OBJECTIVE:To study the utility of intratumoral and peritumoral radiomic models in distinguishing between benign and malignant thyroid micronodules. METHODS:CT-enhanced images of 607 patients with thyroid micronodules were obtained, and an additional 100 patients with micronodules were collected as an independent test group on the basis of the sequence of their examination times. The peritumoral region of interest (ROI) was delineated by expanding outward by 1 and 2 mm. Radiomics features were extracted from the 3-dimensional volume of interest (VOI), which included intratumoral, peritumoral 1 mm, peritumoral 2 mm, intratumoral+peritumoral 1 mm and 2 mm in the arterial phase, venous phase, and combined arteriovenous phase. All of the data were randomly divided into training and validation groups at an 8:2 ratio. The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) method was utilized for regression dimension reduction. Radscore, clinical, and combined models were constructed. A nomogram was used to assess the performance of the fusion model, and an external test group was utilized to test the model's performance. RESULTS:The intratumoral+peritumoral 1 mm radiomic model based on arterial-venous phase fusion had the best prediction performance (AUC training group 0.838; 95% CI: 0.802-0.873; validation group 0.827; 95% CI: 0.755-0.899; external test group 0.770; 95% CI: 0675-0.864). The combined model constructed with the radscore and clinical model was the optimal model (AUC training group 0.859; 95% CI: 0.826-0.892; validation group 0.876; 95% CI: 0817-0.935; external test group 0.810; 95% CI: 0.723-0.897). CONCLUSION:Both the intratumoral and peritumoral radiomic models had good diagnostic efficacy in differentiating benign and malignant thyroid micronodules.

BACKGROUND:Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes. PURPOSE:To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging. MATERIAL AND METHODS:All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves. RESULTS:In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05). CONCLUSION:It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

目的 探讨基于双参数磁共振影像组学联合血清前列腺特异性抗原(PSA)列线图模型预测前列腺癌(PCa)Gleason分级的临床价值.方法 回顾性分析经病理证实的338例PCa患者的影像及临床资料,高危组(Gleason评分＞7分)185例,中低危组(Gleason评分≤7分)153例.利用分割软件手动勾画所有患者的病灶感兴趣区并进行高通量特征提取,经过筛选和降维处理后构建影像组学预测模型.受试者工作特征曲线(ROC)用于评估模型对Gleason分级的预测效能.结果 分别构建基于T2WI序列、ADC序列和T2WI+ADC序列的影像组学模型,三组模型在测试组中的曲线下面积(AUC)分别为0.763、0.765、0.780.列线图预测模型由年龄、总前列腺特异性抗原(TPSA)、游离前列腺特异性抗原(FPSA)及影像组学评分构成,列线图预测模在测试组中的AUC为0.874,对PCa Gleason分级的预测效能最高.结论 由年龄、TPSA、FPSA及影像组学评分构建的列线图预测模型对PCa Gleason分级具有较高的诊断效能.

ObjectiveWe explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis.MethodsA total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0–2) and an unfavourable outcome group (mRS of 3−6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram.ResultsThe FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876−0.968), 0.875 (95% CI: 0.815−0.935), and 0.895 (95% CI: 0.840−0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90−0.98) and test set (AUC = 0.94; 95% CI: 0.87−1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88−1) and validation set 2 (AUC = 0.90; 95% CI: 0.768−1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram.ConclusionThis non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.

目的:探讨剪切波弹性成像(SWE)联合超声加权评分法对乳腺肿块诊断的应用价值.方法:回顾性选取2018 年5 月至2020 年5 月本院收治的120 例乳腺肿块患者作为研究对象,其中良性组62 例,恶性组58 例.两组均行乳腺常规超声和SWE检查,比较两组SWE最大值、SWE最小值、平均值和超声加权评分,并采用ROC曲线分析剪切波弹性成像评分联合超声加权评分法对乳腺肿块的诊断效能.结果:乳腺超声加权评分恶性组为(7.97±4.01)分,明显高于良性组(4.48±2.34)分(t =5.771,P<0.05);超声加权评分和SWE联合超声加权评分对乳腺肿块分级的诊断两组结果比较,差异无统计学意义(P>0.05).SWE最大值、SWE平均值良性组低于恶性组(P<0.05);两组SWE最小值比较,差异无统计学意义(P>0.05);二元Logistic回归分析结果提示,SWE最大值、SWE平均值及超声加权评分均为乳腺良恶性肿块的影响因素(P<0.05);ROC曲线分析显示,联合SWE最大值、SWE平均值和超声加权评分法诊断乳腺肿块的AUC为 0.931,高于超声加权评分法的 0.763,SWE最大值的0.799,SWE平均值的0.769(Z分别为4.188;3.241;3.938,P<0.05).结论:剪切波弹性成像评分联合超声加权评分法可提高乳腺肿块诊断的准确度,有助于乳腺癌的早期筛查诊断.

