Background: Computed tomography (CT) features and clinical characteristics have been shown in recent studies to be effective predictive indicators for risk stratification of thymic epithelial tumors. High-risk thymoma and thymic carcinoma (HRT-TC) are highly aggressive and are associated with poor prognoses. The aim of this study is to evaluate the predictive value of CT features and clinical characteristics to assess postoperative progression in patients with HRT-TC. Methods: Clinical and enhanced CT data were retrospectively collected from patients who underwent HRT-TC surgery between June 1, 2012, and June 1, 2022. A univariate Cox regression analysis was conducted to identify the risk factors associated with postoperative progression. A multivariate Cox regression analysis was then used to determine the independent risk factors. Three-year and 5-year single-factor models as well as multifactorial combined models were then constructed based on the results of these analyses to assess their efficacy, accuracy, and net benefit. The best-performing model was selected to create a nomogram for a consistency assessment. Results: A total of 215 patients were included in the study. The multivariate Cox regression analysis revealed that independent prognostic factors that influenced postoperative progression were the tumor length (hazard ratio [HR] = 1.027; 95% confidence interval [CI] = 1.004-1.049, P = .018), tumor resection (HR = 4.122; 95% CI = 2.054-8.274, P < .001), and the mediastinal vascular invasion (MVI; HR = 2.779; 95% CI = 1.140-6.775, P = .025). The 3-year and 5-year combined models demonstrated superior predictive efficacy, accuracy, and net benefits. The nomogram and calibration curves showed that the predicted risk probabilities from the nomogram aligned well with actual observations. Conclusions: A nomogram based on clinical and CT features provided effective predictions of progression following HRT-TC. This prognostic tool holds significant value for clinicians to guide therapeutic decisions and personalize survival assessments.

Noninvasive and accurate prediction of clinical staging is crucial for patients with cervical cancer (CC). The aim of our study was to develop a clinical model, a diffusion-weighted imaging (DWI)-based radiomics model, and a combined model for staging prediction. We retrospectively enrolled a total of 234 patients with histopathologically confirmed cervical cancer. The patients were divided into a training set (n = 163) and a testing set (n = 71). Radiomics features were extracted from tumor regions in DWI sequences. Clinical features were obtained through univariate and multivariate regression analyses. The clinical model and the DWI-based radiomics model were constructed using the selected features. A nomogram was also created to combine radiomics elements with clinical factors for predicting early-stage and advanced-stage CC. The area under the curve (AUC) values for the radiomics signature, which included features from the tumor’s region of interest (ROI) based on DWI, were 0.928 in the training set and 0.801 in the testing set. When the nomogram combined clinical data with the radiomics signature, the AUC values improved to 0.949 for the training set and 0.847 for the testing set in predicting staging. The novel nomogram, which integrates clinical factors with radiomics features extracted from DWI sequences, demonstrates good performance in staging CC, assisting clinicians in making informed treatment decisions.

BackgroundPatients with locally advanced cervical cancer (LACC) have been advised to undergo radical chemoradiotherapy. To determine whether local recurrence or distant metastasis (LRDM) will occur in patients with locally advanced cervical cancer (LACC) after chemoradiotherapy, this study aims to develop and validate a model using clinical and radiomic parameters.MethodsA total of 118 patients with LACC who were treated with radiotherapy combined with chemotherapy were included. They were divided into a training set (n=83) and a validation set (n=35) at an 7:3 ratio. All patients' diffusion-weighted imaging (DWI) images were uploaded to the ITK-SNAP software. Regions of interest (ROIs) were manually delineated, and a radiomic model was constructed using radiomic features by the LightGBM algorithm. A comprehensive model was constructed by integrating clinical and radiomic features and was visualized as a nomogram. The area under the curve (AUC) values were used to evaluate their predictive performance, and Decision curve analysis (DCA) was employed to assess the clinical utility of the predictive models. The calibration curves were used to assess the agreement between predicted and observed outcomes for the LRDM in both cohorts.ResultsSeven variables were finally chosen for modeling using the least absolute shrinkage and selection operator (LASSO) regression analysis. The AUC values for the training and test sets of the DWI radiomic model were 0.789 and 0.785, respectively. AUC values for the training and test sets were 0.897 and 0.889, respectively, for the combined model LGBM-nomogram that used DWI and clinical characteristics. The nomogram worked remarkably well in both the training and test cohorts, as shown by the calibration curves.ConclusionThe model integrating DWI and clinical features has shown high value in non-invasive prediction of LRDM, which may aid in treatment and prognostication.

