肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效

doi:10.12122/j.issn.1673-4254.2025.09.10

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (9): 1903-1918.doi: 10.12122/j.issn.1673-4254.2025.09.10

• • 上一篇

肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效

黄启智¹^,²(), 谢戴鹏³, 姚霖彤², 李洽轩⁴, 吴少伟², 周海榆¹^,²()

^1.广东省心血管病研究所，广东广州 510080
^2.广东省人民医院（广东省医学科学院），广东广州 510080
^3.中山大学中山医学院生物化学与分子生物学系，广东广州 510080
^4.浙江大学医学院附属第二医院肺移植科，浙江杭州 310000

收稿日期:2025-04-12 出版日期:2025-09-20 发布日期:2025-09-28
通讯作者: 周海榆 E-mail:hccxxzz@163.com;zhouhaiyu@gdph.org.cn
作者简介:黄启智，硕士，医师，E-mail: hccxxzz@163.com
基金资助:
国家自然科学基金(82472064);广东省国际科技合作计划(2022A0505050048);广东省自然科学基金(2024A1515012369);北京希思科临床肿瘤学研究基金(Y-HS202102-0038)

Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer

Qizhi HUANG¹^,²(), Daipeng XIE³, Lintong YAO², Qiaxuan LI⁴, Shaowei WU², Haiyu ZHOU¹^,²()

^1.Guangdong Provincial Institute of Cardiovascular Diseases, Guangzhou 510080, China
^2.Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
^3.Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
^4.Department of Lung Transplantation, Second Affiliated Hospital of Zhejiang University, Hangzhou 310000, China

Received:2025-04-12 Online:2025-09-20 Published:2025-09-28
Contact: Haiyu ZHOU E-mail:hccxxzz@163.com;zhouhaiyu@gdph.org.cn
Supported by:
National Natural Science Foundation of China(82472064)

摘要/Abstract

摘要：

目的开发基于胸部CT的肿瘤微环境特异性影像组学模型，结合临床信息构建诺莫图用于预测晚期非小细胞肺癌（aNSCLC）免疫检查点抑制剂（ICIs）疗效。方法整合TCGA、GEO和TCIA数据库的转录组与CT影像数据，通过加权基因共表达网络分析（WGCNA）在GEO队列中筛选ICIs治疗相关基因（IRGs），随后在TCGA基于IRGs构建机器学习预后模型并探究高低风险组患者的肿瘤免疫微环境特征；通过 “PyRadiomics” 软件包提取TCIA中lung_3队列的影像组学特征，筛选出与IRGs相关（|r|>0.4）的94个特征。回顾性分析2016年1月~2020年12月在广东省人民医院接受首程ICIs治疗的210例aNSCLC患者，按7∶3的比例分为训练组（n=147）与验证组（n=63），在训练组中通过最小绝对收缩和选择算子筛选影像特征，结合Logistic回归构建临床-影像组学联合模型及诺莫图预测ICIs疗效，采用受试者工作曲线曲线、校准曲线和决策曲线分析评估模型性能。结果 WGCNA筛选出84个与免疫反应激活通路相关的IRGs，基于与IRGs相关的肿瘤微环境特异性影像组学标签联合临床特征的ICIs疗效预测模型在训练组（AUC=0.725，95% CI：0.644~0.807）和验证组（AUC=0.706，95% CI：0.577~0.836）的表现均优于单一模型，且能有效预测aNSCLC患者生存情况。诺莫图的校准曲线与决策曲线分析证实其临床实用价值。结论本研究建立的“基因组-影像组-临床”多维预测体系为aNSCLC的ICIs治疗疗效评估提供了可解释的生物标志物组合及临床决策工具。

关键词: 非小细胞肺癌, 免疫检查点抑制剂, 肿瘤微环境, 机器学习, 影像组学

Abstract:

