Cancer phototheranostics involving optical imaging-guided photodynamic therapy(PDT)and photothermal therapy(PTT)is a localized noninvasive approach in treating cancer.Mitochondria-targeted near-infrared(NIR)cyanines are excellent therapeutic photosensitizers of cancer.However,most mitochondria-targeted cyanines exist in the form of hydrophobic structures,which in vivo may cause cyanine aggregation during blood circulation,resulting in poor biocompatibility and limited therapeutic efficacy.Therefore,we developed a trade-off strategy by encapsulating mitochondria-targeted cyanines into liposomal bilayers(CyBI7-LPs),which balanced hydrophilicity that favored blood circulation and hydrophobicity that enhanced mitochondria tumor targeting.Moreover,CyBI7-LPs greatly minimized photobleaching of cyanine as self-generated reactive oxygen species(ROS)could rapidly escape from the liposomal bilayer,affording enhanced PTT/PDT efficacy.Bioorthogonal-mediated targeting strategy was further employed to improve uptake of tumor cells by modifying the liposomal surface to generate CyBI7-LPB.The CyBI7-LPB probe produced a tumor-to-background ratio(TBR)of approximately 6.4 at 24 HPI.Guiding by highly sensitive imaging resulted in excellent anti-tumor therapy outcomes using CyBI7-LPB due to the enhanced photothermal and photodynamic effects.This proposed liposomal nanoplatform exhibited a simple and robust approach as an imaging-guided synergistic anti-tumor therapeutic strategy.
Xianghan ZhangSumei ZhaoZhiqing GaoJialin ZhouYuqiong XiaJie TianChanghong ShiZhongliang Wang
目的使用影像组学方法构建一个影像组学标签分类模型，对肺肿瘤良恶性进行分类预测。方法分析四川大学华西医院80例怖肿瘤患者的CT影像学数据，分割肿瘤区域，提取肿瘤形状、大小、强化程度、纹理和小波变换共485个影像组学特征。利用Lasso算法筛选出与肿瘤良恶性鉴别最密切的组学特征，并使用Logistic回归构建诊断肿瘤良恶性的预测模型。采用受试者工作特征（receiver operating characteristic．ROC）曲线及其曲线下面积（area under curve，AUC）来评估该影像组学标签在训练集和验证集中的效能。结果选取3个影像组学特征构建出影像组学标签，具有很好的预测分类效果。训练集的AUC为0870（95％CI：0760-0978J，灵敏度为0.870，特异度为0.818；验证集的AUC为0.853（95％CI：0.717-0.989），灵敏度为0.882，特异度为0.778。结论随着CT在临床诊断中的广泛使用，真有望成为辅助检测肿瘤良恶性的非侵入手段。
Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.
