Non-contrast enhanced MRI for efficiency evaluation of high-intensity focused ultrasound in adenomyosis ablation.

OBJECTIVE: To investigate the value of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in evaluating the therapeutic effect of high-intensity focused ultrasound (HIFU) in adenomyosis ablation. MATERIAL AND METHODS: One hundred eighty-nine patients with adenomyosis were treated with HIFU. The ablation areas on T2WI and DWI sequences were classified into different types: type I, relatively ill-defined rim or unrecognizable; subtype IIa, well-defined rim with hyperintensity; subtype IIb, well-defined rim with hypointensity. The volume of ablation areas on T2WI (VT2WI) and DWI (VDWI) was measured and compared with the non-perfused volume (NPV), and linear regression was conducted to analyze their correlation with NPV. RESULTS: The VT2WI of type I and type II (subtype IIa and subtype IIb) were statistically different from the corresponding NPV (p = 0.004 and 0.024, respectively), while no significant difference was found between the VDWI of type I and type II with NPV (p = 0.478 and 0.561, respectively). In the linear regression analysis, both VT2WI and VDWI were positively correlated with NPV, with R2 reaching 0.96 and 0.97, respectively. CONCLUSIONS: Both T2WI and DWI have the potential for efficient evaluation of HIFU treatment in adenomyosis, and DWI can be a replacement for CE-T1WI to some extent.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.

PURPOSE: To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC). MATERIALS AND METHODS: From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC. RESULTS: For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models. CONCLUSION: The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.

OBJECTIVES: To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors. METHODS: Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model. RESULTS: MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001). CONCLUSIONS: The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS: • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.

Prehospital Ultra-Early Neurological Deterioration in Intracerebral Hemorrhage: Definition, Prevalence, and Association with Outcomes.

INTRODUCTION: The objective of this study was to define prehospital ultra-early neurological deterioration (UND) and to investigate the association with functional outcomes in patients with intracerebral hemorrhage (ICH). METHODS: We conducted a prospective cohort study of consecutive acute ICH patients. The stroke severity at onset and hospital admission was assessed using the Chongqing Stroke Scale (CQSS), and prehospital UND was defined as a CQSS increase of ≥2 points between symptoms onset and admission. Early neurological deterioration (END) was defined as the increase of ≥4 points in NIHSS score within the first 24 h after admission. Poor outcome was defined as a modified Rankin Scale (mRS) of 4-6 at 3 months. RESULTS: Prehospital UND occurred in 29 of 169 patients (17.2%). Patients with prehospital UND had a median admission NIHSS score of 17.0 as opposed to those without prehospital UND with a median NIHSS score of 8.5. There were three patterns of neurological deterioration: prehospital UND only in 21 of 169 patients (12.4%), END but without prehospital UND in 20 of 169 patients (11.8%), and continuous neurological deterioration in both phases in 8 patients (4.7%). Prehospital UND was associated with worse 3-month outcomes (median mRS score, 4.0 vs. 2.0, p = 0.002). After adjusting for age, time from onset to admission, END, and systolic blood pressure, prehospital UND was an independent predictor of poor outcome (odds ratio [OR] 3.27, 95% confidence interval [CI] 1.26-8.48, p = 0.015). CONCLUSION: Prehospital UND occurs in approximately 1 in 7 patients between symptom onset and admission and is associated with poor functional outcome in patients with ICH. Further research is needed to investigate the prehospital UND in the prehospital phase in the triage of patients with ICH.

Reticulation Sign on Thin-Section CT: Utility for Predicting Invasiveness of Pure Ground-Glass Nodules.

