Tirapazamine (TPZ) is a bioreductive agent with selective antitumor activity under hypoxic conditions; however, its systemic toxicity and delivery efficiency limit its clinical application. This study aimed to develop a TPZ delivery system based on nanobubbles (NBs), combining ultrasound-targeted microbubble destruction (UTMD) and transcatheter arterial chemoembolization (TACE) for treating hepatocellular carcinoma (HCC) to enhance therapeutic efficacy and reduce adverse effects. TPZ-NBs were fabricated using a high-shear dispersion method and characterized for their physicochemical properties, encapsulation efficiency, and drug release profiles. In vitro studies evaluated the effects of TPZ-NBs on hepatoma cell proliferation and migration under hypoxic conditions. Using a rabbit VX2 liver cancer model, we compared the control group, TACE group, TPZ-NBs + TACE group, and TPZ-NBs + UTMD + TACE group in terms of tumor necrosis, drug delivery efficiency, and safety profiles. TPZ-NBs demonstrated favorable encapsulation efficiency (35.81

PURPOSE:The aim of the study is to investigate the feasibility of using dual-source computed tomography (CT) combined with low flow rate and low tube voltage for postchemotherapy image assessment in cancer patients. METHODS:Ninety patients undergoing contrast-enhanced CT scans of the upper abdomen were prospectively enrolled and randomly assigned to groups A, B, and C (n = 30 each). In group A, patients underwent scans at 120 kVp with 448 mgI/kg. Patients in group B underwent scans at 100 kVp with 336 mgI/kg. Patient in group C underwent scans at 70 kVp with of 224 mgI/kg. Quantitative measurements including the CT number, standard deviation of CT number, signal-to-noise ratio, contrast-to-noise ratio, subjective reader scores, and the volume and flow rate of contrast agent were evaluated for each group. RESULTS:There was no statistically significant difference in the subjective image scores within the three groups except for the kidney (all P > 0.05). Group C showed significantly higher CT values, lower noise levels, and higher signal-to-noise ratio and contrast-to-noise ratio values in the majority of the regions of interest compared to the other groups ( P < 0.05). In group C, the contrast agent dose was decreased by 46% compared to group A (79.48 ± 12.24 vs 42.7 ± 8.6, P < 0.01), and the contrast agent injection rate was reduced by 22% (2.7 ± 0.41 vs 2.1 ± 0.4, P < 0.01). CONCLUSIONS:The use of 70 kVp tube voltage combined with low iodine flow rates prove to be a more effective approach in solving the challenge of compromised blood vessels in postchemotherapy tumor patients, without reducing image quality and diagnostic confidence.

目的:基于术前CT构建预测肝门部胆管癌(pCCA)神经侵犯(PNI)的影像组学模型,并评价其效能.方法:回顾性分析本院 2013 年 2 月-2021 年 2 月 149 例经病确诊的 pCCA 患者的临床资料,其中PNI组患者 108 例,无PNI组患者 41 例.采用R语言将所有患者按 3:1 比例随机分为训练集和验证集.在静脉期图像上,沿肿瘤边缘在所有层面上手动勾画 3D感兴趣区(ROI),使用 3D Slicer提取影像组学特征.采用组内相关系数(ICC)、相关性分析去除冗余特征,采用随机森林算法(RF)对所有临床、影像组学特征进行重要性排序,并选取前 18 个重要特征构建 RF 模型.使用准确性、敏感性、特异性及受试者操作特征(ROC)曲线评价模型效能.结果:在训练集中,RF 模型的准确性、敏感性、特异性均为 100%,ROC 曲线下面积 AUC 为 1;在验证集中,RF 模型的准确性为 70.3%,敏感性为59.3%,特异性为 100%,AUC 为 0.846(0.713～0.979).结论:基于增强 CT 图像建立的影像组学模型可用于术前无创性预测pCCA患者的PNI状态.

