Retrospective data from 261 lung cancer patients who underwent thoracic radiotherapy combined with immunotherapy at the Harbin Medical University Cancer Hospital between January 2018 and May 2023 were collected. These data encompassed the clinical, immunological, and dosimetric parameters of the patients. Patients were selected based on specific criteria. The criteria for inclusion include: (1) aged ≥18 years; (2) had primary lung cancer confirmed by pathology; (3) received lung lesion radiotherapy combined with immunotherapy; (4) had complete dose-volume parameters and other information available; and (5) had a follow-up period exceeding 3 months postradiotherapy to acquire comprehensive pre- and posttreatment imaging data. The criteria for exclusion include: (1) active pulmonary infection; (2) pneumonia or abscess irrelevant to the tumor; (3) atelectasis or pleural effusion; and (4) loss to follow-up or incomplete imaging data. The study received approval from the hospital’s ethical review committee. Due to this research was a retrospective study, patient informed consent was not required.

Univariate and multivariate regression analyses of clinical baseline characteristics are performed to identify 12 clinical indicators potentially associated with SP. After extracting multimodal features, we integrate them and feed them into a DNN-based prediction module, as illustrated in the Figure 1 . The clinical data, traditional radiomic features, and deep image features are normalized and dimensionally reduced using principal component analysis (PCA) to enhance computational efficiency and reduce storage requirements. The dimensionally reduced data are concatenated and fed into a fully connected layer for final prediction. The formula is as followed:

where F c is the clinical features, F t is the radiomic features and F d is the deep image features. L is the number of fully connected layers. W i represents the weight matrix, b i is the bias vector and σ i is the activation function for i-th layer. In the study, we used LeakyReLU function as the σ . The loss function we used as follows:

where y ^ i is the label, y i is the prediction of our method.

This study included 261 lung cancer patients received a combination of thoracic radiotherapy and immunotherapy at Harbin Medical University Cancer Hospital between January 2018 and May 2023. Among them, 36.02% (94 patients) experienced SP, with 6.13% (16 patients) classified as G3+ pneumonitis. Table 2 presents the baseline characteristics of the patients. In the SP group, the median age was 61 years, with males comprising 81.9% of the group. Additionally, 88.3% of the patients had a PS score ranging from 0 to 1, and 53.2% had a history of smoking. Squamous cell carcinoma was the most prevalent histological type at 51.1%, while SCLC accounted for 25.5%. In terms of disease staging, T2-stage disease was present in 36.2% of patients, while N2-stage disease was noted in 63.8% of individuals. Importantly, 71.3% of the patients received a radiotherapy dose of 60 Gy or higher, and 78.7% underwent 4 or more cycles of chemotherapy. At the time of patient follow-up, the median number of immune drug cycles administered was 4. The most commonly used immunotherapy drug was camrelizumab (23.4%), followed by tislelizumab (20.2%). Notably, 12.8% of patients received combination immunotherapy drugs. Twelve clinical indicators potentially linked to SP were identified, including the ECOG PS score, T stage, N stage, radiotherapy dose, number of immunotherapy cycles, preradiotherapy NLR, preradiotherapy PLR, preradiotherapy LMR, preradiotherapy SII, V5, V20, and MLD. Based on the univariate analysis, the PS score (HR: 3.556, 95% CI: 1.270-9.955, P=0.016), T-stage (HR: 1.558, 95% CI: 1.198-2.026, P=0.001), and PLR before radiotherapy (HR: 1.003, 95% CI: 1.000-1.006, P=0.028) were significantly linked to the occurrence of SP. The multivariate analysis indicated that PS and T-stage were independent predictors for SP (HR: 3.322, 95% CI: 1.148-9.615, P=0.027; HR: 1.501, 95% CI: 1.147-1.965, P=0.003). Refer to Table 3 for the results of both univariate and multivariate analyses on SP.

To validate the predictive capability of our proposed algorithm for symptomatic pneumonitis caused by lung cancer, we compared it with the classical machine learning algorithm RF and the classical deep learning algorithm. Additionally, we discussed the impact of multimodal data on the algorithm.

