# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 5 Conclusions  
Radiomics is a research field in which several algorithms are exploited to extract quantitative high-dimensional characteristics from images. Several authors have already used this methodology to address classification problems that involve radiological images. The use of algorithms for the processing and reduction of features may allow the design and implementation of more robust classification models that are able to improve the capabilities of automatic intelligent systems to support decisions in the biomedical field. However, the reproducibility of radiomic studies on external validation data is considered a crucial point. In this study, we extracted a reduced set of radiomic features to classify the histological subtype in lung nodules to discriminate between adenocarcinoma cases from other histological classes. In this work, we used three datasets, namely D1 and D2, which were provided by [PERSON] et al., and D3, which was built starting from a local cohort of lung CTs opportunely processed to extract radiomic features. Starting from D1, we designed and implemented a pipeline based on univariate analysis to select the most discriminative features to discriminate between lung adenocarcinoma and other histological types. Then, four experiments were performed to investigate whether and how the number of features, the input dataset, and the model affect the classification performance and the reproducibility of radiomic studies. Our analyses revealed that the best configuration for reproducibility on our local dataset D3 is the one using D1 for training the model. However, also combining D1 and D2 as a single training set led to good performances on data from D3. In any case, internal cross-validations evaluated in Experiment 1 allowed us to reach higher performances, highlighting the importance of considering external validation datasets when performing radiomics studies, so that the real effectiveness of the discovered feature set can be measured. Eventually, the best result for the D3 dataset comes from internal cross-validation, but then this result would be less generalisable on unseen data. Such a conclusion is also endorsed by other authors, such as [PERSON] et al., who also performed external validations in a similar case study, but with a reduced cohort for external validations, obtaining similar results (AUC of 0.72 with Naive Bayes' classifier) [31]. Future works may include the comparison between different kinds of enhancement modalities from CT scans before performing the radiomics feature extraction and the correlation of radiomic signatures with genomics data. The collection of new data may also help in the direction of designing a robust radiomics pipeline for histological and genomic classification. Conceptualization, B.P., M.G. and V.B. ; methodology, A.B., N.A., D.B. and B.P. ; formal analysis, A.B., N.A. and B.P. ; data curation, N.A., E.G., F.C., M.G. and B.P. ; writing--original draft preparation, A.B., N.A., D.B. and B.P. ; writing--review and editing, all authors; visualization,  
Figure 8: ROC curves for the considered external validations. (**a**) External validation of the SVM classifier (5 features) trained on D1 and validated on D2. (**b**) External validation of the AdaBoost classifier (7 features) trained on D1 and validated on D3. (**c**) External validation of the AdaBoost Classifier (10 features) trained on the merged dataset D1, D2 and validated on D3. B.P. and N.A. ; supervision, A.B., M.G. and V.B. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical review and approval were waived for this study since this was a retrospective observational study with anonymised data. Patient consent was waived due to the fact that this was a retrospective observational study with anonymised data, already acquired for medical diagnostic purposes.