目的 探讨非侵入性肝纤维化诊断模型对肝细胞癌(HCC)载药微球化疗栓塞(DEB-TACE)术后发生急性肝功能恶化(ALFD)风险的预测及生存分析.方法 回顾性分析288 例经DEB-TACE治疗的HCC患者的临床资料.按照文中标准将患者分为ALFD组与对照组,对比两组患者的基线资料及术前肝功能指标,采用受试者工作特征(ROC)曲线分析术前 Child-Pugh 评分、APRI、FIB-4 对 DEB-TACE 术后 ALFD 的预测价值.利用 Kaplan-Meier对数秩检验及Cox风险回归模型分析生存率,通过多因素Cox比例风险回归模型筛选出独立预后影响因素.结果 除丙氨酸转氨酶水平外,两组间其他肝功能指标差异均有统计学意义(P<0.05).术前Child-Pugh评分、APRI、FIB-4 的曲线下面积分别为0.763、0.735、0.740,均无显著性差异,APRI、FIB-4 最佳截断点值分别为1.42 和4.47.分层分析提示,APRI>1.42 或FIB-4>4.47 是DEB-TACE术后ALFD的危险因素.生存分析表明,APR≤1.42 与APRI>1.42 时的中位生存期分别为32.5 个月、19 个月(P =0.005);FIB-4≤4.47 与FIB-4>4.47 时中位生存期分别为32.5 个月、20.5 个月(P =0.097);多因素Cox风险回归模型分析得到最大肿瘤直径和门静脉侵犯为独立预后影响因素.结论 基于APRI、FIB-4 的非侵入性肝纤维化模型对评估肝细胞癌DEB-TACE术后发生ALFD的风险及生存具有一定的临床应用价值.

目的 探讨微卫星高度不稳定型(MSI-H)胃癌的CT及临床病理学特征.方法 回顾性分析201例胃癌患者的临床资料,包括33例MSI-H,168例微卫星稳定型(MSS)/微卫星低度不稳定型(MSI-L).统计分析临床、CT及病理学特征与微卫星不稳定型(MSI)状态的相关性.结果 MSI-H组的性别(P=0.018)、肿瘤长径(P＜0.001)、肿瘤部位(P＜0.001)、程序性细胞死亡配体1(PD-L1)表达(P=0.007)与MSS/MSI-L组相比有显著统计学差异,单因素和多因素logistic回归分析显示性别(女性)、肿瘤部位(胃窦)、PD-L1阳性及肿瘤长径为MSI-H的独立预测因子.结论 性别(女性)、肿瘤部位(胃窦)、PD-L1阳性及肿瘤长径可作为MSI-H的独立预测因子,用以MSI-H胃癌的初始筛查.

Introduction:Mutation patterns have been extensively explored to decipher the etiologies of hepatocellular carcinoma (HCC). However, the study and potential clinical role of mutation patterns to stratify high-risk patients and optimize precision therapeutic strategies remain elusive in HCC.Methods:Using exon-sequencing data in public (n=362) and in-house (n=30) cohorts, mutation signatures were extracted to decipher relationships with the etiology and prognosis in HCC. The proteomics (n=159) and cell-line transcriptome data (n=1019) were collected to screen the implication of sensitive drugs. A novel multi-step machine-learning framework was then performed to construct a classification predictor, including recognizing stable reversed gene pairs, establishing a robust prediction model, and validating the robustness of the predictor in five independent cohorts (n=900).Results:Two heterogeneous mutation signature clusters were identified, and a high-risk prognosis cluster was recognized for further analysis. Notably, mutation signature cluster 1 (MSC1) was featured by activated anti-tumor immune and metabolism dysfunctional states, higher genomic instability (high TMB, SNV neoantigen, indel neoantigens, and total neoantigens), and a dismal prognosis. Notably, MSC performed as an independent risk factor than clinical traits (eg, stage, vascular invasion). Additionally, afatinib and canertinib were recognized which might have potential therapeutic implications in MSC1, and the targets of these drugs presented a higher expression in both gene and protein levels in HCC.Discussion:Our studies may provide a promising platform for improving prognosis and tailoring therapy in HCC.