Objective To construct a nomogram for predicting the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (aNSCLC) by integrating chest CT radiomics signature that reflects the tumor microenvironment (TME) and clinical parameters of the patients. Methods Transcriptomic and CT imaging data from TCGA, GEO and TCIA databases were integrated for weighted gene co-expression network analysis (WGCNA) of the GEO cohort to identify the immunotherapy-related genes (IRGs) associated with ICIs response. A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups. Radiomics features were extracted from TCIA lung_3 cohort using PyRadiomics, and 94 features showing strong association with IRGs (|r|>0.4) were selected. A retrospective cohort consisting of 210 aNSCLC patients receiving first-line ICIs at Guangdong Provincial People's Hospital was analyzed and divided into training (n=147) and validation (n=63) groups. Least absolute shrinkage and selection operator was used for radiomic features selection, and logistic regression was applied to construct a combined clinical-radiomic model and nomogram for predicting ICIs therapy response. The performance of the model was evaluated using ROC curve, calibration curve, and decision curve analysis. Results WGCNA identified 84 IRGs enriched in immune activation pathways. The combined model outperformed individual models in both the training (AUC=0.725, 95% CI: 0.644-0.807) and validation cohorts (AUC=0.706, 95% CI: 0.577-0.836). Calibration curve and decision curve analyses confirmed the clinical efficacy of the nomogram for predicting ICIs therapy response in aNSCLC patients. Conclusion The genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC, potentially facilitating personalized immunotherapy decision-making.

Key words: non-small cell lung cancer, immune checkpoint inhibitors, tumor microenvironment, machine learning, radiomics

黄启智, 谢戴鹏, 姚霖彤, 李洽轩, 吴少伟, 周海榆. 肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效[J]. 南方医科大学学报, 2025, 45(9): 1903-1918.

Qizhi HUANG, Daipeng XIE, Lintong YAO, Qiaxuan LI, Shaowei WU, Haiyu ZHOU. Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer[J]. Journal of Southern Medical University, 2025, 45(9): 1903-1918.