ObjectiveTo explore the feasibility of using a contrast-enhanced CT image-based radiomics model to predict central cervical lymph node status in patients with thyroid nodules.MethodsPretreatment clinical and CT imaging data from 271 patients with surgically diagnosed and treated thyroid nodules were retrospectively analyzed. According to the pathological features of the thyroid nodules and central lymph nodes, the patients were divided into three groups: group 1: papillary thyroid carcinoma (PTC) metastatic lymph node group; group 2: PTC nonmetastatic lymph node group; and group 3: benign thyroid nodule reactive lymph node group. Radiomics models were constructed to compare the three groups by pairwise classification (model 1: group 1 vs group 3; model 2: group 1 vs group 2; model 3: group 2 vs group 3; and model 4: group 1 vs groups (2 + 3)). The feature parameters with good generalizability and clinical risk factors were screened. A nomogram was constructed by combining the radiomics features and clinical risk factors. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were performed to assess the diagnostic and clinical value of the nomogram.ResultsFor radiomics models 1, 2, and 3, the areas under the curve (AUCs) in the training group were 0.97, 0.96, and 0.93, respectively. The following independent clinical risk factors were identified: model 1, arterial phase CT values; model 2, sex and arterial phase CT values; model 3: none. The AUCs for the nomograms of models 1 and 2 in the training group were 0.98 and 0.97, respectively, and those in the test group were 0.95 and 0.87, respectively. The AUCs of the model 4 nomogram in the training and test groups were 0.96 and 0.94, respectively. Calibration curve analysis and DCA revealed the high clinical value of the nomograms of models 1, 2 and 4.ConclusionThe nomograms based on contrast-enhanced CT images had good predictive efficacy in classifying benign and malignant central cervical lymph nodes of thyroid nodule patients.

Tumour rupture of gastrointestinal stromal tumours (GISTs) has been considered to be a remarkable risk factor because of its unfavourable impact on the oncological outcome. Although tumour rupture has not yet been included in the current tumor-node-metastasis classification of GISTs as a prognostic factor, it may change the natural history of a low-risk GIST to a high-risk GIST. Originally, tumour rupture was defined as the spillage or fracture of a tumour into a body cavity, but recently, new definitions have been proposed. These definitions distinguished from the prognostic point of view between the major defects of tumour integrity, which are considered tumour rupture, and the minor defects of tumour integrity, which are not considered tumour rupture. Moreover, it has been demonstrated that the risk of disease recurrence in R1 patients is largely modulated by the presence of tumour rupture. Therefore, after excluding tumour rupture, R1 may not be an unfavourable prognostic factor for GISTs. Additionally, after the standard adjuvant treatment of imatinib for GIST with rupture, a high recurrence rate persists. This review highlights the prognostic value of tumour rupture in GISTs and emphasizes the need to carefully take into account and minimize the risk of tumour rupture when choosing surgical strategies for GISTs.