目的 探讨基于CT检查影像组学食管癌根治术后吻合口增厚性质预测模型的构建及其应用价值.方法 采用回顾性队列研究方法.收集2013年1月至2021年6月郑州大学第一附属医院收治202例食管鳞癌患者的临床病理资料;男147例,女55例;年龄为(63±8)岁.202例患者采用随机数法以7:3比例随机分为训练集141例和验证集61例.患者均行食管癌根治术和CT增强检查.观察指标:(1)吻合口恶性增厚的影响因素分析.(2)预测模型构建与评估.(3)3种预测模型性能比较.运用Kolmogorov-Smirnov检验连续变量的正态性.正态分布的计量资料以(x)±s表示,组间比较采用t检验.偏态分布的计量资料以M(Q1,Q3)表示,组间比较采用Mann-WhintneyU检验.计数资料以绝对数表示,组间比较采用x2检验或Fisher确切概率法.采用Kappa检验和组内相关系数(ICC)评估2名医师CT主观征象和测量的CT数值变量一致性,Kappa值＞0.6、ICC＞0.6认为一致性较好.单因素分析采用对应的统计学方法.多因素分析采用Logistics逐步回归模型.绘制受试者工作特征(ROC)曲线,以曲线下面积(AUC)及Delong检验、决策曲线评估模型的诊断效能及临床实用性.结果 (1)吻合口恶性增厚的影响因素分析.202例食管鳞癌患者中,吻合口恶性增厚97例,吻合口炎性增厚105例.2位医师CT主观征象和测量CT数值变量一致性,Kappa值和ICC均＞0.6.多因素分析结果显示:吻合口最大厚度、增厚组织CT强化方式是食管癌根治术后吻合口恶性增厚的独立影响因素[风险比=1.46,3.09,95％可信区间(CI)为1.26～1.71,1.18～8.12,P＜0.05].(2)预测模型构建与评估.①临床预测模型:纳入多因素分析结果吻合口最大厚度、增厚组织CT强化方式构建临床预测模型.ROC曲线显示:临床预测模型训练集的AUC、准确度、灵敏度、特异度分别为0.86(95％CI为0.80～0.92)、0.77、0.77、0.80;验证集上述指标分别为0.78(95％CI为0.65～0.89)、0.77、0.77、0.80.Delong检验结果显示:训练集和验证集AUC比较,差异无统计学意义(Z=1.22,P＞0.05).②影像组学预测模型:提取202例患者854个影像组学特征,最终筛选出2个影像组学特征(wavelet-LL_firstorder Maximum和original_shape_VoxelVolume),用于构建影像组学预测模型.ROC曲线显示:影像组学预测模型训练集的AUC、准确度、灵敏度、特异度分别为0.87(95％CI为0.81～0.93)、0.80、0.75、0.86;验证集上述指标分别为0.73(95％CI为0.63～0.83)、0.80、0.76、0.94.Delong检验结果显示:训练集和验证集AUC比较,差异无统计学意义(Z=-0.25,P＞0.05).③联合预测模型.联合多因素分析结果和影像组学特征构建联合预测模型.ROC曲线显示:联合预测模型训练集的AUC、准确度、灵敏度、特异度分别为0.93(95％CI为 0.89～0.97)、0.84、0.90、0.84;验证集上述指标分别为0.79(95％CI为0.70～0.88)、0.89、0.86、0.91.Delong检验结果显示:训练集和验证集AUC比较,差异无统计学意义(Z=0.22,P＞0.05).(3)3种预测模型性能比较.Hosmer-Lemeshow拟合优度检验结果显示:临床预测模型、影像组学预测模型、联合预测模型的拟合度均较好(x2=4.88,7.95,4.85,P＞0.05).Delong检验结果显示:联合预测模型分别与临床预测模型和影像组学预测模型AUC比较,差异均有统计学意义(Z=2.88,2.51,P＜0.05);临床预测模型与影像组学预测模型比较,差异无统计学意义(Z=-0.32,P＞0.05).校准曲线显示:联合预测模型的预测能力良好.决策曲线显示:联合预测模型对吻合口增厚性质的评估能力优于临床预测模型和影像组学预测模型.结论 吻合口最大厚度、增厚组织CT强化方式是食管癌根治术后吻合口恶性增厚的独立影响因素;影像组学预测模型可鉴别吻合口良恶性增厚,联合预测模型诊断效能最优.

Rationale and Objectives: Acute liver function deterioration (ALFD) following drug-eluting beads transarterial chemotherapy embolism (DEB-TACE) was considered a risk factor for prognosis in patients with hepatocellular carcinoma (HCC). In this study, we aimed to develop and validate a nomogram for the prediction of ALFD after DEB-TACE. Materials and Methods: A total of 288 patients with HCC from a single center were randomly divided into a training dataset (n = 201) and a validation dataset (n = 87). The univariate and multivariate logistic regression analyses were performed to determine risk factors for ALFD. The least absolute shrinkage and selection operator (LASSO) was applied to identify the key risk factors and fit a model. The performance, calibration, and clinical utility of the predictive nomogram were assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results: LASSO regression analysis determined six risk factors with fibrosis index based on four factors (FIB-4) as the independent factor for the occurrence of ALFD after DEB-TACE. Gamma-glutamyltransferase, FIB-4, tumor extent, and portal vein invasion were integrated into the nomogram. In both the training and validation cohorts, the nomogram demonstrated promising discrimination with AUC of 0.762 and 0.878, respectively. The calibration curves and DCA revealed good calibration and clinical utility of the predictive nomogram. Conclusion: The nomogram-based risk of ALFD stratification may improve clinical decision-making and surveillance protocols for patients with a high risk of ALFD after DEB-TACE.

