IntroductionTo explore the value of enhanced computed tomography (CT) -derived extracellular volume (ECV) combined with systemic immune-inflammation index (SII) in predicting tumor budding (TB) grading of rectal cancer.Materials and MethodsThe clinical and imaging data of 177 rectal cancer patients were retrospectively analyzed, and we divided them into a low-grade and medium-high group according to pathological TB count. ECV and SII values between the two groups were compared. Intra-class correlation coefficient (ICC) was used to detect the consistency of measurements among observers. Binary logistic regression was used to analyze the correlations between variables and TB grading of rectal cancer. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficiency of statistically significant parameters and their combination. Area under the curve (AUC), its 95% confidence interval, and the corresponding Youden index, sensitivity, and specificity were calculated.ResultsAmong the 177 rectal cancer patients, 108 were low-grade and 69 were medium-high grade. ECV values measured by two physicians showed good consistency (ICC = 0.98). ECV value of low-grade (21.76% ± 4.89%) was lower than that of medium-high grade TB group (27.91% ± 4.77%) (P < .001). SII value was lower in low-grade group (492.14 ± 239.56) than in medium-high grade TB group (825.02 ± 529.38). In the multivariate analysis, ECV value [odds ratio (OR): 1.339 (95% CI: 1.194-1.502)] and SII value [OR: 1.004 (95% CI: 1.002-1.005)] were independent risk factors for predicting TB grading. In the training set, AUCs of ECV, SII, and their combination in evaluating TB grading of rectal cancer were 0.838 (95% CI: 0.760-0.905), 0.755 (95% CI: 0.663-0.829), and 0.889 (95% CI: 0.832-0.943), respectively. In the test set, the corresponding AUCs were 0.741 (95% CI: 0.626-0.870), 0.716 (95% CI: 0.554-0.849), and 0.815 (95% CI: 0.711-0.913). Decision curve analysis (DCA) showed that the combination had higher clinical value than using ECV or SII alone.ConclusionThe combination of ECV and SII can non-invasively evaluate TB grading of rectal cancer before surgery, potentially providing a reference for preoperative risk stratification as a decision-support tool.

Introduction Lymphovascular invasion (LVI), an aggressive pathological manifestation of breast cancer, is closely associated with increased risk of distant metastasis and poor prognosis. This study proposes a novel modeling strategy that integrates MRI-derived microvascular atlas parameters with the TwinsSVT deep learning architecture to enable noninvasive prediction of LVI status in breast cancer patients and to explore its biological interpretability.Materials and Methods A total of 436 breast cancer patients from two medical centers, all pathologically confirmed postoperatively, were retrospectively enrolled. All patients underwent high-resolution multi-b-value diffusion-weighted imaging (DWI) prior to surgery. From the MRI data, four types of microvascular simulation parameter maps were reconstructed within tumor regions: apparent diffusion coefficient (ADC), mean flow velocity (v_m), velocity dispersion (v_s), and angiographic branching index (ANB), aiming to characterize intratumoral microcirculation and vascular structural complexity. These functional parametric maps were individually input into separate encoder branches of the TwinsSVT model to extract multi-scale spatial features. A multi-layer Transformer fusion module was then employed to capture structural interactions across modalities, thereby constructing a multi-parametric fusion model. Model performance was evaluated using metrics including area under the curve (AUC) and F1 score.Results Compared with single-parameter models, the multi-parametric fusion model demonstrated significantly improved predictive performance, with AUCs of 0.881 (95% CI: 0.781-0.982) and 0.859 (95% CI: 0.764-0.953) in internal and external validation cohorts, respectively. Grad-CAM visualizations revealed that the model predominantly focused on tumor margins and regions of high vascular density, suggesting a strong correlation between the model's attention and actual pathological structures.Conclusion The deep learning model constructed based on MRI-derived microvascular simulation atlases enables noninvasive preoperative prediction of LVI status in breast cancer patients. By effectively capturing structural information and offering biological interpretability, the model holds promise as a robust imaging-based tool for precision subtyping and clinical decision support.

