O-RADS

O - RADS
  • 文章类型: Editorial
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  • 文章类型: Journal Article
    背景:大型语言模型显示出改善放射学工作流程的希望,但是它们在结构化放射任务(例如报告和数据系统(RADS)分类)上的表现仍未得到探索。
    目的:本研究旨在评估3个大型语言模型聊天机器人-Claude-2、GPT-3.5和GPT-4-在放射学报告中分配RADS类别并评估不同提示策略的影响。
    方法:这项横断面研究使用30个放射学报告(每个RADS标准10个)比较了3个聊天机器人,使用3级提示策略:零射,几枪,和指南PDF信息提示。这些病例的基础是2018年肝脏影像学报告和数据系统(LI-RADS),2022年肺部CT(计算机断层扫描)筛查报告和数据系统(Lung-RADS)和卵巢附件报告和数据系统(O-RADS)磁共振成像,由董事会认证的放射科医生精心准备。每份报告都进行了6次评估。两名失明的评论者评估了聊天机器人在患者级RADS分类和总体评级方面的反应。使用Fleissκ评估了跨重复的协议。
    结果:克劳德-2在总体评分中获得了最高的准确性,其中少量提示和指南PDF(提示-2),在6次运行中获得57%(17/30)的平均准确率,在k-pass投票中获得50%(15/30)的准确率。没有及时的工程,所有聊天机器人都表现不佳。结构化示例提示(prompt-1)的引入提高了所有聊天机器人整体评分的准确性。提供prompt-2进一步改进了Claude-2的性能,GPT-4未复制的增强。TheinterrunagreementwassubstantialforClaude-2(k=0.66foroverallratingandk=0.69forRADScategorization),对于GPT-4来说是公平的(两者的k=0.39),对于GPT-3.5来说是公平的(总体评分k=0.21,RADS分类k=0.39)。与Lung-RADS版本2022和O-RADS相比,2018年的所有聊天机器人均显示出更高的准确性(P<0.05);在2018年LI-RADS版本中,使用prompt-2,Claude-2实现了75%(45/60)的最高总体评分准确性。
    结论:当配备结构化提示和指导PDF时,Claude-2显示了根据既定标准(如LI-RADS版本2018)将RADS类别分配给放射学病例的潜力。然而,当前一代的聊天机器人滞后于根据最新的RADS标准对案件进行准确分类。
    BACKGROUND: Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored.
    OBJECTIVE: This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies.
    METHODS: This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots\' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ.
    RESULTS: Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2\'s performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018.
    CONCLUSIONS: When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.
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  • 文章类型: Journal Article
    目的:本研究的目的是评估附件肿块实体组织的表观扩散系数(ADC)分析对优化肿瘤特征的贡献,并可能完善O-RADSMRI4类别的风险分层。
    方法:对EURAD队列进行回顾性分析,以选择具有实体组织和可行ADC测量的附件肿块的所有患者。两名放射科医生独立测量实体组织的ADC值,不包括坏死区域,周围结构,和磁化率伪影。分析了总体人群中扩散定量参数的显着差异以及根据实体组织的形态学方面的差异,以确定其对ADC可靠性的影响。使用受试者工作特征曲线(ROC)来确定用于区分O-RADSMRI评分4群体中的侵入性和非侵入性肿瘤的ADC的最佳截止值。
    结果:最终研究人群包括180名女性,平均年龄为57±15.5(标准差)岁;年龄范围:19-95岁),其中93名良性,23边界线,和137个恶性肿块。在边界肿块(1.310×10-3mm2/s(Q1,Q3:1.152,1.560×10-3mm2/s)中,实体组织的ADC中值大于良性肿块(1.035×10-3mm2/s;Q1,Q3:0.900,1.560×10-3mm2/s)(P=0.002),良性肿瘤与侵袭性肿块(0.850×10-3mm2,0.750s)相比,P<10实体组织对应于不规则间隔或乳头状突起的18.6%(47/253),在46.2%(117/253)的壁瘤或混合肿块中,并在35.2%(89/253)的附件质量中达到纯固体质量。在混合肿块或有壁结节的肿块亚组中,侵袭性肿块的ADC(0.830×10-3mm2/s(Q1,Q3:0.738,0.960)显着低于边界(1.385;Q1,Q3:1.300,1.930)(P=0.0012)和良性肿块(P=0.04)。1.08×10-3mm2/s的ADC截止值对于识别混合或壁结节亚组的侵袭性病变具有71.4%的灵敏度和100%的特异性,AUC为0.92(95%置信区间:0.76-0.99)。
    结论:附件肿块实体组织的ADC分析可以帮助区分O-RADSMRI4类别中的侵入性肿块,尤其是在混合肿块或壁结节中。
    OBJECTIVE: The purpose of this study was to evaluate the contribution of apparent diffusion coefficient (ADC) analysis of the solid tissue of adnexal masses to optimize tumor characterization and possibly refine the risk stratification of the O-RADS MRI 4 category.
