Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.
Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.
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多尺度病理图像纹理特征是可切除肺腺癌的预后因素：一项多中心回顾性研究
DOI：
10.1186/s12967-022-03777-x
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发表时间：
2022-12-14
影响因子：
7.4
通讯作者：
Liu, Zhenbing
中科院分区：
文献类型：
--
作者：
Wang, Yumeng;Pan, Xipeng;Lin, Huan;Han, Chu;An, Yajun;Qiu, Bingjiang;Feng, Zhengyun;Huang, Xiaomei;Xu, Zeyan;Shi, Zhenwei;Chen, Xin;Li, Bingbing;Yan, Lixu;Lu, Cheng;Li, Zhenhui;Cui, Yanfen;Liu, Zaiyi;Liu, Zhenbing
研究背景肿瘤组织形态学分析对可切除肺腺癌（LUAD）的预后判断具有重要意义。计算机提取的图像纹理特征先前已被证明与结果相关。然而，一个全面的，定量的，和可解释的预测仍然有待developed.MethodsIn这个多中心研究，我们包括患者从四个独立的队列与可切除的LUAD。设计了一种自动化流水线，用于在多种放大倍数下从苏木精和伊红（H&E）染色的全载玻片图像（WSIs）中的肿瘤区域提取纹理特征。根据LASSO方法选择的总生存期（OS）的判别性纹理特征，构建多尺度病理图像纹理特征（MPIS）。在发现集（n = 111）和3个外部验证集（V1，n = 115; V2，n = 116;和V3，n = 246）中，通过单变量和多变量分析评价MPIS对OS的预后价值。我们构建了一个包含临床病理变量和MPIS的考克斯比例风险模型，以评估MPIS是否可以改善预后分层。我们还进行了组织基因组学分析，探讨纹理特征和生物pathways.ResultsA组的8个纹理特征，构建MPIS之间的关联。在多变量分析中，发现集中MPIS越高，OS越差（HR 5.32，95%CI 1.72-16.44;P= 0.0037）和三个外部验证集（V1：HR 2.63，95%CI 1.10- 6.29，P = 0.0292; V2：HR 2.99，95%CI 1.34- 6.66，P = 0.0075; V3：HR 1.93，95%CI 1.15- 3.23，P = 0.0125）。在发现集（C指数，0.837 vs. 0.798）和三个外部验证集（V1：0.704 vs. 0.679; V2：0.728 vs. 0.666; V3：0.696 vs. 0.669）中，与基于临床病理变量的模型相比，整合临床病理变量和MPIS的模型具有更好的OS区分度。此外，所确定的纹理特征与生物途径，如细胞因子活性，细胞骨架的结构成分，和细胞外基质结构constituent.ConclusionsMPIS是一个独立的预后生物标志物，是强大的和可解释的。MPIS与临床病理变量的整合改善了可切除LUAD的预后分层，并可能有助于提高个性化术后护理的质量。
BackgroundTumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed.MethodsIn this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways.ResultsA set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72–16.44;P= 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10–6.29,P= 0.0292; V2: HR 2.99, 95%CI 1.34–6.66,P= 0.0075; V3: HR 1.93, 95%CI 1.15–3.23,P= 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent.ConclusionsMPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.
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影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
空间结构和肿瘤浸润淋巴细胞的排列，以预测早期非小细胞肺癌中复发的可能性。
DOI：
10.1158/1078-0432.ccr-18-2013
发表时间：
2019-03-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Corredor G;Wang X;Zhou Y;Lu C;Fu P;Syrigos K;Rimm DL;Yang M;Romero E;Schalper KA;Velcheti V;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
5.5
作者：
Chen L;Zeng H;Xiang Y;Huang Y;Luo Y;Ma X
通讯作者：
Ma X
影响因子：
19.5
作者：
Selvaraju, Ramprasaath R.;Cogswell, Michael;Batra, Dhruv
通讯作者：
Batra, Dhruv