# Integrative Nomogram of Computed Tomography Radiomics, Clinical, and Tumor Immune Features for Analysis of Disease-Free Survival of NSCLC Patients with Surgery

Authors: Dianhui Xiu; Yan Mo; Chaohui Liu; Yu Hu; Yanjing Wang; Yiming Zhao; Tiantian Guo; Kailiang Cheng; Chencui Huang; Lin Liu; Min Cheng
Journal: Journal of Oncology
 (2023)

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## Abstract

To improve prognosis of cancer patients and determine the integrative value for analysis of disease-free survival prediction, a clinic investigation was performed involving with 146 non-small cell lung cancer (NSCLC) patients (83 men and 73 women; mean age: 60.24 years ± 8.637) with a history of surgery. Their computed tomography (CT) radiomics, clinical records, and tumor immune features were firstly obtained and analyzed in this study. Histology and immunohistochemistry were also performed to establish a multimodal nomogram through the fitting model and cross-validation. Finally, Z test and decision curve analysis (DCA) were performed to evaluate and compare the accuracy and difference of each model. In all, seven radiomics features were selected to construct the radiomics score model. The clinicopathological and immunological factors model, including T stage, N stage, microvascular invasion, smoking quantity, family history of cancer, and immunophenotyping. The C-index of the comprehensive nomogram model on the training set and test set was 0.8766 and 0.8426 respectively, which was better than that of the clinicopathological-radiomics model (Z test, P =0.041<0.05), radiomics model and clinicopathological model (Z test, P =0.013<0.05 and P =0.0097<0.05). Integrative nomogram based on computed tomography radiomics, clinical and immunophenotyping can be served as effective imaging biomarker to predict DFS of hepatocellular carcinoma after surgical resection.


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## Body

## 1. Introduction

Lung cancer is the most prevalent malignant tumor. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer, which remains the leading cause of cancer-related death worldwide [1, 2]. Although early-stage lung cancer can be treated by surgery, more than 70% of patients still die from recurrence and metastasis [3]. In recent years, immunotherapy has been successfully applied in clinical trials for the treatment of NSCLC [4]. However, the response rate is only 20% [5]. The pathological tumor-node-metastasis (pTNM) stage is the most important postoperative prognostic factor, but it does not fit all patients. An effective therapy needs to identify the patients’ risk of recurrence, progression, and survival rate. Therefore, it is important to have an individualized assessment of the prognosis for this complex and heterogeneous entity and a validated model that can be applied to each individual.Even though the current dominant pTNM staging, mutational status, genotypic characteristics, tumor metabolism, and immune-related elements for prognostic and predictive potential in different neoplasms related to NSCLC are still decisive. A computed tomography (CT) scan makes it easier to locate tumors than a chest X-ray because it can also show the tumor size and mass, shape, and location in the lung tissue and can find the enlarged lymph nodes that may contain metastatic cancer cells. In addition, radiomics can extract a large amount of information from images of a computed tomography (CT) scan, magnetic resonance imaging (MRI), position emission tomography (PET), and ultrasound using specific and sophisticated algorithms and software, which may further support the artificial diagnosis method in the future.Moreover, studying tumor immune microenvironment (TIME) is rapidly emerging for prognosis and treatment in today’s immunotherapy [6]. Specifically, one of the immune targets is programmed death-ligand PD-L1, and the efficient immune reaction against cancer by tumor infiltrating lymphocytes (TILs) has provided more relevant prognostic by observing PD-L1-mediated tumor immune escape. According to PD-L1 status and the presence or absence of TILs, most known tumors have been classified into the following four categories: adaptive immune resistance with PD-L1 positive and high TILs (type I), immune ignorance with PD-L1 negative and low TILs (type II), intrinsic induction with PD-L1 positive and low TILs (type III), and immune tolerance with PD-L1 negative and high TILs (type IV). It was reported that a high proportion of type I (∼38%) and type II (∼41%) tumors observed in human melanoma had the best prognosis [7]. In addition, CD3+ and CD8+ TILs are positively associated with better prognosis in a large series of studies on NSCLC, but CD8+ is independent of other prognostic variables. Since the immune system decisively triggers the development of NSCLC, our hypothesis is that incorporating these immune parameters into current radiomic models may improve predictive power.Nomogram, a visual statistical prognostic tool that integrates graphical and mathematical representations of clinical prediction models and different types of predictive markers, has become more and more interested in cancer research [8]. This study aimed to develop and validate prognostic models that can intersect and also integrate radiological, clinical, and TIME models for surgically resected NSCLC patients. With this approach, we defined radioimmunoclinical features that could have a significant impact on clinical outcomes. Our preliminary results suggest that the strategies involving TIME, CT imaging, and clinical data analysis may enhance predictive power for lung cancer.


## 2. Patients and Methods

### 2.1. Patient Selection

This retrospective study was approved by the Institutional Ethical Committee of Sino-Japan Union Hospital of Jilin University. A total of 146 cases of NSCLC (83 men and 73 women; mean age, and 60.24 years ± 8.637), who underwent surgical resection at the Unit of Thoracic Surgery of Sino-Japan Union Hospital during January 2010 and December 2015, were enrolled in this study. All patients were diagnosed according to the pTNM staging system from the 8th American Joint Committee on Cancer (AJCC). Inclusion criteria include the following: (1) stage I to stage IIIb; (2) complete clinical and pathological information; (3) preoperative thoracic thin-section CT images (from Picture Archiving and Communication System, PACS workstation); (4) adequate paraffin-embedded blocks of tumor sections for immunohistochemical (IHC) analysis. The exclusion criteria were those patients with (1) autoimmune diseases; (2) pneumonitis not related to the tumor; (3) with immunotherapy before surgery; and (4) metastasized or combined with other tumors. All patients were randomly stratified in a 70 : 30 ratio to form a training group (n = 102) and a validation set (n = 44). The model was trained by the method of 5-foldcross-validation [9], and the model performance was tested based on an independent-validation.