    METHODS: The EURAD cohort was retrospectively analyzed to select all patients with an adnexal mass with solid tissue and feasible ADC measurements. Two radiologists independently measured the ADC values of solid tissue, excluding necrotic areas, surrounding structures, and magnetic susceptibility artifacts. Significant differences in diffusion quantitative parameters in the overall population and according to the morphological aspect of solid tissue were analyzed to identify its impact on ADC reliability. Receiver operating characteristics curve (ROC) was used to determine the optimum cutoff of the ADC for distinguishing invasive from non-invasive tumors in the O-RADS MRI score 4 population.
    RESULTS: The final study population included 180 women with a mean age of 57 ± 15.5 (standard deviation) years; age range: 19-95 years) with 93 benign, 23 borderline, and 137 malignant masses. The median ADC values of solid tissue was greater in borderline masses (1.310 × 10-3 mm2/s (Q1, Q3: 1.152, 1.560 × 10-3 mm2/s) than in benign masses (1.035 × 10-3 mm2/s; Q1, Q3: 0.900, 1.560 × 10-3 mm2/s) (P= 0.002) and in benign tumors compared by comparison with invasive masses (0.850 × 10-3 mm2/s; Q1, Q3: 0.750, 0.990 × 10-3 mm2/s) (P < 0.001). Solid tissue corresponded to irregular septa or papillary projection in 18.6% (47/253), to a mural nodule or a mixed mass in 46.2% (117/253), and to a purely solid mass in 35.2% (89/253) of adnexal masses. In mixed masses or masses with mural nodule subgroup, invasive masses had a significantly lower ADC (0.830 × 10-3 mm2/s (Q1, Q3: 0.738, 0.960) than borderline (1.385; Q1, Q3: 1.300, 1.930) (P= 0.0012) and benign masses (P= 0.04). An ADC cutoff of 1.08 × 10-3 mm2/s yielded 71.4% sensitivity and 100% specificity for identifying invasive lesions in the mixed or mural nodule subgroup with an AUC of 0.92 (95% confidence interval: 0.76-0.99).
    CONCLUSIONS: ADC analysis of solid tissue of adnexal masses could help distinguish invasive masses within the O-RADS MRI 4 category, especially in mixed masses or those with mural nodule.
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  • 文章类型: Journal Article
    准确和快速区分良性和恶性卵巢肿块对于优化患者管理至关重要。本研究旨在建立基于超声图像的列线图,影像组学,和深度迁移学习功能,根据卵巢附件报告和数据系统(O-RADS)自动将卵巢肿块分为低风险和中高风险的恶性肿瘤病变。
    纳入1,080例患者的超声图像,其中1,080例卵巢肿块。由深圳大学华南医院683名患者组成的培训队列。在深圳大学总医院收集了由397名患者组成的测试队列。工作流程包括图像分割,特征提取,特征选择,和模型建设。
    预训练的Resnet-101模型实现了最佳性能。在不同的单模态特征和融合特征模型中,列线图达到了最高水平的诊断性能(AUC:0.930,准确性:84.9%,灵敏度:93.5%,特异性:81.7%,PPV:65.4%,净现值:97.1%,精度:65.4%)。列线图的诊断指标高于初级放射科医生,在该模型的帮助下,初级放射科医生的诊断指标显着提高。校准曲线显示,列线图的预测与卵巢肿块的实际分类之间具有良好的一致性。决策曲线分析表明,列线图在临床上有用。
    与初级放射科医生相比,该模型表现出令人满意的诊断性能。它有可能提高初级放射科医生的专业知识水平,并为卵巢癌筛查提供快速有效的方法。
    UNASSIGNED: Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS).
