The impact of margins in laryngeal cancer patients treated with transoral laser microsurgery: a systematic review and meta-analysis.

PURPOSE: In laryngeal squamous cell carcinoma (LSCC) treated with transoral laser microsurgery (TOLMS), the status of margins significantly affected local control. When a positive or close margin is present, there is no ubiquitous consensus regarding further treatments. The rationale of the present systematic review and meta-analysis is to investigate the survival impact of the status of the margins in patients affected by LSCC treated with TOLMS. DATA SOURCES: PubMed, EMBASE, and Cochrane Library. METHODS: We performed a systematic search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria were: patients affected by LSCC, staged according to the American Joint Committee on Cancer Staging System and treated by TOLMS without any previous treatment; margins status (close, positive, negative) and the adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) of overall survival, disease-specific survival, and disease-free survival has to be reported. RESULTS: Nine studies were deemed eligible for the qualitative analysis, and 3 for the quantitative analysis to investigate the association between margin status and OS. The cumulative number of patients was 3130. The sample size ranged from 96 to 747 patients. The follow-up period ranged from 0 to 201 months. The meta-analysis results show that positive margins have an aHR of 1.30 yet with CI range (0.56 to 2.97). CONCLUSIONS: Our current meta-analysis results are unable to definitively assess the real impact of resection margins on OS. Few authors provide accurate data regarding position and types of margins. Further prospective or high-quality studies are required.

From Hype To Reality: A Narrative Review on the Promising Role of Artificial Intelligence in Larynx Cancer Detection and Transoral Microsurgery.

Early larynx cancer detection plays a crucial role in improving treatment outcomes and recent studies have shown promising results in using artificial intelligence for larynx cancer detection. Artificial intelligence also has the potential to enhance transoral larynx microsurgery. This narrative review summarizes the current evidence regarding its use in larynx cancer detection and potential applications in transoral larynx microsurgery. The utilization of artificial intelligence in larynx cancer detection with white light endoscopy and narrow-band imaging helps improve diagnostic accuracy and efficiency. It can also potentially enhance transoral larynx microsurgery by aiding surgeons in real-time decision-making and minimizing the risk of complications. However, further prospective studies are warranted to validate the findings, and additional research is necessary to optimize the integration of artificial intelligence in our clinical practice.

Can High-Frequency Intraoral Ultrasound Predict Histological Risk Factors in Oral Squamous Cell Carcinoma? A Preliminary Experience.

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.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.

Introduction: Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images. Materials and Methods: A dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets. Results: 219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%. Conclusion: SegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.