### 2.2. Follow-Up and Prognostic Information

The survival information was acquired through telephone inquiries, medical records, and death certificates. The end point of this study was disease-free survival (DFS), that is, the time from the operation to the date of the first recorded evidence of clinical (local or regional) recurrence or distant metastasis as confirmed by histological evidence, or death by any related causes. The project begun in January 2017, and the deadline date of follow-up was December 2021. The baseline of clinical-pathologic data including age, sex, smoking status, family history, staging (T stage, N stage, and clinical stage), pathological features (vascular, nerve, pleural and bronchial invasion, the residue of bronchial stump, and operation style), and the documented date of these baseline’s CT imaging, were obtained from the medical records (Table1).Table 1
Patient demographics in the study.
CharacteristicAllSurvival groupDeath groupP valueNo. of patients14690 (61.6%)56 (38.4%)Age60.24 ± 8.63759.733 ± 8.75161.054 ± 8.4650.371aSex0.076bMale83 (56.8%)46 (51.1%)37 (66.1%)Female63 (43.2%)44 (48.9%)19 (33.9%)Family history of cancer35 (24.0%)23 (25.6%)12 (21.4%)0.0057bSmoking quantity (cigarette)0.007∗d<1w72 (49.3%)50 (55.6%)22 (39.3%)1w–20w18 (12.3%)9 (10.0%)9 (16.1%)20w–50w48 (32.9%)24 (26.7%)24 (42.9%)>50w8 (5.5%)7 (7.8%)1 (1.8%)Tumor immune contexturePD-L10.418d−106 (72.6%)69 (76.7%)37 (66.1%)+16 (11.0%)9 (10.0%)7 (12.5%)++15 (10.3%)8 (8.9%)7 (12.5%)+++9 (6.2%)4 (4.4%)5 (8.9%)CD80.346d−84 (57.5%)51 (56.7%)33 (58.9%)+43 (29.5%)29 (32.2%)14 (25.0%)++18 (12.3%)9 (10.0%)9 (16.1%)+++1 (0.7%)1 (1.1%)0 (0.0%)CD30.734d−21 (14.4%)15 (16.7%)6 (10.7%)+27 (18.5%)16 (17.8%)11 (19.6%)++67 (45.9%)41 (45.6%)26 (46.4%)+++31 (21.2%)18 (20.0%)13 (23.2%)CD40.440d−58 (39.7%)36 (40.0%)22 (39.3%)+39 (26.7%)21 (23.3%)18 (32.1%)++45 (30.8%)31 (34.4%)14 (25.0%)+++4 (2.7%)2 (2.2%)2 (3.6%)Immunophenotyping0.0315dType I27 (18.5%)14 (15.6%)13 (23.2%)Type II71 (48.6%)44 (48.9%)27 (48.2%)Type III35 (17.9%)25 (27.8%)10 (17.9%)Type IV13 (8.9%)7 (7.8%)6 (10.7%)Histologic structure0.452cSquamous cell carcinoma41 (28.1%)22 (24.4%)19 (33.9%)Adenocarcinoma100 (68.5%)65 (72.2%)35 (62.5%)Other NSCLC5 (3.4%)3 (3.3%)2 (3.6%)T stage0.002∗dT192 (63.0%)67 (74.4%)25 (44.6%)T244 (30.1%)17 (18.9%)27 (48.2%)T35 (3.4%)3 (3.3%)2 (3.6%)T45 (3.4%)3 (3.3%)2 (3.6%)N status0.0094∗dIA68 (46.6%)55 (61.1%)13 (23.2%)IB11 (7.5%)4 (4.4%)7 (12.5%)IIA6 (4.1%)3 (3.3%)3 (5.4%)IIB34 (23.3%)19 (21.1%)15 (26.8%)IIIA25 (17.1%)9 (10.0%)16 (28.6%)IIIB2 (1.4%)0 (0.0%)2 (3.6%)Microvascular invasion37 (25.3%)17 (18.9%)20 (35.7%)0.023∗bPerineural invasion11 (7.5%)5 (5.6%)6 (10.7%)0.409bSegment resection residue5 (3.4%)4 (4.4%)1 (1.8%)0.696cVisceral pleural invasion36 (24.7%)21 (23.3%)15 (26.8%)0.638bBronchial invasion18 (12.3%)9 (10.0%)9 (16.1%)0.278bSurgical method0.738cLunglobectomy112 (76.7%)71 (78.9%)41 (73.2%)Pneumonectomy7 (4.8%)3 (3.3%)4 (7.1%)Pulmonary wedge resection15 (10.3%)9 (10.0%)6 (10.7%)Pulmonary sleeve lobectomyCT featuresSpicule sign102 (69.9%)58 (64.4%)44 (78.6%)0.070bLobulation sign132 (90.4%)82 (91.1%)50 (89.3%)0.716bSpinous protuberant sign26 (17.8%)14 (15.6%)12 (21.4%)0.367bVascular-bronchial convergent sign28 (19.2%)15 (16.7%)13 (23.2%)0.329bPleural indentation sign91 (62.3%)55 (61.1%)36 (64.3%)0.700bPure ground-glass opacity12 (8.2%)12 (13.3%)0 (0%)0.011∗cSolid opacity110 (75.3%)58 (64.4%)52 (92.9%)0.000∗∗bPart-solid ground-glass27 (18.5%)23 (25.6%)4 (7.1%)0.005∗∗cCavitation10 (6.8%)3 (3.3%)7 (12.5%)0.073cVacuole sign23 (15.8%)18 (20.0%)5 (8.9%)0.074bNecrosis1 (0.7%)0 (0.0%)1 (1.8%)0.810cCalcification12 (8.2%)6 (6.7%)6 (10.7%)0.578baIndependent samples t-test, bchi-square test, ccorrected chi-square test, and dMann–Whitney U test.


### 2.3. CT Image Acquisition

All patients were examined using Aquilion ONE 320 slice CT (Toshiba, Japan) and 64-MDCT scanner (GE, USA). The CT scanning parameters included a tube voltage of 100 to 130 kV. Entire lung volume from the apices to the pleural recesses and reconstructed with a slice thickness ranging 0.625 mm at end-inspiration in the craniocaudal direction, All captured images were reconstructed with a sharp high kernel and were displayed with standard lung (width, 1600 HU; level, −600 HU) and standard mediastinal window settings (width, 400 HU; level, 40 HU). At the same time, we collected 12 CT-semantic labels of NSCLC patients, including internal signs (density, necrosis, cavitation, vacuolar sign, cavity sign, and calcification) and marginal signs (spicule sign, lobulation sign, spinous protuberant sign, vascular-bronchial convergent sign, and pleural indentation sign).


### 2.4. Immunohistochemical Analysis

Formalin-fixed paraffin-embedded tissues were prepared from the surgically resected NSCLC specimens. Deparaffinized, antigenically retrieved tissues were studied for immunohistochemistry as described [10]. Consecutive sections were used for staining with selected anti-human antibodies, and secondary anti-rabbit antibodies conjugated with horseradish peroxidase were used. All first antibodies and secondary antibodies were purchased from Wuxi Aorui Dongyuan Biotechnology Co. Ltd. (Hefei, Anhui, China). Tumor types and stages were simultaneously determined by 2–3 senior pathologists’ consensus. Immunohistochemical images were taken with a Leica DM RB E research microscope using a Leica DC 100 digital camera (Leica Microsystems, Heidelberg, Germany). The images were directly transmitted to a computer with a Leica DC Viewer version 3.2 and saved as tiff files without editing. The percentage of positive IHC-stained tumor cells was calculated by pathologist using categories of <25%, 26–50%, 51–75%, and >75%.


### 2.5. Construction and Validation of the Radiomic Nomogram

For clearance, a flowchart of this study is detailed in Figure1. Six key steps were included in our study: region of interest (ROI) segmentation, imaging feature extraction, radiomics score calculation, univariate Cox analysis of risk factors, multivariate Cox regression, and the establishment and evaluation of comprehensive integrative nomogram.Figure 1
The flowchart displaying the selection of patients with NSCLC according to the exclusion criteria. The development and validation of Cox regression and nomogram were all conducted by using the official packages of “glmnet,” “rms,” “survival,” and “survminer” in R language.


#### 2.5.1. ROI Segmentation

The original CT images of all patients (DICOM) were uploaded on the deepwise multimodal research platform (https://www.deepwise.com) for segmentation and imaging feature extraction to sort out the region-of-interest (ROI). Two experienced radiologists with 7 and 8 years of clinical experience in chest CT study independently recorded the segment of ROI. When disagreements were encountered, senior imaging experts (10 years of clinical experience in chest CT study) guided the completion of the segmentation. An open-source Python package was used as a platform to extract 2107 radiomics features from the nonfiltered segmented ROI [11].


#### 2.5.2. Feature Extraction

As the postprocess of CT images demands, a high-pass filter, low-pass wavelet filter, and Laplacian Gaussian filter with different parameters were used to obtain more realistic images [12]. The extracted high-throughput radioman features include three categories: the first-order features describe the pixel situation of the image, the shape features describe the lesion, and the texture features describe the internal or surface texture of the lesion including gray level co-occurrence matrix (GLCM). A detailed description of these features is available online and can be accessed on January 22, 2022, at https://pyradiomics.readthedocs.io/en/latest/features.html. The pyradiomic of Python 3.0 (Python Software Foundation, https://www.python.org/) was used to extract imaging features. A total of 2,107 CT image features were extracted from each ROI, and Z-score standardization [13] was performed to form quantified high-throughput CT image features.


#### 2.5.3. Feature Selection and Radiomics Signature Construction

Considering the redundancy of the features and reducing model overfitting, the most useful predictive features were selected using the Spearman correlation test and the least absolute shrinkage and selection operator (LASSO) Cox regression [14]. Firstly, the LASSO Cox regression model was used to select the features most associated with the survival status of the training cohort before the Spearman correlation test was used to reduce feature redundancy. The LASSO method can shrink the coefficients of variables unrelated to survival to zero, and thus, the features with nonzero coefficient were selected. A radiomics score (Rad-score) [15] was computed for each patient through a linear combination of the selected features weighted by their respective coefficients. A weighted log-rank test (G-rho rank test, rho = 1) was used to test the difference between the high-risk and low-risk groups. Kaplan–Meier survival analysis was applied to assess the association between radiomics signature and survival. The patients were classified into high-risk or low-risk groups according to the Rad-score, whose threshold was identified by using the X-tile. In addition, univariate Cox regression analysis was used for other risk features highly correlated with survival, such as CT semantics, clinicopathologic, and TIME parameters [16, 17]. Finally, we combined the above-selected risk features with the Rad-score and used the backward selection method [18] to incorporate the abovementioned risk factors into the multivariate Cox regression model.