目的 探讨甲状腺双能增强CT扫描时减少患者锁骨及右锁骨下静脉对比剂伪影优化方案的效能.方法 选取行甲状腺增强CT检查患者300例,对扫描图像进行对照质量分析.在增强CT检查中分别以传统方案和优化方案进行随机检查,根据方案将影像资料分为3组,对照组(A组)自然平躺,对比剂和生理盐水分别以3 mL/s和2.5 mL/s固定速率推注;实验组下颌上抬,颈下放置圆柱形海绵枕,对比剂推注时间设定为20 s,生理盐水速率分别设为B组(2.5 mL/s)和C组(5 mL/s).客观评价:从甲状腺CT值、噪声(SD1)、噪声比(SNR)、对比信噪比(CNR)、右锁骨下静脉伪影指数(AI)方面对3组图像进行质量分析比较差异.主观评价:对甲状腺内部结构强化层次及边缘视觉效果、锁骨和右锁骨下对比剂静脉伪影影响程度进行评分.结果 3组方案记录的数据多组间比较,甲状腺平均CT值、AI差异有统计学意义(P<0.05).两两比较实验组(B、C组)的甲状腺平均CT值均高于A组,均有统计学差异(P<0.05),B组与C组在甲状腺平均CT值上无明显统计学差异(P>0.05);3组方案在AI大小关系为:C组B组>A组,且两两比较均有统计学差异(P<0.05).3组方案多组间比较在SD1、SNR、CNR上差异无明显统计学意义(P>0.05).结论 体位、对比剂注射时间、生理盐水冲刷技术的优化方案有助于减少甲状腺增强CT的锁骨和右锁骨下静脉对比剂伪影.

目的 探讨双能CT碘浓度在定量预测甲状腺乳头状癌颈部淋巴结转移的临床价值.方法 回顾性分析97例甲状腺癌患者术前双能CT图像,依据病理结果分为非颈部淋巴结转移组与颈部淋巴结转移组.术前均行双能CT平扫加增强扫描,并经Liver VNC软件测量病灶及正常甲状腺组织碘浓度(IC),测得正常甲状腺碘浓度(1C甲状腺)、病灶碘浓度(IC病灶)、病灶与IC甲状腺差值(ICD)及ICD与IC甲状腺比值(ICDNR病灶),两组间各参数采用两独立样本t检验,绘制受试者工作特征(ROC)曲线并比较其诊断效能.结果 分别比较非淋巴结转移组与淋巴结转移组平扫、动脉期及静脉期IC病灶、ICD病灶、ICDNR病灶的值,平扫及动脉期在两组变量的比较中无明显统计学意义(P＞0.05),静脉期IC病灶、ICD病灶、ICDNR病灶的值的差异均有统计学意义(P＜0.05);根据静脉期结果绘制ROC曲线,曲线下面积(AUC)分别为0.477、0.744、0.730,三者截断值分别为1.65、1.35、0.283,敏感度分别为0.90、0.725、0.825,特异度分别为0.175、0.667、0.579.结论 静脉期IC病灶、ICD病灶、ICDNR病灶的值能有效预测甲状腺乳头状癌颈部淋巴结转移,其中静脉期ICD病灶诊断效能最高.

目的 探讨最佳颈部单能量CT值鉴别诊断甲状腺腺瘤与乳头状癌的价值.方法 回顾性分析2018年1月至2019年7月淮安市第一人民医院收治入院且经术后病理确诊的171例甲状腺肿瘤患者颈部CT资料,依据术后病理将患者分为腺瘤组(81例)及乳头状癌组(90例).比较两组患者术前最佳颈部单能量CT检查的病灶CT值(CT病灶)、甲状腺CT病灶与同一层面甲状腺正常组织CT值(CT正常组织)的比值(NCT T)及甲状腺CT病灶与同一层面颈总动脉CT值(CT颈总动脉)的比值(NCT A).以肿瘤组织病理检查结果为金标准,绘制最佳颈部单能量CT值诊断腺瘤受试者工作特征(ROC)曲线,计算CT病灶、NCTT、NCTA诊断甲状腺腺瘤及乳头状癌的灵敏度、特异度,比较CT病灶、NCTA及NCTT诊断腺瘤的ROC曲线下面积(AUC).结果 乳头状癌组CT病灶、NCTA、NCTT较腺瘤组较低,差异有统计学意义(P<0.05);CT病灶、NCTA、NCTT诊断甲状腺腺瘤的曲线下面积分别为0.949、0.924、0.927,其截断值分别为134.8 HU、0.860、0.844,对应的灵敏度分别为0.864、0.877、0.790,特异度分别为0.922、0.844、0.956.结论 甲状腺腺瘤患者最佳颈部单能量CT值较乳头状癌患者显著升高.颈部单能量CT值对甲状腺腺瘤与乳头状癌鉴别诊断有一定的价值,其中CT病灶识别甲状腺腺瘤的效能最高.

