基于影像组学的非小细胞肺癌生存预后研究

洪 慧, 刘晓 辉*
东南大学　生物科学与医学工程学院

摘要


非小细胞肺癌（NSCLC）生存风险分层对临床决策至关重要。本研究基于影像组学，构建MLP模型预测NSCLC患者生存风险，并在来自TCIA-NSCLC-Radiomics的422例数据集上验证其有效性。实验结果显示，MLP模型测试集AUC=0.84，优于对比模型，证明了MLP在生存风险预测中的潜力。

关键词


非小细胞肺癌；CT图像；影像组学；生存风险

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参考


[1]赵珂嘉，刘成武 & 刘伦旭.（2024）.《IASLC第九版肺癌TNM分期》解读.中国胸心血管外科临床杂志，31（04），489-497.

[2]胡玉川，张欣，崔光彬.（2017）.影像组学在肺癌中的应用研究进展.放射学实践，32（12），1239-1241.

[3]Wang,S., Wei,Y., Li,Z., Xu,J., &Zhou, Y.(2022).Development and validation of an MRI Radiomics- Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer. Breast Cancer(Dove Medical Press), 14,335-342.

[4]Hosny,A.,Parmar,C.,Coroller,T.P.,etal.(2018).Deep learning for lung cancer prognostication:A retrospective multi-cohort radiomics study. PLOS Medicine, 15(11), e1002711.

[5]Saad,M.B.,Hong,L.,Aminu,M.,etal.(2023).Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.The Lancet Digital Health, 5(7), e404-e420.

[6]Yolchuyeva,S., Ebrahimpour,L., etal.(2024). Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics. Journal of Translational Medicine,22,42.

[7]Yu,L., Tao,G., Zhu,L., etal.(2019).Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.BMC Cancer, 19,464.

[8]Lai,Y.H.,Chen,W.N.,Hsu,T.C.,etal.(2020).Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.Scientific Reports, 10,4679.


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