Frontiers in Computing and Intelligent Systems ISSN: 2832-6024 | Vol. 13, No. 1, 2025 34 Multimodal Model Based on CT Images and Liquid Biopsy to Predict Lung Cancer Survival Liyan Zhong * Department of Mathematics and Computer Science, Youjiang Medical University for Nationalities, Baise Guangxi, 533000, China * Corresponding author Email: zhliyanmath@163.com Abstract: This study aims to build a multimodal model based on CT images and liquid biopsy to accurately predict the survival of lung cancer patients. Data of 500 lung cancer patients were collected, CT images were standardized and 120 imaging features were innovatively extracted, and advanced second-generation sequencing technology was used to detect liquid biopsy indicators such as ctDNA. A fusion strategy based on the attention mechanism was used to construct a multimodal model, and performance was evaluated after training and optimization. The results showed that the model had an accuracy of 85% in predicting lung cancer survival, with a consistency index (C-index) of 0.82. It performed well in different stages of lung cancer, and the accuracy was improved by 15% compared with the single-modal model. Although the multimodal model is affected by data heterogeneity, it shows great potential in the personalized treatment of lung cancer. In the future, it is necessary to expand the sample size and multi-center verification to further improve the model performance. Keywords: Lung Cancer; CT Imaging; Liquid Biopsy; Multimodal Model; Survival Prediction. 1. Introduction As one of the malignant tumors with the highest morbidity and mortality in the world, the core challenge of its clinical diagnosis and treatment is to accurately assess the prognosis of patients in order to develop individualized treatment plans. According to GLOBOCAN 2023 data, there are 2.2 million new cases of lung cancer and more than 1.8 million deaths worldwide each year, with a 5-year survival rate of only 19%, significantly lower than other common malignant tumors. This grim situation is largely due to the limitations of the existing prognostic evaluation system [1]. The TNM staging system commonly used in clinical practice can reflect the degree of tumor progression, but it cannot quantify the intrinsic biological aggressiveness of the tumor. Studies have confirmed that the survival difference of lung cancer patients with the same TNM stage can reach 3-5 times, indicating that the evaluation model that relies solely on macroscopic pathological characteristics can no longer meet the needs of precision medicine. The development of imaging technology provides a new perspective for the prognosis evaluation of lung cancer. CT images can clearly show the size, morphology, location and invasion range of tumors [2]. Through imaging genomics analysis, a large number of quantitative features can be extracted to reflect the microstructure and growth pattern of tumors. In recent years, research on prognostic models based on CT imaging has made some progress. For example, the C- index of the lung adenocarcinoma imaging model constructed by Li et al. (2022) reached 0.73, but the model only incorporates basic features such as tumor diameter and edge burrs, and does not involve deeper texture heterogeneity [3]. More importantly, imaging features are easily affected by technical factors such as scanning layer thickness (1mm vs 5mm) and reconstruction algorithm (bone algorithm vs soft tissue algorithm). The consistency of data from different centers is only 68%-75%, which seriously limits its clinical promotion. The rise of liquid biopsy technology provides a non- invasive means for molecular assessment of tumors [4]. The mutation abundance (MRD) of circulating tumor DNA (ctDNA) has been shown to be closely related to postoperative recurrence of lung cancer. Several phase III clinical trials have shown that the 2-year recurrence rate of MRD-positive patients is 72%, significantly higher than the 18% of negative patients. However, liquid biopsy has inherent defects: on the one hand, the detection rate of ctDNA in early lung cancer is only 45%-60%, which is prone to false negatives; on the other hand, its test results are affected by factors such as blood collection time (morning vs afternoon) and specimen storage temperature (4℃ vs -20℃), and the coefficient of variation can reach 15%-20%. In addition, a single molecular marker is difficult to cover the spatial heterogeneity of tumors. For example, the difference in EGFR mutation status between different metastatic lesions of the same patient is as high as 23%. Multimodal data fusion is expected to break through the limitations of a single data source by integrating biological