selected depending on the patient's characteristics; they include the radiotherapy, the chemotherapy, the surgical resection, the immunotherapy, but also targeted therapy [1; 4; 5]. We acknowledge that, nowadays, the standard procedure for stage III patients with NSCLC accounts chemoradiotherapy followed by immunotherapy [6]. However, when considering the guidelines at the time of this work, for stage III NSCLC, whether they were resectable or not, chemoradiotherapy (even concurrent followed by immunotherapy) was the recommended therapeutic approach [7; 8], because showed better results than those that were attained by radiotherapy or by radiotherapy followed by chemotherapy [9; 10]. Indeed, [PERSON] et al. showed that patients randomly assigned to two different concurrent chemoradiotherapyopathies had a longer survival time than patients who underwent sequential radio and chemotherapy [9]. The Overall Survival (OS), defined as the length of time from the date of diagnosis until the patient death, helps to identify patient subgroups with better or worse prognosis, so to hypothesize intensified treatment options for the latter group. Unfortunately, local recurrence and distant metastasis are still an issue [11] and, therefore, the strategies to improve OS are urgently needed. In order to help the personalised medicine to make further steps facilitating the identification of the correct treatment for each patient, in recent years we have observed a raising interest in the development and application of Artificial Intelligence (AI) methods to oncology, similarly to what already happened with success in other medical fields [12; 13; 14; 15; 16]. In turns out this has first favoured the birth of radiomics, and now it is boosting the interests of the scientific community on its potential. Radiomics is based on the extraction of quantitative biomarkers, also called features, from medical images, such as CT, MRI, X-ray and PET, in order to diagnose, predict clinical outcome, or guide clinical decisions [17; 18]. The first studies reported promising performances in different clinical applications for diagnostic and prognostic purposes [19; 20; 21; 22; 23]. Several radiomics studies have been conducted on NSCLC, but, to the best of our knowledge, the discovery of a signature associated to the Overall Survival is still an issue deserving more research efforts. In this respect, the contribution of this manuscript is twofold. First, we investigate the use of different Volumes-Of-Interest (VOIs) by exploiting quantitative biomarkers computed not only from the Gross Tumour Volume (GTV), which is what can be seen, palpated, or imaged, but also from other two larger VOIs commonly used in radiation treatment clinical practice, which are Clinical Target Volume (CTV) and Planning Target Volume (PTV). The first take into account the subclinical disease spread and the last allows for uncertainties in planning or treatment delivery. The idea, inspired by the radiotherapy practice adopted when planning the treatments, is that the area surrounding the tumour may contain important information that can be quantitatively exploited by a computer-based approach, although it cannot be discerned by the visual interpretation. In fact, this area around the tumour could catch also how the cancer infiltrates the nearby healthy tissues and, hence, potentially it could help predicting the disease behaviour and patient survival. The other contribution of this manuscript introduces the Local Binary Pattern (LBP) in the field of radiomics. LBP is a texture measure that is successfully used in computer vision, that here we extend to a three-dimensional (3D) environment and we apply with prognostic values in oncology, extending the set of texture features typically used in the radiomics literature, which are based on the two-dimensional (2D) and 3D second-order joint probability functions and their derivatives.