OBJECTIVE:To analyze the pathophysiological changes in the inner ear in different hearing loss patterns by observing the magnetic resonance imaging (MRI) characteristics of patients with sudden sensorineural hearing loss (SSNHL) and to evaluate the diagnostic value of T1WI/T2WI combined with delayed enhanced intravenous 3D-FLAIR MR technique. METHODS:In this single-center retrospective study, we performed T1WI, T2WI, and delayed enhanced intravenous 3D-FLAIR MR in 215 patients with SSNHL. Two radiologists independently assessed the MR image. Patients diagnosed with SSNHL were classified into 4 groups according to the audiogram of hearing loss: low-frequency group, flat audiogram group, high-frequency group, and profound deafness group. The MRI findings were classified into inner ear hemorrhage, endolymphatic hydrops (EH), labyrinthitis, or hemorrhagic labyrinthitis, and statistical analysis was performed. RESULTS:A total of 215 cases (99 males/116 females; aged 46.69 ± 15.06) were included. Patients with the T1WI and T2WI scan accounted for 158 cases, patients with delayed enhancement accounted for 176 cases, and those with T1WI, T2WI, and delayed enhancements accounted for 118 cases. The abnormal MRI results' rates in the low-frequency group, flat audiogram group, high-frequency group, and profound deafness group were 25.9%, 33.78%, 24.32%, and 97.82%, respectively. The profound deafness group exhibited significantly higher incidences of inner ear hemorrhage (28.26%), labyrinthitis (45.65%), and hemorrhagic labyrinthitis (23.91%) compared to the low-frequency group (0.00%, 6.90%, 0.00%; P < .001), flat audiogram group (2.70%, 13.51%, 1.35%; P < .001), and high-frequency group (5.41%, 13.51%, 0.00%; P < .001). EH were more prevalent in the low-frequency group (18.97%) and flat audiogram group (16.22%) than in the profound deafness group (0.00%; P = .007). CONCLUSIONS:MRI technology can show the pathological change process of the inner ear partially, patients with SSNHL may present with labyrinthitis, hemorrhagic labyrinthitis, EH, hemorrhage of the inner ear, which may be interrelated or mutually transformed, and may be related to the severity and prognosis of the disease.

To develop a nomogram model which combined clinical inflammatory indicators and CT radiomics features to predict progression free survival (PFS) in esophageal squamous cell carcinoma (ESCC) after radical operation. 258 ESCC patients receiving surgical operation treatment were retrospectively collected from July 2017 to March 2019. Clinical data, laboratory results, pathology results, pre-operative CT data, and survival outcomes were analyzed. Using cox proportional hazards regression model to assess the relationship between relevant clinicopathological factors and PFS. C-index and calibration curve were used to evaluate the nomogram model. Survival curves were obtained using the Kaplan-Meier and comparisons were made by using the log-rank test. The inflammatory model, radiomics model and nomogram model all have good predictive efficacy for predicting PFS of ESCC patients in both training and test set. Significant differences were found between the nomogram model and inflammatory model and the radiomics model (DeLong test, Z = 3.869 and 3.195, P < 0.001, P = 0.001). Decision curve analysis (DCA) results revealed the net benefit of nomogram model was better than that of inflammatory model and radiomics model. Kaplan-Meier results showed significant difference in PFS between high-risk and low-risk group in Radscore and nomogram model (P < 0.001), and the high-risk group was prone to postoperative recurrence and poor PFS. The nomogram model developed by combining inflammatory indicators and radiomics features, which is helpful for risk stratification and follow-up work, and improving ESCC patients’ prognosis.