#### 2.5.4. Establishment of Nomograms and the Validation

The univariate and multivariate Cox regression analyses were performed in the training cohort to identify potential independent risk factors. Then, based on the results of multivariate analysis, a radiomics nomogram integrating immunological radiomics features and independent clinicopathological risk factors was constructed to predict the postoperative survival status [19, 20]. The discriminative power of the nomogram was assessed using the C-index. The calibration performance was measured by a calibration curve describing the agreement between the predicted and observed survival probabilities. The clinical value of the nomogram was assessed in the entire cohort by the decision curve analysis (DCA), which was generated by calculating the net benefit at different threshold probabilities.


### 2.6. Statistical Analysis

The statistical description and statistical test of the variables were based on R version 3.6.3 (https://www.r-project.org/) and deepwise DxAI platform (https://dxonline.deepwise.com). The independent sample t-test was used for numerical variables, which are normally distributed. The χ2 test was used for disordered categorical variables and the Mann–Whitney U test was used for unidirectional ordered categorical variables. The differences in categorical variables between the survival and death groups were compared. The Z-test was used to optimize the multifactorial COX selecting process and to evaluate the differences between the models at C-index. A weighted logarithmic rank test (G-Rho rank test, Rho = 1) was used to evaluate the difference in Kaplan–Meier (KM) survival analysis curves between the high-risk and low-risk groups. This study is a bilateral significance test, and the significance level is 0.05. P<0.05 is considered to be a statistically significant difference between the groups.


## 2.1. Patient Selection

This retrospective study was approved by the Institutional Ethical Committee of Sino-Japan Union Hospital of Jilin University. A total of 146 cases of NSCLC (83 men and 73 women; mean age, and 60.24 years ± 8.637), who underwent surgical resection at the Unit of Thoracic Surgery of Sino-Japan Union Hospital during January 2010 and December 2015, were enrolled in this study. All patients were diagnosed according to the pTNM staging system from the 8th American Joint Committee on Cancer (AJCC). Inclusion criteria include the following: (1) stage I to stage IIIb; (2) complete clinical and pathological information; (3) preoperative thoracic thin-section CT images (from Picture Archiving and Communication System, PACS workstation); (4) adequate paraffin-embedded blocks of tumor sections for immunohistochemical (IHC) analysis. The exclusion criteria were those patients with (1) autoimmune diseases; (2) pneumonitis not related to the tumor; (3) with immunotherapy before surgery; and (4) metastasized or combined with other tumors. All patients were randomly stratified in a 70 : 30 ratio to form a training group (n = 102) and a validation set (n = 44). The model was trained by the method of 5-foldcross-validation [9], and the model performance was tested based on an independent-validation.


## 2.2. Follow-Up and Prognostic Information

The survival information was acquired through telephone inquiries, medical records, and death certificates. The end point of this study was disease-free survival (DFS), that is, the time from the operation to the date of the first recorded evidence of clinical (local or regional) recurrence or distant metastasis as confirmed by histological evidence, or death by any related causes. The project begun in January 2017, and the deadline date of follow-up was December 2021. The baseline of clinical-pathologic data including age, sex, smoking status, family history, staging (T stage, N stage, and clinical stage), pathological features (vascular, nerve, pleural and bronchial invasion, the residue of bronchial stump, and operation style), and the documented date of these baseline’s CT imaging, were obtained from the medical records (Table1).Table 1
Patient demographics in the study.
CharacteristicAllSurvival groupDeath groupP valueNo. of patients14690 (61.6%)56 (38.4%)Age60.24 ± 8.63759.733 ± 8.75161.054 ± 8.4650.371aSex0.076bMale83 (56.8%)46 (51.1%)37 (66.1%)Female63 (43.2%)44 (48.9%)19 (33.9%)Family history of cancer35 (24.0%)23 (25.6%)12 (21.4%)0.0057bSmoking quantity (cigarette)0.007∗d<1w72 (49.3%)50 (55.6%)22 (39.3%)1w–20w18 (12.3%)9 (10.0%)9 (16.1%)20w–50w48 (32.9%)24 (26.7%)24 (42.9%)>50w8 (5.5%)7 (7.8%)1 (1.8%)Tumor immune contexturePD-L10.418d−106 (72.6%)69 (76.7%)37 (66.1%)+16 (11.0%)9 (10.0%)7 (12.5%)++15 (10.3%)8 (8.9%)7 (12.5%)+++9 (6.2%)4 (4.4%)5 (8.9%)CD80.346d−84 (57.5%)51 (56.7%)33 (58.9%)+43 (29.5%)29 (32.2%)14 (25.0%)++18 (12.3%)9 (10.0%)9 (16.1%)+++1 (0.7%)1 (1.1%)0 (0.0%)CD30.734d−21 (14.4%)15 (16.7%)6 (10.7%)+27 (18.5%)16 (17.8%)11 (19.6%)++67 (45.9%)41 (45.6%)26 (46.4%)+++31 (21.2%)18 (20.0%)13 (23.2%)CD40.440d−58 (39.7%)36 (40.0%)22 (39.3%)+39 (26.7%)21 (23.3%)18 (32.1%)++45 (30.8%)31 (34.4%)14 (25.0%)+++4 (2.7%)2 (2.2%)2 (3.6%)Immunophenotyping0.0315dType I27 (18.5%)14 (15.6%)13 (23.2%)Type II71 (48.6%)44 (48.9%)27 (48.2%)Type III35 (17.9%)25 (27.8%)10 (17.9%)Type IV13 (8.9%)7 (7.8%)6 (10.7%)Histologic structure0.452cSquamous cell carcinoma41 (28.1%)22 (24.4%)19 (33.9%)Adenocarcinoma100 (68.5%)65 (72.2%)35 (62.5%)Other NSCLC5 (3.4%)3 (3.3%)2 (3.6%)T stage0.002∗dT192 (63.0%)67 (74.4%)25 (44.6%)T244 (30.1%)17 (18.9%)27 (48.2%)T35 (3.4%)3 (3.3%)2 (3.6%)T45 (3.4%)3 (3.3%)2 (3.6%)N status0.0094∗dIA68 (46.6%)55 (61.1%)13 (23.2%)IB11 (7.5%)4 (4.4%)7 (12.5%)IIA6 (4.1%)3 (3.3%)3 (5.4%)IIB34 (23.3%)19 (21.1%)15 (26.8%)IIIA25 (17.1%)9 (10.0%)16 (28.6%)IIIB2 (1.4%)0 (0.0%)2 (3.6%)Microvascular invasion37 (25.3%)17 (18.9%)20 (35.7%)0.023∗bPerineural invasion11 (7.5%)5 (5.6%)6 (10.7%)0.409bSegment resection residue5 (3.4%)4 (4.4%)1 (1.8%)0.696cVisceral pleural invasion36 (24.7%)21 (23.3%)15 (26.8%)0.638bBronchial invasion18 (12.3%)9 (10.0%)9 (16.1%)0.278bSurgical method0.738cLunglobectomy112 (76.7%)71 (78.9%)41 (73.2%)Pneumonectomy7 (4.8%)3 (3.3%)4 (7.1%)Pulmonary wedge resection15 (10.3%)9 (10.0%)6 (10.7%)Pulmonary sleeve lobectomyCT featuresSpicule sign102 (69.9%)58 (64.4%)44 (78.6%)0.070bLobulation sign132 (90.4%)82 (91.1%)50 (89.3%)0.716bSpinous protuberant sign26 (17.8%)14 (15.6%)12 (21.4%)0.367bVascular-bronchial convergent sign28 (19.2%)15 (16.7%)13 (23.2%)0.329bPleural indentation sign91 (62.3%)55 (61.1%)36 (64.3%)0.700bPure ground-glass opacity12 (8.2%)12 (13.3%)0 (0%)0.011∗cSolid opacity110 (75.3%)58 (64.4%)52 (92.9%)0.000∗∗bPart-solid ground-glass27 (18.5%)23 (25.6%)4 (7.1%)0.005∗∗cCavitation10 (6.8%)3 (3.3%)7 (12.5%)0.073cVacuole sign23 (15.8%)18 (20.0%)5 (8.9%)0.074bNecrosis1 (0.7%)0 (0.0%)1 (1.8%)0.810cCalcification12 (8.2%)6 (6.7%)6 (10.7%)0.578baIndependent samples t-test, bchi-square test, ccorrected chi-square test, and dMann–Whitney U test.