目的 观察深度学习重建(DLR)联合 Smart去金属伪影(MAR)算法对颈部 CT图像中口腔金属植入物伪影的影响.方法 回顾性分析 60 例口腔内存在金属植入物患者的颈部 CT 资料,其中 19 例存在颈部病变且受金属伪影干扰;分别以自适应迭代重建(ASIR-V,重建百分比为 50%)联合 Smart MAR(IR+S组)、DLR-H(重建强度为高水平)联合Smart MAR(DH+S组)、DLR-M(重建强度为中水平)联合 Smart MAR(DM+S组)、DLR-H(DH 组)及DLR-M(DM组)重建静脉期图像,通过计算各组病变/舌部软组织噪声(SD1)、头夹肌噪声(SD2)及伪影指数(AI)对图像进行客观评估;以Likert量表对图像整体及显示病灶质量进行主观评分;比较各组主、客观评估结果的差异.结果 5 组图像 SD1、SD2 及AI差异均有统计学意义(P均<0.05).SD1 及 AI 在 DH+S组、DM+S组、IR+S组、DH 组及 DM组依次升高(P均<0.05);SD2 在DH+S组、DM+S组及IR+S组依次升高(P均<0.05),而在DH+S组与DH 组、DM+S组与DM组均无统计学差异(P均>0.05).存在颈部病变的 6 例(6/19,31.58%)及无颈部病变的 4 例(4/41,9.76%)可于 IR+S 组、DH+S组及DM+S组中发现DH 组及DM组中不存在的伪影.5 组图像整体及显示病灶质量评分差异均有统计学意义(P均<0.05),在DH+S组、DM+S组及 IR+S组依次降低(P 均<0.05),但均高于 DH 组及 DM组(P 均<0.05),而DM组与DH 组间差异均无统计学意义(P均>0.05).结论 以DLR联合Smart MAR算法重建口腔金属植入物患者颈部CT图像的噪声、AI、图像整体质量及显示病灶质量均较好,但存在无法去除金属伪影的可能.

目的 探讨深度学习图像重建(DLIR)算法、多模型迭代重建(ASiR-V)算法和滤波反投影(FBP)算法在低管电压条件下对腹部平扫CT图像质量的影响.方法 前瞻性搜集因病情需要行全腹部CT平扫检查的 56 例患者,根据体质量指数(BMI)将入选患者分为A组(18 kg/m2≤BMI<24 kg/m2,管电压 80 kVp,n =29)、B组(24 kg/m2≤BMI<29 kg/m2,管电压100 kVp,n =27).所有图像数据均进行FBP、权重为50%的ASiR-V(ASiR-V50%)和高强度DLIR(DLIR-H)图像重建.采用Kruskal-Wallis H检验比较不同重建算法图像间的各项客观评价指标[噪声、肝脏和胰腺的信号噪声比(SNR)、肝脏和胰腺的对比噪声比(CNR)]和主观评价指标(噪声、总体图像质量),组内两两比较采用Bonferroni校正检验.结果 A组和B组的DLIR-H图像的噪声,肝脏、胰腺SNR均显著优于FBP和ASiR-V50%,差异均有统计学意义(P值均<0.05).在 80 kVp组和 100 kVp组,DLIR-H的噪声较FBP降低66.8%和68.7%,较ASiR-V50%降低46.1%和48.7%.DLIR-H肝脏和胰腺的CNR高于FBP,差异有统计学意义(P<0.05),DLIR-H 和 ASiR-V50%、ASiR-V50%和 FBP 间肝脏和胰腺的 CNR 差异无统计学意义(P 值均>0.05).主观评分上,A组和B组DLIR-H的主观噪声和总体图像质量高达4 分以上,均高于FBP和ASiR-V50%(P值均<0.05).结论 与ASiR-V50%和FBP相比,DLIR-H降低了图像噪声,提高了图像质量,在辐射剂量优化方面有更大的潜力.