目的 探讨分析冠状动脉周围脂肪(PCAT)CT值与斑块性质及所在分支的相关性.方法 通过冠状动脉CTA检查,入组管腔轻-中度狭窄的患者178例(疾病组),管腔无狭窄的106例(对照组),测量疾病组中不同冠状动脉分支中、不同性质斑块与同支正常区域的PCAT的CT值,并测量所有病例的心外膜脂肪(EAT)及左胸壁皮下脂肪CT值.结果 疾病组较对照组平均年龄大,平均心率快,高血压的发生率高,EAT及左胸壁皮下脂肪CT值较高(P＜0.05).对比各性质斑块、各冠状动脉分支斑块与同支正常区域PCAT的CT值,发现部分钙化、非钙化斑块PCAT的CT值对比正常差异有统计学意义(P＜0.05).右冠状动脉(RCA)斑块、左主干-左前降支(LM-LAD)斑块PCAT的CT值对比正常差异有统计学意义(P＜0.05).分析斑块性质及冠状动脉分支部位对斑块PCAT的CT值的影响,发现钙化斑块PCAT的CT值较部分钙化及非钙化斑块PCAT的CT值低,且差异有统计学意义(P＜0.05),而冠状动脉分支部位对斑块PCAT的CT值无明显影响(F = 0.603,P = 0.548).结论 PCAT、EAT及左胸壁皮下脂肪CT值与冠状动脉粥样硬化的斑块性质存在相关性,一定程度上对心血管病的诊断及预后有潜在的预测价值.

目的 探讨肝脏占位CT虚拟成像中残余肝体积在肝脏储备功能评估的价值.方法 回顾性分析肝脏占位患者63例,术前均行肝脏占位CT虚拟成像、吲哚氰绿排泄试验(ICGR15)及Child-Pugh评分,ICGR15低于10％为正常,将患者分为<10％组(A组)及≥10％组(B组),对两组患者的一般情况、实验室指标、肝脏体积(LV)、肿瘤体积(TV)、虚拟手术体积(VSV)及残余肝体积(RLV)进行统计学分析,分析RLV与其他指标间的相关关系,以ROC曲线评价RLV预测肝功能不全的效能.结果 与A组患者相比,B组患者的年龄、总胆红素水平增高,Child-Pugh评分增高,白蛋白水平降低,RLV降低,差异有统计学意义(P<0.05),进行Spearman相关性分析,RLV与ICGR15呈负相关,差异有统计学意义(P<0.05).ROC曲线显示RLV值预测肝功能不全的AUC为0.659(P<0.05),灵敏度为0.767.结论 通过肝脏占位CT虚拟成像进行残余肝体积计算,能够进一步评估肝脏储备功能.

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Virtual chemistry experiment is an important teaching tool in middle schools. It can not only help students understand the experimental principles, but also help them memorize the experimental procedures. However, there are still some problems in virtual chemistry experiments. First, in existing research, user intentions are often misunderstood and cannot be accurately understood. Second, the existing research fail to identify the user's wrong actions, which reduce the accuracy of the experiment. Third, the user sense of operation and realism is not strong during the experiment, which reduces the user's experience. Finally, the lack of navigation guidance for experimental operations increases user learning time. In order to solve these problems. This paper proposes a scheme for establishing an intelligent navigational chemical laboratory based on multimodal fusion. First of all, we design a new smart beaker structure with perceptual ability, which can be used to complete most chemical experiments and give users a real sense of experience. Besides, we propose a multimodal fusion understanding algorithm, which reduces the misidentification of the experiment and better understands the real intention of the user. Finally, intelligent navigation and wrong behavior recognition functions are added to the experimental equipment, which improves the efficiency of human-computer interaction. The results show that Compared with the existing virtual laboratory or system, the chemical laboratory scheme proposed in this paper through multimodal fusion understanding algorithm greatly reduces the user's memory load and improves the success rate of the experiment. Moreover, through the combination of virtual and real, the virtual chemistry experiment not only improves the authenticity of the operation, but also stimulates the students' interest in learning, which is well received by users.