## 2.3. CT Image Acquisition

All patients were examined using Aquilion ONE 320 slice CT (Toshiba, Japan) and 64-MDCT scanner (GE, USA). The CT scanning parameters included a tube voltage of 100 to 130 kV. Entire lung volume from the apices to the pleural recesses and reconstructed with a slice thickness ranging 0.625 mm at end-inspiration in the craniocaudal direction, All captured images were reconstructed with a sharp high kernel and were displayed with standard lung (width, 1600 HU; level, −600 HU) and standard mediastinal window settings (width, 400 HU; level, 40 HU). At the same time, we collected 12 CT-semantic labels of NSCLC patients, including internal signs (density, necrosis, cavitation, vacuolar sign, cavity sign, and calcification) and marginal signs (spicule sign, lobulation sign, spinous protuberant sign, vascular-bronchial convergent sign, and pleural indentation sign).


## 2.4. Immunohistochemical Analysis

Formalin-fixed paraffin-embedded tissues were prepared from the surgically resected NSCLC specimens. Deparaffinized, antigenically retrieved tissues were studied for immunohistochemistry as described [10]. Consecutive sections were used for staining with selected anti-human antibodies, and secondary anti-rabbit antibodies conjugated with horseradish peroxidase were used. All first antibodies and secondary antibodies were purchased from Wuxi Aorui Dongyuan Biotechnology Co. Ltd. (Hefei, Anhui, China). Tumor types and stages were simultaneously determined by 2–3 senior pathologists’ consensus. Immunohistochemical images were taken with a Leica DM RB E research microscope using a Leica DC 100 digital camera (Leica Microsystems, Heidelberg, Germany). The images were directly transmitted to a computer with a Leica DC Viewer version 3.2 and saved as tiff files without editing. The percentage of positive IHC-stained tumor cells was calculated by pathologist using categories of <25%, 26–50%, 51–75%, and >75%.


## 2.5. Construction and Validation of the Radiomic Nomogram

For clearance, a flowchart of this study is detailed in Figure1. Six key steps were included in our study: region of interest (ROI) segmentation, imaging feature extraction, radiomics score calculation, univariate Cox analysis of risk factors, multivariate Cox regression, and the establishment and evaluation of comprehensive integrative nomogram.Figure 1
The flowchart displaying the selection of patients with NSCLC according to the exclusion criteria. The development and validation of Cox regression and nomogram were all conducted by using the official packages of “glmnet,” “rms,” “survival,” and “survminer” in R language.


### 2.5.1. ROI Segmentation

The original CT images of all patients (DICOM) were uploaded on the deepwise multimodal research platform (https://www.deepwise.com) for segmentation and imaging feature extraction to sort out the region-of-interest (ROI). Two experienced radiologists with 7 and 8 years of clinical experience in chest CT study independently recorded the segment of ROI. When disagreements were encountered, senior imaging experts (10 years of clinical experience in chest CT study) guided the completion of the segmentation. An open-source Python package was used as a platform to extract 2107 radiomics features from the nonfiltered segmented ROI [11].


### 2.5.2. Feature Extraction

As the postprocess of CT images demands, a high-pass filter, low-pass wavelet filter, and Laplacian Gaussian filter with different parameters were used to obtain more realistic images [12]. The extracted high-throughput radioman features include three categories: the first-order features describe the pixel situation of the image, the shape features describe the lesion, and the texture features describe the internal or surface texture of the lesion including gray level co-occurrence matrix (GLCM). A detailed description of these features is available online and can be accessed on January 22, 2022, at https://pyradiomics.readthedocs.io/en/latest/features.html. The pyradiomic of Python 3.0 (Python Software Foundation, https://www.python.org/) was used to extract imaging features. A total of 2,107 CT image features were extracted from each ROI, and Z-score standardization [13] was performed to form quantified high-throughput CT image features.


### 2.5.3. Feature Selection and Radiomics Signature Construction

Considering the redundancy of the features and reducing model overfitting, the most useful predictive features were selected using the Spearman correlation test and the least absolute shrinkage and selection operator (LASSO) Cox regression [14]. Firstly, the LASSO Cox regression model was used to select the features most associated with the survival status of the training cohort before the Spearman correlation test was used to reduce feature redundancy. The LASSO method can shrink the coefficients of variables unrelated to survival to zero, and thus, the features with nonzero coefficient were selected. A radiomics score (Rad-score) [15] was computed for each patient through a linear combination of the selected features weighted by their respective coefficients. A weighted log-rank test (G-rho rank test, rho = 1) was used to test the difference between the high-risk and low-risk groups. Kaplan–Meier survival analysis was applied to assess the association between radiomics signature and survival. The patients were classified into high-risk or low-risk groups according to the Rad-score, whose threshold was identified by using the X-tile. In addition, univariate Cox regression analysis was used for other risk features highly correlated with survival, such as CT semantics, clinicopathologic, and TIME parameters [16, 17]. Finally, we combined the above-selected risk features with the Rad-score and used the backward selection method [18] to incorporate the abovementioned risk factors into the multivariate Cox regression model.


### 2.5.4. Establishment of Nomograms and the Validation

The univariate and multivariate Cox regression analyses were performed in the training cohort to identify potential independent risk factors. Then, based on the results of multivariate analysis, a radiomics nomogram integrating immunological radiomics features and independent clinicopathological risk factors was constructed to predict the postoperative survival status [19, 20]. The discriminative power of the nomogram was assessed using the C-index. The calibration performance was measured by a calibration curve describing the agreement between the predicted and observed survival probabilities. The clinical value of the nomogram was assessed in the entire cohort by the decision curve analysis (DCA), which was generated by calculating the net benefit at different threshold probabilities.


## 2.5.1. ROI Segmentation

The original CT images of all patients (DICOM) were uploaded on the deepwise multimodal research platform (https://www.deepwise.com) for segmentation and imaging feature extraction to sort out the region-of-interest (ROI). Two experienced radiologists with 7 and 8 years of clinical experience in chest CT study independently recorded the segment of ROI. When disagreements were encountered, senior imaging experts (10 years of clinical experience in chest CT study) guided the completion of the segmentation. An open-source Python package was used as a platform to extract 2107 radiomics features from the nonfiltered segmented ROI [11].


## 2.5.2. Feature Extraction

As the postprocess of CT images demands, a high-pass filter, low-pass wavelet filter, and Laplacian Gaussian filter with different parameters were used to obtain more realistic images [12]. The extracted high-throughput radioman features include three categories: the first-order features describe the pixel situation of the image, the shape features describe the lesion, and the texture features describe the internal or surface texture of the lesion including gray level co-occurrence matrix (GLCM). A detailed description of these features is available online and can be accessed on January 22, 2022, at https://pyradiomics.readthedocs.io/en/latest/features.html. The pyradiomic of Python 3.0 (Python Software Foundation, https://www.python.org/) was used to extract imaging features. A total of 2,107 CT image features were extracted from each ROI, and Z-score standardization [13] was performed to form quantified high-throughput CT image features.


## 2.5.3. Feature Selection and Radiomics Signature Construction

Considering the redundancy of the features and reducing model overfitting, the most useful predictive features were selected using the Spearman correlation test and the least absolute shrinkage and selection operator (LASSO) Cox regression [14]. Firstly, the LASSO Cox regression model was used to select the features most associated with the survival status of the training cohort before the Spearman correlation test was used to reduce feature redundancy. The LASSO method can shrink the coefficients of variables unrelated to survival to zero, and thus, the features with nonzero coefficient were selected. A radiomics score (Rad-score) [15] was computed for each patient through a linear combination of the selected features weighted by their respective coefficients. A weighted log-rank test (G-rho rank test, rho = 1) was used to test the difference between the high-risk and low-risk groups. Kaplan–Meier survival analysis was applied to assess the association between radiomics signature and survival. The patients were classified into high-risk or low-risk groups according to the Rad-score, whose threshold was identified by using the X-tile. In addition, univariate Cox regression analysis was used for other risk features highly correlated with survival, such as CT semantics, clinicopathologic, and TIME parameters [16, 17]. Finally, we combined the above-selected risk features with the Rad-score and used the backward selection method [18] to incorporate the abovementioned risk factors into the multivariate Cox regression model.


## 2.5.4. Establishment of Nomograms and the Validation

The univariate and multivariate Cox regression analyses were performed in the training cohort to identify potential independent risk factors. Then, based on the results of multivariate analysis, a radiomics nomogram integrating immunological radiomics features and independent clinicopathological risk factors was constructed to predict the postoperative survival status [19, 20]. The discriminative power of the nomogram was assessed using the C-index. The calibration performance was measured by a calibration curve describing the agreement between the predicted and observed survival probabilities. The clinical value of the nomogram was assessed in the entire cohort by the decision curve analysis (DCA), which was generated by calculating the net benefit at different threshold probabilities.


