Companies are making significant efforts to improve leakage detection efficiency. Therefore, this paper proposes the identification of leak zones using a probabilistic neural network (PNN) and model-based localization in real water supply networks. First, a large water supply network was divided into several areas using a fuzzy c-means clustering algorithm optimized with a genetic-simulated annealing algorithm (FCM-GSAA). Then the leak zone was identified using a PNN. Second, the specific leak locations were determined using a quantum genetic algorithm (QGA). The proposed method was then applied to the Qingdao water supply network in Shandong Province, China. Compared with the traditional model-based localization using QGA, which meant that the PNN were not used in advance to narrow the leakage range, the accuracy of the PNN-QGA model was improved by 12.5%, and the average calculation time was ten times faster. Therefore, this method can formulate a leak location plan reasonably and efficiently.

The operational condition of fire water supply aims to ensure the continuous and reliable supply of high-pressure water in emergency situations. Assuming a fire breaks out in a mountain village located far from the city center, due to the significantly higher flow rate and velocity of the water supply pipeline compared to normal operating conditions, any malfunction or shutdown of the pump caused by improper operation could result in catastrophic damage to the pipeline system. In response to the call for sustainable development, addressing this urgent academic challenge means finding a way to safely and economically maintain a continuous water supply to the target water demand point, even under extreme accident conditions. In this paper, drawing on engineering examples, we considered air tanks with varying process parameters installed at multiple locations within a water conveyance system to prevent water hammer and ensure water supply safety. To ensure that air tanks are of high quality and cost-effective after procurement and use, a multi-objective optimization design model comprising fitting, optimization, and evaluation plates was constructed, aimed at selecting certain process parameters. In the multi-objective optimization design model, Latin hypercube sampling improved by simulated annealing (LHS-SA), stepwise regression analysis (SRA), the Multi-Objective Whale Optimization Algorithm (MOWOA), and the Multi-Criteria Decision Analysis (MCDA) method with various weight biases are used to ensure the rationality of the optimization process. By comparing the optimization results obtained using these different MCDA methods, it is evident that the results output after AHP-EWM evaluation tend to be economic indicators, whereas the results output after FN-MABAC evaluation tend to be safety indicators. In addition, according to the sensitivity analysis of weight distribution, it can be inferred that the changes in maximum transient pressure head caused by water hammer have the most significant impact on final decision-making.

Hydrological and hydraulic modelling are crucial for flood forecasting and disaster early warning. While integrating neural networks with hydrological and hydraulic models enhances flood prediction performance, the generalization capability of neural networks tends to degrade under conditions of limited sample availability. Mechanistic models like HEC-RAS exhibit limitations in complex hydrological scenarios or when key hydrological data are missing. In this study, a novel approach that combination of HEC-HMS and HEC-RAS with neural networks is proposed. High-quality hydrological data are generated through HEC-RAS to address the issue of missing observed data. And the simulation data are used to drive a neural network model (LSTM) for flood water level prediction. When the mechanism model has insufficient data or it is difficult to directly solve nonlinear dynamic processes, neural networks can compensate for the limitations of the mechanism model through data-driven modelling using high-quality data generated by simulation. The synergy between above approaches significantly enhances prediction reliability. Experimental results demonstrate that the LSTM model, enhanced by HEC-RAS-generated data, achieves excellent performance, with R2 of 0.950 and a MSE of 0.034 m. This provides a new paradigm of "physical mechanism-data driven"

The companies responsible for water supply networks are making significant efforts to improve leakage detection efficiency. This study proposes a novel leak localization method. First, the pressure-driven background leakage model (PDBLM) is established using a nonlinear genetic algorithm that considers the relationship between background leakage and pressure. Second, a fuzzy c-means clustering algorithm optimized by a genetic simulated annealing algorithm (FCMGSAA) and sparrow search algorithm optimized probabilistic neural networks (SSAPNNs) are chosen for the leak location model. To reduce the output classes, a large water supply network is divided into several areas using an FCMGSAA. The areas are used as learning labels, reducing the output classes of the SSA. Then, the leak location model is used to detect the leak area based on a real case in Qingdao, China. Compared with generalized regression neural networks (GRNNs), support vector machine (SVM), convolutional neural networks (CNNs) and back-propagation neural networks (BPNNs), the accuracy of the proposed method is improved by 70, 43.33, 10 and 6.67%, respectively. The proposed method can improve leakage detection efficiency, reduce water loss and contribute to the effective management of water resources.

