Objective To investigate the clinical application value of MRI Radiomics score(Radscore)combined with clinicopatho-logical features in predicting postoperative progression-free survival(PFS)of patients with endometrial cancer(EC).Methods A total of 127 patients with EC were selected.The radiomic features of the lesions were extracted from T2 WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images.The features were screened by random forest model and Radscore was calcu-lated.Simultaneously,clinical and pathological characteristics of patients were collected and incorporated,and multivariate Cox regression analysis was used to screen the risk factors related to PFS.The MRI Radscore and clinicopathological features were mapped to the nomogram,and the performance of nomogram was evaluated by receiver operating characteristic(ROC)curve and calibration curve.Results Multivariate Cox regression analysis showed that progesterone receptor(PR),human epididymis protein 4(HE4)and MRI Radscore were independent risk factors for predicting PFS in patients with EC(P＜0.05).The area under the curve(AUC)of the predicted PFS at 1,3 and 5 years after surgery were 0.91,0.804 and 0.776,respectively.Calibration curves showed that nomogram had a good fit in predicting PFS in patients with EC 1,3 and 5 years after surgery.Conclusion The nomogram con-structed based on multi-sequence MRI Radscore and clinicopathological features has favorable accuracy and stability in predicting postoperative PFS in individuals diagnosed with EC.
Objective To evaluate the potency of applying an artificial intelligence(AI)based model for classifying vertebral fractures in lumbar X-ray images.Methods Patients who underwent lateral lumbar X-ray and MRI were retrospectively selected.Based on MRI results,the vertebrae were categorized as fresh fractures,old fractures,and normal vertebrae.A ResNet-18 classification model was constructed using delineated region of interest(ROI)on the X-ray images,and the model's performance was evaluated.Results A total of 272 patients(662 vertebrae)were included in this study.The vertebrae were randomly divided into training(n=529)and validation(n=133)sets.The model's performance in discerning normal vertebrae,fresh fractures,and old fractures revealed accuracy of 0.91,0.42,and 0.75,and the sensitivity were 0.91,0.408,and 0.72,while the specificity were 0.796,0.892,and 0.796,respectively.Conclusion The X-ray-based ResNet-18 AI model has significant accuracy for distinguishing old fractures and normal vertebrae;However,the model's accuracy needs further improvement for distinguishing fresh fractures.
目的 分析非小细胞肺癌(NSCLC)瘤体及含瘤周影像组学模型对其 Ki-67 表达状态的预测效能,进而探讨基于最优影像组学模型的联合模型列线图的预测效能.方法 回顾性分析经病理证实为 NSCLC且有Ki-67 表达结果的 301 例患者的临床及影像学资料.利用瘤体及瘤体联合瘤周 5 mm、10 mm 组织 3 种感兴趣区(ROI)提取影像组学特征.使用最大相关最小冗余(MRMR)和最小绝对收缩和选择算子(LASSO)2 种算法进行特征筛选.对 3 组 ROI所筛选特征分别使用逻辑回归(LR)及支持向量机(SVM)分类器构建影像组学模型.最终在 6 组模型中寻找最优影像组学模型并计算影像组学评分.利用二元 logistic 回归模型构建临床-影像组学联合模型列线图,使用曲线下面积(AUC)及校准曲线等评估模型效能.结果 基于瘤体联合LR分类器构建的影像组学模型表现最好[训练集 AUC 0.855,95％置信区间(CI)0.803～0.906;测试集 AUC 0.850,95％CI 0.768～0.932].联合模型在训练集中 AUC 0.905(95％CI 0.862～0.948),在测试集中 AUC 0.864(95％CI 0.789～0.939).结论 6 组模型中,瘤体联合 LR构建的影像组学模型预测 NSCLC Ki-67 表达状态的效能最高.基于联合模型列线图有助于临床鉴别 NSCLC Ki-67 表达状态.
目的 探讨CT直接征象及间接征象对肠穿孔术前诊断的临床价值.方法 回顾性分析经手术证实的 44 例肠穿孔患者临床及CT表现,将CT表现分为直接征象和间接征象.将所有患者以具备直接征象(A组)、间接征象≥3 个(B组)及间接征象＜3 个(C组)作为诊断条件分为 3 组,以术中诊断作为金标准,对各组术前诊断符合率进行对比分析,同时对好转及治愈的患者平均住院时间进行统计.结果 诊断符合率比较:A组 100％;B组 84％;C组 40％.3 组间肠穿孔诊断符合率差异有统计学意义(P＜0.05);而 A组与B组组间比较差异无明显统计学意义(P＞0.05);B组与C组组间比较差异有统计学意义(P＜0.05).平均住院时间比较:A组(13.5±6.21)d;B组(17.2±8.34)d;C组(28.1±22.9)d.A组与B组比较差异无明显统计学意义(P＞0.05);B组与 C组比较差异有统计学意义(P＜0.05).结论 CT直接征象对肠穿孔的诊断价值高,可明显减少患者平均住院时间;当直接征象不显著时,间接征象越多,诊断准确性越高,患者平均住院时间也越少.
目的 探讨高分辨率磁共振成像(HR-MRI)在发生于头面部中高危区域的皮肤基底细胞癌(BCC)的术前诊断中的价值.方法 收集13例头面部中高危区域BCC患者的临床及影像资料,对病变的大小、信号特征、边缘、强化、动态增强时间-信号强度曲线(TIC)类型、深度及累及层次进行观察和评估.结果 病灶平均最大径为(19.4±11.0)mm.T1WI均呈等或低信号,T2WI信号表现多样.9例病灶边界模糊.10例病灶均匀强化,所有病灶均呈等、低强化,TIC表现为I型或 Ⅱ型.病灶平均深度为(7.2±3.2)mm,9例累及皮下脂肪以下深层结构.结论 BCC在HR-MRI表现多样,低强化、低灌注为相对特异表现.HR-MRI可以术前无创评估病变的范围及对周围结构的浸润,对指导治疗和手术决策具有较大价值.
