基于MR-T2WI的深度学习与影像组学联合临床特征预测宫颈癌淋巴脉管间隙浸润OA北大核心CSTPCD
Predicting lymph-vascular space invasion in cervical cancer based on MR-T2WI with deep learning and radiomic features combined with clinical features
目的 观察基于MR-T2WI的深度迁移学习(deep transfer learning,DTL)特征、影像组学特征及临床特征构建的联合模型(列线图)在术前预测宫颈癌淋巴脉管间隙浸润(lymph vascular space invasion,LVSI)的价值.材料与方法 回顾性分析178例经术后病理证实为宫颈癌的患者病例,其中70例LVSI(+)、108例LVSI(-),按照8∶2划分为训练集[142例,54例LVSI(+)、88例LVSI(-…查看全部>>
Objective:To explore the value of preoperative prediction of cervical cancer lymph-vascular space invasion(LVSI)by combining deep transfer learning features based on MR-T2WI,radiomic features,and clinical characteristics.Materials and Methods:Data of 178 patients with cervical cancer by postoperative pathology,including 70 cases with LVSI(+)and 108 cases with LVSI(-)were retrospectively analyzed.The patients were divided into training set[n=142,including 54 …查看全部>>
林宝金;龙先凤;吴朝霞;梁莉莉;卢子红;甘武田;朱超华
广西壮族自治区人民医院放疗物理技术室,南宁 530021||清华大学,北京 100084广西壮族自治区人民医院放疗物理技术室,南宁 530021清华大学,北京 100084广西壮族自治区人民医院放疗物理技术室,南宁 530021北京大学肿瘤医院放射治疗科,北京 100142广西壮族自治区人民医院放疗物理技术室,南宁 530021广西壮族自治区人民医院放疗物理技术室,南宁 530021
临床医学
宫颈癌淋巴脉管间隙浸润影像组学磁共振成像深度迁移学习
cervical cancerlymph-vascular space invasionradiomicsmagnetic resonance imagingdeep transfer learning
《磁共振成像》 2024 (3)
130-136,7
Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project(No.Z-A20230042)Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project(No.S2022014). 广西壮族自治区卫生健康委科研课题(编号:Z-A20230042)广西医疗卫生适宜技术开发与推广应用项目(编号:S2022014)
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