Objective:: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. Methods:: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. Results:: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. Conclusion:: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Purpose: The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model. Methods: In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models' performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility. Results: Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664-0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781-0.906) and 0.836 (95% CI: 0.732-0.940) in the training and testing cohorts, respectively. Conclusion: The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Background:The mutation status of epidermal growth factor receptor (EGFR) in lung adenocarcinoma is significantly associated with postoperative progression-free survival. Computed tomography (CT)-based radiomics analysis may have potential value in predicting EGFR mutation status. This study aims to explore the predictive capacity of radiomics analysis for EGFR mutation status in lung adenocarcinomas presenting as ground-glass nodules (GGNs). Methods:We included 199 GGNs confirmed by histopathology from 2016 to 2020. The clinical factors and radiographic characteristics were counted and evaluated. All GGNs were manually delineated and the radiomics features were extracted, using the least absolute shrinkage and selection operator for feature selection. Then the radiographic, radiomics, and combined nomogram model were constructed respectively, and compared with each other. Decision curve analysis (DCA) was used to assess the clinical usefulness of the models, while receiver operating characteristic curves and calibration curves were used to evaluate their predictive performance. Results:Univariate analysis revealed five variables that were significantly different between the EGFR mutant and wild-type groups. Fifteen radiomics features were significantly associated with EGFR mutations. Among the three models, both the radiomics [area under the curve (AUC) =0.818] and the nomogram (AUC =0.820) had good discriminatory ability in predicting EGFR mutation status and performed consistently in the validation cohort (AUC =0.805, and 0.833, respectively), with higher predictive performance than the radiographic model. The DCA showed that when it comes to EGFR mutation status prediction, the nomogram and the radiomics model showed better overall net benefit than the radiographic model. Conclusions:For preoperatively predicting the status of EGFR mutation in lung adenocarcinomas manifesting as GGNs, the CT-based radiomics analysis will be valuable.

目的 探讨妇科癌肉瘤(CS)的影像特征及其临床价值.方法 回顾性分析经病理确诊的42 例女性原发性生殖系统癌肉瘤患者的影像资料,观察并评估病灶的位置、形态、大小、数目、边缘以及强化方式,分析邻近器官受侵及转移情况,总结特征并与病理相对照.结果 15 例卵巢癌肉瘤病灶呈囊实性巨大肿块,多类圆形或椭圆形,瘤体最大径为5.3～24.2 cm;27 例子宫癌肉瘤病灶多呈类圆形、椭圆形、类三角形,Ⅰ型瘤体最大径 3.5～11.8cm,Ⅱ型子宫内膜厚度为1.3～2.8 cm.妇科CS密度、信号混杂,多边界欠清或不清,增强扫描多轻中度强化,可见包膜及迂曲增粗血管影,周围可见侵犯征象,腹盆腔可见淋巴结转移及少至大量腹腔积液.结论 妇科CS 在影像表现上具有一定的特征,影像检查可以评估病变的位置、大小、数目、边缘、与邻近结构的关系、是否存在转移等,为临床诊治提供重要依据.

目的 探讨肺上皮样血管内皮瘤(P-EHE)的影像表现及临床、病理特点.方法 回顾性分析经病理证实的 9 例 P-EHE患者的临床病理资料及影像表现,总结分析其特点.结果 9 例患者病灶发生于两肺 9 例,位于肝脏 4 例,肋骨 3 例,胸膜 2 例,髋关节 1 例,肘关节 1 例,颈椎 1 例,腹膜后 1 例.胸部CT表现为两肺多发小结节 7 例,肺内肿块 4 例,胸膜增厚 2 例,胸腔积液 2 例.正电子发射断层显像/计算机体层成像(PET/CT)示结节或肿块葡萄糖代谢增高或轻度增高.结论 P-EHE患者 CT表现具有一定特征,多发结节常<2 cm,多位于肺门或胸膜下血管束周围,肝脏、骨骼或胸膜等部位相继发现病变,最终确诊有赖于病理以及免疫组化检查.