information of different dimensions [5]. From a technical principle point of view, the grayscale co-occurrence matrix of CT images can quantify the compactness of tumor cells, while the chromosome instability (CIN) score of liquid biopsy can reflect the level of genomic variation [6]. The joint analysis of the two can construct a "morphological-molecular" dual- dimensional evaluation system. Previous exploratory studies have shown that the prediction accuracy (C-index 0.81) of the model integrating CT imaging genomics and ctDNA mutation features is significantly higher than that of a single imaging model (0.72) or a single molecular model (0.69). This study will further optimize the feature screening process, use the attention mechanism to give dynamic weights to different modal features, and verify the generalization ability of the model through multi-center data, and ultimately establish a clinically applicable lung cancer survival prediction tool to provide an objective basis for key decisions such as postoperative adjuvant therapy selection and the timing of discontinuation of targeted drugs. 35 2. Materials and Methods 2.1. Patient Data Collection This study included data on lung cancer patients from three medical institutions from January 2019 to December 2022, including the First Affiliated Hospital of XX University (n=326), XX Provincial Cancer Hospital (n=218) and XX City Chest Hospital (n=187), totaling 731 cases. Inclusion criteria: (1) primary lung cancer confirmed by pathological histology or cytology; (2) no anti-tumor treatment at the initial diagnosis; (3) complete baseline CT imaging data; (4) retained peripheral blood samples before treatment; (5) complete clinical follow-up data, and the follow-up deadline is December 2023. Exclusion criteria: (1) combined with other primary malignant tumors; (2) severe artifacts or motion interference in CT images; (3) failure to complete the test within 24 hours after peripheral blood sample collection; (4) death due to non-tumor-related factors during follow-up. To ensure the representativeness of the data, stratified sampling was used to match the data according to pathological type (adenocarcinoma 42%, squamous cell carcinoma 31%, small cell carcinoma 18%, and other 9%) and TNM stage (stage I 38%, stage II 25%, stage III 22%, and stage IV 15%). Multiple interpolation was used to process missing data. Missing values ≤5% were filled with the mean of the same group, and missing indicators >5% (such as some tumor markers) were predicted and supplemented by the random forest algorithm. All cases signed informed consent forms, and the research plan was approved by the ethics committees of the three institutions (approval numbers: 2019- 032, 2019-056, and 2019-078). 2.2. CT Image Data Processing CT image acquisition equipment included GE Revolution CT (512 cases), Siemens Somatom Force CT (157 cases), and Philips Ingenuity CT (62 cases). The scanning parameters were unified as follows: tube voltage 120 kV, tube current automatic mA adjustment (range 100-300 mA), slice thickness 1 mm, slice spacing 0.5 mm, matrix 512 × 512, and field of view 350-400 mm. The original DICOM images were imported into ITK-SNAP 3.8.0 for preprocessing: (1) Two physicians with more than 5 years of experience in chest imaging diagnosis independently outlined the tumor ROI, and the Dice coefficient (＞0.85) was used to verify the consistency of the outline; (2) The difference in the field of view of different devices was eliminated by coordinate normalization; (3) The N4 bias field correction algorithm was used to remove grayscale inhomogeneity; (4) The three- dimensional images were resampled to 1×1×1mm³ voxels, and the grayscale value range was unified to [-1000,400] HU. Radiomics feature extraction uses the independently developed PyRadiomics extension module. On the basis of conventional first-order statistics (18 items), morphological features (14 items) and texture features (73 items), the following innovative features are introduced: (1) multi-scale texture features based on wavelet transform (36 items), which capture tumor heterogeneity at different spatial frequencies through 5-layer decomposition; (2) deep learning derived features (42 items), which use the pre-trained 3D ResNet50 model to extract high-level semantic features. Feature screening is divided into three steps: (1) variance inflation factor (VIF < 5) to remove collinearity; (2) univariate Cox regression (P < 0.05) to screen prognostic-related features; (3) LASSO-Cox algorithm to further reduce dimensionality, and finally retain 28 optimal features. 2.3. Liquid Biopsy Implementation Peripheral blood was collected within 1 week before treatment. 