图/表 14

参考文献 35

[1]	Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024[J]. CA A Cancer J Clinicians, 2024, 74(1): 12-49. doi：10.3322/caac.21820
[2]	Mok TSK, Wu YL, Kudaba I, et al. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial[J]. Lancet, 2019, 393(10183): 1819-30.
[3]	Wu YL, Zhang L, Fan Y, et al. Randomized clinical trial of pembrolizumab vs chemotherapy for previously untreated Chinese patients with PD-L1-positive locally advanced or metastatic non-small-cell lung cancer: KEYNOTE-042 China Study[J]. Int J Cancer, 2021, 148(9): 2313-20. doi：10.1002/ijc.33399
[4]	Gray J, Rodríguez-Abreu D, Powell SF, et al. FP13.02 pembrolizumab + pemetrexed-platinum vs pemetrexed-platinum for metastatic NSCLC: 4-year follow-up from KEYNOTE-189[J]. J Thorac Oncol, 2021, 16(3): S224. doi：10.1016/j.jtho.2021.01.141
[5]	Robinson AG, Vicente D, Tafreshi A, et al. 97O First-line pembrolizumab plus chemotherapy for patients with advanced squamous NSCLC: 3-year follow-up from KEYNOTE-407[J]. J Thorac Oncol, 2021, 16(4): S748-9. doi：10.1016/s1556-0864(21)01939-0
[6]	André T, Shiu KK, Kim TW, et al. Pembrolizumab in microsatellite-instability-high advanced colorectal cancer[J]. N Engl J Med, 2020, 383(23): 2207-18. doi：10.1056/nejmoa2017699
[7]	Samstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types[J]. Nat Genet, 2019, 51(2): 202-6. doi：10.1038/s41588-018-0312-8
[8]	Kim K, Khang D. Past, present, and future of anticancer nanomedicine[J]. Int J Nanomedicine, 2020, 15: 5719-43. doi：10.2147/ijn.s254774
[9]	Freitas-Dias C, Gonçalves F, Martins F, et al. Interaction between NSCLC cells, CD8⁺ T-cells and immune checkpoint inhibitors potentiates coagulation and promotes metabolic remodeling-new cues on CAT-VTE[J]. Cells, 2024, 13(4): 305. doi：10.3390/cells13040305
[10]	He BX, Dong D, She YL, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker[J]. J Immunother Cancer, 2020, 8(2): e000550. doi：10.1136/jitc-2020-000550
[11]	Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study[J]. Lancet Oncol, 2018, 19(9): 1180-91. doi：10.1016/s1470-2045(18)30413-3
[12]	Tomassini S, Falcionelli N, Bruschi G, et al. On-cloud decision-support system for non-small cell lung cancer histology characterization from Thorax computed tomography scans[J]. Comput Med Imaging Graph, 2023, 110: 102310. doi：10.1016/j.compmedimag.2023.102310
[13]	Clark K, Vendt B, Smith K, et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository[J]. J Digit Imag, 2013, 26(6): 1045-57. doi：10.1007/s10278-013-9622-7
[14]	Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer[J]. Science, 2015, 348(6230): 124-8. doi：10.1126/science.aaa1348
[15]	Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments[J]. Bioinformatics, 2012, 28(6): 882-3. doi：10.1093/bioinformatics/bts034
[16]	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9: 559. doi：10.1186/1471-2105-9-559
[17]	Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering[J]. J Stat Softw, 2012, 46(11): i11. doi：10.18637/jss.v046.i11
[18]	Liu HW, Zhang W, Zhang YH, et al. Mime: a flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection[J]. Comput Struct Biotechnol J, 2024, 23: 2798-810. doi：10.1016/j.csbj.2024.06.035
[19]	Wu TZ, Hu EQ, Xu SB, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data[J]. Innovation (Camb), 2021, 2(3): 100141. doi：10.1016/j.xinn.2021.100141
[20]	Hänzelmann S, Castelo R, Guinney J. GSVA gene set variation analysis for microarray and RNA-seq data[J]. BMC Bioinformatics, 2013, 14: 7. doi：10.1186/1471-2105-14-7
[21]	Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data[J]. Nat Commun, 2013, 4: 2612. doi：10.1038/ncomms3612
[22]	Jiang P, Gu SQ, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response[J]. Nat Med, 2018, 24(10): 1550-8. doi：10.1038/s41591-018-0136-1
[23]	Vickers AJ, Cronin AM, Elkin EB, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers[J]. BMC Med Inform Decis Mak, 2008, 8: 53. doi：10.1186/1472-6947-8-53
[24]	Meng XJ, Huang ZQ, Teng FF, et al. Predictive biomarkers in PD-1/PD-L1 checkpoint blockade immunotherapy[J]. Cancer Treat Rev, 2015, 41(10): 868-76. doi：10.1016/j.ctrv.2015.11.001
[25]	Scholler N, Perbost R, Locke FL, et al. Tumor immune contexture is a determinant of anti-CD19 CAR T cell efficacy in large B cell lymphoma[J]. Nat Med, 2022, 28(9): 1872-82. doi：10.1038/s41591-022-01916-x
[26]	Huang YH, Wei LH, Hu YL, et al. Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer[J]. Front Oncol, 2021, 11: 706733. doi：10.3389/fonc.2021.706733
[27]	Wang GZ, Ding FE, Chen KG, et al. CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment[J]. J Transl Med, 2024, 22(1): 683. doi：10.1186/s12967-024-05393-3
[28]	Campesato LF, Barroso-Sousa R, Jimenez L, et al. Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice[J]. Oncotarget, 2015, 6(33): 34221-7. doi：10.18632/oncotarget.5950
[29]	Zhou ZH, Guo WJ, Liu DQ, et al. Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning[J]. Sci Rep, 2023, 13(1): 4469. doi：10.1038/s41598-023-31189-4
[30]	Yang B, Zhou L, Zhong J, et al. Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer[J]. Respir Res, 2021, 22(1): 189. doi：10.1186/s12931-021-01780-2
[31]	Liu YP, Jiao Y, He D, et al. Deriving time-varying cellular motility parameters via wavelet analysis[J]. Phys Biol, 2021, 18(4): 046007. doi：10.1088/1478-3975/abfcad
[32]	McNamara MG, Templeton AJ, Maganti M, et al. Neutrophil/lymphocyte ratio as a prognostic factor in biliary tract cancer[J]. Eur J Cancer, 2014, 50(9): 1581-9. doi：10.1016/j.ejca.2014.02.015
[33]	Viers BR, Boorjian SA, Frank I, et al. Pretreatment neutrophil-to-lymphocyte ratio is associated with advanced pathologic tumor stage and increased cancer-specific mortality among patients with urothelial carcinoma of the bladder undergoing radical cystectomy[J]. Eur Urol, 2014, 66(6): 1157-64. doi：10.1016/j.eururo.2014.02.042
[34]	Valero C, Lee M, Hoen D, et al. Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors[J]. Nat Commun, 2021, 12(1): 729. doi：10.1038/s41467-021-20935-9
[35]	Bryant AK, Sankar K, Strohbehn GW, et al. Prognostic and predictive value of neutrophil-to-lymphocyte ratio with adjuvant immunotherapy in stage III non-small-cell lung cancer[J]. Lung Cancer, 2022, 163: 35-41. doi：10.1016/j.lungcan.2021.11.021