Objective: To explore whether preoperative contrast-enhanced computed tomogrpahy (CT) can predict lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC), and provide a reliable reference for the formulation of clinical individualized treatment plans. Methods: This retrospective study enrolled 228 patients with surgically resected and pathologically confirmed ESCC, including 36 patients with LVI and 192 patients without LVI. All patients underwent contrast-enhanced CT (CECT) scan within 2 weeks before the operation. Tumor size (including tumor length and maximum tumor thickness), tumor-to-normal wall enhancement ratio (TNR), and gross tumor volume (GTV) were obtained. All clinical features and CECT-derived parameters associated with LVI were analyzed by univariate and multivariate analysis. The independent predictors for LVI were identified, and their combination was built by multivariate logistic regression analysis, using the significant variables from the univariate analysis as inputs. Results: Univariate analysis of clinical features and CECT-derived parameters revealed that age, TNR, and clinical N stage (cN stage) were significantly associated with LVI. The multivariable analysis results demonstrated that age (odds ratio [OR]: 5.32, 95% confidence interval [CI]: 2.224-12.743, P<.001), TNR (OR: 5.399, 95% CI: 1.609-18.110, P  =  .006), and cN stage (cN1: OR: 2.874, 95% CI: 1.182-6.989, P  =  .02; cN2: OR: 6.876, 95% CI: 2.222-21.227) were identified to be independent predictors for LVI. The combination of age, TNR, and cN stage achieved a relatively higher area under the curve (AUC) (0.798), accuracy (ACC) (65.4%), sensitivity (SEN) (69.4%), specificity (SPE) (79.7%), positive predictive value (PPV) (77.4%), and negative predictive value (NPV) (71.6%). Conclusions: The combination of clinical features and CECT-derived parameters may be effective in predicting LVI status preoperatively in ESCC.

We report a probable case of Aspergillus basicranial infection diagnosed by pathogenic serological examination presenting atypical initial manifestations, and highlight the importance of serological examination to avoid treatment delay and disease management. An 84-year-old diabetic patient presented with right peripheral nerve palsy, intolerable otalgia, hearing loss, dysphagia, hoarseness, and bucking. The patient was diagnosed a probable Aspergillus skull base osteomyelitis with cranial neuritis and meningitis of central nervous system. Galactomannan test was used in combination with 1-3-β-D-glucan and magnetic resonance imaging to follow-up during the continuous treatment of voriconazole. To date, the patient has remained in clinical remission for over 39 months but the drug cannot be stopped safely.

To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected the clinical, imaging and pathological data of 149 GISTs patients. We randomly assigned the patients in a ratio of 7:3 to a training set (104 cases) and a validation (45 cases) set. We divided the patients into low and high Ki-67 expression group according to postoperative pathology. CT semantic features were analyzed from preoperative enhancement CT images and radiomics features were extracted from venous phase-enhanced images. We used intraclass correlation coefficient, maximal relevance and minimal redundancy and least absolute shrinkage and selection operator method to screen radiomics features and build radiomics label. 6 ML models were used for model construction. Receiver operating characteristic curves were used to evaluate the predictive efficiency of ML models. SHAP analysis was used to explain the contribution of different variables and their risk threshold. AUC of radscores in predicting Ki-67 expression of GIST patients were 0.749 and 0.729 in training and validation set. Among the 6 ML models, SVM exhibited best prediction accuracy. AUC of SVM model in predicting Ki-67 expression of GIST patients were 0.840, 0.767 and 0.832 in training, validation and test set. SHAP analysis showed that radscores and tumor diameter had highly positive contribution to the model. Therefore, the interpretable SVM model can predict Ki-67 expression of GISTs patients individually before surgery, which can provide reliable imaging biomarkers for clinical treatment decisions.

Purpose:Aspergillus basicranial infection is a rare disease, often associated with delayed or unproven diagnosis, the management of which is unclear. Methods:We report a probable case of otogenic skull base aspergillus osteomyelitis,review invasive basicranial osteomyelitis,manage and follow up the patient by galactomannan(GM) test and magnetic resonance imaging(MRI) during the treatment of voriconazole. Results:

Background and purposeTo develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC).Materials and methodsThe clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status.ResultsWe constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses.The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model.ConclusionsThis study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC.

