Transoral laser exoscopic surgery of the larynx: state of the art and comparison with traditional transoral laser microsurgery.

Objective: To evaluate the efficacy of transoral laser exoscopic surgery (TOLES) in a unicentric series of patients affected by benign and malignant glottic and supraglottic lesions, and compare outcomes with those of transoral laser microsurgery (TOLMS). Methods: To demonstrate the non-inferiority of TOLES in terms of operative time, margin status and complication rates, we compared outcomes of 93 patients treated by TOLES between July 2021 and July 2023 with those of a match-paired group of 107 historical patients treated by TOLMS. To perform a multiparametric ergonomic evaluation of TOLES vs TOLMS, we used observational methods for biomechanical overload risk assessment and wearable technologies comparing 15 procedures with TOLES vs a paired match of 13 surgeries performed with TOLMS by the same surgeon. Results: No significant differences were found in terms of surgical duration, positive margins, or complications between TOLES and TOLMS. Ergonomics assessment by inertial measurement units and electromyographic surface electrodes demonstrated a reduced biomechanical overload with TOLES compared to TOLMS. Conclusions: The many advantages of TOLES, such as its superior didactic value, better digital control of light even through small-bored laryngoscopes, improved binocular vision, and increase in surgical performance by 3 or 4-hand techniques, are difficult to be quantified. In contrast, its non-inferiority in terms of oncological results and better ergonomics compared to TOLMS are demonstrated herein.

The psychosocial needs of head and neck cancer patients: a multicenter study.

INTRODUCTION: Cancer is among the most frequent pathologies and a major cause of death and disability. Scientific research and surgeons focus mainly on aspects relating to etiopathogenesis, diagnosis, and treatment, but often neglect the psychological needs of patients and related social factors. The aim of this study is to investigate the psychological and social needs of patients affected by head and neck cancer to improve patient management and achieve more empathetic care. MATERIALS AND METHODS: The Need Evaluation Questionnaire (NEQ) was administered to adult patients who had to undergo surgery in 4 Italian tertiary head and neck cancer centers the day before the operation. RESULTS: 188 patients affected by stage I-IV head and neck tumors were enrolled. The main needs expressed by patients fall under the categories of either "information and dialogue", (64.3% more information about future conditions, 50% more information about treatments, 45% more information about the diagnosis, 44% more reassurance) or "spiritual support" (50% need to talk to someone who had the same experience as them). CONCLUSIONS: Common methods of explaining diagnosis, treatment, and prognosis have proven unsatisfactory to patients and most also require more psychosocial support. This highlights the need to implement interventions and activities that are increasingly geared toward supporting the psychological and relational aspects of the care journey.

Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.

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.

Head and neck squamous cell carcinoma in HIV patients.

PURPOSE OF REVIEW: The 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 FINDINGS: PLWH 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 CD4+ T-cell count at diagnosis are the most relevant. SUMMARY: Pathogenesis, 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 evidence-based studies represents a significant research gap to be addressed.

Artificial intelligence in clinical endoscopy: Insights in the field of videomics.

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.