BACKGROUND. Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. OBJECTIVE. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. METHODS. This retrospective study included 795 patients (mean age, 53.4 ± 11.1 [SD] years; 254 men, 541 women) with a total of 876 pGGNs on thin-section CT that underwent resection between January 2015 and April 2022. Two fellowship-trained thoracic radiologists independently reviewed unenhanced CT images to assess the pGGNs for a range of features, including diameter, attenuation, location, shape, air bronchogram, bubble lucency, vascular change, lobulation, spiculation, margins, pleural indentation, and the reticulation sign (defined as multiple small linear opacities resembling a mesh or a net); differences were resolved by consensus. The relationship between the reticulation sign and lesion invasiveness on pathologic assessment was evaluated. RESULTS. On pathologic assessment, the 876 pGGNs included 163 nonneoplastic and 713 neoplastic pGGNs (323 atypical adenomatous hyperplasias [AAHs] or adenocarcinomas in situ [AISs], 250 minimally invasive adenocarcinomas [MIAs], and 140 invasive adenocarcinomas [IACs]). Interobserver agreement for the reticulation sign, expressed as kappa, was 0.870. The reticulation sign was detected in 0.0% of nonneoplastic lesions, 0.0% of AAHs/AISs, 6.8% of MIAs, and 54.3% of IACs. The reticulation sign had sensitivity of 24.0% and specificity of 100.0% for a diagnosis of MIA or IAC and sensitivity of 54.3% and specificity of 97.7% for a diagnosis of IAC. In multivariable regression analyses including all of the assessed CT features, the reticulation sign was a significant independent predictor of IAC (OR, 3.64; p = .001) but was not a significant independent predictor of MIA or IAC. CONCLUSION. The reticulation sign, when observed in a pGGN on thin-section CT, has high specificity (albeit low sensitivity) for invasiveness and is an independent predictor of IAC. CLINICAL IMPACT. Those pGGNs that show the reticulation sign should be strongly suspected to represent IAC; this suspicion may guide risk assessments and follow-up recommendations.

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.

BACKGROUND: Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning. MATERIALS AND METHODS: This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively. RESULTS: A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit. CONCLUSIONS: The radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

Significance of marginal vessels in differentiating peripheral small-cell lung cancer and benign lung tumor.

BACKGROUND: Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis. PURPOSE: To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs. MATERIAL AND METHODS: A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared. RESULTS: Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%). CONCLUSION: For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.

Prevalence and Characteristics of Adrenal Tumors in an Unselected Screening Population : A Cross-Sectional Study.

BACKGROUND: With the widespread use of advanced imaging technology, adrenal tumors are increasingly being identified. OBJECTIVE: To investigate the prevalence and characteristics of adrenal tumors in an unselected screening population in China. DESIGN: Cross-sectional study. (ClinicalTrials.gov: NCT04682938). SETTING: A health examination center in China. PATIENTS: Adults having an annual checkup were invited to be screened for adrenal tumors by adrenal computed tomography. MEASUREMENTS: The participants with adrenal tumors had further evaluation for malignancy risk and adrenal function. RESULTS: A total of 25 356 participants were screened, 351 of whom were found to have adrenal tumors, for a prevalence of 1.4%. The prevalence increased with age, from 0.2% in participants aged 18 to 25 years to 3.2% in those older than 65 years. Among 351 participants with adrenal tumors, 337 were diagnosed with an adrenocortical adenoma, 14 with another benign nodule, and none with a malignant mass. In 212 participants with an adenoma who completed endocrine testing, 69.3% were diagnosed with a nonfunctioning adenoma, 18.9% with cortisol autonomy, 11.8% with primary aldosteronism, and none with pheochromocytoma. Proportions of nonfunctioning adenomas were similarly high in various age groups (72.2%, 67.8%, and 72.2% in those aged <46, 46 to 65, and ≥66 years, respectively). LIMITATION: Only 212 of 337 participants with an adrenocortical adenoma had endocrine testing. CONCLUSION: The prevalence of adrenal tumors in the general adult screening population is 1.4%, and most of these tumors are nonfunctioning regardless of patient age. Cortisol and aldosterone secretion are the main causes of functional adenomas. PRIMARY FUNDING SOURCE: National Key Research and Development Program of China and National Natural Science Foundation of China.

DARU-Net: A dual attention residual U-Net for uterine fibroids segmentation on MRI.

PURPOSE: Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images. METHODS: The proposed method is based on U-Net architecture and integrates two attention mechanisms: channel attention of squeeze-and-excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU-Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI). RESULTS: The average DSC, precision, recall, and JI of DARU-Net reached 0.8066 ± 0.0956, 0.8233 ± 0.1255, 0.7913 ± 0.1304, and 0.6743 ± 0.1317. Compared with U-Net and other deep learning methods, DARU-Net was more accurate and stable. CONCLUSION: This work proposed an optimized U-Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU-Net was able to accurately segment uterine fibroids from MR images.

The value of HPV genotypes combined with clinical indicators in the classification of cervical squamous cell carcinoma and adenocarcinoma.

