Academic Radiology最新文献

Rationale and objectives: To investigate the diagnostic value of perfluorobutane-enhanced ultrasound (US) examinations for differentiating benign from malignant subpleural lung lesions.

Methods: This single-center, retrospective study enrolled consecutive patients with subpleural lung lesions between January 2022 and March 2023. The cause of the lung lesions was confirmed by biopsy and follow-up examinations. The lesions were continuously evaluated using perfluorobutane-enhanced US for 0-180 s, and washout (WT) was observed after 3, 5, and 10 min. Univariate and multivariate analyses were used to identify significant US features, which were evaluated for their diagnostic performance. The diagnostic performance of combining several features for predicting malignant lung lesions was also assessed by multivariate logistic regression analysis.

Results: Seventy cases were included (17 benign lesions [13 men, 4 women; mean age: 57.5 ± 12.2 years] and 53 malignant lesions [41 men, 12 women; mean age: 63.3 ± 11.6 years]). Peak intensity (PI), arrival time (AT), and WT after 10 min significantly differed between malignant and benign lesions. The sensitivity and accuracy were significantly higher for 10-minute WT than for AT (both p < 0.05). The area under the curve of the combined diagnostic evaluation with AT, PI, and 10-minute WT was 0.897 (95% [CI]: 0.806-0.988), which was significantly higher than that of AT or PI alone.

Conclusion: Perfluorobutane-enhanced US can differentiate benign from malignant lung lesions, and combining AT, PI, and 10-minute WT for diagnostic purposes performed better than a single feature.

Rationale and objectives: We aimed to evaluate possible elasticity changes in the menisci of patients with type 2 diabetes mellitus using shear wave elastography (SWE).

Materials and methods: The medial and lateral menisci of the right and left knee of 40 patients (20 males, 20 females) with type 2 diabetes mellitus and 40 healthy controls (20 males, 20 females) were evaluated between June 2024 and September 2024. All patients and the control group were evaluated with MRI for meniscal pathology. Medial and lateral meniscal thicknesses were measured in the coronal plane in grayscale US mode. In both groups, the SWE measurement range was set to 0-8.2m/s and 0-200kPa and 2 mm ROIs were placed on the medial and lateral meniscal bodies of both knees in the coronal plane. The stiffness values of the meniscus were measured three times and the mean value of these three measurements was recorded.

Results: There was no significant difference between meniscal thickness in diabetic patients and control group (p > 0.05). Bilateral meniscal stiffness values of diabetic patients were higher than the control group and there was a statistically significant difference (p < 0.05). There were moderate to strong positive correlations between meniscal stiffness values and fasting blood glucose and HA1c values in the diabetic patients(p < 0.05).

Conclusion: This is the first study to demonstrate that meniscus stiffness increases in diabetic patients. SWE is a quantitative imaging method that can be used to detect meniscal pathologies that may develop due to diabetes.

Rationale and objectives: This study evaluated the diagnostic performance of ¹⁸F-fluorocholine (FCH) PET/CT as the first-line functional imaging method for preoperative localization of hyperfunctioning parathyroid glands (HPGs) in patients with primary hyperparathyroidism (PHPT).

Materials and methods: This retrospective single-center study included 80 consecutive patients with PHPT, referred for FCH PET/CT between January 2018 and July 2022, and who subsequently underwent surgery. The diagnostic performance of FCH PET/CT was compared to histological results for per-lesion analysis, and to postoperative resolution of biochemical PHPT for per-patient analysis.

Results: ¹⁸F-FCH-PET/CT revealed 95 positive foci in 77/80 patients and was negative in 3/80 patients. Postoperative resolution of HPT was obtained in 67/80 patients (84%). Per-lesion analysis showed 80 true positives, five true negatives, 11 false negatives, and eight false positives. Seven PET-positive foci could not be compared to histology. In a first per-lesion analysis, excluding these seven anomalies, sensitivity and positive predictive value (PPV) of FCH PET/CT were 88% (95% CI: 79-94) and 91% (95% CI: 87-94), respectively. In a second per-lesion analysis considering the seven anomalies as false positives (maximum bias analysis), PPV was 84% (95% CI: 80%-87%). By per-patient analysis, FCH PET/CT correctly identified and located all pathological glands in 56/80 (70%, 95% CI: 59-80) patients.

