Lung cancer is a significant contributor to global cancer mortality (1). Lung cancer is classified into two main groups (2, 3): non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), of which about 85% of patients belong to NSCLC, which includes lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and other histological subtypes. In NSCLC patients, LUAD accounts for the largest proportion. With the development of low-dose computed tomography(LDCT) lung cancer screening, a large number of patients with early-stage NSCLC have been screened, and in particular, a considerable number of patients with stage I LUAD have been screened (4, 5), for which surgical resection is the preferred treatment (6). However, studies have found that the risk of recurrence remains high, even with a 20-50% recurrence rate for completely resected stage I LUAD (7). Therefore, assessment of postoperative recurrence is crucial for the prognosis of stage I LUAD.

Currently, most studies have focused on assessing benign and malignant tumours (8), disregarding the prognostic impact of subtle changes in the peritumoural microenvironment (9, 10). Furthermore, studies on the prognosis of LUAD have primarily concentrated on evaluating the prognosis of intermediate and advanced lung cancer based on genes and treatment regimens (11–14), while neglecting the impact of certain clinical factors such as immunohistochemistry and density on the prognosis. It is important to note that due to the heterogeneity of tumours (15, 16), even at the same stage, the prognosis can vary significantly. Moreover, most of the previous studies have been on two-dimension (2D) and three-dimension (3D) prognostic models (17, 18), and nowadays some scholars have started to study 2.5-dimension (2.5D) models (19) as well. Through the peritumoural radiomics prognostic study of stage I LUAD (20, 21), this study not only makes up for the shortcomings of previous studies, but also develops a new 2.5D peritumoural radiation-clinical omics model. Compared with previous 2D or 3D radiomics features, the method is newer and more effective in studying the prognosis of LUAD.

In recent years, much attention has been paid to an emerging technology, radiomics, which automatically extracts a large number of imaging features from medical imaging data in a high-throughput manner; it appears to offer an almost unlimited range of imaging biomarkers, and shows great potential in oncology for detecting, diagnosing, evaluating prognosis, and predicting response to treatment (24–26). Furthermore, an increasing number of scholars are conducting radiomics studies on the interstitium of peripheral lung cancer, which refers to the tissue surrounding the primary tumour, and achieving favourable outcomes (17, 27, 28). This demonstrates the importance of the peritumoural region in radiomics analysis (29).

Tumour radiomics is widely used for prognostic prediction in LUAD (27). However, few studies have applied peritumoural imaging features to aid in the prediction of stage ILUAD, and the selection of the peritumoural region remains controversial. Previous studies have defined the peritumoural region as ranging from 1.5 to 20 mm (8, 30, 31). Wu et al. concluded that peritumour radiomic features based on CT images are reliable for predicting the prognosis of non-small cell carcinoma (28). The study also noted that the peritumoural region should ideally extend 15 mm, 20 mm or 30 mm from the tumour border. Chen et al. measured the bulk tumour volume as well as the bulk tumour volume in the peritumoural 3mm, peritumoural 6mm and peritumoural 9mm regions by extracting the radiomic feature regions (18), and finally constructed the bulk tumour volume of peritumoural 9mm region based on the extraction of the radiomics features had the highest AUC (training set = 0.82, internal validation = 0.75, external validation = 0.67). Liu et al. conducted another study where they extracted radiomics features from intratumoural to peritumoural 3mm, peritumoural 3mm and peritumoural 6mm regions (17). The study demonstrated that features from the intratumoural 3mm to peritumoural 3mm region had higher predictive performance. In a study using radiomics to predict early recurrence, Wang et al. selected 2.1 mm, 4.2 mm, and 8.4 mm as the peritumoural regions, extracted 2D and 3D deep learning image features, and constructed a radiomics model via an air cavity diffusion model, which resulted in good performance in both internal validation cohort and external validation cohort, demonstrating its potential for assisting in post-surgical treatment strategies (7). Wang et al. investigated 8 models of tumour perimeter 5mm, 10mm, 15mm, 20mm as well as tumour-perimeter 5mm, tumour-perimeter 10mm, tumour-perimeter 15mm, tumour-perimeter 20mm, and found that nomogram based on the combined model of tumour-perimeter 10mm and clinical features had a high predictive efficiency for STAS status in NSCLC patients (32). It can be seen that the researchers chose different peritumoural regions, but the best performing peritumoural features essentially consisted of features in the 3-9mm peritumoural regions. In addition, it was also found in previous studies that only intratumoural features were used to predict the prognosis of LUAD (33–36), whereas in this study, the use of peritumoural features performed well.

