## 3 Medical Justification
### Example Study Case: Medical Part  
For the medical part of the case study, we present the case of a 62-year-old male patient admitted to the thoracic surgery department with a right upper lobe tumor, diagnosed 6 months before by performing a thoracic CT scan. The patient decided at that time not to undergo surgery, not even a biopsy, and assumed to wait and see if the lung mass grows in size. At the time of admission, the patient's general status was worse, having symptoms such as a productive cough, fatigue, and dyspnea. The CT scan showed an increase in the diameter of the mass by 5 cm, having all the characteristics of a malignant lung tumor (Figure 3A-D), just as we described them for our study, see Figure 3 below. This time, the patient accepted the surgical procedure. Thoracotomy was performed. The proposed surgical procedure was a right upper lobectomy with radical mediastinal lymphadenectomy, but the intra-operative aspect (lung mass adherent to middle lobe blood  
Figure 3: (**A**,**B**) coronal view; (**C**) sagittal view; (**D**) axial view of a right upper lobe mass presenting all the aspects of malignancy: size over 5 cm, gross spiculation, attenuation alternances (part solid, part GGO), punctuate calcification, no tumoral fatty tissue, positive bronchial sign with a mean value of 34 HU and a growth rate of 5 cm over 6 months period. vessels and lack of horizontal fissure) determined an increased lung resection--upper-middle bi-lobectomy. The postoperative outcome of the patient was favorable, but we recorded a decrease in FEV/FEV 1 by almost 40%. The pathology report was a hamartoma, with chronic pneumonia surrounding the tumor, with no malignant cells present both in the resected lung parenchyma and in all the mediastinal nodal stations. This case proves that even experienced surgeons can be tricked by the clinical and imaging aspects of lung masses or nodules into strongly believing that the correct diagnosis is lung cancer when, in fact, it is a benign tumor. We applied our pipeline processing to this specific case, and given the novel correlations and the feature explanation provided by our intelligent system, we infer that the surgeon could have changed his opinion regarding the surgery in view of these parameter interpretations. We synchronize with the exact technical results for this case in Section 4.1 of the technical part. We want to emphasize here why an intelligent processing system powered by a deep learning classifier, as we propose, could have been very useful considering other treatment options, such as conservative treatment and further imaging follow-up and reserve lung resection as a last resort. Advances in artificial intelligence, both in terms of algorithms and hardware, with the development of new, very capable processors that use neural engines and bionic chipsets, have opened a wide range of possibilities for data scientists to apply these technologies to a vast array of domains, including medicine, to help surgeons and physicians improve their medical and surgical procedures and to help them minimize errors. This leads to a more precise, faster diagnosis and a better understanding of diseases and treatments both for doctors and patients. For a thoracic surgeon, the intelligent system developed using these modern technologies, similar to the one proposed in this paper, could prove to be very valuable in conducting a correct clinical and imaging diagnostic in the fight against lung cancer for two main reasons. First, this tool could compensate for the lack of experience of younger surgeons when interpreting imaging data and establishing the optimal moment for surgery and the extent of lung resection.