摘要
目的建立预测不同病理进展期乳腺导管原位癌(DCIS)预后的模型, 并评估其效能。方法回顾性分析2014年11月至2020年12月深圳市人民医院、北京大学深圳医院、深圳市罗湖区人民医院接受乳腺X线摄影检查的273例不同进展期乳腺DCIS患者的完整病例资料。患者均为女性, 年龄26~86(49±11)岁。其中110例纳入单纯DCIS+乳腺导管原位癌伴微浸润(DCIS-MI)组, 163例纳入浸润性导管癌伴导管原位癌(IDC-DCIS)组, 对患者临床、影像及病理特征进行分析。影像特征提取采用乳腺Mammo AI融合模型及基于深度学习的自然语言处理(NLP)技术的诊断报告结构化模型。对各组患者按照6∶4比例通过Python软件随机分为训练集和验证集, 以单因素分析和多因素logistic回归分析筛选预测因子, 选择赤池信息量准则值最小者构建预测模型。绘制受试者操作特征曲线评估预测模型效能。结果单纯DCIS+DCIS-MI组以雌激素受体(-)或人表皮生长因子受体2(3+)为预后不良参考标准, 预后不良62例, 预后良好48例;IDC-DCIS组以诺丁汉预后指数为参考标准, 预后不良33例, 预后中等73例, 预后良好57例。单纯DCIS+DCIS-MI组中, DCIS核分级、乳腺X线摄影可疑形态钙化、DCIS病理亚型、伴微浸润共4个预测因子被用于建立模型;IDC-DCIS组中, 神经/脉管侵犯、Ki67水平、DCIS分子亚型、DCIS成分占比、乳腺X线摄影伴随征象共5个预测因子被用于建立模型。训练集中模型预测单纯DCIS+DCIS-MI预后不良的曲线下面积(AUC)为0.92(95%CI 0.84~1.00), 验证集中为0.90(95%CI 0.82~0.99);训练集中模型预测IDC-DCIS预后不良的AUC为0.84(95%CI 0.76~0.93), 验证集为0.78(95%CI 0.64~0.91)。结论基于深度学习联合NLP所建立的模型能有效预测不同病理进展期DCIS预后状态, 有利于DCIS风险分层, 为临床决策提供参考。
Objective To establish the predictive models for the prognosis of ductal carcinoma in situ(DCIS)at different pathological stages,and to evaluate the predictive performance of the models.Methods Complete data of 273 patients with confirmed DCIS at different pathological stages who underwent mammography examination in Shenzhen People′s Hospital,Peking University Shenzhen Hospital and Shenzhen Luohu People′s Hospital from November 2014 to December 2020 were retrospectively collected,including 110 cases in the DCIS+ductal carcinoma in situ with microinvasion(DCIS-MI)group and 163 cases in the invasive ductal carcinoma(IDC)-DCIS group.The clinical,imaging and pathological features were analyzed.Mammary Mammo AI fusion model and deep learning-based natural language processing(NLP)structured diagnostic report model were used for image feature extraction.Patients in each group were randomly divided into training set and validation set with a ratio of 6∶4,and the predictors were screened by univariate and multivariate logistic regression analysis.The lowest Akaike information criterion value of each group was selected to construct the final predictive model.The receiver operating characteristic(ROC)curve was drawn to evaluate the performance of each model.Results Taking estrogen receptor(-)or human epidermal growth factor receptor 2(3+)as the poor prognostic reference,there were 62 cases considered with poor prognosis and 48 cases with good prognosis in DCIS+DCIS-MI group;while in the IDC-DCIS group,taking the Nottingham prognostic index as the reference,33 cases were considered with poor prognosis,73 cases with moderate prognosis,and 57 cases with good prognosis.Four predictive factors were screened to construct the DCIS+DCIS-MI-group predictive model,including DCIS nuclear grade,calcification with suspicious morphology in mammography,DCIS pathologic subtype and DCIS with microinvasion.Five predictive factors were screened to construct the IDC-DCIS-group predictive model,including neural or vascular invasion,Ki67 l
作者
李霖
欧阳汝珊
林小慧
李萌
赖小慧
李增艳
成官迅
马捷
Li Lin;Ouyang Rushan;Lin Xiaohui;Li Meng;Lai Xiaohui;Li Zengyan;Cheng Guanxun;Ma Jie(The Second Clinical Medical College,Jinan University,Shenzhen 518020,China;Department of Radiology,Shenzhen People′s Hospital,Shenzhen 518020,China;Department of Radiology,Luohu People′s Hospital,Shenzhen 518000,China;Department of Radiology,Peking University Shenzhen Hospital,Shenzhen 518036,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2022年第11期1215-1222,共8页
Chinese Journal of Radiology
基金
广东省医学科学技术研究基金(A2018493)
国家高性能医疗器械创新中心开放基金(NMED2021MS-01-001)。
