Journal of Southern Medical University ›› 2025, Vol. 45 ›› Issue (9): 1903-1918.doi: 10.12122/j.issn.1673-4254.2025.09.10
Qizhi HUANG1,2(
), Daipeng XIE3, Lintong YAO2, Qiaxuan LI4, Shaowei WU2, Haiyu ZHOU1,2()
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: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.
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URL: https://www.j-smu.com/EN/10.12122/j.issn.1673-4254.2025.09.10
Fig.1 Flowchart of the study. We integrated the transcriptomic and CT imaging data from the TCGA, GEO, and TCIA databases and used weighted gene co-expression network analysis (WGCNA) to identify immune checkpoint inhibitors (ICIs)-related genes (IRGs) in the GEO cohort. A machine learning prognostic model was constructed in TCGA, and the tumor immune microenvironment characteristics of the patients in high- and low-risk groups were explored based on these IRGs. We extracted radiomic features from the lung_3 and identified 94 features significantly correlated with IRGs (|r|>0.4). We retrospectively analyzed 210 patients with advanced non-small cell lung cancer (aNSCLC), who were divided into the training group and validation group in a 7:3 ratio. In the training group, we used LASSO to select the imaging features and combined them with logistic regression to construct a clinical-radiomics nomogram for predicting ICIs efficacy. The performance of the model was evaluated using ROC curves, calibration curves, and decision curves.
| 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) |
Tab.1 Clinical information of GEO patients
| 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) |
Fig.2 Principal component analysis (PCA) of mRNA-sequencing data before (A) and after (B) batch effect correction.
Fig.4 Kyoto Encyclopedia of Genes and Genomes (A) and Gene Ontology (B) pathway analysis of the IRGs.
| 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) |
Tab.2 Baseline data of the patient cohorts from the TCGA database
| 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) |
Fig.5 Predictive models constructed using 101 kinds of machine learning.
Fig.7 Distribution of TME immune infiltration components. A: Distribution of TME immune cell infiltration components of the training set by ssGSEA. B: Distribution of TME immune cell infiltration components of the validation set by ssGSEA. C: ESTIMATE scores of the training set. D: ESTIMATE scores of the validation set. E: TIDE in the training set. F: TIDE in the validation set (B). *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.
| Variables | Training group | Testing group | ||||||
|---|---|---|---|---|---|---|---|---|
| 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) | ||||
Tab.3 Baseline characteristics of the real-world cohort
| Variables | Training group | Testing group | ||||||
|---|---|---|---|---|---|---|---|---|
| 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) | ||||
Fig.8 An example of tumor segmentation. A: Primary tumor lesion in pre-segmentation state. B: Axial image single-layer regions of interest manually outlined using 3D Slicer software.
| 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 |
Tab.4 List of radiomics features and coefficients
| 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 |
Fig.9 LASSO regression model identifies 7 radiomics features. A: Relationship between L1 norm and model coefficients. B: MSE corresponding to different regularization parameter λ [Log (λ)] during cross-validation.
| Variables | Univariate | Multivariate | ||
|---|---|---|---|---|
| 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) |
Tab.5 Clinical information analyzed by univariate and multivariate logistic regression
| Variables | Univariate | Multivariate | ||
|---|---|---|---|---|
| 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) |
Fig.10 Evaluation of the multimodal model. A,B: ROC curves of the multimodal model in training group (A) and validation group (B). C: Nomogram for predicting the efficacy of ICIs treatment in aNSCLC. D,E: Calibration curves for the Nomogram in training group (D) and validation group (E). F,G: Decision curve analysis for the multimodal model in training group (F) and validation group (G).
Fig.11 Kaplan-Meier progression-free survival curve analysis for the radiomics model in the training group (A) and validation group (B).
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