Abstract Background The study was to develop a radiomics model based on a high-resolution CT (HRCT) scan to noninvasively analyze the benign and malignant sub-centimeter pure ground glass nodule (pGGN). Methods The study included 235 patients with 251 sub-centimeter pGGN (training cohort: n=176; validation cohort: n=75) who underwent preoperative HRCT scans. The volume of interest was manually delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select the useful radiomics features. The multivariable logistic regression was used to select the clinically important risk factors. The mean CT value model, imaging features model, radiomics model, and combined model were constructed, and the performance was evaluated by receiving operator characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A nomogram based on the combined model was developed. Results Gender, mean diameter, shape, margin, and mean CT value were independent clinical risk predictors for predicting the malignancy of sub-centimeter pGGN, and enrolled them to build the clinical predictive model. A total of 39 radiomics features were selected to build the radiomics predictive model. In the validation cohort, the area under the curve (AUC) of the radiomics model (AUC=0.877) and combined model (AUC=0.898) were higher than the mean CT value model (AUC=0.670) and imaging features model (AUC=0.733) (all P<0.05). Conclusion The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Objectives:To develop and validate a nomogram model based on radiomics features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma.Methods:A total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomics features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomics features and construct radiomics score (radscore). A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA).Results:A total of 1316 radiomics features were extracted. A radiomics signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radscore between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomics features showed improved accuracy, specificity, positive predictive value, and AUC for predicting VPI, compared to the radiomics model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram with clinical features is beyond the traditional clinical and radiomics features.Conclusion:A nomogram model combining radiomics and clinical features is effective in non-invasively prediction of VPI in patients with lung adenocarcinoma.

Objectives: To establish a radiomics nomogram for preoperative prediction of Ki-67 proliferation index in stage T1a-b lung adenocarcinoma. Methods: A total of 206 patients with pathologically confirmed lung adenocarcinoma who underwent CT scans within 2 weeks preoperatively from January 2016 to June 2020 were retrospectively included. Ki-67 index <= 10% was considered low expression, and Ki-67 index > 10% was considered high expression. The primary cohort was randomized with a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 61). The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used for feature selection, and radiomics signature was constructed. Univariate and multivariate logistic regression analyses were used to identify clinically important risk factors and radiomics signature associated with Ki-67 proliferation index, which were then combined into radiomics nomogram. Results: Tumor maximum diameter (P = 0.005), lobulation (P = 0.002), absent of vacuole (P < 0.001), and Radscore (P < 0.001) were independent risk predictors of high Ki-67 proliferation index expression. The radiomics nomogram showed good predictive efficacy. The AUC, sensitivity, specificity and accuracy of radiomics nomogram in the training and validation cohorts were 0.91 (95% CI: 0.86-0.96), 87.9%, 80.5%, 83.4% and 0.85 (95% CI: 0.75-0.94), 71.9%, 82.8% and 77.0%. Decision curve analysis further demonstrated the clinical utility of the nomogram. Conclusions: Radiomics nomogram provide a non-invasive method to predict Ki-67 proliferation index preoperatively in stage T1a-b lung adenocarcinoma, which might be the supplementary information for clinicians to choose the appropriate treatment program.

