BACKGROUND Mismatch repair deficient/microsatellite instability-high(MMR-D/MSI-H)colorectal cancers(CRCs)possess a distinctive genomic profile that results in a spectrum of phenotypic attributes setting them apart fro...BACKGROUND Mismatch repair deficient/microsatellite instability-high(MMR-D/MSI-H)colorectal cancers(CRCs)possess a distinctive genomic profile that results in a spectrum of phenotypic attributes setting them apart from their mismatch repair proficient(MMR-P)or microsatellite stable(MSS)counterparts.CRCs have several prognostic factors,including stage,tumor differentiation,location,lymphovascular and perineural invasion,tumor budding,tumor infiltrating lymphocytes,lymph node yield(LNY),and lymph node ratio(LNR).AIM To determine the unique phenotypic characteristics of MMR-D/MSI-H CRCs and leverage the conventional wisdom of LNY and LNR with the distinctive characteristics of MMR-D/MSI-H CRCs.METHODS This retrospective analysis involved 223 stage I-III CRC patients who underwent surgical resection without neoadjuvant treatment.Clinical and histological features were obtained from patient records and by re-examining the hematoxylin and eosin-stained slides.MMR/MSI status was evaluated for all patients using either MMR immunohistochemistry or MSI testing.RESULTS Of the 223 patients in our study,87(39.01%)were MMR-D/MSI-H CRCs while 136(60.99%)were MMR-P/MSS CRCs.The MMR-D/MSI-H CRCs exhibited significant statistical differences compared to the MMR-P/MSS CRCs in several factors,including location,stage,tumor budding,lymphovascular and perineural invasion,lymphocytic response,LNY,LNR,and size of uninvolved lymph nodes.LNY and LNR were significantly higher in MMR-D/MSI-H group compared with the MMR-P/MSS group(P=0.003 and P<0.001,respectively).Also,the interquartile range of the largest uninvolved lymph node was 1 cm(0.8 cm-1.2 cm)in MMR-D/MSI-H CRCs compared to 0.7 cm(0.6 cm-0.97 cm)in MMRP/MSS CRCs.The overall survival for the MMR-P/MSS CRC group was 71%at five years,and the MMR-D/MSIH CRC group was 92%at five years(P<0.001).CONCLUSION MMR-D/MSI-H CRCs possess a unique genomic profile that leads to distinct phenotypic characteristics,including an enhanced immune response.This distinctive profile underscores the substantial prognostic and predictive value of MMR-D/MSI-H status in CRC.展开更多
BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it d...BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.展开更多
随着乳腺疾病筛查的深入开展,乳腺导管原位癌(ductal carcinoma in situ,DCIS)的检出率日益增高。DCIS患者的复发风险存在异质性,但目前的临床治疗缺乏差异性,甚至仍有过度治疗的倾向。临床能否区分不同复发进展风险的患者群体,并对低风...随着乳腺疾病筛查的深入开展,乳腺导管原位癌(ductal carcinoma in situ,DCIS)的检出率日益增高。DCIS患者的复发风险存在异质性,但目前的临床治疗缺乏差异性,甚至仍有过度治疗的倾向。临床能否区分不同复发进展风险的患者群体,并对低风险DCIS选择合理的临床治疗和随访方式成为关注的热点。本文结合临床实践与研究进展,对病理诊断中如何评估患者的复发风险以及相关的形态学指标进行分析,以增进对DCIS生物学特征的认识,探索未来的发展方向。展开更多
目的基于细胞药动学探究斑蝥素与黄芩苷配伍在亚细胞水平的分布规律,阐明其抗肝癌的增效机制。方法以人肝癌HepG2细胞为模型,采用超高效液相色谱-串联质谱(ultra-high performance liquid chromatography-tandem mass spectrometry,UPLC...目的基于细胞药动学探究斑蝥素与黄芩苷配伍在亚细胞水平的分布规律,阐明其抗肝癌的增效机制。方法以人肝癌HepG2细胞为模型,采用超高效液相色谱-串联质谱(ultra-high performance liquid chromatography-tandem mass spectrometry,UPLC-MS/MS)定量分析单药(斑蝥素6μg/mL、黄芩苷30μg/mL)及配伍组(斑蝥素6μg/mL+黄芩苷30μg/mL)给药后12 h内,整体细胞及细胞核、线粒体、内质网和溶酶体各细胞器中药物浓度的动态变化,并应用Phoenix WinNonlin软件非房室模型计算药动学参数。结果在整体细胞水平,配伍使斑蝥素的胞内药时曲线下面积(area under the curve,AUC_(0~t))增加48.9%,清除率降低(P<0.05),但未显著影响黄芩苷的药动学行为。在亚细胞层面,配伍使斑蝥素与黄芩苷在细胞核、溶酶体、线粒体、内质网内的AUC_(0~t)分别增加了93.5%、46.4%、38.3%、52.3%和68.4%、40.0%、41.0%、46.7%(P<0.05、0.01)。此外,配伍后2种药物在线粒体内达峰时间(t_(max))提前,且斑蝥素在内质网中的平均驻留时间(mean residence time,MRT_(0~t))显著延长(P<0.01),表明两者配伍实现了时空协同的药物递送。结论斑蝥素/黄芩苷配伍可通过协同优化药物在细胞核、线粒体、内质网及溶酶体等关键亚细胞结构的分布,进而可能通过诱导DNA损伤、加速线粒体介导细胞凋亡及促进内质网应激等途径,增强抗肝癌效果,为基于细胞器靶向的中药配伍设计提供了理论依据。展开更多