Conclusion: ¹⁸F-Fluorocholine PET/CT appears to be an effective pre-surgical imaging method for localization of hyperfunctioning parathyroid tissue in patients with PHPT.

Rationale and objectives: Non-muscle-invasive bladder cancer (NMIBC) is highly recurrent, with each recurrence potentially progressing to muscle-invasive cancer, affecting patient prognosis. Intratumoral heterogeneity plays a crucial role in NMIBC recurrence. This study investigated a novel habitat-based radiomic analysis for stratifying NMIBC recurrence risk.

Materials and methods: A retrospective collection of 382 NMIBC patients between 2015 and 2021 from two medical institutions was carried out. Patients' CT images were collected across three phases, with tumor sites delineated within the bladder. Intratumoral habitats were identified using K-means clustering on 19 texture features of the tumor sites, followed by the extraction of 107 radiomic features per habitat with PyRadiomics. These features were integrated into machine learning algorithms to develop a habitat-based model (HBM) for predicting two-year recurrence of NMIBC patients. The clinical and multiphase radiomic models were also constructed for comparison, with the Delong test comparing their diagnostic efficiency. The impact of HMB on patients' recurrence-free survival and the correlation between HBM and tumor-stroma ratio were further analyzed.

Results: Three distinct habitats were identified within NMIBC. The HBM showed an AUC of 0.932 (95% CI: 0.906 - 0.958) in the training cohort and 0.782 (95% CI: 0.674 - 0.890) in the validation cohort for predicting two-year recurrence. With comparison between different models, The HBM is demonstrated to possess superior diagnostic efficacy to the clinical model (p < 0.001) in the training cohort. However, no significant difference was noted between the multiphase and clinical models (p = 0.130) in the training cohort. The HBM score effectively distinguished the recurrence-free survival of NIMBC patients and demonstrated a significant correlation with the tumor-stroma ratio.

Conclusions: Habitat-based radiomics, coupled with machine learning, efficiently predicts NMIBC recurrence. Further research on habitat-based radiomics offers potential improvement in clinical management of NMIBC.

Rationale and objectives: This investigation sought to create a nomogram to predict the ablation effect after microwave ablation in patients with hepatocellular carcinoma, which can guide the selection of microwave ablation for small hepatocellular carcinomas.

Methods: In this two-center retrospective study, 233 patients with hepatocellular carcinoma treated with microwave ablation (MWA) between January 2016 and December 2023 were enrolled and analyzed for their clinical baseline data, laboratory parameters, and MR imaging characteristics. Logistic regression analysis was used to screen the features, and clinical and imaging feature models were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).

Results: Two models and a nomogram were developed to predict ablation outcomes after MWA based on a training set (n = 182, including complete ablation: 136, incomplete ablation: 46) and an external validation set (n = 51, complete ablation: 36, incomplete ablation: 15). The clinical models and nomogram performed well in the external validation cohort. The AUC of the nomogram was 0.966 (95% CI: 0.944- 0.989), with a sensitivity of 0.935, a specificity of 0.882, and an accuracy of 0.896.

Conclusions: Combining clinical data and imaging features, a nomogram was constructed that could effectively predict the postoperative ablation outcome in hepatocellular carcinoma patients undergoing MWA, which could help clinicians provide treatment options for hepatocellular carcinoma patients.

Rationale and objectives: The study aimed to analyze the patterns and frequency of Level V lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC), identify its risk factors, and construct predictive models for assessment.

Methods: We conducted a retrospective analysis of 325 PTC patients who underwent thyroidectomy and therapeutic unilateral bilateral modified radical neck dissection from October 2020 to January 2023. Patients were randomly allocated into a training cohort (70%) and a validation cohort (30%). The radiomics signature model was developed using ultrasound images, applying the minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression to extract high-throughput quantitative features. Concurrently, the clinic signature model was formulated based on significant clinical factors associated with Level V LNM. Both models were independently translated into nomograms for ease of clinical use.