Objective:To explore the value of multiphasic CT-based radiomics signature in predicting the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN).Methods:The multiphasic CT images of patients with pSPN confirmed by postoperative pathology in the First Affiliated Hospital of Zhengzhou University from January 2012 to January 2021 were analyzed retrospectively. There were 23 cases of invasiveness and 59 cases of non-invasiveness. The region of interest(ROI) was artificially delineated layer by layer in the plain scan, arterial-phase and venous-phase images, respectively. The 1 316 image features were extracted from each ROI. The data set was divided into training and validation sets with a ratio of 7∶3 by stratified random sampling, and synthetic minority oversampling technique (SMOTE) algorithm was used for oversampling in the training set to generate invasive and non-invasive balanced data for building the training model. The constructed model was validated in the validation set. The receiver operating characteristic(ROC) analysis was used to evaluate model performance and the Delong′s test was applied to compare the area under the ROC curve (AUC) of different predict models. The improvement for classification efficiency of each independent model or their combinations were also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices.Results:After feature extraction, 2, 6 and 3 features were retained to construct plain-scanned model, arterial-phase and venous-phase models, respectively. Seven independent-phase and combined-phase models were established. Except the plain-scanned model, the AUC values of other models were greater than 0.800. The arterial-phase model had the best efficiency for classification among all independent-phase models. The AUC values of arterial-phase model in the SMOTE training and validation sets were 0.913 and 0.873, respectively. By combining the radiomics signature of the arterial-phase and venous-phase models, the AUC values of training and validation sets increased to 0.934 and 0.913 respectively. There were no significant differences of the AUC values between the scan-arterial venous-phase model and arterial venous-phase model in both training and validation sets (both P>0.05). The NRI and IDI indexes showed that the combined form of plain-scan model and arterial-venous-phase model could not significantly improve the classification efficiency in the validation set (both NRI and IDI<0). Conclusions:The arterial-phase CT-based radiomics model has a good predictive performance in the invasive behavior of pSPN, and the combination with a venous-phase radiomics model can further improve the model performance.

Objective:To investigate the efficiency of deep learning image reconstruction (DLIR) algorithm in the image quality and detection of hypovascular hepatic metastases under low radiation doses in comparison with adaptive statistical iterative construction-V (ASiR-V).Methods:Fifty-six patients with suspected hypovascular hepatic metastases who needed abdominal enhanced CT scans were collected prospectively in the First Affiliated Hospital of Zhengzhou University from January to April 2021. The patients received conventional radiation dose with tube current-time products of 400 mA CT scans in the first venous phase, low-dose CT scans in the second venous phase, which were set as tube current-time products of 280 mA for group A (19 cases), 200 mA for group B (19 cases) and 120 mA for group C (18 case), respectively. The images of first venous phase and 3 groups of second venous phase were both reconstructed with ASiR-V60% and high-DLIR (DLIR-H). Quantitative parameters [image noise, liver and portal vein signal to noise ratio (SNR), contrast to noise ratio (CNR)] and qualitative parameters (overall image quality, lesion conspicuity, diagnostic confidence) were compared between ASiR-V60% and DLIR-H images, and the effective radiation dose (ED) and the lesion detectability of each group was recorded. The paired t test was used to compare quantitative parameters, whereas the Wilcoxon signed-rank test of paired data was used to compare qualitative parameters. Results:In the second venous phase, ED was (5.56±0.35) mSv in group A, (3.88±0.23) mSv in group B, and (2.42±0.23) mSv in group C, with a decrease of 30%, 50% and 70% compared with the first venous phase, respectively. Moreover, with the decrease of radiation dose, the noise gradually increased, and the CNR lesions, SNR liver and SNR portal vein all gradually decreased. DLIR-H images had statistically better quantitative scores than ASiR-V60% images when the same radiation dose was applied (all P<0.001). Furthermore, the qualitative parameters of each group decreased with the decrease of radiation dose. Under the same radiation dose, the overall image quality, lesion conspicuity and diagnostic confidence of DLIR-H were higher than those of ASiR-V60% (all P<0.001). All lesions [100% (84/84)] were detected by ASIR-V60% and DLIR-H in group A, 92.0% (75/81) in group B, and 88.0% (79/89) in group C. Conclusions:Compared with ASiR-V60%, DLIR-H could reduce image noise, improve overall image quality and lesion conspicuity of hypovascular hepatic metastases as well as increase diagnostic confidence under different radiation doses.