BACKGROUND: To investigate the differences in HPV genotypes and clinical indicators between cervical squamous cell carcinoma and adenocarcinoma and to identify independent predictors for differentiating cervical squamous cell carcinoma and adenocarcinoma. METHODS: A total of 319 patients with cervical cancer, including 238 patients with squamous cell carcinoma and 81 patients with adenocarcinoma, were retrospectively analysed. The clinical characteristics and laboratory indicators, including HPV genotypes, SCCAg, CA125, CA19-9, CYFRA 21-1 and parity, were analysed by univariate and multivariate analyses, and a classification model for cervical squamous cell carcinoma and adenocarcinoma was established. The model was validated in 96 patients with cervical cancer. RESULTS: There were significant differences in SCCAg, CA125, CA19-9, CYFRA 21-1, HPV genotypes and clinical symptoms between cervical squamous cell carcinoma and adenocarcinoma (P < 0.05). Logistic regression analysis showed that SCCAg and HPV genotypes (high risk) were independent predictors for differentiating cervical squamous cell carcinoma from adenocarcinoma. The AUC value of the established classification model was 0.854 (95% CI: 0.804-0.904). The accuracy, sensitivity and specificity of the model were 0.846, 0.691 and 0.899, respectively. The classification accuracy was 0.823 when the model was verified. CONCLUSION: The histological type of cervical cancer patients with persistent infection of high-risk HPV subtypes and low serum SCCAg levels was more prone to being adenocarcinoma. When the above independent predictors occur, the occurrence and development of cervical adenocarcinoma should be anticipated, and early active intervention treatment should be used to improve the prognosis and survival of patients.

Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography.

BACKGROUND: Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis. METHODS: From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy. RESULTS: Compared to patients with benign patchy GGOs, malignant cases were older (P <  0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P <  0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135-6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027-14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062-40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124-8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079-12.482; P < 0.001). CONCLUSIONS: In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.

Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas.

OBJECTIVES: To explore the feasibility and effectiveness of machine learning (ML) based on multiparametric magnetic resonance imaging (mp-MRI) features extracted from transfer learning combined with clinical parameters to differentiate uterine sarcomas from atypical leiomyomas (ALMs). METHODS: The data of 86 uterine sarcomas between July 2011 and December 2019 and 86 ALMs between June 2013 and June 2017 were retrospectively reviewed. We extracted deep-learning features and radiomics features from T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI). The two feature extraction methods, transfer learning and radiomics, were compared. Random forest was adopted as the classifier. T2WI features, DWI features, combined T2WI and DWI (mp-MRI) features, and combined clinical parameters and mp-MRI features were applied to establish T2, DWI, T2-DWI, and complex multiparameter (mp) models, respectively. Predictive performance was assessed with the area under the receiver operating characteristic curve (AUC). RESULTS: In the test set, the T2, DWI, T2-DWI and complex mp models based on transfer learning (AUCs range from 0.76 to 0.81, 0.80 to 0.88, 0.85 to 0.92, and 0.94 to 0.96, respectively) outperformed the models based on radiomics (AUCs of 0.73, 0.76, 0.79, and 0.92, respectively). Moreover, the complex mp model showed the best prediction performance, with the Resnet50-complex mp model achieving the highest AUC (0.96) and accuracy (0.87). CONCLUSIONS: Transfer learning is feasible and superior to radiomics in the differential diagnosis of uterine sarcomas and ALMs in our dataset. ML models based on deep learning features of nonenhanced mp-MRI and clinical parameters can achieve good diagnostic efficacy. KEY POINTS: • The ML model combining nonenhanced mp-MRI features and clinical parameters can distinguish uterine sarcomas from ALMs. • Transfer learning can be applied to differentiate uterine sarcomas from ALMs and outperform radiomics. • The most accurate prediction model was Resnet50-based transfer learning, built with the deep-learning features of mp-MRI and clinical parameters.

Radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer.

OBJECTIVE: To investigate the radiological classification, gene-mutation status, and surgical prognosis of synchronous multiple primary lung cancer (sMPLC). METHODS: From January 2013 to October 2019, 192 consecutive patients with sMPLC were investigated. The clinical, CT, molecular, and pathological features of all patients were analyzed. Furthermore, the prognosis of 89 patients who only underwent surgical resection was evaluated. RESULTS: Among 192 patients, all lesions pathologically confirmed or highly suspected as tumors based on radiological findings were retrospectively analyzed, and the CT findings of sMPLC were classified into three types: (I) all lesions manifested as solid nodules/masses (14.06%, 27/192), (II) all lesions manifested as subsolid nodules/masses (43.23%, 83/192), and (III) tumor lesions manifested as a combination of ≥ 2 of the following patterns: solid nodules/masses, subsolid nodules/masses, cystic airspace, and focal consolidation (42.71%, 82/192). For 252 tumors undergoing epidermal growth factor receptor (EGFR)-mutation testing, the EGFR-mutation rate was higher in subsolid tumors than that in solid tumors (p < 0.05). Among 19 patients with all tumors undergoing surgery and driver-gene testing, genetic heterogeneity was prevalent among the multiple tumors (63.16%,12/19). The highest clinical stage of non-I, ipsilateral distribution of tumors, and CT classification of I indicated a poor prognosis for patients with sMPLC (all p < 0.05). CONCLUSION: Subsolid lesions are the most common presentation of sMPLC. Genetic heterogeneity in driver mutations among sMPLC may be present. Prognosis in patients with sMPLC is determined by the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors. KEY POINTS: • Synchronous multiple primary lung cancer (sMPLC) has three types of CT findings. • Genetic heterogeneity may be prevalent among the multiple tumors. • Prognosis in patients with sMPLC is associated with the highest clinical TNM stage, distribution, and radiological classification among the multiple tumors.