## 2.6. Statistical Analysis

The statistical description and statistical test of the variables were based on R version 3.6.3 (https://www.r-project.org/) and deepwise DxAI platform (https://dxonline.deepwise.com). The independent sample t-test was used for numerical variables, which are normally distributed. The χ2 test was used for disordered categorical variables and the Mann–Whitney U test was used for unidirectional ordered categorical variables. The differences in categorical variables between the survival and death groups were compared. The Z-test was used to optimize the multifactorial COX selecting process and to evaluate the differences between the models at C-index. A weighted logarithmic rank test (G-Rho rank test, Rho = 1) was used to evaluate the difference in Kaplan–Meier (KM) survival analysis curves between the high-risk and low-risk groups. This study is a bilateral significance test, and the significance level is 0.05. P<0.05 is considered to be a statistically significant difference between the groups.


## 3. Results

### 3.1. Clinical-Pathological Parameters and CT Semantic Features of NSCLC Patients

A total of 146 patients (90 patients survived, aged 59.733 ± 8.751 years; 56 patients died, aged 61.054 ± 8.465 years) were statistically analyzed, and 13 clinicopathological features and 12 CT-semantic features were detailed in Table1. According to the results of the chi-square analysis, there were statistically significant differences among groups in 5 parameters, including microvascular invasion (P=0.023), family history of cancer (P=0.0057), pure ground-glass opacity (P=0.011), solid opacity (P=0.0005), and part-solidground-glass opacity (P=0.005). According to the Mann–Whitney U test results of the ordered categorical variables, statistically significant differences between groups in 4 factors, including clinical stage (P=0.000), T stage (P=0.00018), N stage (P=0.00057), and smoking level (P=0.007), were identified.


### 3.2. Tumor Immune Microenvironment and Survival Outcome

According to the Mann–WhitneyU test results of ordered categorical variables, no significant difference between groups in four factors including PD-L1, CD8, CD4, and CD3 (P>0.05) and the immunophenotyping based on CD8 T cell and PD-L1 showedstatistically significant differences (P=0.0315). The expression of PD-L1, CD8, CD4, and CD3 were shown according to the positive percentage in Figure 2. The 1-, 3-, and 5-year DFS rate was 74.93% and 87.95%, 46.23% and 74.19%, and 39.75% and 72.67%, respectively, between high and low groups, which were statistically significant difference. The median DFS of the high-risk group was 990 days.Figure 2
Representative immunohistochemistry of the resected samples from NSCLC patients by CD3, CD4, CD8, and PD-L1, that indicated a progression of tumor staging. The thumbnail indicates where the image was located, so some strong stained stroma area may be excluded.Accordingly, patients were divided into high (Type I) and low (Type II, Type III, and Type IV) groups. The expression of CD8 and PD-L1, namely, Type I, had been found to be the worse prognosis than the other types. The immunophenotyping based on CD8 T cell and PD-L1 according to pTNM staging are shown in Figure3.Figure 3
Immunophenotyping by tumor immune microenvironment (TIME) indicated by PD-L1 and CD8 TILs. The thumbnail indicates where the image was located, so actually strong tumor area may be excluded. (I): type (I) adaptive immune resistance (PD-L1 positive and high CD8 TILs). II: type II, immune ignorance (PD-L1 negative and low CD8 TILs). III: type III, intrinsic induction (PD-L1 positive and low CD8 TILs), IV: type IV, immune tolerance (PD-L1 negative and high CD8 TILs).


### 3.3. Construction of the Radiomics Score Based on Radiomics Signatures

A total of 2,107 radiomic features were extracted from the CT images including 414 first-order features, 14 morphological features, and 1,679 textural features. Finally, seven radiomics features were selected using the Spearman correlation test and the 5-foldcross-validation LASSO Cox regression method in the training set (n = 102). Figure 4(a) shows the Pearson correlation coefficients of different features, indicating that different features have different correlations. The closer the correlation coefficient to 1 or −1, the stronger the linear correlation is; the closer the correlation coefficient is to 0, the less linear correlation degree of features could be. Therefore, according to the feature selection results, 7 radiomics features were significant features, and their correlation coefficients were calculated and shown in Figure 4(b), indicating that the pairwise correlation between these features is smaller (correlation coefficients = −0.5∼0∼0.5). The names, modeling coefficients, and categories of the 7 features are shown in Table 2.Figure 4
Heat map of matrix similarity analysis of radiomics features based on Pearson’s correlation coefficients. Highly correlated clusters are located along the diagonal demonstrating strong correlations. (a) Twenty-eight features before selection; (b) seven features after the selection.
(a)(b)Table 2
Selected 7 CT radiomics features, their coefficients, and categories.
FeatureCoefExp (coef)Categories1log_sigma_1_0_mm_3D_glcm_Imc23.1966.493Texture2wavelet_LLH_gldm_DependenceVariance0.047610.0306Texture3wavelet_HHH_glszm_LowGrayLevelZoneEmphasis−6.5570.090Texture4logarithm_firstorder_Median0.0002380.0005196First order5lbp_2D_firstorder_Median0.98670.3293First order6lbp_2D_gldm_DependenceEntropy0.34580.4947Texture7lbp_3D_k_glszm_ZonePercentage−274.2101.7TextureFinally, to explore the significance of high-throughput CT image features more intuitively, we selected two typical patients with non-small cell carcinoma and showed 7 significant radiomics features in the lesion ROI on CT images in Figure5.Figure 5
Radiomics feature maps of the seven selected features. A 5-foldcross-validation was used to reduce overfitting for each image. The images enhanced by the processing pipleline and recorded from left to right: the original CT imaging, log_sigma_1_0_mm_3D_glcm_Imc2, wavelet_LLH_gldm_DependenceVariance, wavelet_HHH_glszm_LowGrayLevelZoneEmphasis, logarithm_firstorder_Median, lbp_2D_firstorder_Median, lbp_2D_gldm_DependenceEntropy, lbp_3D_k_glszm_ZonePercentage. Top row: F, 59 years, IB stage, DFS = 20 months. Bottom row: M, 49 years, IIB stage, DFS>5 years.The radiomics signature was constructed, with a Rad-score calculated by using the following formula:“R-score = 3.196∗log_sigma_1_0_mm_3D_glcm_Imc2, +0.04761∗wavelet_LLH_gldm_DependenceVariance.  −6.557∗wavelet_HHH_glszm_LowGrayLevelZoneEmphasis,  +0.000238∗logarithm_firstorder_Median, +0.9867∗lbp_2D_firstorder_Median, +0.3458∗lbp_2D_gldm_DependenceEntropy,  −274.2∗lbp_3D_k_glszm_ZonePercentage.”We selected the inflection point value of receiver operating characteristic (ROC) curve as the cutoff value (maximally selected rank statistics calculated based on maxstat.text function of maxstat package in R), and the optimal cutoff for the Rad-score was 1.135. Accordingly, patients were divided into high (>1.135) and low (≤1.135) groups. Also, the 1-, 3-, and 5-year DFS rate was 76.11% and 97.45%, 33.42% and 91.23%, and 27.31% and 87.90%, respectively, between high and low groups, which were statistically significant.