背景与目的:肝脏血管周上皮样细胞瘤(PEComa)在临床上较为少见,且部分潜在恶性.临床上患者多为体检时偶然发现,也有少数患者出现腹部疼痛或不适、发热乏力或消瘦等症状.由于其缺乏特异性的症状和影像学表现,所以临床上较易发生误诊并影响治疗.本研究通过总结既往病例的诊疗经验并结合国内外文献复习,旨在进一步认知并掌握肝脏PEComa的诊断及治疗.方法:通过电子病历系统收集苏州大学附属第一医院普外科2014年1月-2021年10月收治的肝脏PEComa病例数据,包括术前影像学资料、实验室检查、术中资料、术后病理及免疫组化等,进行回顾性分析,根据围术期治疗情况、术后随访数据,结合国内外相关文献对该病的临床特点、影像学表现、治疗、病理结果及预后进行总结.结果:共纳入患者17例,其中6例为男性,11例为女性;年龄25～68岁,平均(45.7±13.7)岁.3例主诉右上腹不适,4例合并乙肝病毒感染,甲胎蛋白(AFP)均未见明显异常;所有患者均经过术前影像学诊断,但准确率仅为5.9％(1/17);1例行术前穿刺活检.所有患者均行手术治疗,其中7例行腹腔镜手术,10例行开放手术;8例行解剖性肝段或肝叶切除术,9例行局部切除术.手术时间50.0～250.0 min,平均(133.6±52.8)min;术中出血量 50～400 mL,平均(138.2±116.6)mL;住院期间无二次手术、无死亡病例;术后住院时间3～11d,平均(6.1±2.4)d.17例患者均为肝脏单发肿瘤,肿瘤直径1.5～9.0cm,平均(4.0±2.4)cm,经手术治疗后病理证实为肝脏PEComa,其中15例患者完成免疫组化,Melan A阳性率90.0％(9/10),黑色素瘤抗体HMB-45阳性率93.3％(14/15),平滑肌肌动蛋白SMA 阳性率 92.9％(13/14),S-100 蛋白阳性率 35.7％(5/14),CD34 阳性率 57.1％(8/14),Ki-67 指数2％～12％.所有病例Clavien-Dindo术后并发症分级均为Ⅰ级,出院后行规律随访随访时间1.0～91.0个月,平均随访时间(46.0±28.1)个月,1例已完成随访,1例失访,其余患者目前均未出现复发.结论:肝脏PEComa是一种相对罕见的肝脏间叶性肿瘤,绝大多数为良性肿瘤,恶性罕见.该病好发于中年女性,一般无特殊临床表现,术前影像学诊断准确率较低,易与其他肝脏肿瘤混淆,其确诊依赖术后病理学检查.首选治疗方案为手术切除,潜在恶性病例需行长期规律随访,总体预后良好.

Objective To evaluate histogram analysis of ADC and intravoxel incoherent motion (IVIM) for detecting the prostate cancer(PCa) in the transition zone(TZ).Methods A total of 49 patients underwent preoperative DW-MRI (b of 0-1000 s/mm2) were prospectively collected and processed by monoexponential and biexponential IVIM model for quantitation of apparent diffusion coefficients (ADCs),perfusion fraction (f),diffusivity (D) and pseudo-diffusivity (D*).Histogram analysis was performed by outlining entire tumour regions of interest (ROIs).These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of magnetic resonance/transrectal ultrasound (MR/TRUS) fusion guided biopsy.The diagnostic ability of differentiating the PCa from BHP in TZ was analysed by ROC regression.Results Twenty-two(28 focus) cases of PCa in PZ and 26(33 focus) cases of BPH were confirmed by pathology.Mean ADC,median ADC,10th percentile ADC,90th percentile ADC,kurtosis,skewness of ADC and mean D,median D and 90th percentile D differed significantly between PCa and BPH in TZ.The highest classification accuracy was achieved by the mean ADC(0.857) and mean D(0.841).Logistic regression models based on mean ADC and mean D led to an AUC of 0.936.Conclusion Monoexponential DWI and biexponential IVIM could potentially improve the differentiation of prostate cancer in TZ,the combination of mean ADC and mean D performs better than the parameters alone in the diagnosis of PCa in TZ.