Objective: To construct a simple scoring model for predicting the biological risk of gastrointestinal stromal tumors based on enhanced computed tomography (CT) features. Methods: The clinicopathological and imaging data of 149 patients with primary gastrointestinal stromal tumor were retrospectively analyzed in our hospital. According to the risk classification, the patients were divided into low-risk group and high-risk group. The features of enhanced CT were observed and recorded. Univariate and multivariate logistic regression models were used to determine the predictors of high-risk biological behaviors of gastrointestinal stromal tumor, and then a simple scoring model was constructed according to the regression coefficients of each predictor. The receiver operating characteristic curve was used to evaluate the predictive ability of the model. Results: There was no significant difference between the risk classification of gastrointestinal stromal tumor with gender and age (P = .168, .320), while significant difference was found between the tumor size and location (P < .001). Univariate and multivariate logistic regression analyses showed that tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, and venous phase contrast enhancement rate were independent predictors of the biological risk of gastrointestinal stromal tumor (P < .05). The area under the curve value of tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, and venous phase contrast enhancement rate as the high-risk predictor of gastrointestinal stromal tumor were 0.955, 0.729, 0.680, and 0.807, respectively. Receiver operating characteristic curve results showed that the area under the curve of the scoring model constructed based on enhanced CT features was 0.941 (95% confidence interval: 0.891-0.973). When the total score was >1, the sensitivity of the scoring model in diagnosing gastrointestinal stromal tumor was 85.58%, the specificity was 88.89%, the positive predictive value was 88.51%, the negative predictive value was 86.04%, and the accuracy was 86.18%. The results of DeLong test showed that the area under the curve of the scoring model was better than that of the receiver operating characteristic curve of tumor size, enlarged vessels feeding or draining the mass, peritumoral lymph node enlargement, venous phase contrast enhancement rate, and other indicators alone in predicting the high risk of gastrointestinal stromal tumor, and the differences were statistically significant (Z = 26.510, P < .001; Z = 3.992, P < .001; Z = 6.353, P < .001; Z = 4.052, P = .013). Conclusion: The simple scoring model based on enhanced CT features is a simple and practical clinical prediction model, which is helpful to make preoperative individualized treatment plan and improve the prognosis of gastrointestinal stromal tumor patients.

目的 通过食管癌增强CT图像特征建立简易评分模型,预测食管鳞状细胞癌(ESCC)P53阳性表达的风险,为临床治疗方案的选择提供重要依据.方法 搜集南京医科大学附属淮安第一医院2017年12月至2020年12月间228例ESCC患者.根据术后免疫组织化学病理结果分为P53阴性组(n=78)和阳性组(n=150).评估术前增强CT图像,记录食管癌患者年龄、性别、肿瘤位置、大小(长度、厚度)、CT强化程度.采用多元Logistic回归筛选独立预测因子并建立评分模型,采用受试者工作特征(ROC)曲线评估评分模型及独立预测P53阳性表达的效能.应用Hosmer-Lemeshow检验和校准曲线评估模型拟合度.结果 多元Logistic回归结果显示肿瘤长度、CT强化程度是P53阳性表达的独立预测因子(P＜0.05),其诊断P53阳性表达的曲线下面积(AUC)分别为0.822、0.636.基于以上2个CT特征建立评分模型,评分模型诊断P53阳性表达的AUC为0.835(95％CI:0.779～0.892),当以评分1分为阈值时,评分模型诊断P53阳性表达的灵敏度为85.3％,特异度为78.2％、阳性预测值为79.6％、阴性预测值为76％、准确率为71.2％.拟合优度Hosmer-Lemeshow检验结果显示P=0.305,校准曲线提示评分模型预测P53阳性表达的风险与实际风险之间具有良好的一致性.结论 基于增强CT图像特征所建立的简易评分模型可准确、客观地预测ESCC组织P53阳性表达的风险,有助于治疗方案的选择及改善患者的预后.