The study of pressure change characteristics under failure conditions of pipeline sections, such as pipe burst and leakage loss, is of great significance to the safety of urban water supply network and leakage prevention and control. At present, most of the related technical research is based on the steady-state hydraulic model, which has low accuracy and cannot accurately obtain the transient law of water flow in the pipeline under the failure conditions. To solve this problem, a transient hydraulic model of pipeline failure is established based on the Navier-Stokes equations, the finite volume method and the hydrodynamic control equations, which is solved by the finite element analysis method to realize the pipeline transient pressure simulation and transient characteristic analysis. On this basis, based on the transient pressure data, the pipeline transient pressure simulation and prediction model based on the LSTM neural network is constructed to analyze the pressure response characteristics of the pipeline under the failure conditions, and to dynamically predict the transient pressure of the pipeline at multiple points during the occurrence of bursting, leakage, and so on. Example applications show that the average absolute percentage error of pipeline transient pressure prediction is stabilizes at less than 0.2%, and the constructed pipeline failure transient hydraulic model and transient pressure simulation model have high computational accuracy. The coupled application of the two models can realize the dynamic simulation and prediction of the pressure under the pipeline failure conditions, which can help to provide an important theoretical basis for the further development of scientific and efficient leakage state pattern identification and accurate leakage location through the transient pressure characterization, and provide effective technical support for water supply enterprises in leakage monitoring and control.

For long-distance water transmission projects utilizing cascade pressurized pump stations in mountainous regions, the configuration and optimization of water hammer protection equipment are indispensable. In practical engineering applications, the setup of water hammer protection equipment is not solely determined by the pipeline's ultimate pressure. Hence, a comprehensive evaluation of safety and cost factors should be conducted, while ensuring that the pipeline's carrying capacity is not exceeded. This paper opts for sealed airbag vessels as the water hammer protection equipment, aiming to optimize water transmission safety and equipment procurement costs. Utilizing the Bentley Hammer v10.08 and MATLAB R2023 software platforms, and incorporating the Latin Hypercube Sampling (LHS) method decomposed by Cholesky, stepwise regression analysis, Multiobjective Particle Swarm Optimization (MOPSO) algorithm, and fuzzy membership normalization evaluation method, a conceptual analysis module is developed. This module simulates the sampling of the optimization variables within the constraints to obtain a high-quality set of expected samples. A high-precision objective function formulation was derived by fitting the data with higher dispersion. A comprehensive evaluation of several Pareto optimal solutions was performed to identify a high-reliability water hammer protection scheme for the entire pipeline. It indicates that the optimal selection of water hammer protection equipment reduces transient positive pressure head and transient negative pressure head by 37.66 % and 63.74 % respectively, compared to the system without protection equipment. And compared to the equipment purchase cost before optimization, it can save 28.37%. This research will provide theoretical guidance for rational selection of sealed airbag air vessels.

This paper presents a Simulink-based dynamic model of an eight-axle distributed-drive heavy-duty vehicle to analyze coupled roll-yaw instability under complex scenarios. A criterion framework is proposed to identify rollover and sideslip risks. Through simulations involving crosswinds, split-friction roads, and sharp turns, a speed-steering angle safety boundary is extracted. Results show that the boundary decreases nonlinearly with increased steering input and load. The proposed model effectively captures critical instability and provides quantitative insight for path planning and active safety control.

Objective:This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics. Methods:We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations. Among the 232 patients, 89 were identified as MVI-positive and 143 as MVI-negative. Regarding tumor differentiation, 56 patients were well-differentiated, 145 moderately, and 31 poorly. Multivariate logistic regression analysis was employed to establish a prediction model for variables showing significant differences. Additionally, the diagnostic performance of various indicators were evaluated using ROC analysis. Results:Among the qualitative data, significant differences (P<0.05) were observed between the MVI-positive and MVI-negative groups in 5 items such as peritumoral enhancement. In terms of quantitative data, the MVI-positive group exhibited higher maximum tumor length, AST, ALT, AFP levels and the ALBI score (P<0.05). Conversely, CT values in the arterial phase (AP), portal venous phase (PVP), and PT levels were lower in the MVI-positive group (P<0.05). Multivariate Logistic regression analysis identified ALBI score, PT level, CT value in PVP, and tumor capsule as independent risk factors for MVI occurrence (AUC: 0.71, 0.58, 0.66, and 0.60). The combined diagnostic AUC value was 0.82 (95% CI: 0.76-0.87). Significant differences were found among different differentiation grade groups in 10 items such as non-smooth tumor margin (P<0.05). Conclusion:Preoperative dynamic contrast-enhanced CT examination in patients with HCC can be utilized to predict the presence of MVI. When combined with clinical characteristics, these imaging signs demonstrate good predictive performance for MVI status. Furthermore, this approach has significant implications for determining the differentiation grade of tumors.

