| 摘要: |
| 目的 使用术前超声影像量化特征建立粗梁团块型肝癌区分模型。方法 回顾性收集我院2017年8月1日至2020年10月1日经手术切除治疗的肝细胞癌患者。经过临床信息可用性筛选及病理切片再阅后产生研究队列并按70%：30%比例产生训练集及验证集。提取训练集术前超声影像量化特征并进行χ2值排序法筛选。使用随机森林法训练粗梁团块型肝癌区分模型后在验证集上评估建模性能。结果 共纳入79例粗梁团块型及其他类型肝细胞癌。术前AFP水平、Edmondson-Steiner分化分级、卫星灶情况、微血管侵犯情况在粗梁团块型及其他类型肝细胞癌中存在统计学差异,年龄、性别、HBV感染情况不具有统计学显著的组间差异。特征筛选算法选择高维纹理特征进行亚型预测,最终随机森林模型在验证集上AUC=0.895、准确度为0.833、精确度为0.833、灵敏度为60%、特异度为89.5%。结论 使用术前超声影像量化特征可建立粗梁团块型肝癌区分模型,具有高特异度并有望与其他模态区分模型互补,改善肝细胞癌患者预后。 |
| 关键词: 肝细胞癌 肿瘤 超声检查 |
| DOI： |
| 投稿时间：2021-12-19修订日期：2022-02-21 |
| 基金项目:重庆市自然科学基金重点项目(cstc2019jcyj-zdxmX0019) |
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| The pre-operative ultra-sound imaging quantitative features based macrotrabecular-massive hepatocellular carcinoma differentiation. |
| gujin,zhangyu,zhouzefen,zhangwenfang |
| () |
| Abstract: |
| Objective To build macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) differentiation model based on the quantitative pre-operative ultrasound features. Methods The hepatocellular carcinoma patients that underwent surgery in our hospital from Augest 1,2017 to Oct 1, 2020 were retrospectively collected. After clinical and pathological sorting, the collected cases were separated into training set (70%) and validation set (30%). The quantitative pre-operative ultrasound features were extracted from training set data and selected by chi-square algorithm. Then, the trained random forest model performance was evaluated on validation set data. Results A total of 79 MTM-HCC and other HCCs were included in this study. The pre-operative AFP level, Edmondson-Steiner grade, satellite lesion and microvascular invasion status distribution were significantly different between MTM-HCC and other HCCs, while the age, sex, HBV infection status was not. The feature selection algorithm used high-dimension texture features in MTM-HCC prediction. The final random forest model achieved AUC=0.895, Accuracy=0.833, Precision=0.833, Sensitivity=60% and Specificity=89.5% on validation set. Conclusions The prognosis-benefit MTM-HCC differentiation model could be built based on the quantitative pre-operative ultrasound features, which has high specificity and was complementary with other differentiation model. |
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