目的 采用静息态功能磁共振成像探讨轻度经前综合症(PMS)患者海马及其亚区功能连接(FC)的模式与激素的相关性.方法 选取20例轻度PMS患者在卵泡期及黄体晚期分别行静息态脑功能磁共振检查,选取双侧海马及其亚区为感兴趣区,采用配对t检验比较卵泡期和黄体期脑功能连接差异,分析差异脑区与神经精神学量表及激素水平的相关性.结果 与卵泡期比,黄体晚期PMS患者表现为左海马头部与右侧中央前回、右侧额叶、右侧海马尾部与左枕叶功能连接增强(GRF校正,P＜0.05);左侧海马尾部与左侧扣带回、前额叶、右侧海马及海马体部与双侧额上回、额内上回功能连接减低(GRF校正,P＜0.05).双侧海马尾部功能连接差异脑区与激素水平呈负相关(左侧r=-0.507,P=0.023;右侧r=-0.487,P=0.030).结论 轻度PMS患者黄体晚期较卵泡期存在功能连接的改变,激素可能参与这一过程的调节.

目的 探讨磁共振扩散加权成像(DWI)表观扩散系数(ADC)值与食管癌病理预后因素的相关性.方法 回顾性分析本院70例食管癌患者的影像及病理资料,术前行常规MRI及DWI检查,测量肿瘤ADC值(包括平均ADC值和最小ADC值),分别探讨食管癌病灶ADC值与病理分化程度、淋巴结不同转移状态、表皮生长因子受体(EGFR)及Ki-67不同表达水平的相关性.结果 食管癌不同分化程度(高、中、低)间的平均ADC值及最小ADC值差异均有统计学意义(P＜0.05),病理分化程度与食管癌病灶平均ADC值及最小ADC值均呈显著正相关(r=0.870,P＜0.001;r=0.825,P＜0.001);有、无淋巴结转移组间肿瘤平均ADC值及最小ADC值差异均无统计学意义(P＞0.05),淋巴结转移状态与病灶平均ADC值及最小ADC值均无明显相关性(r=-0.029,P=0.811;r=0.019,P=0.875);食管癌病灶平均ADC值及最小ADC值在EGFR、Ki-67表达中差异均有统计学意义(P＜0.001),平均ADC值、最小ADC值与肿瘤组织EGFR、Ki-67的表达程度均呈负相关性[(r=-0.460,P＜0.001;r=-0.468,P＜0.001);(r=-0.768,P＜0.001;r=-0.706,P＜0.001)]. 结论 食管癌平均ADC值及最小ADC值与病理分化程度及肿瘤组织EGFR、Ki-67的表达程度有一定相关性,ADC值可以作为食管癌临床治疗方案的选择及预后评估的无创性客观量化指标.

PURPOSES:To investigate the relationship between apparent diffusion coefficient (ADC) value and p53 and ki-67 expression in esophageal squamous cell carcinoma (ESCC) patients. MATERIALS AND METHODS:Clinical, pathologic and MRI findings of 55 ESCC patients were retrospectively analyzed. Immunohistochemical assay was used to determine the expression level of p53 and ki-67 in esophageal carcinoma tissues. The correlations between the ADC value (including ADCmax, ADCmean and ADCmin) and p53 and ki-67 expression level were explored. RESULTS:Significant differences of the ADCmean values were found between positive and negative expression of p53 and between high and low expression of ki-67 in 55 patients of ESCC (P = 0.008, P = 0.036). Receiver operation characteristic (ROC) curve analysis showed that the cutoff value of ADCmean value with positive expression of p53 was 1.475 × 10-3 mm2/s, the area under the curve (AUC) was 0.775, and the sensitivity and specificity were 80.0%, 70.0%, respectively. While the cutoff value for the ADCmean value with high expression of ki-67 was 1.590 × 10-3 mm2/s, the AUC was 0.713, and the sensitivity and specificity were 66.7%, 76.5%, respectively. The ADCmean values were significantly negatively correlated with the expression level of p53 and ki-67 (r = -0.403, P = 0.008; r = -0.329, P = 0.036). CONCLUSION:The ADCmean values of ESCC were related with the expression level of p53 and ki-67 in tumor tissue, which may be served as a non-invasive biological indicator to predict the proliferation of ESCC cells and judge the prognosis of patients.

