## 5 Conclusions  
In the present work, the main objective was the classification of lung tumours into adenocarcinoma or non-adenocarcinoma, with the negative class encompassing two different histological subtypes (SCC and LCC). For that, two separate pipelines were employed: the classical ML approach, with the classification performed by the RF and XGBoost based on radiomic features, and the DL approach, with the features being extracted and classified by a ResNet and a Hybrid ViT. Furthermore, these models received as input just one 2D tumour slice or a 3D tumour volume. The models were trained in a public dataset and tested on two public external datasets. Overall, the models with 3D inputs led to superior performances, as anticipated, due to their capability to capture more comprehensive and generalized information from the histology of the tumours. The best-performing model was the 3D Hybrid ViT classifier, achieving an AUC of 0.869 and a balanced accuracy of 0.816 in the internal test set. However, the performance of the models did not generalize to the external datasets. The outcomes achieved were acceptable for the internal test set and in line with what is available in the literature. The performance in the external datasets shows the importance of evaluating the models in different datasets to understand the generalization capability of the models. In future work, different classifiers and DL architectures should be explored, along with the potential ensemble of the two pipelines. Also, direct classification between histological subtypes, such as ADC _vs_ SCC or ADC _vs_ LCC, could be tested, as it would reduce the high diversity in the negative class, possibly leading to improved results. Additionally, increased amounts of training data are required, particularly for the deep learning approach, where the scarcity of data has a more negative impact due to the inherent complexity of these types of models. Hence, the employment of larger datasets or the implementation of diverse domain-specific data augmentation techniques should be considered to promote the generalization of the models. Concluding, the accurate classification of lung adenocarcinoma is of utmost importance, given the elevated incidence of this histological type. Although there is room for improvement in the proposed models, the conducted experiments demonstrated the potential of predicting lung adenocarcinoma from CT scans using ML and DL models. The prediction of the tumour subtype using only CT scans can have the potential to contribute to less invasive methods in clinical practice to perform the tumour characterization needed for personalized treatments. Conceptualization, M.G., T.M., H.P.O. and T.P. ; methodology, M.G. and T.M. ; data curation, M.G. and T.M. ; writing--original draft preparation, M.G. and T.M. ; writing--review and editing, M.G., T.M., E.M.R., H.P.O. and T.P. ; supervision, H.P.O. and T.P. All authors have read and agreed to the published version of the manuscript. This work is financed by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology) within the project LUCCA, with reference 2022.03488.PTDC, and two PhD Grants Numbers 2023.02607.BD and 2024.04595.BD. The databases used in the experiments of this work ensure, according to the corresponding published description article, that the necessary ethical approvals regarding data access were obtained. **Informed Consent Statement:** The databases used in the experiments of this work ensure, according to the corresponding published description article, that the necessary informed consent was obtained from all subjects involved in the study.