Based on these previous studies, we selected peritumoural 3mm, peritumoural 6mm, peritumoural 9mm, peritumoural 12mm and peritumoural 15mm as peritumoural regions, but unlike them, we used low-dose lung cancer screening CT plain images and performed 2.5D radiological feature extraction. In this study, we found that the combined peritumoural 9mm radiation-clinical omics model had the highest diagnostic efficacy (AUC=0.865) compared to the tumour and the rest of the peritumour models, with good AUC and sensitivity, specificity, NPV, and PPV in both the internal validation cohort and the external validation cohort, and that it outperformed the conventional unimodal model. By combining a peritumoural 9mm radiomics model with clinical factors, we have also created a visual nomogram with high predictive power and net benefit in the evaluation of recurrence after surgery for stage ILUAD. Our study provides a new approach to prognostic assessment, helps to adjust the treatment plan for patients with stage ILUAD, and enables AI-personalised management of the prognosis of these patients.

Multifactorial logistic regression analysis identified T-stage, neuron-specific enolase assay, Ki67 and nodule type as independent predictors of recurrence after surgery for stage ILUAD, which can be used for clinical modelling. Higher clinical stage, Ki67 percentage, and percentage of nodal solid component imply higher proliferation and invasiveness of tumour cells and higher risk of postoperative recurrence, which is consistent with previous reports (37–39). In addition, multifactorial logistic regression showed that neuron-specific enolase assay and nodule type were also independent predictors of postoperative recurrence, but the clinical-omics features were not significant; therefore, we developed a nomogram combining some of the independent predictors in combination with peritumoural 9mm radiomic features to predict the probability of recurrence in patients with stage ILUAD. In clinical practice, the patient’s clinical information and radiological score(radscore) are added to the nomogram to obtain multiple probability scales, and then the total score of the nomogram is calculated, which shows the probability of recurrence. Notably, there was a significant improvement in the AUC of the nomogram compared to a single radiomics and clinical model. It can gain valuable treatment time for patients with stage ILUAD that may recur, and it can help to develop a more rational and effective treatment plan. When it is known that a patient has a high probability of recurrence after surgery, some adjuvant treatments such as chemoradiotherapy or targeted drugs can be taken to reduce the chance of recurrence.

In addition, DeLong test of AUC for each model showed that in the training cohort, the AUC values for the nomogram were significantly different from those of the peritumoural 9mm radiomics model and the clinical model (P < 0.05). The results of the study showed that the combined radiation-clinical omics model performed better than the single model, and that clinical parameters also play an important role in predicting postoperative recurrence for stage ILUAD.

The different models constructed in this study not only provide intratumoural and peritumoural biological information, but also give some guidance for clinical treatment. Furthermore, by comparing the diagnostic performance of the different peritumour models, the peritumoural 9mm model had the best predictive performance overall, possibly due to the higher reproducibility of radiomics features the further away from the intratumour area. This finding may be related to the presence of homogeneous lung parenchyma in the distal peritumoural area (31). Thus, in our study, the peritumoural 9mm model showed better predictive performance than the other models. According to the recommendations of the NCCN guidelines for NSCLC 2024, 4th edition, for most patients with NSCLC, the margin requirement is to ensure that the lung parenchyma margin distance is ≥ 2 cm or ≥ the size of the tumour nodule (40), and it was found that the peritumoural region was often extended from the tumour border to 15 mm, 20 mm, or 30 mm (30, 41, 42). However, in our study, when extending to 20 mm peritumour, we found it difficult to avoid thick blood vessels and bronchioles, and complex extrapulmonary tissues, so we only extended to 15 mm peritumour.

This study has several limitations. Firstly, it is a retrospective study and there may be recurrent cases in the 2018 cases so far. Secondly, the sample size in this study was small and the predictive efficiency of the external validation cohort may be erroneous, and due to the small sample size, we could not perform survival analysis, and more large sample studies are needed for further validation in the future.

In summary, the combined 2.5D peritumoural 9mm radiation-clinical omics model is more accurate than the tumour and the rest of the peritumoural model in predicting the prognosis of clinical stage ILUAD, and may serve as an effective non-invasive predictive tool, which may provide value in decision-making and defining personalised treatments. However, since most of the studies were conducted retrospectively, further prospective, multicentre and biologically relevant studies based on prospective, multicentre and biologically relevant studies should be carried out in order to facilitate its clinical application.