Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically, these technologies have the objective to extract insights from complex datasets that cannot easily be analyzed by conventional statistical methods. While promising results have been obtained for various -omics datasets, radiological images, and histopathologic slides, analysis of videoendoscopic frames still represents a major challenge. In this context, videomics represents a burgeoning field wherein several methods of computer vision are systematically used to organize unstructured data from frames obtained during diagnostic videoendoscopy. Recent studies have focused on five broad tasks with increasing complexity: quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions inside frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Herein, we present a broad overview of the field, with a focus on conceptual key points and future perspectives.
SUMMARY Objective To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx. Methods A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure. Results Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001). Conclusions The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.
Purpose of reviewThe introduction of antiretroviral therapy has significantly impacted the incidence of head and neck squamous cell carcinoma (HNSCC) in people living with HIV (PLWH). This manuscript aims to give an overview of the evidence in the literature about this population. Recent findingsPLWH have an increased incidence of HNSCC, with earlier age and more advanced stage at diagnosis. This epidemiologic trend may be explained by combining traditional and HIV-related risk factors. With the improvement of global health status, more patients are candidate for complex therapeutic strategies with curative intent. To date, it is still debated whether HIV-infected patients possess a profile of increased risk regarding treatment-related toxicity and survival outcomes, with the literature still lacking substantial evidence. Among the prognostic factors that can guide the clinician in selecting the most appropriate treatment strategy, age, site/subsite, stage, HIV viral load, and CD4þ T-cell count at diagnosis are the most relevant. SummaryPathogenesis, treatment characteristics, oncologic outcomes, and prognostic factors of HNSCC in PLWH are still debated. Given the increasing incidence of HNSCC in PLWH, the need for dedicated evidencebased studies represents a significant research gap to be addressed.
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