| 4 | Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma:A step towards precision medicine显示文摘The high rate of early recurrence in hepatocellular carcinoma(HCC)post curative surgical intervention poses a substantial clinical hurdle,impacting patient outcomes and complicating postoperative management.The advent of machine learning provides a unique opportunity to harness vast datasets,identifying subtle patterns and factors that elude conventional prognostic methods.Machine learning models,equipped with the ability to analyse intricate relationships within datasets,have shown promise in predicting outcomes in various medical disciplines.In the context of HCC,the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies.This editorial comments on the study carried out exploring the merits and efficacy of random survival forests(RSF)in identifying significant risk factors for recurrence,stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models(CPH).In doing so,the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine. | Abhimati Ravikulan Kamran Rostami | 2024 | World Journal of Gastroenterology2024，30，5： | 0 |
| 5 | From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring显示文摘In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment. | Mariana Michelle Ramírez-Mejía Nahum Méndez-Sánchez | 2024 | World Journal of Gastroenterology2024，30，7： | 0 |
| 6 | 未分化甲状腺癌预后随机生存森林模型的构建及预测效果分析显示文摘目的探讨未分化甲状腺癌(ATC)预后的影响因素,评估构建的随机生存森林(RSF)模型在ATC预后预测中的应用价值。方法选择2004-2015年美国国立癌症研究所的监测、流行病学和最终结果(SEER)数据库中经组织病理学诊断为ATC的患者707例,采用简单随机法将所有患者分为训练集(495例)和验证集(212例)。采用单因素Cox比例风险模型分析影响训练集患者总生存(OS)的相关因素。采用基于最小赤池信息量准则(AIC)的多因素Cox比例风险模型分析上述变量并进行筛选,基于筛选出的变量构建预测OS的传统Cox模型;采用RSF算法对单因素Cox回归分析中P<0.05的变量进行分析,筛选重要的5个特征,纳入基于最小AIC的多因素Cox比例风险模型,采用筛选出的变量构建预测OS的RSF-Cox模型。采用时间依赖受试者工作特征(tROC)曲线及曲线下面积(AUC)、校正曲线、决策曲线、综合Brier评分(IBS),通过训练集和验证集来评估各模型预测OS的效能。结果单因素Cox回归分析显示,年龄、是否接受化疗、淋巴结转移情况、是否接受放疗、手术方式、肿瘤浸润程度、肿瘤数量、肿瘤长径和诊断时年份这9个变量是ATC预后的影响因素(均P<0.05)。基于最小AIC(4855.8)的多因素Cox回归分析显示,年龄较小(61~70岁比>80岁:HR=0.732,95%CI 0.560~0.957,P=0.023;≤50岁比>80岁:HR=0.561,95%CI 0.362~0.870,P=0.010)、接受化疗(是比否:HR=0.623,95%CI 0.502~0.773,P<0.001)、接受放疗(是比否:HR=0.695,95%CI 0.559~0.866,P=0.001)、接受手术(叶切除比未手术或未知:HR=0.712,95%CI 0.541~0.939,P=0.016;全切或次全切比未手术或未知:HR=0.535,95%CI 0.436~0.701,P<0.001)、肿瘤长径(≤2 cm比>6 cm:HR=0.495,95%CI 0.262~0.938,P=0.031;>2 cm且≤4 cm比>6 cm:HR=0.714,95%CI 0.520~0.980,P=0.037;>4 cm且≤6 cm比>6 cm:HR=0.699,95%CI 0.545~0.897,P=0.005)是ATC患者OS的独立保护因素;淋巴结转移(N1未知比N0:HR=1.664,95%CI 1.158~2.390,P=0.006;N1b比N0:HR=1.312,95%CI 1.029~1.673,P=0.028)、更具侵袭性的肿瘤浸润程度(组别3比组别1:HR=1.492,95%CI 1.062~2.096,P=0.021;组别4比组别1:HR=1.636,95%CI 1.194~2.241,P=0.002)是ATC患者OS的独立危险因素,诊断时年份(2010-2015年比2004-2009年:HR=1.166,95%CI 0.962~1.413,P=0.118)虽无统计学意义,但将其纳入可提高传统Cox模型的效能,故也将其纳入传统Cox模型。采用RFS算法,筛选出手术方式、肿瘤长径、年龄分组、是否接受化疗和肿瘤数量5个变量,基于最小AIC(4884.6)的多因素Cox回归分析显示,接受化疗(是比否:HR=0.574,95%CI 0.476~0.693,P<0.001)、手术方式(叶切除比未手术或未知:HR=0.730,95%CI 0.567~0.940,P=0.015;全切或次全切比未手术或未知:HR=0.527,95%CI 0.423~0.658,P<0.001)、肿瘤长径(≤2 cm比>6 cm:HR=0.428,95%CI 0.231~0.793,P=0.007;>2 cm且≤4 cm比>6 cm:HR=0.701,95%CI 0.513~0.958,P=0.026;>4 cm且≤6 cm比>6 cm:HR=0.681,95%CI 0.536~0.866,P=0.002)是ATC患者OS的独立影响因素,基于这3个变量构建RSF-Cox模型。tROC曲线分析显示,在训练集中RSF-Cox模型预测6、12、18个月OS率的AUC分别为93.56、92.62、90.80,在验证集中分别为93.05、92.47、90.20;在训练集中传统Cox模型分别为89.00、87.76、85.24,在验证集中分别为86.22、83.68、82.86。预测6、12、18个月OS率时,RSF-Cox模型校准曲线较传统Cox模型更接近45°,RSF-Cox模型决策曲线的临床净获益均高于传统Cox模型。RSF-Cox模型IBS(0.089)低于传统Cox模型(0.111)。结论基于接受化疗、手术方式、肿瘤长径构建的RSF模型可以有效预测ATC患者的OS。 | 乔菲菲 侯庆 吴雨雷 | 2023 | 肿瘤研究与临床2023，35，8： | 0 |
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