Non-potable water accounts for a large part of the total demand. The recycling of buildings greywater as an alternative water source is an effective way to save water resources. In this paper, a novel process combining vacuum ultraviolet (VUV), granular active carbon (GAC) and ultrafiltration (UF) was studied to removal of hardto-biodegrade organic pollutants from greywater. When VUV is placed in front of GAC, the performance of pollutant removal and membrane fouling control is better. Compared with UF, the removal efficiencies of UV254, linear anionic surfactant (LAS), dissolved organic carbon (DOC), chemical oxygen demand with potassium dichromate (CODCr) and NH4+-N by VUV/GAC/UF were increased by 23.30 %, 24.84 %, 39.77 %, 34.15 % and 31.97 %, respectively. There was high concentration of proteins in greywater, mainly tryptophan-like proteins and microbial derived proteins, which contributed the most to membrane fouling. Compared with UF, the VUV/ GAC/UF coupling removal efficiencies of above proteins reached 99.39 % and 95.55 %, respectively. The coupling process of VUV and GAC can effectively improve the performance of UF membrane. After three cycles of VUV/GAC/UF filtration, the membrane specific flux is 0.88, which is similar to the initial membrane specific flux. The total membrane fouling resistance, reversible membrane fouling resistance and irreversible membrane fouling resistance decreased by 69.35 %, 93.72 % and 55.70 %, respectively. In summary, the above research provides theoretical support for the prospects of the application of greywater reuse technology in buildings.

The electric wheel has an advantage of independently, accurately, and promptly controlling torque in response. However, current distributed drive steering control strategies fail to fully leverage this capability. To fill this gap, this paper proposes a composite electronic moment power steering (EMPS) control strategy for multi-axle distributed drive vehicles, based on dynamic modeling and analysis. The proposed control strategy integrates EMPS with direct yaw moment control (DYC), enhancing steering flexibility and fault tolerance in the steering system at low speeds, while also ensuring vehicle stability at high velocities. It adopts a hierarchical control architecture, wherein the upper controller utilizes a nonlinear state observer for the joint estimation of multi-objective parameters, and the lower controller is responsible for the accurate tracking of the steering angle by EMPS, the yaw rate tracking by DYC, and the assignment of weights to the composite controller. By establishing and analyzing a detailed vehicle model and an electric drive steering axle dynamics model, a multi-dimensional feasible domain for EMPS is proposed, ensuring the safety and smoothness of steering maneuvers. The co-simulation of MATLAB/Simulink and TruckSim are conducted to verify the effectiveness of the composite control strategy. The EMPS controller is proved to have robust steering angle tracking performance, with the β -β̇ trajectory consistently converging within the stability zone, maintaining a sufficient margin for tire longitudinal force under the composite steering control. Additionally, real vehicle testing confirms the effectiveness of EMPS and the redundancy tolerance of the steering functionality in distributed drive multi-axle vehicles equipped with EMPS.

Purpose To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM). Methods Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression. Results Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001). Conclusion The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.

In-wheel motors (IWMs) are considered ideal drivetrains for electric vehicles (EVs), but their applications remain preliminary. In particular, the torque density of IWMs cannot meet the performance requirements of all vehicle types. This review reports the evolutionary progress of IWMs toward torque density improvement and discusses four critical technologies together for the first time: deceleration mode, electromagnetic topology, heat dissipation, and in-wheel structure. The direct drive, outer rotor, and water cooling IWMs are well-suited to most passenger vehicles. Furthermore, the adaptability of IWMs to vehicle types is analyzed. Medium and large passenger and sport utility vehicles have limited installation space for the reducer and largely depend on IWMs’ torque. When the torque weight density of an IWM with structural components improves, IWMs will be adopted widely. Further evolution of IWMs will involve employing novel materials, refined design optimization, and seamless structural integration. Novel materials will enhance the torque output capability and transcend existing limitations. The intelligent design optimization balances torque and efficiency, achieving the required energy conversion quality. The degree of structural integration determines the weight and reliability of the entire IWM and its auxiliary parts.