目的 分析血氧水平依赖功能MRI与颞叶癫痫(TLE)患者的认知功能(包括语言功能、执行功能和记忆功能)的联系,探索患者脑组织功能机制原理.方法 选取25例TLE患者为试验组及25名正常人为对照组,均给与神经心理学测试以及记忆功能评估,比较两组在言语智商(VIQ)、操作智商(PIQ)、总智商(FIQ)以及记忆智商(MQ)的差异,同时比较两组功能MRI的数据.结果 对照组的神经心理学测试、记忆功能评估以及VIQ、PIQ、FIQ和MQ的评分均明显高于试验组,差异有统计学意义(均P<0.01).试验组和对照组在脑部组织的正性激活均存在明显差异(均P<0.05).在进行语言测试时,试验组和对照组间的正性激活部位不同表现在右侧额前叶、颞外侧回和左侧小脑、角回边缘侧、楔前叶及中央后回,而负性激活区域为左侧额前叶皮质和右侧小脑、角回边缘侧及扣带前回;在进行执行功能测试时,正性激活部位不同表现在右侧额前叶、颞叶、小脑前叶及左侧颞上回,脑组织负性激活区没有明显差别;在进行记忆功能测试时,正性差异位于双侧扣带前回及楔前叶,而负性激活差异位于左侧额下回及中央后回.结论 认知功能与皮质下的连接功能相关,TLE和非TLE人群在进行不同的任务测试时,被激活的脑组织区域也存在明显的差异.

Aiming at the problem of the difficulty of real-time accurate sensing and positioning of virtual objects in virtual experiments in middle schools, this paper designs a real-time acquisition algorithm of three-dimensional position information of virtual objects based on monocular vision. The designed target object binding can only perceive objects in a virtual scene in real time and accurately calculate its three-dimensional position information under an ordinary monocular camera. Finally, it is verified on the experimental teaching platform of fusion of reality and reality, which significantly reduces The difficulties in the interaction process enhance the user's sense of interaction and experience.

This study focused on the application of functional magnetic resonance imaging and neuropsychology in diagnosis of vascular mild cognitive impairment (MCI) and the exploration of its relevant factors. The study enrolled 28 patients with vascular MCI in an observation group and 30 healthy individuals in a control group. All patients underwent magnetic resonance imaging. An automatic segmentation algorithm based on graph theory was adopted to process the images. Age, sex, disease course, Montreal Cognitive Assessment score, regional homogeneity, and amplitude of low-frequency fluctuation levels were recorded. There were no significant differences in age, gender, and course of disease between the observation group and the control group (P > 0.05). The level of regional homogeneity in the left posterior cerebellum in the observation group was significantly higher than that in the control group (P < 0.05).The regional homogeneity level of bilateral cingulate cortex was negatively correlated with Montreal Cognitive Assessment score (P < 0.05). The amplitude of low-frequency fluctuation of bilateral inferior parietal lobe, parietal lobe, and prefrontal lobe in the observation group was significantly lower than that in the control group, and the amplitude of low-frequency fluctuation of bilateral anterior cingulate gyrus, superior medial frontal gyrus, orbital frontal gyrus, right middle frontal gyrus, and right auxiliary motor area was higher than that in the control group (P < 0.05). Heart disease, such as myocardial infarction and atrial fibrillation, is a high risk factor for vascular MCI. Functional magnetic resonance imaging combined with an automatic segmentation algorithm can noninvasively observe the changes of a patient's brain tissue, which can be used in the recognition of vascular MCI. The global network attributes of patients with depression tend to be more randomized and have stronger resilience under targeted attacks.