近年来，CT图像算法中基于深度学习的图像重建（DLIR）技术不断发展，日益成熟，目前已经逐步应用于临床实践中。DLIR算法较常规迭代重建算法具有在降低辐射剂量和图像噪声的同时不改变图像纹理，保持或提高解剖细节显示能力、总体图像质量和医生诊断信心的众多优势。因此，笔者重点就DLIR算法的原理、优劣势及其在人体各系统的临床应用进展进行综述，旨在进一步提高对DLIR算法的认识，并对其可能的应用情景提供借鉴。

目的 探讨低辐射剂量联合深度学习重建算法(DLIR)在提高肝转移图像质量和诊断能力中的应用价值.方法 前瞻性搜集因临床需要行上腹部CT增强检查的肝转移患者30例,静脉期采用标准辐射剂量扫描联合前置多模型迭代重建算法V(ASiR-V)40％算法行图像重建(对照组),加扫第二静脉期采用50％低辐射剂量扫描并联合DLIR算法三个强度水平[低(L)、中(M)、高(H)]行图像重建(实验组).对所有图像行客观评价[图像噪声、肝转移病灶的对比噪声比(CNR)、肝脏和门静脉的信噪比(SNR)]及主观评价[总体图像质量和病灶显示能力].采用单因素方差分析和Kruskal-Wallis H检验比较4组图像的客观和主观评价指标.结果 DLIR-L组的噪声高于ASiR-V40％组(P＜0.05),病灶CNR、肝脏和门静脉SNR、总体图像质量和病灶显示能力均低于ASiR-V40％,但两组的肝脏SNR与总体图像质量差异均无统计学意义.DLIR-M组和ASiR-V40％组的噪声、病灶CNR、肝脏和门静脉SNR、总体图像质量与病灶显示能力差异均无统计学意义.与ASiR-V40％组相比,DLIR-H组的噪声减低(P＜0.05),病灶CNR、肝脏和门静脉SNR和病灶显示能力相当,总体图像质量提高(P＜0.05).在DLIR 3组间,噪声随重建强度水平(DLIR-L、DLIR-M、DLIR-H)的升高而降低,总体图像质量和病灶显示能力随强度水平的提高而提高,且各组间差异有统计学意义(P＜0.05).病灶CNR、肝脏和门静脉SNR随DLIR强度的提高而降低,但仅DLIR-H与DLIR-L组差异有统计学意义(P＜0.05).结论 与ASiR-V40％相比,采用DLIR-H重建在减低50％辐射剂量条件下提高总体图像质量且保持肝转移病灶诊断能力不降低.

RATIONALE AND OBJECTIVES:To develop and validate a nomogram for the prediction of stent dysfunction after transjugular intrahepatic portosystemic shunt (TIPS) placement in patients with hepatitis B cirrhosis.MATERIALS AND METHODS:From 2012 to 2020, 355 patients with hepatitis B cirrhosis who underwent TIPS placements were enrolled in this study. A multivariable logistic regression analysis was applied to determine independent risk factors for the nomogram construction. Discrimination, calibration, and clinical usefulness of the prediction model were assessed by using receiver operating characteristic curves, calibration scatter plots, and a decision curve analysis (DCA).RESULTS:Independent factors for TIPS stent dysfunction included diabetes, previous splenectomy, the shunting branch of the portal vein, and stent position, which were used to construct the nomogram. The AUC values in the training and validation cohorts were 0.817 (95% CI: 0.731-0.903) and 0.804 (95% CI: 0.673-0.935), respectively, which suggested a good predictive ability. The calibration curves in both cohorts revealed good agreement between the predictions and actual observations. The DCA curve indicated that when the threshold probability ranged from 2% to 88%, the nomogram could provide clinical usefulness and a net benefit.CONCLUSION:The nomogram that we developed could be conveniently used to predict TIPS stent dysfunction in patients with hepatitis B cirrhosis.