The KEYNOTE-799 trial investigated the safety profile of pembrolizumab when used together with concurrent chemoradiotherapy for patients with inoperable LA-NSCLC. Findings showed that the occurrence of any-grade pneumonitis in both groups were 37.5% and 26.4%, while rates of G3+ pneumonitis were 8.0% and 6.9%, respectively (19). In real-world scenarios, patients often present complex conditions, such as undergoing multiple treatment cycles, having underlying illnesses, or being elderly. These individuals frequently harbor pneumonia-related risk factors, leading to their exclusion from clinical trials. In a retrospective study encompassing lung cancer patients receiving durvalumab immunoconsolidation postchemoradiotherapy across 10 institutions, 63.5% of patients developed pneumonitis during immunoconsolidation therapy, with SP accounting for 33.7% and grade 3 pneumonitis accounting for 8% (10). A real-world Japanese investigation of pneumonitis occurrence during durvalumab consolidation postchemoradiotherapy in NSCLC patients revealed an occurrence of any-grade pneumonitis of 83%, an incidence of SP of 34%, an incidence of G3+ pneumonitis of 7%, and a fatal pneumonitis rate of 1% (20). In this study, any-grade pneumonitis accounted for 76.63%, SP accounted for 36.02%, and grade 3 or above pneumonitis accounted for 6.13%, aligning closely with Japanese real-world research data but surpassing clinical trial outcomes. This finding indicates the critical importance of tailored prevention and management strategies for radiotherapy-immune-related pulmonary toxicity in diverse patient populations. A recent extensive meta-analysis highlighted a 20.2% incidence of symptomatic RP postchemoradiotherapy in 24,527 patients with LA-NSCLC. Notably, the analysis revealed a 33.6% occurrence of G2+ pneumonitis in patients who were treated with a combination of chemoradiotherapy and immunotherapy, indicating a higher risk of symptomatic RP than in patients receiving radiotherapy alone (21). Similarly, a comprehensive meta-analysis, which reviewed data from 836 patients NSCLC who received chemoradiotherapy, reported an overall incidence of symptomatic RP at 29.8% following chemoradiotherapy (22), which was lower than the 36.02% incidence of SP in this study. This suggests that incorporating immunotherapy elevates the risk of SP beyond grade 2.

In this study, the occurrence of SP was found to be independently linked to the ECOG PS score and T stage, which is consistent with existing research findings. The evaluation of tumor size through T staging shows that T1 tumors have a reduced risk of SP compared to T2 tumors (23), and tumors in stage I-II have an inferior risk of RP compared to those in stage III-IV (24). In addition to reflecting systemic immune status, immunological parameters offer insights into the immune microenvironment within tumor tissues. Studies indicate that these parameters correlate with lung cancer patient prognosis postradiotherapy and chemotherapy (25, 26). Our univariate analysis revealed a connection between the pre-RT PLR and the occurrence of SP, which is consistent with prior research. Shan et al. (27) indicated that the pre-treatment PLR is a predictor of RP occurrence in patients who are receiving stereotactic body radiation therapy (SBRT). Although dosimetric parameters did not show an association with SP in our study, recent research underscores their relevance to pulmonary toxicity in lung cancer patients undergoing radiotherapy with immunotherapy. Landman et al. (28) highlighted a significant relationship between pneumonia incidence in radioimmunoassay-treated lung cancer patients and higher bilateral lung V5, V20, and MLD. Additionally, Koffer et al. (29) reported that bilateral lung V20 and MLD can predict SP occurrence in lung cancer patients postcombined radioimmunotherapy. Dosimetric parameters were integrated into our model based on these studies.