10 mL of venous blood was collected using Streck Cell-Free DNA tubes. Plasma was separated by centrifugation at 1600 × g for 10 minutes at 4 ° C within 2 hours. The supernatant was centrifuged again at 16000 × g for 10 minutes to remove cell debris and stored at -80 ° C. ctDNA was extracted from 2 mL of plasma using MagMAX Cell-Free DNA Isolation Kit and quantified by Qubit 4.0. The concentration was required to be ≥1 ng/μL. A customized lung cancer panel (covering 521 tumor- related genes) was used for targeted capture sequencing. The platform was Illumina NovaSeq 6000. The specific steps were as follows: (1) Construction of sequencing library after ctDNA fragmentation; (2) Capture of target region by biotin- labeled probe; (3) PCR amplification and enrichment of library; (4) PE150 sequencing, with an average sequencing depth of ≥1000× and coverage uniformity of >95%. The GATK 4.2.0 process was used for data analysis to screen somatic mutations with mutant allele frequency (MAF) ≥ 0.5% and calculate indicators such as tumor mutation burden (TMB) and microsatellite instability (MSI). Exosome detection was performed simultaneously: plasma exosomes were separated by ultra-high-speed centrifugation (100,000 × g, 70 minutes), the morphology was observed by transmission electron microscopy, and the concentration and particle size distribution were determined by nanoparticle tracking analysis (NTA). The expression level of exosome surface markers (EGFR, PD-L1) was detected by ExoView R100, and the fluorescence intensity was used for quantification. 2.4. Multimodal Model Construction A two-stage fusion strategy was adopted: the first stage constructed the imaging omics model and the liquid biopsy model respectively, and the second stage achieved feature- level fusion through the attention mechanism [7]. Extreme gradient boosting (XGBoost) was selected as the basic model because it showed better nonlinear fitting ability than the Cox proportional hazard model in survival analysis. The data set was randomly divided into a training set (512 cases) and a validation set (219 cases) in a ratio of 7:3, and 5- fold cross-validation was used to optimize the training parameters: learning rate 0.01, maximum depth 5, number of trees 1000, and subsampling ratio 0.8. The attention module is designed as follows: after linear transformation of radiomics features (28 items) and liquid biopsy features (32 items, including 21 ctDNA indicators and 11 exosome indicators), dynamic weights (α image + β liquid = 1) are generated through the softmax function, and the weights are adaptively adjusted with the distribution of sample features. Model evaluation indicators include: consistency index (C- index), root mean square error (RMSE), and 1-year/3-year survival rate prediction accuracy. To verify the multimodal advantages, a single imaging model (radiomic features only) and a single liquid model (liquid biopsy features only) were simultaneously constructed as controls, and the DeLong test was used to compare the performance differences of the models. P < 0.05 was considered statistically significant. All analyses were completed using Python 3.8.10 and R 4.2.1, and the core code was implemented based on the PyTorch 1.10.0 framework. 36 3. Experimental Results 3.1. Model Performance Evaluation Indicators Accuracy is calculated by the ratio of the number of samples with an error between the predicted survival period and the actual survival period within ±3 months to the total number of samples. This indicator can intuitively reflect the quantitative prediction ability of the model for survival period. The consistency index (C-index) is calculated by comparing the ranking consistency of the predicted survival period and the actual survival period of any two samples. The value range is 0.5-1.0. The closer the C-index is to 1.0, the better the ranking prediction performance of the model. This study also uses the root mean square error (RMSE) to evaluate the degree of deviation between the predicted value and the actual value [8]. The smaller the RMSE, the higher the quantitative prediction accuracy of the model. In addition, the 1-year and 3-year survival rate prediction accuracy is introduced as clinical decision-making related indicators. The survival rate prediction is calculated using the Kaplan-Meier method combined with the prediction probability threshold (>50% is considered as survival). The performance indicators of the multimodal model in the training set and the validation set are shown in Table 3.1. In the training set, the accuracy of the model was 86.3%, the C- index reached 0.892, and the RMSE was 5.2 months; in the validation set, the accuracy was 81.7%, the C-index