目的 探讨CT影像组学特征鉴别肾透明细胞癌(ccRCC)和肾非透明细胞癌(non-ccRCC)的价值,构建准确性较高的预测模型.方法 回顾性分析经病理证实的54例ccRCC和25例non-ccRCC.患者术前均行肾脏多期相CT扫描.使用ITK-SNAP软件人工逐层分割各期相病灶,生成三维ROI.对ROI进行高通量特征采集,并以7︰3的比例随机选择训练组和测试组,应用Spearman相关分析和LASSO降维处理进行特征筛选,构建预测模型,用十折交叉验证方式结合ROC曲线验证各模型的预测效能.结果 基于平扫期、皮髓质期、实质期、排泄期及增强3期综合数据构建了5个鉴别ccRCC与non-ccRCC的影像组学模型,5个模型在训练组中的AUC分别为0.93(95％CI:0.85～1.00)、0.98(95％CI:0.94～1.00)、0.93(95％CI:0.84～1.00)、0.92(95％CI:0.84～1.00)和0.98(95％CI:0.93～1.00),模型准确度分别为88％、84％、92％、84％和98％;5个模型在测试组中的AUC分别为0.83(95％CI:0.67～0.99)、0.95(95％CI:0.89～1.00)、0.91(95％CI:0.82～1.00)、0.91(95％CI:0.80～1.00)和0.96(95％CI:0.88～1.00),模型准确度分别为73％、89％、81％、89％和93％.增强3期综合模型的AUC值和准确度最高.结论 基于CT影像组学特征构建的5个模型对ccRCC和non-ccRCC术前鉴别诊断均有一定价值,增强3期综合模型预测效能最好.

Objective:To investigate the value of CT radiomics mode in differential diagnosis of benign and malignant thyroid nodules.Methods:The clinical and imaging data of 179 patients with thyroid nodules confirmed by pathology from May 2017 to August 2018 were retrospectively analyzed in the Affiliated Huaian First People′s Hospital of Nanjing Medical University. Among the patients, 89 cases were benign nodules and 90 cases were malignant nodules. All patients underwent unenhanced and enhanced CT scan before operation. The stratified random sampling method was used to divide patients into a training group (143 cases) and a testing group (36 cases) according to a ratio of 8∶2. The A.K software was used to extract 378 imaging omics features based on preoperative CT images, and then Spearman correlation analysis and least absolute shrinkage and selection operator regression analysis were used for feature selection and model construction. The receiver operating characteristic (ROC) curve was used to verify the model in the training group and the testing group, and the efficacy of imaging omics features to predict benign and malignant thyroid nodules was evaluated.Results:After feature screening, 16 radiomics features were used to construct an identification model between benign and malignant thyroid nodules. In the training group, the area under the ROC curve (AUC) was 0.92 [95% confidence interval (CI): 0.88-0.97], the sensitivity and specificity were 88.7%, 82.0%, and the diagnostic accuracy of the model was 91.1%. In the testing group, AUC was 0.90 (95 %CI: 0.81-0.98), sensitivity and specificity were 88.5%, 84.6%, and the diagnostic accuracy of the model was 88.2%. Conclusion:The CT radiomics mode has a good diagnostic performance in the identification of benign and malignant thyroid nodules.

目的 探讨CT影像组学模型在预测浸润性肺腺癌(IAC)分化程度的价值.方法 回顾性分析2017年10月至2018年8月经手术病理或穿刺病理结果证实为IAC的患者133例,其中,106例作为训练组,27例作为测试组.根据病理结果,将所有患者分为高、中分化组(67例)和低分化组(66例),所有患者均在本院行胸部CT平扫.使用病灶分割软件ITK-SNAP手动分割每个患者的病灶,勾画每个层面病灶最大界面作为感兴趣区(ROI),对所有病灶CT图像进行高通量特征采集,提取出396个CT特征.对所得特征进行LASSO降维处理,构建得到预测模型,采用受试者工作特征曲线(ROC)评价模型的预测效能.结果 采用LASSO回归降维方法筛选保留10个特征参数,预测模型在训练组中的曲线下面积(AUC)为0.84(95％ CI:0.762～0.923),敏感度和特异度分别为59.6％和97.9％,模型的准确度为79.4％,阳性预测值为97.0％,阴性预测值为71.0％;在测试组中预测模型的AUC为0.83 (95％CI:0.698～0.971),敏感度和特异度分别为75.0％和84.2％,测试的准确度为79.3％,阳性预测值为83.2％,阴性预测值为76.3％.结论 基于CT影像组学模型预测IAC的分化程度有重要价值.