    UNASSIGNED: The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction.
    UNASSIGNED: The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful.
    UNASSIGNED: This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.
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  • 文章类型: Systematic Review
    本研究旨在系统地比较卵巢-附件报告和数据系统与国际卵巢肿瘤分析简单规则的诊断性能,以及对卵巢癌和附件肿块风险分层的adneXa模型中不同肿瘤的评估。
    由两名独立审稿人对截至2023年7月的相关研究的在线数据库进行了文献检索。汇总估计与分层汇总接收器操作特征模型合并。使用诊断准确性研究质量评估-2和诊断准确性研究质量评估-比较工具评估纳入的研究的质量。进行元回归和亚组分析以探索不同临床设置的影响。
    共有13项研究符合纳入标准。卵巢附件报告和数据系统与AdneXa模型中不同增生性增生的评估之间的8项头对头研究的合并敏感性和特异性分别为0.96(95%CI0.92-0.98)和0.82(95%CI0.71-0.90)。0.94(95%CI0.91-0.95)和0.83(95%CI0.77-0.88),分别,以及卵巢附件报告和数据系统与国际卵巢肿瘤分析简单规则之间的七项正面研究,合并的敏感性和特异性分别为0.95(95%CI0.93-0.97)和0.75(95%CI0.62-0.85)。0.91(95%CI0.82-0.96)和0.86(95%CI0.76-0.93),分别。在敏感性(P=0.57和P=0.21)和特异性(P=0.87和P=0.12)方面,卵巢附件报告和数据系统与AdneXa模型中不同增生性评估以及国际卵巢肿瘤分析简单规则之间没有发现显着差异。在所有三个指南的研究中都观察到了显著的异质性。
    这三项指南均显示出高诊断性能,3种指南在敏感性和特异性方面没有观察到显著差异.
    UNASSIGNED: This study aims to systematically compare the diagnostic performance of the Ovarian-Adnexal Reporting and Data System with the International Ovarian Tumor Analysis Simple Rules and the Assessment of Different NEoplasias in the adneXa model for risk stratification of ovarian cancer and adnexal masses.
    UNASSIGNED: A literature search of online databases for relevant studies up to July 2023 was conducted by two independent reviewers. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic model. The quality of the included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Metaregression and subgroup analyses were performed to explore the impact of varying clinical settings.
    UNASSIGNED: A total of 13 studies met the inclusion criteria. The pooled sensitivity and specificity for eight head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model were 0.96 (95% CI 0.92-0.98) and 0.82 (95% CI 0.71-0.90) vs. 0.94 (95% CI 0.91-0.95) and 0.83 (95% CI 0.77-0.88), respectively, and for seven head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the International Ovarian Tumor Analysis Simple Rules, the pooled sensitivity and specificity were 0.95 (95% CI 0.93-0.97) and 0.75 (95% CI 0.62-0.85) vs. 0.91 (95% CI 0.82-0.96) and 0.86 (95% CI 0.76-0.93), respectively. No significant differences were found between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model as well as the International Ovarian Tumor Analysis Simple Rules in terms of sensitivity (P = 0.57 and P = 0.21) and specificity (P = 0.87 and P = 0.12). Substantial heterogeneity was observed among the studies for all three guidelines.
    UNASSIGNED: All three guidelines demonstrated high diagnostic performance, and no significant differences in terms of sensitivity or specificity were observed between the three guidelines.