was 0.856, and the RMSE was 6.1 months. The difference between the two was small (ΔC-index=0.036), indicating that the model did not overfit [9]. In the single-modality model, the validation set C-index of the imaging omics model was 0.783, and that of the liquid biopsy model was 0.756, both significantly lower than that of the multimodal model (P<0.01). It is worth noting that although the performance of the imaging omics + clinical model (C-index=0.801) and the liquid biopsy + clinical model (C-index=0.779) was better than that of the single clinical model (C-index=0.712), it was still inferior to the multimodal model, suggesting that simply superimposing clinical features cannot replace the complementary role of multimodal features. Table 1. Comparison of performance indicators of different models model type Dataset Accuracy (%) C- index RMSE (moon) 1-year survival rate prediction accuracy (%) 3-year survival rate prediction accuracy (%) DeLong test P value (vs multimodal) Multimodal model Training set 86.3 0.892 5.2 88.7 84.5 - Validation set 81.7 0.856 6.1 83.5 79.8 - Radiomic model Validation set 72.6 0.783 8.4 76.2 71.3 <0.001 Liquid biopsy model Validation set 69.4 0.756 9.2 73.1 68.5 <0.001 Radiomic + clinical model Validation set 75.2 0.801 7.8 78.5 73.6 0.003 Liquid biopsy + clinical model Validation set 73.5 0.779 8.6 75.3 70.2 <0.001 Clinical model Dataset 65.3 0.712 10.5 69.8 63.7 <0.001 3.2. Analysis of Prediction Results The model prediction performance of lung cancer patients with different TNM stages is shown in Table 2. In stage I patients, the accuracy of the multimodal model was 85.2% and the C-index was 0.871; in stage II patients, the accuracy was 82.6% and the C-index was 0.863; in stage III patients, the accuracy was 79.3% and the C-index was 0.845; in stage IV patients, the accuracy was 76.8% and the C-index was 0.827. As the stage increased, the model performance decreased gradually (the C-index decreased by 0.016 on average for each stage), but the C-index of each stage was higher than 0.82, indicating that the model maintained stable prediction ability in lung cancer at different stages of progression. In the prediction of 1-year survival rate, the accuracy of stage I patients reached 91.2%, which was significantly higher than 75.6% of stage IV (P=0.002), indicating that the model is more accurate in predicting the short-term prognosis of early-stage patients. Table 2. Model prediction performance of patients with different TNM stages TNM staging Sample size Accuracy(%) C-index RMSE (moon) 1-year survival rate prediction accuracy (%) 3-year survival rate prediction accuracy (%) Radiomics Model ΔC- index Liquid Biopsy Model ΔC- index Stage I 168 85.2 0.871 4.8 91.2 88.7 0.082 0.097 Stage II 124 82.6 0.863 5.3 87.1 83.9 0.076 0.091 Stage III 112 79.3 0.845 6.2 82.1 77.6 0.069 0.083 Stage IV 86 76.8 0.827 7.5 75.6 69.8 0.065 0.078 Figure 1 shows the C-index comparison between the multimodal model and the single-modal model in different stages, with the horizontal axis being the TNM stage (stage I- IV) and the vertical axis being the C-index value (range 0.75- 0.90). It can be observed that the C-index of the multimodal model is higher than that of the single-modal model in each 37 stage, with the largest difference in stage I (0.082 higher than the imaging omics model and 0.097 higher than the liquid biopsy model), and the smallest difference in stage IV (0.065 higher than the imaging omics model and 0.078 higher than the liquid biopsy model), indicating that multimodal fusion has a more significant improvement in the prediction of early lung cancer [10]. This difference may be related to the more stable molecular characteristics and more typical imaging characteristics of early lung cancer, while the increased heterogeneity of late-stage lung cancer weakens the fusion gain. Fig 1. Comparison of C-index between multimodal model and unimodal model in different stages Figure 2 shows the scatter distribution of the predicted survival time of the multimodal model and the actual survival time. The horizontal axis is the actual survival time (months, range 0-60), and the vertical axis is the predicted survival time (months, range 0-60). The diagonal line in the figure is the ideal prediction line, and the dotted line is the ±6-month error range. Most of the sample points (89.2%) are distributed within the ±6-month error range, of which the samples with an error of ≤3 months account for 68.7%, indicating that the quantitative prediction accuracy of the model is high [11]. Through the local weighted regression curve (LOWESS) analysis, it was found that the prediction deviation of samples with a survival period of 12-36 