The Electromechanical Brake System (EMB) is a type of brake-by-wire system composed of a motor, reduction mechanism, and motion switching mechanism. EMB plays a significant role in promoting high-frequency wire-controlled chassis technology and high-level intelligent driving technology, and has received extensive attention from many domestic and foreign enterprises and research institutes. The braking response speed of existing EMBs is not as pronounced compared with that of the Electrohydraulic Brake System (EHB). Improving the braking response speed of EMBs is crucial for expanding their applications. This paper takes the EMB driven by a high-voltage motor as the research object, and focuses on improving the braking response speed of EMB. This paper completes the research work of EMB design, EMB control algorithm design, EMB simulation platform construction, EMB experimental platform construction and test verification. Simulation and test results show that the high-voltage motor-driven EMB has the ability to eliminate braking clearance within 20 ms and provide maximum braking force within 50 ms, significantly improving the braking response speed.

The independently controlled electric wheels of distributed drive vehicles provide faster and more accurate actuators for vehicle slip ratio control. Meanwhile, the estimation of the slip ratio of electric wheels has been of vital importance for the dynamics control of distributed drive electric vehicles. However, the conventional slip ratio estimation method is hard to accurately estimate the slip ratio under steering conditions without multiple observations, increasing the cost and introducing errors. Considering that the output torque and motor rotation rate of electric wheels can be accurately collected, the novel slip ratio estimation method takes advantage of the signals of the electric wheels and requires fewer vehicle sensors. Based on the torsional vibration model of electric wheel, the slip ratio estimation method was proposed and validated by simulations and experiments. With the drum dynamometer, the slip ratio estimation method was applied to a single electric wheel for testing, proving the feasibility and accuracy of the proposed method. The slip ratio estimation was finally applied to a fuel cell heavy truck for road tests, of which the results show that the error index is reduced from 0.0152 to 0.0064 compared to the conventional slip ratio estimation method, confirming the good estimation performance achievable via the proposed method.

Objective To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. Methods One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). Results Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233–5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247–5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). Conclusion The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. Critical relevance statement A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. Key points • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS. Graphical Abstract

In-wheel motors significantly enhance the dynamics control performance of electric vehicles. This paper investigates pivot steering control applied to commercial trucks with four in-wheel motors. The differential torque and differential speed methods are demonstrated. The suitability of pivot steering based on differential speed control for commercial trucks is assessed. Two differential speed control strategies are designed and compared through simulation: one in which a wheel is locked by the braking system and one in which no wheel is locked. The analysis includes a comprehensive examination of the steering radius and other vehicle states under pivot steering control. The influence of vehicle parameters, including the position of centroid and tire configuration, on the pivot steering effects is explored. The findings contribute some insights into the feasibility and performance optimization of pivot steering in commercial truck applications.

目的 探讨双能量CT的不同定量参数在直肠癌患者术前评估中的诊断价值.方法 回顾性搜集 118例直肠癌患者,术前均行双能量CT扫描.根据病理,分为无淋巴结转移组和有淋巴结转移组、癌结节组和无癌结节组、脉管神经侵犯组和无脉管神经侵犯组.比较不同分组在动静脉期的对比剂含量、碘浓度(IC)、标准化碘浓度(nIC)、电子云密度(Rho)、原子序数(Z)、双能量指数(DEI)、能谱曲线斜率(K)的差异.绘制受试者工作特征(ROC)曲线,比较曲线下面积(AUC),明确诊断效能.结果 定量参数的组间一致性为0.803～0.928.无淋巴结转移组的静脉期nIC明显低于有淋巴结转移组(P<0.001,AUC =0.771).无癌结节组的静脉期Rho高于癌结节组(P =0.037,AUC =0.635).无脉管神经侵犯组的动脉期Rho高于脉管神经侵犯组(P =0.025,AUC =0.648).结论 双能量CT的不同定量参数可用于评估直肠癌患者术前的淋巴结状态、癌结节和脉管神经侵犯情况,为直肠癌患者精准化治疗提供有效依据.