Radiotherapy combined with immunotherapy offers a significant survival benefit for lung cancer patients; however, it also intensifies pulmonary toxicity. This study represents an earlier exploration into predicting pulmonary toxicity resulting from combined radioimmunotherapy. Lung cancer is a complex and heterogeneous disease, particularly in patients receiving combined radioimmunotherapy. These individuals often exhibit diverse baseline characteristics, such as multiple treatment cycles, underlying illnesses, and advanced age, which are common high-risk factors for pneumonia onset and vary widely across patient groups. Individual patient differences, and treatment sensitivities of tumors, clinical data, immunological factors, and dosimetric parameters derived from CT images are integrated to provide comprehensive and personalized insights recognizing the intrinsic heterogeneity and enhancing the accuracy of SP prediction. Krafft et al. (14) created a model to predict RP using pretreatment whole-lung radiomic features combined with clinical and dosimetric data, achieving an AUC of 0.68, which was consistent with our findings. Meanwhile, radiomics has made significant advancements in the diagnosis and prognosis of lung cancer. Lung cancer is one of the most prevalent and deadly malignancies globally, with a relatively low overall five-year survival rate. Therefore, early diagnosis and prognosis are crucial for improving patient survival rates. In recent years, the widespread application of artificial intelligence (AI) technologies in the field of lung cancer, particularly in natural language processing, machine learning, deep learning, and reinforcement learning, has significantly enhanced the accuracy of early diagnosis and prognostic predictions (30). Furthermore, the application of AI has propelled cancer prediction performance to new heights, not only improving diagnostic accuracy but also presenting new opportunities and challenges for clinical implementation (31). In addition, Qiu et al. (32) distinguished between CIP and RP based on pre-treatment CT images, revealing that clinical or radiological parameters such as bilateral lesions (p < 0.001) and sharp margins (p = 0.001) were significantly associated with the identification of CIP and RP. In the validation cohort, the AUC values reached 0.901 and 0.874, respectively. Yang et al. (33) explored the ability of CT radiomics features to predict the EGFR mutation status. The results showed that the AUC values of the unenhanced, arterial and venous phases in the EGFR mutation status training group were 0.6713, 0.8194 and 0.8464, respectively. Wen et al. (34) compared the predictive performance of pretreatment CT-based radiomics signatures and clinicopathological and CT morphological factors for PD-L1 expression level and tumor mutation burden (TMB) status. Radiomics signatures showed good performance for predicting PD-L1 and TMB with AUCs of 0.730 and 0.759, respectively. Predictive models that combined radiomics signatures with clinical and morphological factors dramatically improved the predictive efficacy for PD-L1 (AUC = 0.839) and TMB (AUC = 0.818). Radiomics can extract numerous features but may overlook subtle high-order characteristics. Hence, a DL approach is applied in this study based on the ResNet34 architecture for deep image feature extraction. Unlike manual feature extraction, DL eliminates the need for contouring, reducing variability among different segmentations and enhancing efficiency. Additionally, DL provides detailed information specific to each task within the hidden layers of the neural network. This amplifies the performance of the model by a significant margin. The multidimensional deep network model developed in this study outperforms several other models, demonstrating that integrating machine learning and clinical data can enrich image features with additional information, markedly enhancing predictive accuracy. Moreover, this study aims to create a decision-support tool for clinical use, emphasizing that optimal models often require the combination of multiple factors to enhance decision-making capabilities (12, 35). Consequently, the results affirm that models combining multiple indicators outperform single models. PCA is used to diminish the complexity of image features that have been extracted. This method allows the conversion of high-dimensional data into lower-dimensional form, ensuring that essential information is retained throughout the process. By selecting representative principal components, data redundancy is minimized, data analysis and visualization processes are simplified, and model performance is enhanced.

The combination of radiotherapy and immunotherapy has become the standard treatment for patients with locally advanced, unresectable NSCLC. While this combination enhances anti-tumor efficacy, it also increases the risk of pulmonary complications, particularly SP. The clinical significance of our model lies in its ability to identify independent risk factors, such as ECOG and T stage, which underscores the model’s clinical applicability. These factors are readily available in clinical practice, allowing for the model to be integrated into the routine assessment of lung cancer patients prior to initiating treatment. Moreover, our model relies on pre-treatment CT images, which means it can be easily implemented in clinical settings without the need for additional invasive procedures or extensive follow-up imaging. This convenience enhances its feasibility as a routine assessment tool. The ability to accurately predict SP is crucial for patient management. Our study presents a robust deep imaging radiomics model that serves as a decision-making tool, capable of precisely forecasting the occurrence of SP in lung cancer patients undergoing radiotherapy and immunotherapy. By facilitating the early identification of patients at high risk for SP, the model guides clinicians in proactive monitoring and intervention, enabling the selection of appropriate treatment strategies tailored to individual patient profiles. Consequently, treatment plans can be adjusted accordingly, mitigating the severity of pneumonitis and improving the safety of therapeutic regimens for this vulnerable population.

There are limitations to this research. First, being retrospective, it depends on data from solely one institution and lacks validation from external data; therefore, additional research is necessary to evaluate the model’s applicability. Second, the sample size was limited, underscoring the necessity for more extensive samples in forthcoming studies to improve model precision. Third, in addition to radiomic features, the present study incorporated only clinical, immunological, and dosimetric parameters, omitting biological factors. Studies show that increased baseline levels of GM-CSF and sIL-6R are associated with the development of pneumonitis in lung cancer patients receiving radiochemotherapy and immunotherapy (36), variables not included in this study. Genomic or proteomic data can elucidate pulmonary toxic response mechanisms, suggesting that integrating radiomic features with such data can enhance predictive capabilities. Thus, future research should explore new data types and incorporate additional information to refine the prediction accuracy. Moreover, this study utilized only dose and volume histogram (DVH) parameters to represent the two-dimensional dose distribution within the target body and failed to capture spatial dose distribution characteristics. RP primarily manifests in the locally irradiated lung region. Multiregion-based radiomics can depict the three-dimensional spatial dose distribution and can extract radiomic features from multiple regions, including high-, medium-, and low-dose areas and specific-sized 3D rings formed around the tumor, capturing predictive indicators for the occurrence of pulmonary toxicity better. While this study extracted radiomics features from normal lung tissue and combined them with two-dimensional dose parameters to construct a model, future investigations can incorporate multi-regional three-dimensional omics analysis for enhanced predictive outcomes.