SMOG Index, and Dale-Chall Readability Score (DCRS). Paired t-tests and Bonferroni-corrected p-values were used for statistical analysis.

Results: Qualitative evaluation showed no significant differences between GPT-4 and Claude-3-Opus for any metrics evaluated (all Bonferroni-corrected p-values: p = 1), while they outperformed other assessed models across five qualitative metrics (p < 0.001). GPT-4 had the fewest content and trust-breaking errors, with Claude-3-Opus second. However, all models exhibited some level of trust-breaking and post-therapy misconduct errors, with GPT-4-Turbo and GPT-3.5-Turbo with few-shot prompting showing the lowest error rates, and Mistral-7B and Mistral-8×7B showing the highest. Quantitatively, GPT-4 surpassed Claude-3-Opus in all readability metrics (all p < 0.001), with a median FRE score of 69.01 (IQR: 64.88-73.14) versus 59.74 (IQR: 55.47-64.01) for Claude-3-Opus. GPT-4 also outperformed GPT-3.5-Turbo and Gemini Ultra (both p < 0.001). Inter-rater reliability was strong (κ = 0.77-0.84).

Conclusions: GPT-4 and Claude-3-Opus demonstrated superior performance in generating simplified IR reports, but the presence of errors across all models, including trust-breaking errors, highlights the need for further refinement and validation before clinical implementation.

Clinical relevance/applications: With the increasing complexity of interventional radiology (IR) procedures and the growing availability of electronic health records, simplifying IR reports is critical to improving patient understanding and clinical decision-making. This study provides insights into the performance of various LLMs in rewriting IR reports, which can help in selecting the most suitable model for clinical patient-centered applications.

Rationale and objectives: Identifying intrahepatic cholangiocarcinoma (iCCA) patients who are at high risk for early recurrence (ER) can guide personalized treatment strategy and improve survival. This study aimed to investigate the value of preoperative MRI, especially diffusion-weighted imaging, in predicting ER, including in patients receiving neoadjuvant therapy.

Materials and methods: This study included 175 pathologically-confirmed iCCA patients who underwent curative resection (114 men, 61 women; mean age 59.0 ± 9.56 years). MRI features, particularly apparent diffusion coefficient (ADC), were analyzed and compared between ER and non-ER cases. Survival analyses of ER were evaluated using Cox regression and Kaplan-Meier analysis.

Results: ER occurred in 54.3% (95/175) of patients. Multivariate logistic regression analysis identified tumor ADC as the only independent predictor of ER (odds ratio = 0.034, P < 0.001), with AUCs of 0.758 (95%CI 0.664, 0.836) in the testing cohort and 0.779 (95%CI 0.622, 0.893) in the validation cohort. The optimal ADC threshold was 1.273 × 10^-3 mm²/s. Tumor ADC was comparable to the AJCC 8th staging system in predicting ER (AUC 0.758 vs 0.650 in testing cohort and 0.779 vs 0.661 in validation cohort). Multivariate Cox analysis identified high tumor burden score (HR = 1.109, P = 0.009), non-smooth margin (HR = 2.265, P = 0.008) and tumor ADC (HR = 0.111, P < 0.001) as independent risk factors for ER. Lower ADC values were linked to shorter RFS in both testing and validation cohorts (P < 0.001 and 0.0219), as well as in patients receiving neoadjuvant therapy (P = 0.003).

Conclusion: Preoperative MRI, particularly ADC, can help predict ER in iCCA, regardless of the application of neoadjuvant therapy, comparable to the AJCC 8th staging system.

Rationale and objectives: To evaluate whether parathyroid adenomas can be detected by thoracic radiologists on routine chest CT.

Materials/methods: This retrospective study included patients with hyperparathyroidism evaluated by parathyroid scans and a control group with normal calcium. All had enhanced chest CT within 36 months prior to parathyroid imaging. Chest CTs were reviewed by 3 blinded thoracic radiologists. We report diagnostic accuracy for all positive findings and findings > 8 mm.

Results: Our sample comprised 126 patients, 63 with confirmed hyperparathyroidism and 63 control patients; 6 parathyroid cases were excluded for being out of the field of view. Readers 1, 2, and 3 had sensitivity of 95%, 60%, and 35%, and specificity of 88%, 89%, and 97%, respectively. Specificity increased to 95%, 97%, and 98% when considering only findings larger than 8 mm. Review of false negative studies for reader 1 revealed 3 parathyroid adenomas visualized in retrospect. Review of the 7 false positive studies for reader 1 revealed candidate lesions in all of them attributed to exophytic thyroid nodules or lymph nodes. 90%, 67%, and 40% of the parathyroid adenoma patients had at least 1, 2, and 3 complications respectively. Most prevalent complications were nephrolithiasis (48%) and osteopenia (46%).

Conclusions: Routine contrast-enhanced chest CT can detect the majority of parathyroid adenomas with high specificity.

Clinical relevance/application: Increasing awareness of parathyroid adenomas by chest radiologists allow for detection of enlarged parathyroid glands, diagnosing hyperparathyroidism before clinical presentation.

Rationale and objectives: Accurate prediction of the progression of preclinical Alzheimer's disease (AD) is crucial for improving clinical management and disease prognosis. The objective of this study was to develop and validate clinical-radimoics integrated model to predict the time to progression (TTP) and disease risk stratification of preclinical AD.

Materials and methods: A total of 244 cases (mean age: 73.8 ± 5.5 years, 120 women) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly divided into the training cohort (n = 172) and validation cohort (n = 72) using a 7:3 ratio. Clinical factors were identified by univariate and multivariate COX regression. Radiomics features were extracted from GM, WM and CSF of T₁WI images and selected by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO). Using selected clinical factors and radiomics features, the clinical, radimocis and clinical-radiomics nomogram models were developed for predicting the TTP. The performance of each model was assessed by C-index. The risk stratification ability and predicting efficacy of the clinical-radiomics model were utilizing the Kaplan-Meier curve and receiver operator characteristic (ROC) curve.

Results: The C-index of clinical, radimocis and clinical-radiomics models were 0.852 (95% confidence interval[CI]:0.810-0.893), 0.863 (95%CI:0.816-0.910) and 0.903 (95%:0.870-0.936) in the training cohort and 0.725 (95%CI:0.630-0.820), 0.788 (95%CI:0.678-0.898), 0.813(95%CI:0.734-0.892) in the validation cohort. The AUCs of the multi-predictor nomogram at 1-, 3-, 5- and 7-year were 0.894, 0.908, 0.930, 0.979 in the training cohort and 0.671, 0.726, 0.839, 0.931 in the validation cohort.

Conclusion: In this study, we constructed a clinical-radimoics integrated model to predict the progression of preclinical AD and stratified the risk of disease progression in preclinical AD.