目的 探讨不同ADC值在局部进展期直肠癌(LARC)患者新辅助放化疗(nCRT)后淋巴结转移诊断价值.方法 回顾性纳入65例LARC患者,根据术后病理淋巴结状态,分为有淋巴结转移组和无淋巴结转移组.两名不同年资的影像诊断医师采用容积感兴趣区(ROI)勾画法分别测得nCRT前后ADC的最大值、最小值、均值、差值(最大值与最小值之差),并计算nCRT前后ADC最大值差(治疗后ADC最大值与治疗前ADC最大值之差)、最小值差(治疗后ADC最小值与治疗前ADC最小值之差)、均值差(治疗后ADC均值与治疗前ADC均值之差)以及最大值变化率(ADC最大值差/治疗前ADC最大值)、最小值变化率(ADC最小值差/治疗前ADC最小值)、均值变化率(ADC均值差/治疗前ADC均值).分析上述不同的ADC值在nCRT后有无淋巴结转移中的差异,绘制受试者工作特征曲线(ROC)明确各ADC值预测直肠癌nCRT后淋巴结转移的诊断效能.结果 14个不同ADC参数值的组间一致性较好,组内相关系数(ICC)值为0.408～0.886.nCRT后ADC最大值、ADC最小值、ADC均值、治疗前后ADC最大值差、治疗前后ADC最小值差、nCRT前后ADC均值差、ADC最大值变化率、ADC最小值变化率和ADC均值变化率与nCRT后淋巴结转移有相关性(r=-0.272～-0.434).nCRT后ADC最大值、ADC最小值、ADC均值、治疗前后ADC最大值差、治疗前后ADC最小值差、nCRT前后ADC均值差、ADC最大值变化率、ADC最小值变化率和ADC均值变化率在nCRT后有淋巴结转移组和无淋巴结转移组中差异具有统计学意义(P=0.001～0.03).其中nCRT后ADC均值、ADC均值差在区分有无淋巴结转移的诊断效能最好,曲线下面积(AUC)分别为0.740、0.753.结论 不同ADC值在一定程度上反映直肠癌nCRT后淋巴结状态,测量nCRT后直肠肿瘤ADC均值对nCRT后淋巴结状态进行评估更加方便、准确.

Objective: This study investigates the association of the liver and spleen signal intensity on MRI with anemia in patients with gynecologic cancer. Methods: 332 patients with gynecological cancer and 78 healthy women underwent MRI examination. Liver and spleen MRI parameters and laboratory tests were obtained within 1 week. The signal intensity ratios of liver and spleen to the paraspinal muscle were calculated on gradient-echo T1-weighted images (T1WI) and T2-weighted images (T2WI) in both patients and healthy women, respectively. Results: The ratios of liver and spleen to paraspinal muscle on T1WI and T2WI were lower in patients than in the healthy women, respectively (P<0.0001). The ratios of the liver and spleen to paraspinal muscle on T1WI and T2WI decreased with the increasing stage of anemia and decreasing hemoglobin levels (P<0.001). The ratios of the liver to paraspinal muscle on T1WI, spleen to paraspinal muscle on T1WI, and the liver and spleen to paraspinal muscle on T2WI could predict anemia stage≥1 (AUC=0.576, 0.643, 0.688, and 0.756, respectively), ≥2 (AUC=0.743, 0.714, 0.891, and 0.922, respectively) and 3 (AUC=0.851, 0.822, 0.854, and 0.949, respectively). Conclusion: T2WI-based spleen signal intensity ratios showed the highest potential for non-invasive evaluation of anemia in gynecological cancer.

目的 探讨双能CT的定量参数对局部进展期直肠癌患者新辅助治疗后疗效的评估价值.方法 回顾性纳入21例直肠癌患者,分为完全缓解组(TRG 0)和未完全缓解组(TRG 1-3);有效治疗组(TRG 0-1)和无效治疗组(TRG 2-3).在双能量CT图像上独立采用全肿瘤体积勾画法对双能量CT的不同定量参数进行测定,分析不同的定量参数在上述两组之间的差异,绘制受试者工作特征曲线(ROC)明确各定量参数的诊断效能.结果 治疗前动脉期电子云密度(Rho)、治疗前静脉期Rho、动脉期Rho变化率、静脉期标准化碘浓度(nIC)变化率在治疗有效组和治疗无效组中差异有统计学意义(P<0.05),其AUC值分别为0.685、0.801、0.806、0.898.静脉期双能量指数(DEI)变化率、静脉期Z变化率、治疗前动脉期造影剂含量、治疗前静脉期造影剂含量、治疗后静脉期造影剂含量、治疗前动脉期Rho在新辅助治疗后完全缓解组和非完全缓解组中差异有统计学意义(P<0.05),其AUC值分别为0.727,0.713,0.597,0.667,0.769,0.685.结论 双能量CT测得的定量参数值能反应新辅助治疗后的疗效,其中静脉期nIC变化率在评估有效治疗和无效方面的诊断效能最高,而治疗后静脉期造影剂含量值在评估治疗完全缓解方面诊断效能最高.