Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

Non neoplastic tongue diseases. An epidemiological investigation and diagnostic criteria.

BACKGROUND: Few clinical-epidemiological data regarding tongue diseases were showed in recent literature. The aim of this study was to evaluate the prevalence of non neoplastic tongue pathologies, and to perform an epidemiological, clinical and etiopathogenetic comparable data system on Non-Neoplastic Glossitis (NNG). METHODS: A total of 215 subjects (90 males and 125 females, age range: 6-72 years) have been examined) at the Dental Clinic of the University of Brescia over a period of 2 years. From this group, patients with tongue non neoplastic lesions were selected. Each selected patient with NNG was examined, following the Diagnostic Protocol of the "Department of Oral Pathology and Medicine" of the Dental Clinic of Brescia University. RESULTS: 84 cases of NNG (39%) were observed from January 1997 to October 1998. CONCLUSIONS: The selected group of patients with NNG has been stratified following clinical and etiological criteria, and the results discussed, emphasizing the importance of careful and correct examination of the tongue, in order to bring to light morphological and pathological changes often neglected or misdiagnosed. One should never exclude, moreover the possibility of malignant evolution of some lingual lesions requiring a close follow-up.