目的 观察CT预估保守治疗闭襻性肠梗阻效果的效能.方法 回顾性分析95例经CT诊断并接受保守治疗的无肠缺血征象闭襻性肠梗阻患者;以治疗后梗阻症状好转、随访1个月内无进展、碘剂消化道造影或CT证实梗阻缓解为治疗成功,反之为治疗失败,对比其临床资料及CT所见,分析以CT征象预估保守治疗闭襻性肠梗阻成功的效能.结果 保守治疗成功31例(成功组),治疗失败64例(失败组),组间CT所见梗阻程度、肠系膜水肿、积粪征及移行点距离差异均有统计学意义(P均<0.05).多因素分析结果显示,积粪征[OR=3.19,95％CI(1.15,8.87),P=0.026]和移行点距离[OR=7.35,95％CI(2.82,31.02),P=0.002]是保守治疗成功的影响因素.以受试者工作特征(ROC)曲线确定移行点距离预测闭襻性肠梗阻保守治疗成功的最佳截断值为9.7 mm.以积粪征、移行点距离(≥9.7 mm)预测闭襻性肠梗阻保守治疗成功的敏感度分别为48.39％(15/31)和87.10％(27/31),特异度为75.00％(48/64)和56.25％(36/64);二者联合的敏感度、特异度分别为35.48％(11/31)、87.50％(56/64).结论 根据CT所见积粪征和梗阻移行点距离可在一定程度上预估保守治疗闭襻性肠梗阻的效果.

Background To analyse clinical characteristics and computer tomography (CT) findings of hepatic epithelioid haemangioendothelioma (HEH) and to determine differential features compared with liver metastasis (LM). Methods This retrospective study included 80 patients with histopathologically confirmed HEH ( n = 20) and LM ( n = 60) of different primary tumours who underwent dynamic contrast-enhanced CT scans. CT findings included the location, contour, size, number, margin, and density of lesions, the patterns and degree of contrast enhancement of lesions, vascular invasion and changes in other organs. The enhancement ratio (ER) and tumour-to-normal parenchyma ratio (TNR) were calculated. Receiver operating characteristic curves (ROCs) were used to determine areas under the curve (AUCs). Results About 65% of HEH lesions were located in submarginal areas. Significant differences were observed between HEH and LM patients in age, sex, and tumour marker positivity ( p < 0.05). HEH showed minimal to slight enhancement, thin ring-like enhancement in arterial phase, and slight, homogeneous, progressive enhancement in the portal phase. HEH presented capsule retraction, and the “target” sign and the “lollipop” sign were significantly more frequent than in LM ( p < 0.05). The ER and TNR in the arterial phase of HEH were lower than those of LM ( p < 0.05). AUCs of ER and TNR in the arterial phase were 0.74 and 0.73, respectively. Conclusion Lesions in subcapsular locations, capsular retraction, slight and thin ring-like enhancement, “target” and “lollipop” signs and lower ER and TNR in the arterial phase may represent important features of HEH compared with LM.

患者男,73岁,2个月前发现左侧腰背部皮下肿块,最大径约5 cm,并进行性增大;既往体健,无特殊病史.查体:腰背部左侧触及约8 cm×6 cm×3 cm皮下肿块,质硬,轻度压痛,活动度可,表面皮肤粗糙、红肿,压之不褪色.实验室检查未见明显异常.全身18F-FDG PET/CT:L2水平左侧皮下软组织肿块放射性分布略浓聚,最大标准摄取值(maximum standard uptake value,SUVmax)2.0,最大面积约3.5 cm×2.2 cm,CT 值约35 HU(图1A～1D),颅骨、躯干骨及四肢骨未见明显放射性异常;考虑皮肤恶性肿瘤.

患者 女,50岁,以"腹痛、腹胀伴发热10 d"为主诉人院.既往无乙肝、丙肝病史.实验室检查:肿瘤标记物CA125106 U/mL,CA199 54.2 U/mL;肝功能分级为 Child A 级.上腹部平扫及增强CT示:肝左叶巨大占位,最大径约157.14 mm×134.54 mm,增强后呈中度强化,肝左静脉及下腔静脉可见充盈缺损(图1).穿刺病理活检示(图2):肝组织内见腺体轻度异型增生,可见腺癌细胞浸润;结合免疫组化,诊断肝内胆管细胞癌(intrahepatic cholangiocarcinoma,ICC);TNM 分期为T3N1M0 Ⅲb 期.

Objective To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC). Methods A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed. Result Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model. Conclusion The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.