The present study aimed to evaluate the efficacy of using the prostate imaging reporting and data system (PI-RADS) for the detection of prostate cancer (PCa) in the transitional zone (TZ) by 3T multiparametric magnetic resonance imaging (mpMRI), and to compare the diagnostic performance of PI-RADS V1 to V2 for the detection of PCa in the TZ. A total of 77 patients with suspicious lesions in the prostate TZ (83 cores) identified from mpMRI images acquired at 3T were scored per the PI-RADS system (V1 and V2) criteria. Magnetic resonance/transrectal ultrasound fusion-guided biopsy was performed in patients with at least one lesion classified as category 3 in the PI-RADS V1 assessment. The diagnostic performance of PI-RADS V1 for the detection of PCa in the TZ was compared with PI-RADS V2 by assessing the sensitivity, specificity and receiver operating characteristics. A total of 31 cases of PCa in the TZ and 46 cases of benign prostatic hyperplasia were confirmed by pathology, including 23 cases classified as Gleason score 7 and 54 cases of negative results and Gleason score 6. PI-RADS V2 exhibited a higher area under the curve (AUC, 0.888) compared with V1 (AUC, 0.869). The sensitivity of V2 (75.0%) was higher compared with that of V1 (68.8%), whereas the specificity of V2 (90.2%) was lower compared with that of V1 (96.1%) at PI-RADS scores of 11 and 4, respectively. The ESUR PI-RADS system may indicate the likelihood of PCa in suspicious lesions in the TZ on mpMRI. These results suggest that PI-RADS V2 performs better compared with V1 for the assessment of PCa in the TZ.

Objective To explore the MRI features of supratentorial intra-cerebral ependymoma and improve awareness and accuracy of diagnosis. Methods A retrospective analysis was performed based on the MRI data of 8 patients with pathologically confirmed supratentorial intra-cerebral ependymoma, including 5 males and 3 females. All patients were scanned by MRI and enhanced MRI. The following image features of the lesions were evaluated: location, size, relation with adjacent structures, peritumoral edema, mass effect, MRI signal and enhancement pattern. Results In these 8 cases, 7 cases were cystic-solid and 1 case was solid. All lesions were located around the lateral ventricle. In terms of the degree of peritumoral edema, 2 cases were not obvious, 4 were mild and 2 were severe respectively. Besides, signal of hemorrhage was found in 5 cases, with obvious enhancement in both solid and cystic part. Conclusion The radiology diagnosis of supratentorial intra-cerebral ependymoma is difficult, but the MRI manifestations are still characteristic, which is helpful for preoperative diagnosis.

Objective To analyze the characteristics of dynamic contrast enhanced MR imaging (DCE-MRI)in prostate cancer (PCa)at 3.0T,and to evaluate the diagnostic value of DCE-MRI.Methods 85 patients with suepected PCa received conventional MRI and DCE-MRI.The signal intense-time (SI-T)curve was analyzed.Then the time to maximum (Tmax),the maximum degree of enhancement (STmax%),and the rate of enhancement (Rmax)were calculated.The differences of styles of SI-T curve and the parameters between the positive and negative group were compared respectively.Results 59 cases of PCa were proved by biopsy,and there was no evidence of tumor in 26 cases.507 zones had histopathological results with 250 zones in positive group and 257 zones in negative group .The most common style of SI-T curve in positive group was rapidly ascending followed with descending curve,the most common style of SI-T curve in negative group was persistent ascending curve and plateau curve.The mean values of Tmax,SImax%,Rmax were (69.49±22.53)s,1.74±0.43,7.83±3.80 in positive group respectively,while (175.61±52.64)s,1.05±0.35,1.86±1.10 in negative group respectively,there were statistically significant differences between the two groups(t =-24.24,1 6.34,1 7.75,P <0.01)respectively. The mean values of Tmax,SImax% ,Rmax were (8 9 .1 9 ± 3 1 .7 2 )s,1 .5 8 ± 0 .4 6 ,5 .2 1 ± 3 .3 4 in the low-risk group (Gleason score 2 - 6 )respectively,while (64.25±14.68)s,1.76±0.43,8.25±3.70 in the high-risk group (Gleason score 7-10)respectively, there were statistically significant differences between them(t = 7.09,-8.74,- 7.83,P <0.01).Conclusion 3.0T DCE-MRI has great value in the diagnosis of PCa.

