Lung cancer (LC) remains a major health problem, causing the highest mortality in Europe and worldwide.1 These data are explained by more than 70% of cases diagnosed at an advanced stage with a low survival; therefore, early diagnosis is key because early-stage treatment improves LC prognosis.2 Recently, a shift has been occurring due to the increasingly expanded use of computed tomography across various settings, leading to a rise in the number of incidentally detected pulmonary nodules (PN). On the other hand, this shift is also attributed to the implementation of lung cancer screening (LCS). In both cases, the outcome is early-stage diagnosis. That's how the 2022 American Cancer Society annual report reveals a decline in the incidence of advanced-stage cancer, with a simultaneous annual increase of 4.5% in a localised stage primarily linked to the initiation of LCS in the US a decade ago.3

Despite the known advantages in reducing mortality due to LCS,4–6 not all PN detected are LC. In a systematic review, 21% of trial screens yielded a false positive result (range 1–42%) in baseline LDCT,6 requiring follow-up images or even biopsy. Thus, these false positives can cause potential harm to patients, and they can generate high costs for health services.7–9 Similarly, they occur with the increased incidental finding of PN, as many of these are benign and require close follow-up, which is similar to the approach taken with PN identified through LCS.10–13 In this context, whether the PN is detected incidentally or through LCS, it is crucial to identify high-risk cases and rule out those with low risk.14,15

Fortunately, applying artificial intelligence (AI) techniques to radiomics might make it possible to discriminate between benign and malignant PN.16 Radiomics is an emerging area based on the high-throughput mining of quantitative image features from medical images that allows data to be applied in clinical decision-making to improve diagnostics and prognostics. It is mainly applied to cancer research.17,18 Image analysis with a structured step workflow19 is used to extract complementary features from multiple views (the shape, intensity and texture of features, among other things) which encapsulate different aspects of the lesion. Machine learning is a subfield of AI that encompasses all approaches and allows computers to learn from various types of data. In contrast, deep learning is part of machine learning based on multi-layered artificial neural networks to solve highly complex problems.20,21 This has been applied in radiomics and is used to model such multi-view features to predict clinical outcomes.

The usefulness of radiomics in the early diagnosis of LC is promising.22,23 Although many of these studies yield favourable outcomes, most are based solely on imaging data. Incorporating clinical data related to LC can enhance these findings by improving their application in daily clinical practice, yet there are still few studies with this approach. Among these, Zhang et al., using retrospective data, developed a radiomic and deep learning model to predict the malignancy of PN, achieving an AUC of 0.819 (95% CI: 0.76–0.88).24 Employing a multi-omics approach could represent a significant advancement in applying AI models to the clinical prognosis of LC patients.25

The Radiolung project aims to design an algorithm based on a radiomic signature that, associated with clinical data, can accurately discriminate between LC and benign tumours.

Radiomics has revolutionised medical imaging, and there are currently many working groups dedicated to this research in different fields.18,31 Other AI techniques, such as deep learning, allow us to associate these radiomic characteristics with other data, such as clinical information. Regarding this integrative approach, few working groups are dedicated to LC and PN diagnosis using radiomics and clinical data together, and the retrospective study by Lui et al.32 stands out. They conducted an analysis utilising 20 radiomic features, in addition to age, gender, and PN location. Successfully, they developed a predictive model that achieved an AUC of 0.81 (95% CI: 0.75–0.87). Similar results were published in 2023 by Lin et al.33 With a similar approach, our study yields promising results, as the integration of selected clinical features with radiomic image data based on deep learning techniques can formulate a predictive model that significantly improve results compared to relying solely on image analysis, achieving a positive predictive value of 94% (95% CI: 0.85–0.99) and an AUC of 0.80 (95% CI: 0.67–0.92). It is worth noting that the accuracy showed a slight but non-significant decrease in the integrative model, which could be related to the imbalance in the number of benign and malignant cases in our cohort.