目的:探讨双能量CT及DWI在食管癌病理分级中的应用价值.方法:搜集60例食管癌患者行CT及MRI检查,分别测量CT动、静脉期病灶的NIC、CT强化程度及ADC值,比较食管癌不同分化程度间NIC值、CT强化程度及ADC的差异;对不同分化程度NIC值、ADC值行ROC曲线分析.结果:不同分化程度食管癌NIC值及CT强化程度不同,各组间动静脉期NIC及静脉期CT强化程度差异均有统计学意义(P<0.05),而动脉期CT强化程度差异无统计学意义(P>0.05);动、静脉期NIC值及ADC值诊断中高分化食管癌与低分化食管癌ROC曲线下面积分别为0.801、0.817、0.816.结论:双源CT碘浓度及DWI成像ADC值能反映出食管癌的病理分级,可为临床分期、治疗及预后评价提供更多信息.

Objective: To explore the value of diffusion-weighted imaging for early response detection of locally advanced esophageal squamous cell carcinoma with concurrent chemoradiotherapy. Methods: Fifty-five (42 males, 13 females) patients with locally advanced esophageal cancer who were undergoing chemoradiotherapy were recruited for this study. Diffusion-weighted imaging was performed in all patients before therapy, at the first weekend, the second weekend, and the end of chemoradiotherapy. The rate of change in apparent diffusion coefficient value and the maximum diameter between pretherapy and posttherapy were calculated. Results: Fifty-five patients with locally advanced esophageal squamous cell carcinoma were classified as responders (40 cases) and nonresponders (15 cases). Before chemoradiotherapy, the responders group had a significantly lower apparent diffusion coefficient values than the nonresponders group ( t = −4.815, P = .000). At the 3 time points after chemoradiotherapy (first weekend, second weekend, and the end of chemoradiotherapy), there was no statistically significant difference in apparent diffusion coefficient values between responders and nonresponders ( P > .05). The responders group had a significantly higher rate of change in apparent diffusion coefficient value than the nonresponders group at each time point ( P < .05). At the first weekend of chemoradiotherapy, the rate of change in the maximum diameter was not significantly different in the 2 groups ( t = 0.928, P = .357). There was a negative correlation between the tumor apparent diffusion coefficient value of pretherapy and the reduction ratio of tumor maximum diameter at the end of chemoradiotherapy ( r = −0.592, P = .000). Conclusions: The change rate of apparent diffusion coefficient value by the end of the first week after beginning chemoradiotherapy may be a sensitive indicator to detect the early response to locally advanced esophageal squamous cell carcinoma.

Objective: To investigate the application value of apparent diffusion coefficient value in the pathological type, histologic grade, and presence of lymph node metastases of esophageal carcinoma. Materials and Methods: Eighty-six patients with pathologically confirmed esophageal carcinoma were divided into different groups according to pathological type, histological grade, and lymph node status. All patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging scan, and apparent diffusion coefficient values of tumors were measured. Independent sample t test and 1-way variance were used to compare the difference of apparent diffusion coefficient value in different pathological types, histologic grades, and lymph node status. Correlation between the apparent diffusion coefficient value and the histologic grade was evaluated using Spearman rank correlation test. Receiver operating characteristic curve of apparent diffusion coefficient value was generated to evaluate the differential diagnostic efficiency of poorly and well/moderately differentiated esophageal carcinoma. Results: No significant difference was observed in apparent diffusion coefficient value between esophageal squamous cell carcinoma and adenocarcinoma and in patients between those with and without lymph node metastases ( P > .05). The differences of apparent diffusion coefficient value were statistically significant between different histologic grades of esophageal carcinoma ( P < .05). The apparent diffusion coefficient value was positively correlated with histologic grade ( rs = 0.802). The apparent diffusion coefficient value ≤1.25 × 10−3 mm2/s as the cutoff value for diagnosis of poorly differentiated esophageal carcinoma with the sensitivity of 84.3%, and the specificity was 94.3%. Conclusions: The performance of apparent diffusion coefficient value was contributing to predict the histologic grade of esophageal carcinoma, which might increase lesions characterization before choosing the best therapeutic alternative. However, they do not correlate with pathological type and the presence of lymph node metastases of esophageal carcinoma.