Objective To evaluate the diagnostic value of prostate imaging reporting and data system version 1 (PI-RADS V1) and version 2 (PI-RADS V2) for detection of prostate cancer (PCa) in the transition zone (TZ).Methods Seventy-seven patients with suspicious lesions in TZ on mpMRI were scored according to the PI-RADS system (V1 and V2) before MR-TRUS fusion guided biopsy prospectively.In all of the patients with suspicious tumors,respectively at least one lesion with a PI-RADS V1 assessment category of ≥3,was selected for biopsy.Independent sample t test was used to compare scores of PI-RADS V1 and V2 between PCa and benign prostatic hyperplasia (BPH).The diagnostic performance of PI-RADS V 1 and V2 for detection of PCa in the transition zone was compared by analyzing ROC basing on the results of MR-TRUS fusion guided biopsy.Results A cohort of 77 patients was performed including 31 cases of PCa (32 cores) and 46 cases of BPH (51 cores).PCa (V1:1 1.50±2.79;V2:4.28±0.99) had significantly higher scores of both PI-RADS V1 and PI-RADS V2 than BPH(V1:7.51± 1.63;V2∶2.61 ±0.67) (P＜0.05).Using a PI-RADS V1 score cut-off ≥ 11,sensitivity and specificity in group PCa and BPH were calculated,which were 68.8％(22/32) and 96.1％(49/51) with a area under curve of 0.869;using a PI-RADS V2 score cut-off ≥4,which were 75.0％ (24/32) and 90.2％ (46/51) with a area under curve of 0.888,respectively.Conclusions PI-RADS system can indicate the likelihood of PCa of suspicious lesions in TZ on Mp-MRI.PI-RADS V2 perform better than V 1 for the assessment of prostate cancer in TZ.

Objective To evaluate the prostate imaging reporting and data system(PI-RADS) version 1 and version 2 for detection of prostate cancer (PCa) by multiparametric magnetic resonance imaging (MpMRI) in a consecutive cohort of patients with magnetic resonance imaging/transrectal ultrasonography (MRI-TRUS) fusion-guided biopsy.Methods 30 suspicious lesions including 15 prostate cancer and 15 non cancer at 3.0 T MpMRI were scored according to the PI-RADS V1(≥ 3 scores in at least one MRI sequence)system before MRI-TRUS fusion guided biopsy and correlated to histopathology results.PI-RADS V2 and Likert scores were determined retrospectively,diagnostic accuracy was determined using receiver operating characteristic curve analysis.Results The PI-RADS score of the dominant lesion was significantly higher in patients with PCa compared to patients with negative histopathology (PI-RADS V1:12.10±2.60 vs 7.47±1.98,P<0.05;PI-RADS V2:4.21±1.18 vs 2.79±0.92,P<0.05);Using a Likert score cut-off ≥ 4,a sensitivity of 73.7%,a specificity of 78.9%, positive predictive value of 77.74% and a negative predictive value of 75.00% (AUC=0.778,95%CI:0.63-0.93), a PI-RADS V1 cut-off ≥ 10,a sensitivity of 73.7%,a specificity of 94.7%,positive predictive value of 93.29% and a negative predictive value of 78.26% (AUC=0.911,95%CI:0.82-1.00) and PI-RADS V2 cut-off ≥ 4,a sensitivity of 57.9%, a specificity of 100%, positive predictive value of 100% and a negative predictive value of 73.37% (AUC=0.837,95%CI:0.70-0.97) were achieved.Conclusion The described fusion system is dependable and efficient for targeted MRI-TRUS fusion-guided biopsy.MpMRI PI-RADS scores combined with a novel real-time MRI-TRUS fusion system facilitate sufficient diagnosis of PCa with high sensitivity and specificity,PI-RADS scores appears to be the preferable method for the evaluation of prostate cancer than Likert score, while V2 does not perform better than V1.

目的 分析肾上腺节细胞神经瘤的CT征象以提高对该病的诊断水平.方法 回顾性分析经病理证实的11例肾上腺节细胞神经瘤患者的CT资料.结果 11例肾上腺节细胞神经瘤,左侧4例,右侧7例.肿瘤直径2.6～ 15.1 cm,平均6.62cm;11例肿瘤均有完整包膜.5例呈卵圆形,6例形态不规则;4例肿瘤部分包绕或推移邻近血管,但血管不呈受侵表现.平扫时肿瘤密度均低于肌肉组织,其中3例见散点状钙化.增强扫描后1 1例肿瘤均呈轻到中度强化,其中6例肿瘤呈不均匀强化.结论 肾上腺节细胞神经瘤在CT影像上有一定的特征性,可与肾上腺其他肿瘤鉴别.