Other studies such as Marmon et al.34 and Kammer et al.35 have developed models based on blood biomarkers, clinical data, and radiomics with favourable results. However, it is important to note that these studies use blood biomarkers that are not specific to LC, which may add an additional cost without necessarily improving performance compared to models using only clinical and radiomic data. Furthermore, technically, they use classic machine learning hand-crafted features for radiomic descriptors without incorporating information from surrounding lung tissue. While these studies are relevant, our research stands out for its innovative deep learning approach and its ability to integrate multiple data sources.

Regarding validated predictive models based on clinical data, there are those designed for incidental PN such as the Mayo model,36 while others focus on LCS such as the Brock model.37 Please see the comparative table in the supplementary material. We have applied the Mayo model in our cohort and it exhibits similar performance in terms of PPV of 88% (95% CI: 0.75–0.95), but worst accuracy 63% (95% CI: 0.52–0.74), and AUC of 0.54 (95% CI: 0.36–0.72) (see supplemental material).

A strength of this study is that it employs prospective patient data for individuals who have undergone PN surgery, allowing for the collection of clinical data, chest CT images and histology. Another point to highlight is the novelty of this exploratory study, as it tests various clinical characteristics to identify the most representative ones for association with a deep radiomic model. This emphasizes the importance of integrating tools from daily medical practice with new technologies to enable early cancer diagnosis. Finally, we have compared our model with a conventional and validated clinical model such as the Mayo model, allowing us to demonstrate its validity in our cohort.

As for the limitations of this study, the most significant one is the need to expand the number of cases to improve both the clinical and radiomic models. Therefore, we are considering making the next study multicentric in the upcoming year to validate our exploratory analysis. Another limitation is the low number of benign cases, as these are real-life cases involving PN that have undergone surgery, which are inherently more diagnostically challenging. On the flip side, this situation contributes to the model training to detect suspicious malignancy cases of PN that ultimately prove benign.

Radiomics and AI models focused on LC are evolving across different phases, encompassing aspects such as PN management, diagnosis, treatment, and relapse of LC.38,39 Integrative models with clinical data will be crucial to optimise their effectiveness. These advancements will positively impact patients, particularly in managing PN, because these models will aid in more accurately predicting the likelihood of malignancy, thereby avoiding unnecessary follow-ups, biopsies or surgeries. This situation not only reduces patient anxiety but also minimises potential harm, decreases waiting lists and reduces healthcare system costs.

In conclusion, we firmly believe that this integrative model, combining clinical data and radiomics through deep learning, can aid in diagnosing and managing PN. Although external validation is necessary in a subsequent phase, in the near future it may be directly useful in LCS programmes, thus optimising the early detection of LC and bringing technological advances closer to clinical practice.

This project is supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España grant number PID 2021-126776OB-C21; Barcelona Respiratory Network (BRN) Fundació Ramon Pla i Armengol; “Clinical project” grant, Fundació Acadèmia Ciències Mèdiques de Catalunya i de Balears; “Talents” grant 2020, Fundació La Pedrera and Hospital Universitari Germans Trias i Pujol; Lung Ambition Alliance Grant; and the JMC Legacy Research Fund of Germans Trias i Pujol University Hospital.

The authors declare not to have any conflicts of interest that may be considered to influence directly or indirectly the content of the manuscript.

Sonia Baeza: conceptualization, methodology, data curation, formal analysis, validation, writing-original draft, writing-review and editing, visualisation. Debora Gil: conceptualization, methodology, software, formal analysis, validation, writing-review and editing, supervision. Carles Sanchez: methodology, software, data curation, formal analysis, validation, writing-review and editing. Guillermo Torres: methodology, software, data curation, formal analysis, validation, writing-review and editing. Cristian Tebé: validation, formal analysis, writing-review and editing. João Carmezim: validation, formal analysis, writing-review and editing. Ignasi Guasch: methodology, writing-review and editing. Isabel Nogueira: methodology, writing-review and editing. Samuel Garcia-Reina: methodology, writing-review and editing. Carlos Matínez-Barenys: methodology, writing-review and editing. Jose Luis Mate: methodology, writing-review and editing. Felipe Andreo: conceptualization, writing-review and editing. Antoni Rosell: conceptualization, methodology, formal analysis, writing-review and editing, supervision.

The text is original, and no artificial intelligence techniques have been used to write its content.