Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.
Introduction Obstruction of the lacrimal drainage represents a common ophthalmologic issue. The blockage may interest any level of the lacrimal drainage pathway, and it is important to find the site of obstruction to plan the most appropriate treatment. In this study, findings from magnetic resonance (MR) dacryocystography were compared with findings from endoscopic and surgical procedures to evaluate the accuracy of MR dacryocystography in localizing the site of nasolacrimal duct obstruction. Methods We enrolled twenty-one patients with clinical suspicion of nasolacrimal duct obstruction who underwent dacryoendoscopy and surgery. MR dacryocystography was performed with a heavily T2-weighted fast spin echo sequence in the coronal planes. Before the MRI was performed, a sterile 0.9% NaCl solution was administered into both conjunctival sacs. For each examination, two independent readers (with 8 and 10 years of experience in head and neck imaging) evaluated both heavily 3D space T2-weighted and STIR sequences. Results Stenosis/obstruction of nasolacrimal duct or lacrimal sac was diagnosed in all 21 patients who underwent MRI dacryocystography. In particular, the site of the obstruction was classified as lacrimal sac in 12 (57%) patients, nasolacrimal duct in 6 (29%) patients, and canaliculi in 3 (14%) patients by both readers. By comparison with the evidence resulting from the endoscopy, there were differences between MRI dacryocystography and dacryoendoscopy in the evaluation of the obstruction’s site in three patients, with an overall accuracy of 85.7%. Conclusion MR dacryocystography allows a non-invasive evaluation of the lacrimal drainage pathway, valid for the planning of the most appropriate treatment.
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