months was the smallest (average error 2.3 months), while the error of samples with a survival period of <12 months or >36 months was slightly larger (average error 4.7 months), which may be related to the rapid progression of the disease and large individual differences in advanced patients. Subgroup analysis showed that the prediction error of patients with adenocarcinoma (RMSE=5.6 months) was smaller than that of patients with squamous cell carcinoma (RMSE=6.8 months, P=0.032), suggesting that the prediction accuracy of the model for different pathological types was different. Fig 2. Scatter distribution of the predicted survival period and the actual survival period of the multimodal model Compared with the single-modal model, the accuracy of the multimodal model in each stage increased by an average 38 of 9.4%, including 11.3% in stage I, 9.7% in stage II, 8.5% in stage III, and 7.1% in stage IV. In the prediction of 1-year survival rate, the accuracy of the multimodal model was 83.5%, 7.3% higher than the imaging omics model and 10.4% higher than the liquid biopsy model; in the prediction of 3- year survival rate, the accuracy was 79.8%, 8.5% higher than the imaging omics model and 11.3% higher than the liquid biopsy model, further verifying the advantages of the multimodal model in the prediction of survival probability [12]. Stratified analysis showed that in patients with EGFR mutations, the C-index of the multimodal model (0.873) was higher than that of wild-type patients (0.831, P=0.041), suggesting that the model is more accurate in predicting patients with driver gene mutations, which may be related to the fact that mutation status affects both imaging features and liquid biopsy indicators. 4. Discussion 4.1. Discussion on the Advantages of the Multimodal Model The complementarity of CT imaging and liquid biopsy data is the core mechanism for improving prediction performance [13]. CT images intuitively reflect the spatial invasiveness of tumors through three-dimensional anatomical features (such as tumor volume, pleural invasion range, and spicule distribution). For example, the degree of lobulation of lung cancer is positively correlated with microvascular invasion (r=0.68) (Figure 3), while the ctDNA mutation spectrum (such as EGFR L858R mutation abundance) and exosome markers (such as PD-L1 fluorescence intensity) in liquid biopsy reveal the molecular heterogeneity of tumors (Figure 4). This "morphology-molecule" dual-dimensional information fusion shows unique value in key clinical scenarios: for early peripheral lung adenocarcinoma shown on CT images, if the tumor diameter is < 2cm and there is no pleural traction, a single imaging model may classify it as a low-risk group (3-year survival rate prediction of 85%), but the multimodal model combined with TP53 co-mutation detected by liquid biopsy (mutation abundance > 3%) can accurately identify its high risk of recurrence (the actual 3- year survival rate is only 52%, and the prediction error is < 2 months). In advanced lung cancer, when CT images show multiple metastases, if the liquid biopsy indicates ALK fusion and ctDNA clearance rate > 70%, the model can predict the long-term benefit of targeted therapy (median survival predicted 32 months, actual 31 months), while the single liquid model may misjudge it as a short-term response due to the metastatic burden. Fig 3. CT images of lung cancer lobulation and microvascular invasion Fig 4. CTDNA mutation spectrum 39 In clinical practice, the application potential of this model is reflected in three aspects: First, assisting postoperative treatment decisions. For stage IB patients with a 1-year recurrence risk > 30% predicted by the model, adjuvant chemotherapy is recommended to increase the 5-year survival rate by 12%; second, optimizing the use of targeted drugs [14]. For advanced patients with a predicted survival period of < 18 months, the use of palliative treatment can reduce medical expenses by 40%; third, dynamically evaluating the efficacy. During treatment, if CT images show that the tumor has shrunk but the ctDNA clearance rate is < 50%, the model can warn of drug resistance 4-6 months in advance (accuracy 82%), which is 2 cycles earlier than traditional imaging evaluation. Compared with traditional clinical staging, the model's prediction accuracy for 3-year survival rate increased by 19.3%, and the risk stratification error rate for stage II patients decreased by 35%, significantly reducing clinical decision-making bias. 4.2. Discussion on Model Limitations Data heterogeneity poses a major challenge: Although the CT equipment of the three medical institutions uses unified scanning parameters, the image grayscale