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  • 文章类型: Case Reports
    卵巢Struma是一种单胚层畸胎瘤,其特征是存在>50%的甲状腺组织。它主要是良性的;因此,术前诊断很重要。它通常表现为多房性囊性肿块,但很少表现为主要的实性肿块。在磁共振成像(MRI)上,在动态钆增强T1加权图像上,实体外观的甲状腺肿显示早期信号强度增强,这在组织病理学上表明甲状腺组织的存在与丰富的血管。卵巢附件报告和数据系统(O-RADS)MRI评分是全球范围内用于表征附件病变的经过验证的分类。基于形态学,信号强度,增强MRI上的任何实体组织,该评分系统可用于将附件病变分为5类,从1分(无附件肿块)到5分(恶性肿瘤高危).在非动态对比增强(非DCE)MRI上注射g(Gd)后30-40秒,其信号强度高于子宫肌层的附件固体肿块的得分为5分(恶性肿瘤的高风险)。在非DCEMRI上注射Gd后30秒,我们提出了一个实性表现为卵巢甲状腺肿的病例,其信号强度高于子宫肌层。术前评分为5分。因此,尽管存在良性卵巢肿块,但仍进行了经腹全子宫切除术和双侧附件卵巢切除术.当在非DCEMRI上Gd注射后30-40秒遇到信号强度高于子宫肌层的附件肿块时,鉴别诊断应包括卵巢甲状腺肿,尽管O-RADSMRI得分为5分,但应该讨论情况的管理。
    Struma ovarii is a monodermal teratoma characterized by the presence of >50% thyroid tissue. It is mostly benign; therefore, preoperative diagnosis is important. It usually manifests as a multilocular cystic mass but rarely as a predominantly solid mass. On magnetic resonance imaging (MRI), solid-appearing struma ovarii showed early signal intensity enhancement on dynamic gadolinium-enhanced T1-weighted images, which histopathologically indicates the presence of thyroid tissue with abundant blood vessels. The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI score is a validated classification worldwide for characterizing adnexal lesions. Based on the morphology, signal intensity, and enhancement of any solid tissue on the MRI, the scoring system can be used to classify adnexal lesions into five categories from score one (no adnexal mass) to score five (high risk of malignancy). An adnexal solid mass with a higher signal intensity than that of the myometrium 30-40 seconds after gadolinium (Gd) injection on non-dynamic contrast-enhanced (non-DCE) MRI was assigned a score of 5 (high risk of malignancy).  We present a case of solid-appearing struma ovarii with a higher signal intensity than that of the myometrium 30 seconds after Gd injection on non-DCE MRI, and it was classified as score five preoperatively. Therefore, a total abdominal hysterectomy with bilateral salpingo-oophorectomy was performed despite the presence of a benign ovarian mass. When an adnexal mass with a higher signal intensity than that of the myometrium 30-40 seconds after Gd injection on non-DCE MRI is encountered, struma ovarii should be included in the differential diagnosis, despite the O-RADS MRI score of five and management of the situation should be discussed.
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  • 文章类型: Randomized Controlled Trial
    背景:及时识别和治疗卵巢癌是患者预后的关键决定因素。在这项研究中,我们开发并验证了基于超声(US)成像的深度学习影像组学列线图(DLR_Nomogram),以准确预测卵巢肿瘤的恶性风险,并比较了DLR_Nomogram与卵巢附件报告和数据系统(O-RADS)的诊断性能.
    方法:本研究包括两项研究任务。对于两项任务,患者均以8:2的比例随机分为训练和测试集。在任务1中,我们评估了849例卵巢肿瘤患者的恶性肿瘤风险。在任务2中,我们评估了391例O-RADS4和O-RADS5卵巢肿瘤患者的恶性风险。开发并验证了三个模型来预测卵巢肿瘤中恶性肿瘤的风险。将每个样本的模型的预测结果合并以形成新的特征集,该特征集用作逻辑回归(LR)模型的输入,以构建组合模型。可视化为DLR_列线图。然后,通过受试者工作特征曲线(ROC)评估这些模型的诊断性能.
