Facial nerve palsy following parotid gland surgery:A machine learning prediction outcome approach

Introduction:Machine learning (ML)‐based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now.Three distinct ML models,random forest (RF),K‐nearest neighbor,and artificial neural network (ANN),for the prediction of FNI were evaluated in this mode.Methods:A retrospective,longitudinal,multicentric study was performed,including patients who went through parotid gland surgery for benign tumors at three different university hospitals.Results:Seven hundred and thirty‐six patients were included.The most compelling aspects related to risk escalation of FNI were as follows:(1) location,in the mid‐portion of the gland,near to or above the main trunk of the facial nerve and at the top part,over the frontal or the orbital branch of the facial nerve;(2) tumor volume in the anteroposterior axis;(3) the necessity to simultaneously dissect more than one level;and (4) the requirement of an extended resection compared to a lesser extended resection.By contrast,in accordance with the ML analysis,the size of the tumor (>3 cm),as well as gender and age did not result in a determining favor in relation to the risk of FNI.Discussion:The findings of this research conclude that ML models such as RF and ANN may serve evidence‐based predictions from multicentric data regarding the risk of FNI.Conclusion:Along with the advent of ML technology,an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical,radiological,histological,and/or cytological data.

Self-assembling protein nanocarrier for selective delivery of cytotoxic polypeptides to CXCR4^(+) head and neck squamous cell carcinoma tumors

Loco-regional recurrences and distant metastases represent the main cause of head and neck squamous cell carcinoma(HNSCC) mortality. The overexpression of chemokine receptor 4(CXCR4) in HNSCC primary tumors associates with higher risk of developing loco-regional recurrences and distant metastases, thus making CXCR4 an ideal entry pathway for targeted drug delivery. In this context, our group has generated the self-assembling protein nanocarrier T22-GFP-H6, displaying multiple T22 peptidic ligands that specifically target CXCR4. This study aimed to validate T22-GFP-H6 as a suitable nanocarrier to selectively deliver cytotoxic agents to CXCR4^(+)tumors in a HNSCC model. Here we demonstrate that T22-GFP-H6 selectively internalizes in CXCR4^(+)HNSCC cells, achieving a high accumulation in CXCR4^(+)tumors in vivo, while showing negligible nanocarrier distribution in non-tumor bearing organs. Moreover, this T22-empowered nanocarrier can incorporate bacterial toxin domains to generate therapeutic nanotoxins that induce cell death in CXCR4-overexpressing tumors in the absence of histological alterations in normal organs. Altogether, these results show the potential use of this T22-empowered nanocarrier platform to incorporate polypeptidic domains of choice to selectively eliminate CXCR4^(+)cells in HNSCC. Remarkably, to our knowledge, this is the first study testing targeted proteinonly nanoparticles in this cancer type, which may represent a novel treatment approach for HNSCC patients.