吴洁菡, 宋家伟, 徐畅, 王伟, 杨成文, 刘桂芝, 刘宁波. 基于放疗定位CT的影像组学构建局限期小细胞肺癌预后模型的研究[J]. 中国肿瘤临床, 2023, 50(1): 37-43. DOI: 10.12354/j.issn.1000-8179.2023.20220987
引用本文: 吴洁菡, 宋家伟, 徐畅, 王伟, 杨成文, 刘桂芝, 刘宁波. 基于放疗定位CT的影像组学构建局限期小细胞肺癌预后模型的研究[J]. 中国肿瘤临床, 2023, 50(1): 37-43. DOI: 10.12354/j.issn.1000-8179.2023.20220987
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Jiehan Wu, Jiawei Song, Chang Xu, Wei Wang, Chengwen Yang, Guizhi Liu, Ningbo Liu. Positioning computed tomography-based radiomics for survival prediction in limited-stage small cell lung cancer[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(1): 37-43. DOI: 10.12354/j.issn.1000-8179.2023.20220987
Citation: Jiehan Wu, Jiawei Song, Chang Xu, Wei Wang, Chengwen Yang, Guizhi Liu, Ningbo Liu. Positioning computed tomography-based radiomics for survival prediction in limited-stage small cell lung cancer[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(1): 37-43. DOI: 10.12354/j.issn.1000-8179.2023.20220987
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基于放疗定位CT的影像组学构建局限期小细胞肺癌预后模型的研究

Positioning computed tomography-based radiomics for survival prediction in limited-stage small cell lung cancer

    摘要
  • 摘要:
      目的  采用影像组学方法分析放疗定位CT影像的组学特点，构建预测局限期小细胞肺癌（limit-stage small cell lung cancer，LS-SCLC）患者总生存（overall survival，OS）期、无进展生存（progression-free survival， PFS）期的组学模型，为个体化治疗提供依据。
      方法  回顾性分析天津医科大学肿瘤医院2013年9月至2019年12月193例LS-SCLC患者的放疗定位CT资料，并将患者按照7∶3分为训练组和测试组，勾画患者肿瘤区域（gross tumor volume，GTV）进行特征分析。随访获得的患者预后数据，以t检验和LASSO筛选特征建立随机森林预测模型，以曲线下面积（area under the receiver operating characteristic curve，AUC）对模型进行验证评估。
      结果  患者中位OS为29.77个月，中位PFS为19.03个月。每例患者提取了1 037个影像特征，包含一阶特征、形状特征和纹理特征。分别以OS≤1年或OS≥3年、OS≤1年或OS≥5年、PFS≤6个月或PFS≥24个月作为标准对患者分组，各测试组模型的AUC均值分别为0.73、0.79、0.70。组学特征中original_ngtdm_Strength、wavelet-HHL_ngtdm_Busyness、wavelet-LLH_glcm_ClusterShade和wavelet-LLH_glcm_Correlation等参数具有预测价值。
      结论  基于放疗定位CT的影像组学获得的影像特征模型对LS-SCLC患者预后有一定预测价值，纳入临床因素建立融合模型综合分析可能获得更为理想的结果。

     

    Abstract:
      Objectives  To evaluate the effectiveness of the radiomic features of positioning computed tomography (CT) in prognostication, including overall survival (OS) and progression-free survival (PFS), among patients with limited-stage small cell lung cancer (LS-SCLC) and to improve individualized treatment for them.
      Methods  A total of 193 patients with LS-SCLC, who were admitted to the Tianjin Medical University Cancer Institute & Hospital, were enrolled in this retrospective study, conducted from September 2013 to December 2019. The patients were randomly assigned into the training and testing groups in a ratio of 7:3. The gross tumor volume (GTV) was segmented by experienced radiologists to extract features as regions of interest. The random forest classification was used to further analyze possible prognostic factors, selected via t-test and least absolute shrinkage and selection operator (LASSO). The performance of the models was evaluated considering the area under the receiver operating characteristic curve (AUC).
      Results  The median OS of the whole cohort was 29.77 months, and the median PFS was 19.03 months. A total of 1,037 radiomic features were extracted from the CT location images, including the first-order, shape, and texture features. All patients were selected considering different standards to develop models, including cohorts of OS ≤ 12 months or OS ≥ 36 months, OS ≤ 12 months or OS ≥ 60 months, and PFS ≤ 6 months or PFS ≥ 24 months. The mean AUCs of all models were 0.73, 0.79, and 0.70, respectively. The most important features were original_ngtdm_Strength, wavelet-HHL_ngtdm_Busyness, wavelet-LLH_glcm_ClusterShade, and wavelet-LLH_glcm_Correlation.
      Conclusions  The radiomic model, based on positioning CT, was a viable prognostication tool for LS-SCLC. A combined model that considers clinical factors and radiomic features may help obtain more ideal results.

     

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