Variable	Total (n=43)	GSE126044 (n=16)	GSE135222 (n=27)	Z/χ²	P
Age [year, Median (Q₁, Q₃)]	64.00 (56.50, 68.00)	64.50 (55.75, 68.25)	62.00 (58.00, 68.00)	0.00	>0.999
PFS [month, Median (Q₁, Q₃)]	2.17 (1.05, 7.08)	2.55 (0.95, 8.87)	1.97 (1.18, 6.32)	-0.26	0.792
Gender [n (%)]				0.01	0.929
Female	7 (16.28)	2 (12.50)	5 (18.52)
Male	36 (83.72)	14 (87.50)	22 (81.48)
Histology [n (%)]				-	<0.001
LUAD	7 (16.28)	7 (43.75)	0 (0.00)
LUSC	9 (20.93)	9 (56.25)	0 (0.00)
N/A	27 (62.79)	0 (0.00)	27 (100.00)
Drug [n(%)]				43.00	<0.001
N/A	27 (62.79)	0 (0.00)	27 (100.00)
Nivolumab	16 (37.21)	16 (100.00)	0 (0.00)
PD-L1 expression [n (%)]				-	<0.001
N/A	29 (67.44)	2 (12.50)	27 (100.00)
No	9 (20.93)	9 (56.25)	0 (0.00)
Yes	5 (11.63)	5 (31.25)	0 (0.00)

Variable	Total (n=43)	GSE126044 (n=16)	GSE135222 (n=27)	Z/χ²	P
Age [year, Median (Q₁, Q₃)]	64.00 (56.50, 68.00)	64.50 (55.75, 68.25)	62.00 (58.00, 68.00)	0.00	>0.999
PFS [month, Median (Q₁, Q₃)]	2.17 (1.05, 7.08)	2.55 (0.95, 8.87)	1.97 (1.18, 6.32)	-0.26	0.792
Gender [n (%)]				0.01	0.929
Female	7 (16.28)	2 (12.50)	5 (18.52)
Male	36 (83.72)	14 (87.50)	22 (81.48)
Histology [n (%)]				-	<0.001
LUAD	7 (16.28)	7 (43.75)	0 (0.00)
LUSC	9 (20.93)	9 (56.25)	0 (0.00)
N/A	27 (62.79)	0 (0.00)	27 (100.00)
Drug [n(%)]				43.00	<0.001
N/A	27 (62.79)	0 (0.00)	27 (100.00)
Nivolumab	16 (37.21)	16 (100.00)	0 (0.00)
PD-L1 expression [n (%)]				-	<0.001
N/A	29 (67.44)	2 (12.50)	27 (100.00)
No	9 (20.93)	9 (56.25)	0 (0.00)
Yes	5 (11.63)	5 (31.25)	0 (0.00)