### 3.4. Development and Validation of the Four Nomograms

A Cox regression analysis identified microvascular invasion, T stage, N stage, clinical stage, solid opacity, family history of cancer, and immunophenotyping as independent risk factors (microvascular invasion: HR: 2.8, 95% CI: 1.5, 5.4,P=0.0017; T stage: HR: 2.1, 95% CI: 1.4, 3.1, P=0.00057; N stage: HR: 1.9, 95% CI: 1, 4, 2.6, P=0.00018; clinical stage: HR: 1.6, 95% CI: 1.3, 1.9, P=4e−05; solid opacity: HR: 13, 95% CI: 1.8, 96, P=0.011; family history of cancer: HR: 0.41, 95% CI: 0.17, 0.97, P=0.043; immunophenotyping: HR: 0.61, 95% CI: 0.4, 0.94, P=0.026) (Table 3). Based on the univariate analysis given above, we constructed four multivariate Cox regression models: clinicopathological, radiomics, clinicopathological-radiomics, and comprehensive nomogram. In the optimization stage of the above models, the Z-test was used to optimize the modeling factors, and the variance inflation factor (VIF) was used to test the multicollinearity of the factors. After several iterations of model optimization, four models were finally formed. The C-index and concordance probability for the different models in the training set and test set were summarized in Table 4. We found that the C-index of the comprehensive nomogram model on the training set and test set was 0.8766 and 0.8426, respectively, which was better than that of the clinicopathological-radiomics model (Z test, P=0.041<0.05), radiomics model, and clinicopathological model (Z test, P=0.013<0.05, P=0.0097<0.05). Therefore, the predictive power of the comprehensive nomogram is higher than that of all other models. However, there was no statistical difference between the radiomics model and the clinicopathological model in the performance of the training set and the test set (P>0.05).Table 3
Univariate Cox analysis for multiple factors.
FactorsUnivariate CoxHR (95% CI)P valueSex1.4 (0.71–2.7)0.34Age1 (0.97–1)0.69Immunophenotyping0.61 (0.4–0.94)0.026∗PD-L11.3 (0.99–1.8)0.063CD81.3 (0.86–1.9)0.23CD31.4 (0.94–1.9)0.1CD41 (0.71–1.5)0.91Histologic structure0.99 (0.54–1.8)0.96N stage1.9 (1.4–2.6)0.00018∗T stage2.1 (1.4–3.1)0.00057∗Clinical stage1.6 (1.3–1.9)4e − 05∗Microvascular invasion2.8 (1.5–5.4)0.0017∗Perineural invasion1.9 (0.67–5.4)0.23Segment resection residue0.48 (0.066–3.5)0.47Perineural invasion1.2 (0.58–2.4)0.67Bronchial invasion1 (0.42–2.4)0.97Surgical method1.1 (0.76–1.4)0.76Smoking quantity (cigarette)1.1 (0.8–1.4)0.65Family history of cancer0.41 (0.17–0.97)0.043∗Spicule sign2.2 (0.99–4.8)0.052Lobulation sign0.88 (0.27–2.9)0.83Spinous protuberant sign1.2 (0.52–2.7)0.69Vascular-bronchial convergent sign1.6 (0.78–3.3)0.2Pleural indentation sign1.2 (0.62–2.3)0.58Pure ground-glass opacity1.2e − 08 (0-inf)1Solid opacity13 (1.8–96)0.011∗Part-solid ground-glass0.14 (0.019–1)0.054Cavitation1.8 (0.57–6)0.31Vacuole sign0.76 (0.29–1.9)0.56Necrosis3.9 (0.52–29)0.18Calcification1.9 (0.76–5)0.17Table 4
Performance comparison of different models (C-index∗).
ModelModeling factorsTraining set (n = 102)Test set (n = 34)C-indexC-indexComprehensive nomogram modelImmunophenotyping + TNM stage + microvascular invasion + family history of tumor + solid opacity + rad_score0.87660.8426Clinicopathological-radiomics modelTNM stage + microvascular invasion + family history of tumor + solid opacity + Rad-score0.8730.835Radiomics modelRad-score0.81360.7Clinicopathological modelTNM clinical stage + microvascular invasion + family history of tumor + solid opacity0.79790.7613∗C-index calculates the degree of discrimination between COX model predictions and true values in survival analysis.The results of the multifactor Cox regression analysis of the comprehensive nomogram model in the training set were plotted in the forest map in Figure6.Figure 6
A forest map for each risk factor in the nomogram. The figure shows the grouping variables of the model, the number of patients in the training set, HR and 95% CI, the upper and lower limits of the 95% CI for RR, and theP value. When the upper and lower limits of the 95% CI of a factor RR are >1, that is, when the 95% CI horizontal line in the forest plot falls on the right side of the null line, it can be considered that the mortality rate is greater than the survival probability.


### 3.5. Performance of the Clinicopathology-Immune-Radiomics Nomogram

According to the nomogram calculation, for NSCLC patients with a total score of 545, the 1-, 3- and 5-years death probability were 0.63, 0.531, and 0.165, with a statistically significant difference (P=0.0004<0.05), detailed in Figure 7(a). Meanwhile, we measured the prediction ability of the nomogram in NSCLC patients within 1, 3, and 5 years through the correction curve, the abscissa represents the predicted survival rate, the ordinate represents the actual survival rate, and the diagonal represents the predicted probability which is very close to equal to the actual probability (Figure 7(b)). The results showed that the prediction curve of our model coincides with the diagonal line, indicating that the prediction result of the model is good, which can also be seen from the C-index. The K–M survival curves of two independent factors, Rad-score prediction, and immunophenotype enrichment score prediction, in the high- and low-risk groups are shown in Figures 8(a) and 8(b), respectively.Figure 7
Generation of the nomogram for prognostic risk and clinicopathological characteristics. (a) The comprehensive nomogram for predicting 1-, 3-, and 5-year DFS after surgery; (b) the calibration curves of the comprehensive nomogram. The performance of the validation cohort was shown on the plot relative to the 45-degree line, representing perfect prediction.
(a)(b)Figure 8
Kaplan–Meier analyses of overall survival according to the risk groups. (a) Rad-score prediction. (b) Immunophenotypes score prediction.
(a)(b)


### 3.6. Clinical Utility

In addition, in order to evaluate the diagnostic accuracy of different models and their significance in clinical decision-making, we drew DCA in Figure9 to show the decisive significance of the three models in diagnosing NSCLC patients. The analysis shows that the net benefit rate of the clinicopathological-radiomics model is the highest when the threshold is within the range of 0.2∼0.3, 0.4∼0.5, and 0.9∼1.0. When the threshold is in all the other ranges, the net benefit rate of the comprehensive nomogram is higher than all the other models. The clinicopathological model performed the worst in all the ranges.Figure 9
Decision curve analysis for comparing prognostic factors with three models (curve 1: comprehensive nomogram model, curve 2: clinicopathological-radiomics model, and curve 3: pure clinicopathological model). They-axis measures the net benefit. The comprehensive nomogram model has the highest net benefit at the threshold from 0 to 0.2, 0.3 to 0.4, and 0.5 to 0.9.


## 3.1. Clinical-Pathological Parameters and CT Semantic Features of NSCLC Patients

A total of 146 patients (90 patients survived, aged 59.733 ± 8.751 years; 56 patients died, aged 61.054 ± 8.465 years) were statistically analyzed, and 13 clinicopathological features and 12 CT-semantic features were detailed in Table1. According to the results of the chi-square analysis, there were statistically significant differences among groups in 5 parameters, including microvascular invasion (P=0.023), family history of cancer (P=0.0057), pure ground-glass opacity (P=0.011), solid opacity (P=0.0005), and part-solidground-glass opacity (P=0.005). According to the Mann–Whitney U test results of the ordered categorical variables, statistically significant differences between groups in 4 factors, including clinical stage (P=0.000), T stage (P=0.00018), N stage (P=0.00057), and smoking level (P=0.007), were identified.


## 3.2. Tumor Immune Microenvironment and Survival Outcome

According to the Mann–WhitneyU test results of ordered categorical variables, no significant difference between groups in four factors including PD-L1, CD8, CD4, and CD3 (P>0.05) and the immunophenotyping based on CD8 T cell and PD-L1 showedstatistically significant differences (P=0.0315). The expression of PD-L1, CD8, CD4, and CD3 were shown according to the positive percentage in Figure 2. The 1-, 3-, and 5-year DFS rate was 74.93% and 87.95%, 46.23% and 74.19%, and 39.75% and 72.67%, respectively, between high and low groups, which were statistically significant difference. The median DFS of the high-risk group was 990 days.Figure 2
Representative immunohistochemistry of the resected samples from NSCLC patients by CD3, CD4, CD8, and PD-L1, that indicated a progression of tumor staging. The thumbnail indicates where the image was located, so some strong stained stroma area may be excluded.Accordingly, patients were divided into high (Type I) and low (Type II, Type III, and Type IV) groups. The expression of CD8 and PD-L1, namely, Type I, had been found to be the worse prognosis than the other types. The immunophenotyping based on CD8 T cell and PD-L1 according to pTNM staging are shown in Figure3.Figure 3
Immunophenotyping by tumor immune microenvironment (TIME) indicated by PD-L1 and CD8 TILs. The thumbnail indicates where the image was located, so actually strong tumor area may be excluded. (I): type (I) adaptive immune resistance (PD-L1 positive and high CD8 TILs). II: type II, immune ignorance (PD-L1 negative and low CD8 TILs). III: type III, intrinsic induction (PD-L1 positive and low CD8 TILs), IV: type IV, immune tolerance (PD-L1 negative and high CD8 TILs).


