BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpres...BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.展开更多
Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial ...Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.展开更多
目的了解肿瘤抗原肽的HLA限制性以及其诱导的T细胞能否杀伤肿瘤细胞,探索无关供者来源细胞用于过继性抗肿瘤T细胞治疗。方法使用16个肿瘤抗原肽诱导18名无关供者外周血单个核细胞(PBMC)分化为反应性T细胞,并分析HLA型别;利用NetMHC数据...目的了解肿瘤抗原肽的HLA限制性以及其诱导的T细胞能否杀伤肿瘤细胞,探索无关供者来源细胞用于过继性抗肿瘤T细胞治疗。方法使用16个肿瘤抗原肽诱导18名无关供者外周血单个核细胞(PBMC)分化为反应性T细胞,并分析HLA型别;利用NetMHC数据库预测肽和HLA分子亲和力;选择HLA-A2限制性的肿瘤抗原肽诱导第二组17名无关供者的PBMC进行杀瘤实验,反应性T细胞作为效应细胞,T2细胞及肿瘤抗原肽同源的肿瘤细胞作为靶细胞,测量LDH(乳酸脱氢酶)释放量或者RTCA(实时无标记细胞分析仪)检测效应细胞杀瘤效率,比较HLA-A2+和A2-T细胞杀瘤效率。结果筛出和HLA-A2具有高亲和力的肿瘤抗原肽LM7,可诱导5/11 HLA-A2+为反应性T细胞,其中HLA-A2+纯合子则为3/3,而HLA-A2-者则为2/7。LM7诱导反应性T细胞杀伤肿瘤百分比A2+组明显强于A2-组(60.72±11.28 vs 47.2±4.46,P=0.03)。结论本研究显示NetMHC预测对于纯合子样品更有帮助,肿瘤抗原肽LM7具有HLA-A2限制性,可诱导部分HLAA2+PBMC分化为反应性T细胞,可杀伤肿瘤,应对供者进行HLA筛选并分析其细胞功能,其诱导的反应性T细胞可作为过继性T细胞抗肿瘤治疗的细胞来源。展开更多
基金Supported by Science and Technology Program of Fujian Province,No.2021J01430Joint Funds for the Innovation of Science and Technology of Fujian Province,No.2021Y9229.
文摘BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.
基金supported by the MOE(Ministry of Education of China)Project of Humanities and Social Sciences(23YJAZH169)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T2020017)Henan Foreign Experts Project No.HNGD2023027.
文摘Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.
文摘目的了解肿瘤抗原肽的HLA限制性以及其诱导的T细胞能否杀伤肿瘤细胞,探索无关供者来源细胞用于过继性抗肿瘤T细胞治疗。方法使用16个肿瘤抗原肽诱导18名无关供者外周血单个核细胞(PBMC)分化为反应性T细胞,并分析HLA型别;利用NetMHC数据库预测肽和HLA分子亲和力;选择HLA-A2限制性的肿瘤抗原肽诱导第二组17名无关供者的PBMC进行杀瘤实验,反应性T细胞作为效应细胞,T2细胞及肿瘤抗原肽同源的肿瘤细胞作为靶细胞,测量LDH(乳酸脱氢酶)释放量或者RTCA(实时无标记细胞分析仪)检测效应细胞杀瘤效率,比较HLA-A2+和A2-T细胞杀瘤效率。结果筛出和HLA-A2具有高亲和力的肿瘤抗原肽LM7,可诱导5/11 HLA-A2+为反应性T细胞,其中HLA-A2+纯合子则为3/3,而HLA-A2-者则为2/7。LM7诱导反应性T细胞杀伤肿瘤百分比A2+组明显强于A2-组(60.72±11.28 vs 47.2±4.46,P=0.03)。结论本研究显示NetMHC预测对于纯合子样品更有帮助,肿瘤抗原肽LM7具有HLA-A2限制性,可诱导部分HLAA2+PBMC分化为反应性T细胞,可杀伤肿瘤,应对供者进行HLA筛选并分析其细胞功能,其诱导的反应性T细胞可作为过继性T细胞抗肿瘤治疗的细胞来源。