Factors influencing magnetic resonance imaging finding of endopelvic fascial edema after ultrasound-guided high-intensity focused ultrasound ablation of uterine fibroids.

OBJECTIVE: Investigate the relationships between endopelvic fascial edema and its influencing factors after ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation of uterine fibroids. METHODS: This retrospective study included 688 women with uterine fibroids treated by USgHIFU; based on post-treatment MRI, the patients were divided into two groups: endopelvic fascial edema group and nonedema group. The specific location of fascial edema of each patient was also recorded. Fascial edema and fibroid features and treatment parameters were set as the dependent and independent variables, respectively, and the correlations were studied using univariate and multivariate analyses. The relationship between the pain-related adverse events and location of fascial edema was analyzed by χ2 and fisher's exact tests. RESULTS: Edema and nonedema groups had 556 and 112 patients, respectively. Among the edema patients, posterior fascial edema incidence was the highest. Multifactorial analysis showed that the energy efficiency factor (EEF), fibroid location, and enhancement type were positively associated with endopelvic fascial edema (p < 0.05), while the distance from dorsal surface of the fibroid to sacrum was negatively correlated (p < 0.001). Patients with anterior, posterior and perirectal, and right lateral fascial edemas were associated with lower abdominal pain, sacrococcygeal pain, and leg numbness/pain, respectively. CONCLUSION: Post-USgHIFU ablation, patients were prone to developing endopelvic fascial edema, and some of them experienced pain-related adverse events. The fibroid location, its types of contrast enhancement, the distance from the dorsal surface of the fibroid to the sacrum, and EEF were the influencing factors resulting in the endopelvic fascial edema after USgHIFU ablation.

Evaluation of high intensity focused ultrasound treatment for different types of adenomyosis based on magnetic resonance imaging classification.

OBJECTIVE: To evaluate the mid-term symptom improvement of patients with different types of adenomyosis based on magnetic resonance imaging (MRI) classification after ultrasound-guided high intensity focused ultrasound (USgHIFU) treatment. MATERIALS AND METHODS: A total of 321 patients with adenomyosis who underwent HIFU and completed 18-month follow-up were retrospectively reviewed. Based on the relationship between the adenomyotic lesion and the uterine structural components on T2-weighted imaging (T2WI), adenomyotic lesions were classified as internal, external, full thickness and intramural adenomyosis. Based on the extent of the myometrial involvement, these lesions were further subclassified as asymmetric and symmetric adenomyosis. RESULTS: All patients completed HIFU ablation in one session. The range of median menstrual pain score in patients with asymmetric internal, symmetric internal, asymmetric external, asymmetric full thickness, symmetric full thickness, and intramural adenomyosis was between 6 and 8 points before HIFU, the median menstrual pain score decreased to 2-4 points 18-month post-HIFU (p < .005). The menstrual pain relief rate was 68.3%, 62.1%, 54.7%, 64.1%, 60%, and 100%, respectively. The median menstrual blood volume score range was between 2 and 4 points in the different groups of patients before HIFU, it decreased to 1-3 points 18-month after HIFU with a relief rate of 68.3%, 51.6%, 51.0%, 55.5%, 57.2%, and 100%, respectively. No serious complication occurred in any of these patients. CONCLUSIONS: Based on our results, USgHIFU is safe and effective in the treatment of patients with different subtypes of adenomyosis with mid-term sustained improvement in symptoms of menstrual pain and menstrual blood volume.

Patellar shape is associated with femoral trochlear morphology in individuals with mature skeletal development.