    结果:DLR_Nomogram在预测卵巢肿瘤的恶性风险方面表现出优异的预测性能,如任务1的训练集和测试集的ROC曲线下面积(AUC)值分别为0.985和0.928。其测试集的AUC值低于O-RADS;然而,差异无统计学意义。DLR_列线图在任务2的训练和测试集中分别表现出0.955和0.869的最高AUC值。DLR_Nomogram在Hosmer-Lemeshow测试中对这两个任务均显示出令人满意的拟合性能。决策曲线分析表明,DLR_Nomogram在特定阈值范围内预测恶性卵巢肿瘤方面产生了更大的净临床益处。
    结论:基于美国的DLR_Nomogram显示了准确预测卵巢肿瘤恶性风险的能力,表现出与O-RADS相当的预测功效。
    BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).
    METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).
    RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.
    CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
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  • 文章类型: Journal Article
    2021年,美国放射学会(ACR)卵巢附件报告和数据系统(O-RADS)MRI委员会开发了一种风险分层系统和词典,用于使用MRI评估附件病变。像BI-RADS分类一样,O-RADSMRI为放射科医生和临床医生之间的交流提供了一种标准化的语言。对于放射科医生来说,熟悉O-RADS算法方法以避免错误分类是至关重要的。培训,就像国际卵巢肿瘤分析(IOTA)提供的那样,对于确保O-RADSMRI系统的准确和一致的应用至关重要。诸如O-RADSMRI计算器之类的工具旨在确保算法方法。这篇综述突出了教学要点,珍珠,和使用O-RADSMRI风险分层系统时的陷阱。关键相关性陈述本文重点介绍了在临床实践中使用O-RADSMRI评分系统的珍珠和陷阱。关键点•实体组织被描述为显示对比后增强。•膜内褶皱,管的管端,光滑的墙壁,或隔膜不是实体组织。•低风险TIC没有肩部或高原。中等风险的TIC有肩膀和高原,尽管与子宫外肌层相比,肩部不那么陡峭。
    In 2021, the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee developed a risk stratification system and lexicon for assessing adnexal lesions using MRI. Like the BI-RADS classification, O-RADS MRI provides a standardized language for communication between radiologists and clinicians. It is essential for radiologists to be familiar with the O-RADS algorithmic approach to avoid misclassifications. Training, like that offered by International Ovarian Tumor Analysis (IOTA), is essential to ensure accurate and consistent application of the O-RADS MRI system. Tools such as the O-RADS MRI calculator aim to ensure an algorithmic approach. This review highlights the key teaching points, pearls, and pitfalls when using the O-RADS MRI risk stratification system.Critical relevance statement This article highlights the pearls and pitfalls of using the O-RADS MRI scoring system in clinical practice.Key points• Solid tissue is described as displaying post- contrast enhancement.• Endosalpingeal folds, fimbriated end of the tube, smooth wall, or septa are not solid tissue.• Low-risk TIC has no shoulder or plateau. An intermediate-risk TIC has a shoulder and plateau, though the shoulder is less steep compared to outer myometrium.
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  • 文章类型: Journal Article
    基于卵巢附件报告和数据系统(O-RADS),我们构建了一个列线图模型来预测具有复杂超声形态的附件肿块的恶性可能性。
    在一项多中心回顾性研究中,在2019年1月至2023年4月期间,通过哈尔滨医科大学附属第四医院的电子病历系统,在附件区域共收集了430名受试者的质量.哈尔滨医科大学附属肿瘤医院共有157名受试者被纳入例外验证队列。以病理肿瘤的发现为黄金标准,将受试者分为良性和恶性组。所有患者以7:3的比例随机分配到验证集和训练集。使用逐步回归分析来过滤变量。进行Logistic回归以构建列线图预测模型,这在训练集中得到了进一步的验证。森林情节,C指数,校正曲线,并利用临床决策曲线对模型进行验证,评价其准确性和有效性,将其与现有的附件病变模型(O-RADSUS)进行进一步比较,并评估附件中不同类型的肿瘤(ADNEX)。
    在诊断模型的制备中,遵循四个预测因子作为恶性肿瘤的独立危险因素:O-RADS分类,HE4级,声影,和前突血流评分(均p<0.05)。该模型在C指数为0.959(95CI:0.940-0.977)的训练集中显示出中等的预测能力,在验证集中为0.929(95CI:0.884-0.974),和外部验证集中的0.892(95CI:0.843-0.940)。结果表明,列线图的预测结果与校准曲线的实际结果吻合得很好,新的列线图在决策曲线分析中是临床有益的。
    具有复杂超声形态的附件肿块列线图的风险包含四个特征,这些特征显示出合适的预测能力,并提供了更好的风险分层。其诊断性能明显超过ADNEX型号和O-RADSUS,其筛选性能基本等同于ADNEX模型和O-RADSUS分类。
    UNASSIGNED: Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.