## 3.3. Construction of the Radiomics Score Based on Radiomics Signatures

A total of 2,107 radiomic features were extracted from the CT images including 414 first-order features, 14 morphological features, and 1,679 textural features. Finally, seven radiomics features were selected using the Spearman correlation test and the 5-foldcross-validation LASSO Cox regression method in the training set (n = 102). Figure 4(a) shows the Pearson correlation coefficients of different features, indicating that different features have different correlations. The closer the correlation coefficient to 1 or −1, the stronger the linear correlation is; the closer the correlation coefficient is to 0, the less linear correlation degree of features could be. Therefore, according to the feature selection results, 7 radiomics features were significant features, and their correlation coefficients were calculated and shown in Figure 4(b), indicating that the pairwise correlation between these features is smaller (correlation coefficients = −0.5∼0∼0.5). The names, modeling coefficients, and categories of the 7 features are shown in Table 2.Figure 4
Heat map of matrix similarity analysis of radiomics features based on Pearson’s correlation coefficients. Highly correlated clusters are located along the diagonal demonstrating strong correlations. (a) Twenty-eight features before selection; (b) seven features after the selection.
(a)(b)Table 2
Selected 7 CT radiomics features, their coefficients, and categories.
FeatureCoefExp (coef)Categories1log_sigma_1_0_mm_3D_glcm_Imc23.1966.493Texture2wavelet_LLH_gldm_DependenceVariance0.047610.0306Texture3wavelet_HHH_glszm_LowGrayLevelZoneEmphasis−6.5570.090Texture4logarithm_firstorder_Median0.0002380.0005196First order5lbp_2D_firstorder_Median0.98670.3293First order6lbp_2D_gldm_DependenceEntropy0.34580.4947Texture7lbp_3D_k_glszm_ZonePercentage−274.2101.7TextureFinally, to explore the significance of high-throughput CT image features more intuitively, we selected two typical patients with non-small cell carcinoma and showed 7 significant radiomics features in the lesion ROI on CT images in Figure5.Figure 5
Radiomics feature maps of the seven selected features. A 5-foldcross-validation was used to reduce overfitting for each image. The images enhanced by the processing pipleline and recorded from left to right: the original CT imaging, log_sigma_1_0_mm_3D_glcm_Imc2, wavelet_LLH_gldm_DependenceVariance, wavelet_HHH_glszm_LowGrayLevelZoneEmphasis, logarithm_firstorder_Median, lbp_2D_firstorder_Median, lbp_2D_gldm_DependenceEntropy, lbp_3D_k_glszm_ZonePercentage. Top row: F, 59 years, IB stage, DFS = 20 months. Bottom row: M, 49 years, IIB stage, DFS>5 years.The radiomics signature was constructed, with a Rad-score calculated by using the following formula:“R-score = 3.196∗log_sigma_1_0_mm_3D_glcm_Imc2, +0.04761∗wavelet_LLH_gldm_DependenceVariance.  −6.557∗wavelet_HHH_glszm_LowGrayLevelZoneEmphasis,  +0.000238∗logarithm_firstorder_Median, +0.9867∗lbp_2D_firstorder_Median, +0.3458∗lbp_2D_gldm_DependenceEntropy,  −274.2∗lbp_3D_k_glszm_ZonePercentage.”We selected the inflection point value of receiver operating characteristic (ROC) curve as the cutoff value (maximally selected rank statistics calculated based on maxstat.text function of maxstat package in R), and the optimal cutoff for the Rad-score was 1.135. Accordingly, patients were divided into high (>1.135) and low (≤1.135) groups. Also, the 1-, 3-, and 5-year DFS rate was 76.11% and 97.45%, 33.42% and 91.23%, and 27.31% and 87.90%, respectively, between high and low groups, which were statistically significant.


## 3.4. Development and Validation of the Four Nomograms

A Cox regression analysis identified microvascular invasion, T stage, N stage, clinical stage, solid opacity, family history of cancer, and immunophenotyping as independent risk factors (microvascular invasion: HR: 2.8, 95% CI: 1.5, 5.4,P=0.0017; T stage: HR: 2.1, 95% CI: 1.4, 3.1, P=0.00057; N stage: HR: 1.9, 95% CI: 1, 4, 2.6, P=0.00018; clinical stage: HR: 1.6, 95% CI: 1.3, 1.9, P=4e−05; solid opacity: HR: 13, 95% CI: 1.8, 96, P=0.011; family history of cancer: HR: 0.41, 95% CI: 0.17, 0.97, P=0.043; immunophenotyping: HR: 0.61, 95% CI: 0.4, 0.94, P=0.026) (Table 3). Based on the univariate analysis given above, we constructed four multivariate Cox regression models: clinicopathological, radiomics, clinicopathological-radiomics, and comprehensive nomogram. In the optimization stage of the above models, the Z-test was used to optimize the modeling factors, and the variance inflation factor (VIF) was used to test the multicollinearity of the factors. After several iterations of model optimization, four models were finally formed. The C-index and concordance probability for the different models in the training set and test set were summarized in Table 4. We found that the C-index of the comprehensive nomogram model on the training set and test set was 0.8766 and 0.8426, respectively, which was better than that of the clinicopathological-radiomics model (Z test, P=0.041<0.05), radiomics model, and clinicopathological model (Z test, P=0.013<0.05, P=0.0097<0.05). Therefore, the predictive power of the comprehensive nomogram is higher than that of all other models. However, there was no statistical difference between the radiomics model and the clinicopathological model in the performance of the training set and the test set (P>0.05).Table 3
Univariate Cox analysis for multiple factors.
FactorsUnivariate CoxHR (95% CI)P valueSex1.4 (0.71–2.7)0.34Age1 (0.97–1)0.69Immunophenotyping0.61 (0.4–0.94)0.026∗PD-L11.3 (0.99–1.8)0.063CD81.3 (0.86–1.9)0.23CD31.4 (0.94–1.9)0.1CD41 (0.71–1.5)0.91Histologic structure0.99 (0.54–1.8)0.96N stage1.9 (1.4–2.6)0.00018∗T stage2.1 (1.4–3.1)0.00057∗Clinical stage1.6 (1.3–1.9)4e − 05∗Microvascular invasion2.8 (1.5–5.4)0.0017∗Perineural invasion1.9 (0.67–5.4)0.23Segment resection residue0.48 (0.066–3.5)0.47Perineural invasion1.2 (0.58–2.4)0.67Bronchial invasion1 (0.42–2.4)0.97Surgical method1.1 (0.76–1.4)0.76Smoking quantity (cigarette)1.1 (0.8–1.4)0.65Family history of cancer0.41 (0.17–0.97)0.043∗Spicule sign2.2 (0.99–4.8)0.052Lobulation sign0.88 (0.27–2.9)0.83Spinous protuberant sign1.2 (0.52–2.7)0.69Vascular-bronchial convergent sign1.6 (0.78–3.3)0.2Pleural indentation sign1.2 (0.62–2.3)0.58Pure ground-glass opacity1.2e − 08 (0-inf)1Solid opacity13 (1.8–96)0.011∗Part-solid ground-glass0.14 (0.019–1)0.054Cavitation1.8 (0.57–6)0.31Vacuole sign0.76 (0.29–1.9)0.56Necrosis3.9 (0.52–29)0.18Calcification1.9 (0.76–5)0.17Table 4
Performance comparison of different models (C-index∗).
ModelModeling factorsTraining set (n = 102)Test set (n = 34)C-indexC-indexComprehensive nomogram modelImmunophenotyping + TNM stage + microvascular invasion + family history of tumor + solid opacity + rad_score0.87660.8426Clinicopathological-radiomics modelTNM stage + microvascular invasion + family history of tumor + solid opacity + Rad-score0.8730.835Radiomics modelRad-score0.81360.7Clinicopathological modelTNM clinical stage + microvascular invasion + family history of tumor + solid opacity0.79790.7613∗C-index calculates the degree of discrimination between COX model predictions and true values in survival analysis.The results of the multifactor Cox regression analysis of the comprehensive nomogram model in the training set were plotted in the forest map in Figure6.Figure 6
A forest map for each risk factor in the nomogram. The figure shows the grouping variables of the model, the number of patients in the training set, HR and 95% CI, the upper and lower limits of the 95% CI for RR, and theP value. When the upper and lower limits of the 95% CI of a factor RR are >1, that is, when the 95% CI horizontal line in the forest plot falls on the right side of the null line, it can be considered that the mortality rate is greater than the survival probability.