Variable	Training group (n=698)	Testing group (n=298)	t/χ²	P
Age (year, Mean±SD)	65.34±11.77	64.94±13.48	0.47	0.636
OS (day, Mean±SD)	964.50±963.00	887.70±841.20	1.20	0.232
Gender [n (%)]			1.73	0.188
Female	271 (38.83)	129 (43.29)
Male	427 (61.17)	169 (56.71)
Status [n (%)]			0.59	0.443
Alive	428 (61.32)	175 (58.72)
Dead	270 (38.68)	123 (41.28)
T stage [n (%)]			0.58	0.965
T1	194 (27.79)	88 (29.53)
T2	391 (56.02)	164 (55.03)
T3	83 (11.89)	32 (10.74)
T4	28 (4.01)	13 (4.36)
Tx	2 (0.29)	1 (0.34)
M stage [n (%)]			0.26	0.877
M0	518 (74.21)	222 (74.50)
M1	23 (3.30)	8 (2.68)
Mx	157 (22.49)	68 (22.82)
N stage [n (%)]			-	0.733
N0	456 (65.33)	186 (62.42)
N1	149 (21.35)	73 (24.50)
N2	75 (10.74)	34 (11.41)
N3	6 (0.86)	1 (0.34)
Nx	12 (1.72)	4 (1.34)
Histology [n (%)]			0.04	0.835
LUAD	351 (50.29)	152 (51.01)
LUSC	347 (49.71)	146 (48.99)

Variable	Training group (n=698)	Testing group (n=298)	t/χ²	P
Age (year, Mean±SD)	65.34±11.77	64.94±13.48	0.47	0.636
OS (day, Mean±SD)	964.50±963.00	887.70±841.20	1.20	0.232
Gender [n (%)]			1.73	0.188
Female	271 (38.83)	129 (43.29)
Male	427 (61.17)	169 (56.71)
Status [n (%)]			0.59	0.443
Alive	428 (61.32)	175 (58.72)
Dead	270 (38.68)	123 (41.28)
T stage [n (%)]			0.58	0.965
T1	194 (27.79)	88 (29.53)
T2	391 (56.02)	164 (55.03)
T3	83 (11.89)	32 (10.74)
T4	28 (4.01)	13 (4.36)
Tx	2 (0.29)	1 (0.34)
M stage [n (%)]			0.26	0.877
M0	518 (74.21)	222 (74.50)
M1	23 (3.30)	8 (2.68)
Mx	157 (22.49)	68 (22.82)
N stage [n (%)]			-	0.733
N0	456 (65.33)	186 (62.42)
N1	149 (21.35)	73 (24.50)
N2	75 (10.74)	34 (11.41)
N3	6 (0.86)	1 (0.34)
Nx	12 (1.72)	4 (1.34)
Histology [n (%)]			0.04	0.835
LUAD	351 (50.29)	152 (51.01)
LUSC	347 (49.71)	146 (48.99)