    UNASSIGNED: In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).
    UNASSIGNED: Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.
    UNASSIGNED: The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.
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  • 文章类型: Journal Article
    背景:本研究旨在比较卵巢-附件报告和数据系统(O-RADS)和医生的主观判断在诊断附件肿块恶性风险方面的诊断效率。
    方法:这是2017年至2020年对616个附件肿块的分析。临床发现,术前超声图像,并记录病理诊断。每个附件肿块由医生的主观判断评估,由两名高级医生和两名初级医生进行O-RADS评估。O-RADS1-3级肿块是良性肿瘤,O-RADS4-5级肿块为恶性肿瘤。将所有结果与病理诊断进行比较。
    结果:在616个附件肿块中,469(76.1%)为良性,147例(23.9%)为恶性。O-RADS的曲线下面积与初级医生的主观判断之间没有差异(0.83(95%CI:0.79-0.87)与0.79(95%CI:0.76-0.83),p=0.0888)。高级医生的O-RADS和主观判断曲线下面积相等(0.86(95%CI:0.83-0.89)与0.86(95%CI:0.83-0.90),p=0.8904)。O-RADS对初级医生检测恶性肿瘤的敏感性远高于主观判断(84.4%vs.70.1%)和高级医生(91.2%vs.81.0%)。在检测成熟囊性畸胎瘤和卵巢子宫内膜异位囊肿的主要良性病变的亚组分析中,在使用O-RADS时,初级医生的诊断准确性明显比高级医生差。
    结论:O-RADS在预测附件恶性肿块方面具有优异的表现。可以在一定程度上弥补初级医生的经验不足。在区分各种良性病变方面应有更好的表现。
    This study aimed to compare the diagnostic efficiency of Ovarian-Adnexal Reporting and Data System (O-RADS) and doctors\' subjective judgment in diagnosing the malignancy risk of adnexal masses.
    This was an analysis of 616 adnexal masses between 2017 and 2020. The clinical findings, preoperative ultrasound images, and pathological diagnosis were recorded. Each adnexal mass was evaluated by doctors\' subjective judgment and O-RADS by two senior doctors and two junior doctors. A mass with an O-RADS grade of 1 to 3 was a benign tumor, and a mass with an O-RADS grade of 4-5 was a malignant tumor. All outcomes were compared with the pathological diagnosis.
    Of the 616 adnexal masses, 469 (76.1%) were benign, and 147 (23.9%) were malignant. There was no difference between the area under the curve of O-RADS and the subjective judgment for junior doctors (0.83 (95% CI: 0.79-0.87) vs. 0.79 (95% CI: 0.76-0.83), p = 0.0888). The areas under the curve of O-RADS and subjective judgment were equal for senior doctors (0.86 (95% CI: 0.83-0.89) vs. 0.86 (95% CI: 0.83-0.90), p = 0.8904). O-RADS had much higher sensitivity than the subjective judgment in detecting malignant tumors for junior doctors (84.4% vs. 70.1%) and senior doctors (91.2% vs. 81.0%). In the subgroup analysis for detecting the main benign lesions of the mature cystic teratoma and ovarian endometriosic cyst, the junior doctors\' diagnostic accuracy was obviously worse than the senior doctors\' on using O-RADS.
    O-RADS had excellent performance in predicting malignant adnexal masses. It could compensate for the lack of experience of junior doctors to a certain extent. Better performance in discriminating various benign lesions should be expected with some complement.
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