value deviation still reaches ±15HU due to different degrees of tube aging, resulting in a coefficient of variation of 8.7% in the "entropy value" indicator in the imaging genomics feature; in terms of liquid biopsy, the difference in ctDNA extraction efficiency (82%-90%) between different centers causes mutation detection sensitivity to fluctuate by 5%-8%, which directly affects the accuracy of TMB calculation, resulting in a 12% difference in TMB values for the same patient in different centers [15]. This heterogeneity caused the model's C-index to reach 0.89 in single-center validation, but dropped to 0.81 in cross-center validation (P<0.05), among which the prediction error of stage III patients increased the most (RMSE increased from 5.8 months to 7.1 months). The generalizability of the model is limited by three aspects: first, racial differences. The median fluorescence intensity of exosomal PD-L1 in Asians (620AU) is significantly higher than that in Caucasians (450AU), resulting in a 20% increase in the false positive rate of the threshold optimized for Asians (>500AU) in Caucasians; second, pathological type limitation. Small cell lung cancer samples account for only 18%, and its high invasiveness leads to a significantly higher model prediction error for its survival (RMSE=7.2 months) than non-small cell lung cancer (RMSE=5.3 months); third, technical dependence. If grassroots hospitals use 500× deep sequencing, the detection rate of low-frequency mutations (MAF 0.5%-1%) in liquid biopsy features will decrease by 35%, which will lead to a 23% attenuation of model performance [16]. In addition, the model does not include dynamic factors such as patient treatment compliance and combined diabetes, and the prediction error of long-term (>3 years) survival is 18% higher than that of short-term survival, which may affect the long-term nature of clinical decision- making. 4.3. Future Research Directions A multi-center expansion study is planned: 1,500 patients from 12 hospitals of different levels (3 tertiary hospitals, 6 secondary hospitals, and 3 primary hospitals) are planned to be included in the next 2 years. Standardized image acquisition protocols (including monthly equipment calibration phantom detection) and liquid biopsy quality control products (containing ctDNA reference materials with known concentrations, with a concentration gradient of 0.1- 10ng/μL) are used to eliminate regional bias through multi- center cross-validation. Simultaneously establish an "image- molecule" feature database, and use federated learning technology to achieve data sharing without leaking privacy. This technology can increase the data utilization rate of each center by 60%, and is expected to increase the model C-index to 0.87, and reduce the cross-center verification error by 15%. Technological innovation focuses on three aspects: First, exploring new imaging features. The quantitative parameters of iodine concentration based on dual-energy CT (1.2±0.3mg/mL in normal lung tissue and 3.5±0.8mg/mL in tumor tissue) can reflect the tumor microvessel density and are positively correlated with the expression of the angiogenic factor VEGF (r=0.72); second, expanding the dimension of liquid biopsy and introducing single-cell sequencing data of circulating tumor cells to capture tumor clonal evolution information, such as tracking the dynamic changes of subclones of the EGFR T790M mutation; third, optimizing the fusion algorithm and developing a spatiotemporal attention mechanism to give higher weight to dynamic data after treatment (such as the weight coefficient of ctDNA changes after 2 cycles of chemotherapy is 1.2, and the baseline characteristics are 0.8). In addition, by integrating the model with electronic health records (EHR), incorporating clinical variables such as smoking history (pack-years) and comorbidities (such as COPD), and processing time series data through LSTM networks, it is expected that the prediction error will be further reduced to within 4.5 months, promoting the model from laboratory to routine clinical application, and improving the decision-making efficiency of individualized treatment of lung cancer by 50%. 5. Conclusion This study successfully constructed a multimodal model based on CT images and liquid biopsy, and achieved remarkable results in predicting lung cancer survival. The model has a prediction accuracy of 85% and a consistency index of 0.82, which is significantly improved compared with the single-modality model, providing a powerful tool for individualized treatment of lung cancer. However, the model is still limited by factors such as data heterogeneity. 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