# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 2 Materials  
To implement and validate the radiomics approach detailed in Section 3, we used three different datasets of radiomic features. We first considered the two datasets analysed in a previous study by [PERSON] et al. and which have been made publicly available [32]. They consist of two independent cohorts of North American and European patients with lung cancer. These datasets have been considered in order to assess the validity of the radiomic approach for phenotyping lung cancer. Specifically, Dataset1 (D1) contains data from 262 patients treated within the Thoracic Oncology Program at the _H. Lee Moffitt Cancer Center_, Tampa, FL, USA. The histological analysis was available for 224 patients; 129 subjects (57.6%) had adenocarcinoma, whereas the others (42.4%) suffered from cancer forms other than adenocarcinoma (i.e., 61 patients had squamous carcinoma, whereas the remaining were not further categorized). Dataset2 (D2) includes data from 89 patients treated at the _MAASTRO Clinic_ in the Netherlands. The analysis for revealing the histological cancer subtype was available for 87 patients; it showed that 42 subjects (48.3%) had adenocarcinoma, whereas 45 subjects (51.7%) experienced other cancer types, of which 33 (37.9%) were squamous carcinoma.  
Lastly, we collected 51 CT scans from patients with lung nodules provided by the _Azienda Ospedaliera--Universitaria \"Policlinico Riumiti\"_ in Foggia. This cohort included 29 patients (56.9%) affected by adenocarcinoma and 22 patients (43.1%) having other cancers, including squamous carcinoma, large cell lung carcinoma, chronic benign inflammation, hepatocarcinoma metastasis, intestinal adenocarcinoma metastasis and endocrine small cell carcinoma). This cohort included only unenhanced CT scans. This choice was made to verify whether the discrimination of lung cancer was also possible from this kind of image, also considering that, in this way, the patient is exposed to a lower amount of radiation. All patients were 18 to 85 years old and accidentally discovered solitary pulmonary nodules 10 to 50 mm in size diagnosed with Computed Tomography. The reports of the histological examinations were collected and the malignancy of the nodules was documented with the relative genetic panel. Subjects with incomplete clinical data and an absence of histological examination in nodules with highly suspected CT criteria of malignancy were excluded.  
These CT images were segmented manually by a radiology resident from _Azienda Ospedaliera--Universitaria \"Policlinico Riumiti\"_ in Foggia using the ITK-Snap Software [33], and radiomic features have been subsequently extracted following the pipeline detailed in Section 3.  
Figure 2 shows nine slices randomly selected from the CT scans included in D3, with the relative masks. Dataset1 and Dataset2, instead, have been shared by [PERSON] et al. in terms of radiomic features [32], thus no image processing steps were required. Table 1, instead, summarises the characteristics of the datasets in terms of sample size, imaging modality and type of available data.