文摘BACKGROUND Mismatch repair deficient/microsatellite instability-high(MMR-D/MSI-H)colorectal cancers(CRCs)possess a distinctive genomic profile that results in a spectrum of phenotypic attributes setting them apart from their mismatch repair proficient(MMR-P)or microsatellite stable(MSS)counterparts.CRCs have several prognostic factors,including stage,tumor differentiation,location,lymphovascular and perineural invasion,tumor budding,tumor infiltrating lymphocytes,lymph node yield(LNY),and lymph node ratio(LNR).AIM To determine the unique phenotypic characteristics of MMR-D/MSI-H CRCs and leverage the conventional wisdom of LNY and LNR with the distinctive characteristics of MMR-D/MSI-H CRCs.METHODS This retrospective analysis involved 223 stage I-III CRC patients who underwent surgical resection without neoadjuvant treatment.Clinical and histological features were obtained from patient records and by re-examining the hematoxylin and eosin-stained slides.MMR/MSI status was evaluated for all patients using either MMR immunohistochemistry or MSI testing.RESULTS Of the 223 patients in our study,87(39.01%)were MMR-D/MSI-H CRCs while 136(60.99%)were MMR-P/MSS CRCs.The MMR-D/MSI-H CRCs exhibited significant statistical differences compared to the MMR-P/MSS CRCs in several factors,including location,stage,tumor budding,lymphovascular and perineural invasion,lymphocytic response,LNY,LNR,and size of uninvolved lymph nodes.LNY and LNR were significantly higher in MMR-D/MSI-H group compared with the MMR-P/MSS group(P=0.003 and P<0.001,respectively).Also,the interquartile range of the largest uninvolved lymph node was 1 cm(0.8 cm-1.2 cm)in MMR-D/MSI-H CRCs compared to 0.7 cm(0.6 cm-0.97 cm)in MMRP/MSS CRCs.The overall survival for the MMR-P/MSS CRC group was 71%at five years,and the MMR-D/MSIH CRC group was 92%at five years(P<0.001).CONCLUSION MMR-D/MSI-H CRCs possess a unique genomic profile that leads to distinct phenotypic characteristics,including an enhanced immune response.This distinctive profile underscores the substantial prognostic and predictive value of MMR-D/MSI-H status in CRC.
文摘BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.
文摘随着乳腺疾病筛查的深入开展,乳腺导管原位癌(ductal carcinoma in situ,DCIS)的检出率日益增高。DCIS患者的复发风险存在异质性,但目前的临床治疗缺乏差异性,甚至仍有过度治疗的倾向。临床能否区分不同复发进展风险的患者群体,并对低风险DCIS选择合理的临床治疗和随访方式成为关注的热点。本文结合临床实践与研究进展,对病理诊断中如何评估患者的复发风险以及相关的形态学指标进行分析,以增进对DCIS生物学特征的认识,探索未来的发展方向。
文摘目的基于细胞药动学探究斑蝥素与黄芩苷配伍在亚细胞水平的分布规律,阐明其抗肝癌的增效机制。方法以人肝癌HepG2细胞为模型,采用超高效液相色谱-串联质谱(ultra-high performance liquid chromatography-tandem mass spectrometry,UPLC-MS/MS)定量分析单药(斑蝥素6μg/mL、黄芩苷30μg/mL)及配伍组(斑蝥素6μg/mL+黄芩苷30μg/mL)给药后12 h内,整体细胞及细胞核、线粒体、内质网和溶酶体各细胞器中药物浓度的动态变化,并应用Phoenix WinNonlin软件非房室模型计算药动学参数。结果在整体细胞水平,配伍使斑蝥素的胞内药时曲线下面积(area under the curve,AUC_(0~t))增加48.9%,清除率降低(P<0.05),但未显著影响黄芩苷的药动学行为。在亚细胞层面,配伍使斑蝥素与黄芩苷在细胞核、溶酶体、线粒体、内质网内的AUC_(0~t)分别增加了93.5%、46.4%、38.3%、52.3%和68.4%、40.0%、41.0%、46.7%(P<0.05、0.01)。此外,配伍后2种药物在线粒体内达峰时间(t_(max))提前,且斑蝥素在内质网中的平均驻留时间(mean residence time,MRT_(0~t))显著延长(P<0.01),表明两者配伍实现了时空协同的药物递送。结论斑蝥素/黄芩苷配伍可通过协同优化药物在细胞核、线粒体、内质网及溶酶体等关键亚细胞结构的分布,进而可能通过诱导DNA损伤、加速线粒体介导细胞凋亡及促进内质网应激等途径,增强抗肝癌效果,为基于细胞器靶向的中药配伍设计提供了理论依据。