Variables	Training group				Testing group
Variables	R (n=69)	nR (n=78)	t/χ²	P	R (n=28)	nR (n=35)	t/χ²	P
Age (year, Mean±SD)	60.55±10.48	60.46±9.78	0.05	0.958	59.86±9.34	60.34±8.72	-0.21	0.832
OS (day, Mean±SD)	681.16±322.46	318.42±302.09	6.99	<0.001	719.86±348.41	283.91±237.60	5.65	<0.001
PFS (day, Mean±SD)	515.13±60.73	81.87±49.70	9.89	<0.001	623.96±340.28	89.74±111.86	7.97	<0.001
Gender [n (%)]			10.55	0.001			0.56	0.456
Male	65 (94.20)	58 (74.36)			23 (82.14)	26 (74.29)
Female	4 (5.80)	20 (25.64)			5 (17.86)	9 (25.71)
ECOG [n (%)]			0.97	0.324			3.35	0.067
0-1	65 (94.20)	70 (89.74)			27 (100.00)	29 (82.86)
≥2	4 (5.80)	8 (10.26)			0 (0.00)	6 (17.14)
Smoking status [n (%)]			-	0.031			1.83	0.176
Never smoked	25 (36.23)	42 (53.85)			12 (42.86)	21 (60.00)
Current or former smoker	44 (63.77)	35 (44.87)			16 (57.14)	14 (40.00)
Missing	0 (0.00)	1 (1.28)			-	-
Tumor histologic type [n (%)]			0.05	0.825			1.24	0.265
LUAD	52 (75.36)	60 (76.92)			24 (85.71)	26 (74.29)
LUSC	17 (24.64)	18 (23.08)			4 (14.29)	9 (25.71)
Pathological stage [n (%)]			5.85	0.054			-	0.121
III	7 (10.14)	7 (8.97)			4 (14.29)	3 (8.57)
IVA	31 (44.93)	21 (26.92)			13 (46.43)	9 (25.71)
IVB	31 (44.93)	50 (64.10)			11 (39.29)	23 (65.71)
EGFR mutation [n (%)]			1.24	0.265			0.45	0.504
0	64 (92.75)	68 (87.18)			28 (93.33)	28 (84.85)
1	5 (7.25)	10 (12.82)			2 (6.67)	5 (15.15)
PD-L1 [n(%)]			2.68	0.444			-	0.294
<1%	5 (7.25)	7 (8.97)			1 (3.57)	4 (11.43)
1%-49%	13 (18.84)	10 (12.82)			1 (3.57)	4 (11.43)
≥50%	20 (28.99)	17 (21.79)			8 (28.57)	5 (14.29)
NA	31 (44.93)	44 (56.41)			18 (64.29)	22 (62.86)
LOT [n (%)]			7.10	0.008			3.96	0.047
First line	27 (39.13)	15 (19.23)			14 (50.00)	9 (25.71)
Second line or more	42 (60.87)	63 (80.77)			14 (50.00)	26 (74.29)
ICIs [n (%)]			1.50	0.221			1.16	0.282
Anti PD-1	55 (79.71)	68 (87.18)			21 (75.00)	30 (85.71)
Anti PD-L1	14 (20.29)	10 (12.82)			7 (25.00)	5 (14.29)

肿瘤微环境特异性CT影像组学标签预测非小细胞肺癌免疫治疗疗效

Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer

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参考文献 35

相关文章 15

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Feature	Coefficient
Intercept	0.298
wavelet-HLL-firstorder-Mean	0.483
wavelet-HHH-gldm-LargeDependenceHighGrayLevelEmphasis	0.391
wavelet-HHH-glszm-SmallAreaEmphasis	0.292
wavelet-HHL-gldm-LargeDependenceHighGrayLevelEmphasis	0.289
wavelet-HHL-firstorder-Kurtosis	0.936
wavelet-HHL-glszm-SmallAreaLowGrayLevelEmphasis	0.588
wavelet-LLL-ngtdm-Busyness	0.390

Variables	Univariate		Multivariate
Variables	P	OR (95% CI)	P	OR (95% CI)
Age	0.878	1.00 (0.98-1.03)	-	-
Gender	0.003	0.29 (0.13-0.65)	0.137	0.48 (0.18-1.26)
Smoking status	0.009	2.05 (1.19-3.51)	0.346	1.39 (0.70-2.78)
ECOG	0.028	0.35 (0.13-0.89)	0.164	0.41 (0.12-1.43)
EGFR mutation	0.598	0.79 (0.34-1.88)	-	-
Clinical stage	0.007	0.56 (0.37-0.85)	0.128	0.69 (0.43-1.11)
Lines of therapy	0.002	0.67 (0.52-0.86)	0.438	0.89 (0.65-1.20)
NLR pre	0.398	0.98 (0.93-1.03)	-	-
NLR post	<0.001	0.84 (0.76-0.93)	0.003	0.85 (0.77-0.95)

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