## 4 Results and Discussion
### Limitations  
In comparison with works from the literature based on the same dataset for training, the Lung-PET-CT-Dx, our best model is close in performance but does not achieve yet a performance with an AUC above 0.9. However, it is important to note that most literature works are based on a multi-class classification, and are not focused on adenocarcinoma classification. One example is the work of [PERSON] et al. [51] that achieved an AUC of 0.97, with an SVM model based on radiomic features capable of distinguishing four different subtypes. It is noteworthy that the aggregation of two distinct histological subtypes within the negative class introduces a challenge in comparison with the state-of-the-art results. This factor can impact especially the evaluation of the models in the external datasets where the negative class (Non-ADC) can present a different distribution between the two aggregated subtypes (LCC+SCC). In addition, the training of the models was performed in an unbalanced dataset, systematically balanced by SMOTE or data augmentation, techniques that present limitations especially when applied to a small dataset.  
A limitation of the current work is the lack of generalization capability of the obtained models when evaluated in external datasets. It is important to note that this decay in performance cannot be compared with the literature due to a lack of evaluation of the published models in external test sets [51]. The obtained results indicate how the performance of the models published in the literature not evaluated in an external dataset can underestimate the domain shift problem and the lack of domain generalization of models trained in a single dataset. The drop in performance on external datasets was studied by analysing the results together with the clinical data available, such as age, gender, and staging, and by inspection of the CT scans. However, no patterns were identified that could support any conclusions, which indicates that the performance decline is directly linked to the class imbalance in the datasets. Given that, better techniques to balance the data should be employed, such as the use of domain-specific data augmentation techniques or the use of generative models to create new data samples with higher variability but that remain realistic. In addition, to deal with the lack of datasets with annotation for the nodule subtype, self-supervised learning strategies could be employed to pre-train the deep learning models using malignant nodules from other datasets without the need for subtype information.  
Another limitation of the current work is the assumption that CT scans contain relevant information for lung cancer histology determination. This hypothesis contrasts with daily  
Figure 10: Confusion matrixes for the 3D Hybrid ViT model in (a) the Lung-PET-CT-Dx internal test set and the external test sets: (b) NSCLC-Radiomics and (c) NSCLC-Radiogenomics.  
clinical practice, where radiologists do not rely solely on CT scans for this diagnosis. In future works, the incorporation of interpretability metrics could be considered to identify the regions that the models attend the most to perform classification. Nevertheless, these metrics should be analysed and evaluated by clinical experts for proper interpretation.