BACKGROUND: As several studies have detected correlations between patellar and femoral trochlear development, this raises the question of whether patellar shape is associated with trochlear developmental outcomes. METHODS: Patellar shape and femoral trochlear morphology were retrospectively analyzed in 183 subjects, of whom 61 each were classified as having Wiberg type I, II, and III patellae (groups A, B, and C, respectively). The sulcus angle (SA), lateral trochlea inclination angle (LTA), medial trochlear inclination angle (MTA), lateral facet length (LFL), medial facet length (MFL), lateral trochlear height (LTH), medial trochlear height (MTH), trochlea sulcus height (TH), and lateral-medial trochlear facet distance (TD) were analyzed as a means of evaluating trochlear morphology. Trochlear depth, trochlear condyle asymmetry, and trochlear facet asymmetry were additionally calculated, and differences in trochlear morphology and correlations between trochlear morphology and patellar shape were evaluated. RESULTS: The femoral trochlear parameters of patients in group A differed significantly from those of patients in groups B and C. No significant differences between groups B and C were evident. Patellar shape was positively correlated with LTA, MTA, MFL, trochlear condyle asymmetry, and trochlear facet asymmetry, and was negatively correlated with SA. CONCLUSIONS: These data indicated that patellar shape and trochlear morphology are related to one another,which suggest normalized patella morphology surgery and trochlear surgery are better choices for patients with patella instability. TRIAL REGISTRATION: Retrospectively registered.

Increased Internal Carotid Artery Tortuosity is a Risk Factor for Spontaneous Cervicocerebral Artery Dissection.

OBJECTIVE: Spontaneous cervicocerebral artery dissection (sCCD) is an important cause of ischaemic stroke that often occurs in young and middle aged patients. The purpose of this study was to investigate the correlation between tortuosity of the carotid artery and sCCD. METHODS: Patients with confirmed sCCD who underwent computed tomography angiography (CTA) were reviewed retrospectively. Age and sex matched patients having CTA were used as controls. The tortuosity indices of the cervical arteries were measured from the CTA images. The carotid siphon and the extracranial internal carotid artery (ICA) were evaluated according to morphological classification. The carotid siphons were classified into five types. The extracranial ICA was categorised as simple tortuosity, coiling or kinking. Independent risk factors for sCCD were investigated using multivariable analysis. RESULTS: The study included sixty-six patients with sCCD and 66 controls. There were no differences in vascular risk factors between the two groups. The internal carotid tortuosity index (ICTI) (25.24 ± 12.37 vs. 15.90 ± 8.55, respectively; p < .001) and vertebral tortuosity index (VTI) (median 11.28; interquartile range [IQR] 6.88, 18.80 vs. median 8.38; IQR 6.02, 12.20, respectively; p = .008) were higher in the patients with sCCD than in the controls. Type III and Type IV carotid siphons were more common in the patients with sCCD (p = .001 and p < .001, respectively). The prevalence of any vessel tortuosity, coiling and kinking of the extracranial ICA was higher in the patients with sCCD (p < .001, p = .018 and p = .006, respectively). ICTI (odds ratio [OR] 2.964; p = .026), VTI (OR 5.141; p = .009), and Type III carotid siphons (OR 4.654; p = .003) were independently associated with the risk of sCCD. CONCLUSION: Arterial tortuosity is associated with sCCD, and greater tortuosity of the cervical artery may indicate an increased risk of arterial dissection.

Altered local and matrix functional connectivity in depressed essential tremor patients.

BACKGROUND: Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided a promising way to observe spontaneous brain activity. METHODS: Local FC analyses were performed in forty-one depressed ET patients, 49 non-depressed ET patients and 43 healthy controls (HCs), and then matrix FC and clinical depression severity correlation analyses were further performed to reveal spontaneous neural activity changes in depressed ET patients. RESULTS: Compared with the non-depressed ET patients, the depressed ET patients showed decreased ReHo in the bilateral cerebellum lobules IX, and increased ReHo in the bilateral anterior cingulate cortices and middle prefrontal cortices. Twenty-five significant changes of ReHo clusters were observed in the depressed ET patients compared with the HCs, and matrix FC analysis further revealed that inter-ROI FC differences were also observed in the frontal-cerebellar-anterior cingulate cortex pathway. Correlation analyses showed that clinical depression severity was positively correlated with the inter-ROI FC values between the anterior cingulate cortex and bilateral middle prefrontal cortices and was negatively correlated with the inter-ROI FC values of the anterior cingulate cortex and bilateral cerebellum lobules IX. CONCLUSION: Our findings revealed local and inter-ROI FC differences in frontal-cerebellar-anterior cingulate cortex circuits in depressed ET patients, and among these regions, the cerebellum lobules IX, middle prefrontal cortices and anterior cingulate cortices could function as pathogenic structures underlying depression in ET patients.