Results: The radiomics signature model, without the inclusion of clinical factors, showed high discriminative power with an area under the curve (AUC) of 0.933 in the training cohort and 0.912 in the validation cohort. Conversely, the clinic signature model, composed of tumor margin, simultaneous metastasis, and high-volume lateral LNM, achieved an AUC of 0.749 in the training cohort. The radiomics signature model exhibited superior performance in sensitivity, specificity, positive predictive value, negative predictive value across both cohorts. Decision curve analysis demonstrated the clinical utility of the radiomics signature model, indicating its potential to guide more precise treatment decisions.

Conclusion: The radiomics signature model outperformed the clinic signature model in predicting Level V LNM in PTC patients. The radiomics signature model, available as a nomogram, offers a promising tool for preoperative assessment, with the potential to refine clinical decision-making and individualize treatment strategies for PTC patients with potential Level V LNM.

Rationale and objectives: To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in rectal cancer (RC) patients and to analyze their prognosis.

Materials and methods: Data from 529 RC patients who underwent radical surgery at two centers were retrospectively collected. 379 patients from center one were randomly divided into a training set (n = 265) and an internal validation (invad) set (n = 114) in a 7:3 ratio. 150 patients from center two were included in the external validation (exvad) set. Univariate and multivariate analyses were performed to identify independent clinical predictors and to construct a clinical model. Preoperative mpMRI images were utilized to extract deep features through the ResNet-101 model. Following feature selection, a deep learning model was developed. A nomogram was created by combining the clinical model with the deep learning model. The clinical applicability of each model was assessed using ROC curves, decision curve analysis (DCA), clinical impact curves (CIC), and deLong test. Kaplan-Meier survival analysis was conducted to evaluate prognostic outcome among patients.

Results: Among the 529 patients, 142 (26.8%) were TD positive. In the training set, clinical model was constructed based on clinical independent predictors (cT and cN). 30 deep features were selected to calculate the deep learning radscore (DLRS) and develop the deep learning (DL) model. The AUC values for the clinical model were 0.724, 0.836, and 0.763 in the training set, invad set, and exvad set, respectively. The AUC values for the DL model were 0.903, 0.853, and 0.874, respectively. The nomogram achieved higher AUC values of 0.925, 0.919, and 0.9, respectively. The DeLong test indicated that the predictive performance of the nomogram was superior to both the DL model and the clinical model in training and invad sets. Kaplan-Meier survival analysis showed that both the DL model and the nomogram effectively stratified patients into high-risk and low-risk groups for 3-year DFS (p < 0.05).

Conclusion: The nomogram, which integrates mpMRI-based deep radiomic features and clinical characteristics, effectively predicts preoperative TD status in RC. Both the DL model and the nomogram can effectively stratify patients' 3-year DFS risk.

Rationale and objectives: The combination of functional biologic data and imaging appearance has the potential to add diagnostic information to help the radiologist evaluate breast masses in an efficient, effective, and cost-conscious manner. This is the first clinical evaluation of the Gen 2(Model 9100, 8101) Imagio® System to assess image quality with both the stand-alone internal ultrasound (IUS), ultrasound-only transducer, and the Optoacoustic/Ultrasound (OA/US) duplex probe (1,2). This study assesses palpable and non-palpable breast abnormalities in patients who are referred for diagnostic breast ultrasound work-up. This study is intended to confirm the clinical acceptability of modifications made to the Imagio® System ultrasound component following Premarket Approval (PMA) of the Imagio® Gen 1 version.

Materials and methods: This prospective, single-arm, non-randomized study included 38 patients presenting with a palpable lump and/or imaging abnormality detected at a single investigational site. Each patient had the breast, and if indicated, the axillary lymph nodes imaged with the Gen 2 Imagio® system.

Results: For patients with SenoGram®-predicted Likelihood of Malignancy (LOM) and pathology available (N = 23), observed sensitivity was 100.0% (9/9) with a confidence interval of (66.4%, 100.0%), using a SenoGram®-predicted False Negative Rate (FNR) cut-off of ≤ 2%. Observed specificity was 64.3% (9/14) (Confidence Interval: 35.1%, 87.2%), using a SenoGram®-predicted FNR cut-off of ≤ 2%. At 98% fixed sensitivity, the specificity (fSp) for OA/US + SG was 100.0% while it was 0.0% for IUS. The absolute gain in fSp was 100.0%.

Conclusion: Combining structure with morphology can increase specificity without decreasing sensitivity in a real-world setting.