## 3.5. Performance of the Clinicopathology-Immune-Radiomics Nomogram

According to the nomogram calculation, for NSCLC patients with a total score of 545, the 1-, 3- and 5-years death probability were 0.63, 0.531, and 0.165, with a statistically significant difference (P=0.0004<0.05), detailed in Figure 7(a). Meanwhile, we measured the prediction ability of the nomogram in NSCLC patients within 1, 3, and 5 years through the correction curve, the abscissa represents the predicted survival rate, the ordinate represents the actual survival rate, and the diagonal represents the predicted probability which is very close to equal to the actual probability (Figure 7(b)). The results showed that the prediction curve of our model coincides with the diagonal line, indicating that the prediction result of the model is good, which can also be seen from the C-index. The K–M survival curves of two independent factors, Rad-score prediction, and immunophenotype enrichment score prediction, in the high- and low-risk groups are shown in Figures 8(a) and 8(b), respectively.Figure 7
Generation of the nomogram for prognostic risk and clinicopathological characteristics. (a) The comprehensive nomogram for predicting 1-, 3-, and 5-year DFS after surgery; (b) the calibration curves of the comprehensive nomogram. The performance of the validation cohort was shown on the plot relative to the 45-degree line, representing perfect prediction.
(a)(b)Figure 8
Kaplan–Meier analyses of overall survival according to the risk groups. (a) Rad-score prediction. (b) Immunophenotypes score prediction.
(a)(b)


## 3.6. Clinical Utility

In addition, in order to evaluate the diagnostic accuracy of different models and their significance in clinical decision-making, we drew DCA in Figure9 to show the decisive significance of the three models in diagnosing NSCLC patients. The analysis shows that the net benefit rate of the clinicopathological-radiomics model is the highest when the threshold is within the range of 0.2∼0.3, 0.4∼0.5, and 0.9∼1.0. When the threshold is in all the other ranges, the net benefit rate of the comprehensive nomogram is higher than all the other models. The clinicopathological model performed the worst in all the ranges.Figure 9
Decision curve analysis for comparing prognostic factors with three models (curve 1: comprehensive nomogram model, curve 2: clinicopathological-radiomics model, and curve 3: pure clinicopathological model). They-axis measures the net benefit. The comprehensive nomogram model has the highest net benefit at the threshold from 0 to 0.2, 0.3 to 0.4, and 0.5 to 0.9.


## 4. Discussion

The NSCLC has a better prognosis compared to small cell lung cancer (SCLC) in general because it can be treated through surgery in most cases. However, its possible relapse gives NSCLC patients high challenges too. Since NSCLC accounts for 85% of all lung cancers, it is reasonable to establish more efficient models for research and clinic. Our study made brave challenges to incorporate comprehensive multimodal radiomic, clinicopathological, and tumor immune features for the individualized DFS prediction of surgically resected NSCLC patients. To the best of our knowledge, it is the first time that we report a concise nomogram with eight variables, which provide a feasible and practical reference to clinical professionals for recommending a more appropriate management for NSCLC patients. The integrative nomogram plus different types of biomarkers has also shown to be superior to the clinicopathological, radiomics, and clinicopathological-radiomics model alone, demonstrating a powerful predicting capability. The pTNM staging is the most important postoperative prognostic approach in clinical practice. However, growing evidence suggests that the high-throughput extracted images from CT-scan could reflect tumor biological characteristics and replace some of risk stratification, which demonstrated the survival outcomes through density, compactness, and intratumor heterogeneity. As the features obtained from LASSO were generally accurate and the regression coefficients of most features were shrunk towards zero during overfitting, Lasso-logistic regression was performed to select the texture features to establish the Rad-score [21], which makes the model more accurate to predict [22]. Our Rad-score-based nomograms yielded a better discriminative ability than the traditional pTNM for NSCLC patients [23, 24]. Moreover, our results suggested that the Rad-score could add pTNM staging systems in prognostic stratification as the C-index value increased, thus, the Rad-score complements the diagnosis system. This indicates the clinical importance of our findings for individualized DFS prediction in NSCLC patients. Patients with high CD8+ and high PD-L1 TILs expression had poor survival rate. Upregulation of PD-L1 on tumor cells can inhibit the antitumor activity of CD8+ TILs, which may significantly reduce the prognosis. This observation suggests that the immune activity and tumor immune escape have coevolved, even though each of their existence is likely offset by the coexistence of each other [25]. Our study shows that the classification of the immune microenvironment based on the combination of PD-L1 and CD8+ TILs is better at stratifying patients with different outcomes in NSCLC.All in all, the integrative nomogram improved survival prediction in NSCLC patients may offer a practical reference for individualized management of these patients. Furthermore, the nomogram indicated a superior predictive accuracy and clinical utility of the outcome through the functional analysis, immune cell infiltration, and time-dependent ROC. In addition, the resting CD4 memory T cells, resting mast cells, and neutrophils and their integration may reflect those multiple factors of essential characteristics in patients. As reported, the C-index of the radiomics model was often between 0.60 and 0.67, which has been improved to 0.72 when combined with clinical and genomic features. Recent studies have verified the correlation between TILs and survival in patients with several kinds of tumors. In addition, high expression of PD-L1 was found to be associated with poor survival rate in melanoma, NSCLC, colorectal, and renal cell cancer patients. However, in our study, no separate immune factors could be found as independent prognostic factors to affect the NSCLC survival rate; nevertheless, the tumor immune microenvironment is affected by multiple immune cells, and the prognostic role based on a single factor is still controversial. The increasing evidence confirms that TIME assessment of TILs and PD-L1 score is rapidly emerging as a potential biomarker for prognosis and treatment response. Moreover, classifying cancers into T cell-inflamed tumors (PD-L1 high, CD8 high, and IFN-γ signature) versus noninflamed tumors (immune-excluded and immune-desert), is proving to be possible to predict survival rate based on the immune checkpoint inhibitor (ICI) responses.Many studies have suggested that CD8+ TILs could produce IFN-γ and induce PD-L1 expression in different solid tumors, which indicates a coevolvement of immune activity and tumor immune escape. The survival significance of each of them is neutralized by the coexistence of the counterpart. Our studies suggest that classifying the immune microenvironment based on PD-L1 and CD8+ TIL combination could better stratify patients with different outcomes in NSCLC. The worse survival was observed in patients with high CD8+ TILs and high PD-L1 expression, most likely because tumor immune escape and also the upregulated expression of PD-L1 on tumor cells could inhibit the antitumor activity of CD8+ TILs. However, there are certain limitations to our present study. Firstly, this study is a preliminary exploration with a single center and a relatively small sample size, so there can be potential information bias in the retrospective study. Nonetheless, the adequate patient follow-up (5 years) and the presence of a validated cohort may partially cover this issue. However, external verification by other agencies is necessary. Therefore, in the follow-up research, we will introduce external verification or form multistudy centers to obtain data with a larger sample size for further verification. Secondly, it is intrinsic to the radiomics approach, and it is easily related to the actual poor interpretation of high-throughput extracted data and lack of methodological standardization to reach validated and reproducible features with an impact on patient survival rate. Thus, the underlying mechanism for explaining the prognostic role of our nomogram still needs to be further investigated in the future.Finally, our model may help to build up a deep neural network (DNN) that could be composed of nonlinear modules, which represent multiple levels of abstraction. Each representation can be transformed into a slightly more abstract level, leading to more involved interactions among features. Compared with traditional machine learning methods, deep learning algorithms can extract high-level abstractions from different data sources and provide self-learning capability [26]. It is still a long way to go from nomogram to an artificial intelligent model, but such perspective application would greatly help the diagnosis and prolong patients’ life span. The deep learning signature-based nomogram would be a robust tool for the prognostic prediction in the resected NSCLC patients [27].In conclusion, nomogram integrating radiomics, immunophenotypic, and clinicopathological parameters may not implement the actual risk-free stratification models; however, it may provide a smarter approach especially for surgically resected NSCLC patients who harbor a more aggressive course independently from pTNM. We intend to extend this integrative nomogram approach to unresectable or NSCLC advanced patients to predict the responses within the immunotherapy, guide personalized treatment for ideal candidates who might benefit from such neoadjuvant treatment.



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*Source: 8607062-2023-02-21.xml*