PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS: Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS: ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION: The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.
Despite advancements in multidisciplinary care, oncologic outcomes of oral cavity squamous cell carcinoma (OSCC) have not substantially improved: still, one-third of patients affected by stage I and II can develop locoregional recurrences. Imaging plays a pivotal role in preoperative staging of OSCC, providing depth of invasion (DOI) measurements. However, locoregional recurrences have a strong association with adverse histopathological factors not included in the staging system, and any imaging features linked to them have been lacking. In this study, the possibility to predict histological risk factors in OSCC with high-frequency intraoral ultrasonography (IOUS) was evaluated. Thirty-four patients were enrolled. The agreement between ultrasonographic and pathological DOI was evaluated, and ultrasonographic margins' appearance was compared to the Brandwein-Gensler score and the worst pattern of invasion (WPOI). Excellent agreement between ultrasonographic and pathological DOI was found (mean difference: 0.2 mm). A significant relationship was found between ultrasonographic morphology of the front of infiltration and both Brandwein-Gensler score ≥ 3 (p < 0.0001) and WPOI ≥4 (p = 0.0001). Sensitivity, specificity, positive predictive value, and negative predictive value for the IOUS to predict a Brandwein-Gensler score ≥3 were 93.33%, 89.47%, 87.50%, and 94.44%, respectively. The present study demonstrated the promising role of IOUS in aiding risk stratification for OSCC patients.
A recent study reported that the occurrence of depapillated mucosa surrounding oral tongue squamous cell carcinomas (OTSCC) is associated with perineural invasion (PNI). The present study evaluates the reliability of depapillation as a PNI predictor and how it could affect narrow-band imaging (NBI) performance. This is thus a retrospective study on patients affected by OTSCC submitted to radical surgery. The preoperative endoscopy was evaluated to identify the presence of depapillation. Differences in distribution between depapillation and clinicopathological variables were analyzed. NBI vascular patterns were reported, and the impact of depapillation on those was studied. We enrolled seventy-six patients. After evaluation of the preoperative endoscopies, 40 (53%) patients had peritumoral depapillation, while 59 (78%) had a positive NBI pattern. Depapillation was strongly correlated to PNI, 54% vs. 28% (p = 0.022). Regarding the NBI pattern, there was no particular association with depapillation-associated tumors. The presence of depapillation did not affect the intralesional pattern detected by the NBI, while no NBI-positive pattern was found in the depapillation area. Finally, the NBI-guided resection margins were not affected by depapillation. Peritumoral depapillation is a reliable feature for PNI in OTSCC. NBI margin detection is not impaired by depapillation.