目的:研究肺部影像报告和数据系统(Lung-RADS)分级、CT征象及联合两种方法对孤立性肺结节的定性诊断价值.方法:分析240例孤立性肺结节的CT表现,观察其特征性影像学表现(边界是否光整、有无钙化、胸膜牵拉、毛刺、分叶、支气管截断、支气管充气、空泡、空洞及肿瘤血管征象)并测量大小.按照Lung-RADS分级标准对病灶重新分类,通过对有差异性的各个征象分值累计每个病灶的CT征象计分,对照病理结果,绘制ROC曲线,比较各方法对良恶性孤立性肺结节的诊断价值.结果:Lung-RADS分级诊断孤立性肺结节的敏感度、特异度、符合率分别为54.9％、68.9％、57.5％,ROC曲线下面积(AUC)为0.628(P＜0.05);CT征象分值诊断肺结节的敏感度、特异度、符合率分别为81.5％、91.1％、83.3％,AUC为0.910(P＜0.001);联合两种方法诊断时,诊断孤立性肺结节的敏感度、特异度、符合率分别为97.9％、95.6％、97.5％,AUC为0.988(P＜0.001).结论:Lung-RADS分级、CT征象对鉴别诊断肺结节良恶性均有重要价值,但CT征象评估的诊断效能优于Lung-RADS分级,两者联合应用可为孤立性肺结节的鉴别诊断提供重要依据.

Objective: To determine whether change in apparent diffusion coefficient value could predict early response to chemotherapy in breast cancer liver metastases. Materials and Methods: We retrospectively studied 42 patients (86 lesions) with breast cancer liver metastases who had undergone conventional magnetic resonance imaging and diffusion-weighted imaging (b = 0.700 s/mm2) before and after chemotherapy. Maximum diameter and mean apparent diffusion coefficient value (×10−3 mm2/s) of liver metastases from breast cancer were evaluated. The grouping reference was based on magnetic resonance imaging according to Response Evaluation Criteria in Solid Tumors (RECIST). Analysis of variance and receiver–operating characteristic analyses were performed. Results: Eighty-six metastases were classified as 40 responders and 46 nonresponders. A statistically significant correlation was found between prechemotherapy and postchemotherapy apparent diffusion coefficient values in responders, which were 0.9 ± 0.16 × 10−3 mm2/s, 1.05 ± 0.12 × 10−3 mm2/s, 1.26 ± 0.12 × 10−3 mm2/s, and 1.33 ± 0.87 × 10−3 mm2/s, respectively. No statistically significant difference was found between prechemotherapy and postchemotherapy apparent diffusion coefficient values in nonresponders. Differences were statistically significant between responders and nonresponders at prechemotherapy, 2 weeks after chemotherapy, and 4 weeks after chemotherapy ( P = 0.014, P = .001, and P = .000, respectively). Receiver operating characteristic curves showed that apparent diffusion coefficient values could predict treatment response early at 2 weeks after chemotherapy with 64.5% sensitivity and 91.8% specificity. Conclusion: The change in apparent diffusion coefficient value may be a sensitive indicator to predict early response to chemotherapy in breast cancer liver metastases.