Landscape of innate lymphoid cells in human head and neck cancer reveals divergent NK cell states in the tumor microenvironment.

Natural killer (NK) cells comprise one subset of the innate lymphoid cell (ILC) family. Despite reported antitumor functions of NK cells, their tangible contribution to tumor control in humans remains controversial. This is due to incomplete understanding of the NK cell states within the tumor microenvironment (TME). Here, we demonstrate that peripheral circulating NK cells differentiate down two divergent pathways within the TME, resulting in different end states. One resembles intraepithelial ILC1s (ieILC1) and possesses potent in vivo antitumor activity. The other expresses genes associated with immune hyporesponsiveness and has poor antitumor functional capacity. Interleukin-15 (IL-15) and direct contact between the tumor cells and NK cells are required for the differentiation into CD49a+CD103+ cells, resembling ieILC1s. These data explain the similarity between ieILC1s and tissue-resident NK cells, provide insight into the origin of ieILC1s, and identify the ieILC1-like cell state within the TME to be the NK cell phenotype with the greatest antitumor activity. Because the proportions of the different ILC states vary between tumors, these findings provide a resource for the clinical study of innate immune responses against tumors and the design of novel therapy.

Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence.

This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer.

OBJECTIVES: To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging. METHODS: Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings. These embeddings served as input for a standard support vector machine (SVM) to classify the tissues as neoplastic or normal. The model's discriminative performance was validated using an 80-20 train-validation split. RESULTS: From 38 endoscopic NBI videos, 327 image patches were analyzed. The classification results in the validation cohort demonstrated high accuracy (92%) and precision (89%), with a perfect recall (100%) and an F1-score of 94%. The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.96. CONCLUSION: The use of large vision model-derived embeddings effectively differentiated between neoplastic and normal oropharyngeal tissues. This study supports the feasibility of employing CV foundation models like DINOv2 in the endoscopic evaluation of mucosal lesions, potentially augmenting diagnostic precision in Otorhinolaryngology. LEVEL OF EVIDENCE: 4 Laryngoscope, 134:4535-4541, 2024.

Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer.

BACKGROUND: The prognosis for patients with head and neck cancer (HNC) is poor and has improved little in recent decades, partially due to lack of therapeutic options. To identify effective therapeutic targets, we sought to identify molecular pathways that drive metastasis and HNC progression, through large-scale systematic analyses of transcriptomic data. METHODS: We performed meta-analysis across 29 gene expression studies including 2074 primary HNC biopsies to identify genes and transcriptional pathways associated with survival and lymph node metastasis (LNM). To understand the biological roles of these genes in HNC, we identified their associated cancer pathways, as well as the cell types that express them within HNC tumor microenvironments, by integrating single-cell RNA-seq and bulk RNA-seq from sorted cell populations. RESULTS: Patient survival-associated genes were heterogenous and included drivers of diverse tumor biological processes: these included tumor-intrinsic processes such as epithelial dedifferentiation and epithelial to mesenchymal transition, as well as tumor microenvironmental factors such as T cell-mediated immunity and cancer-associated fibroblast activity. Unexpectedly, LNM-associated genes were almost universally associated with epithelial dedifferentiation within malignant cells. Genes negatively associated with LNM consisted of regulators of squamous epithelial differentiation that are expressed within well-differentiated malignant cells, while those positively associated with LNM represented cell cycle regulators that are normally repressed by the p53-DREAM pathway. These pro-LNM genes are overexpressed in proliferating malignant cells of TP53 mutated and HPV + ve HNCs and are strongly associated with stemness, suggesting that they represent markers of pre-metastatic cancer stem-like cells. LNM-associated genes are deregulated in high-grade oral precancerous lesions, and deregulated further in primary HNCs with advancing tumor grade and deregulated further still in lymph node metastases. CONCLUSIONS: In HNC, patient survival is affected by multiple biological processes and is strongly influenced by the tumor immune and stromal microenvironments. In contrast, LNM appears to be driven primarily by malignant cell plasticity, characterized by epithelial dedifferentiation coupled with EMT-independent proliferation and stemness. Our findings postulate that LNM is initially caused by loss of p53-DREAM-mediated repression of cell cycle genes during early tumorigenesis.

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers.

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.

Cost comparison of treatment for oropharyngeal carcinoma.

OBJECTIVES/HYPOTHESIS: Based on current guidelines, surgical and nonsurgical therapies are viable frontline treatment for patients with locoregional oropharyngeal carcinoma (OPC). We sought to compare financial parameters between chemoradiation and transoral robotic surgery (TORS) in this patient population. STUDY DESIGN: Case-control study. METHODS: In this study we identified patients with selected American Joint Committee on Cancer 7th Edition stage II to IVa OPC treated with TORS between January 2013 and December 2014. Fifteen patients who underwent TORS were stage matched with 15 patients treated with chemoradiation. Total charges and cost data for each patient were analyzed at 4-month and 1-year time points; functional and oncologic outcomes were assessed. RESULTS: There were no significant differences in functional and oncologic outcomes. Patients undergoing TORS had a longer inpatient hospital stay, and most required a nasogastric tube for an average of 3.5 days. There were no local or regional recurrences. Across all time points, the TORS group had lower charges and costs compared to the chemoradiation group, with 14% lower costs at 1 year. In the chemoradiation group, nearly two-thirds of costs came from radiation therapy and pharmacy expenses. Chemotherapy accounted for most pharmacy costs. The costs of operating the surgical robot accounted for a about half of surgical costs. CONCLUSIONS: Selected patients with stage II to IVa oropharyngeal carcinoma treated with TORS may incur lower costs than those treated nonsurgically. With rising healthcare spending, the financial impact of treatment might be considered for those patients eligible for treatment regimens with comparable functional and oncologic outcomes. LEVEL OF EVIDENCE: 3b Laryngoscope, 129:1604-1609, 2019.

A Next-Generation Single-Port Robotic Surgical System for Transoral Robotic Surgery: Results From Prospective Nonrandomized Clinical Trials.

IMPORTANCE: Transoral endoscopic head and neck surgery now plays an important role in the multidisciplinary management of oropharyngeal carcinoma. Previous generations of robotic surgical systems used a multiport system with a rigid stereo-endoscope and 2 wristed instruments that facilitated transoral robotic surgery. OBJECTIVE: To evaluate a new single-port robotic surgical system in head and neck surgery prospectively through concurrent nonrandomized clinical trials. DESIGN, SETTING, AND PARTICIPANTS: Two prospective clinical trials were conducted from December 16, 2016, to December 26, 2017, to assess the safety, feasibility, and performance of a flexible single-port robotic surgical system in 4 institutions, including 3 in the United States and 1 in Hong Kong. A total of 47 patients with tumors of the oropharynx were enrolled and underwent surgery. All patients were classified as having American Society of Anesthesiologists class I to III status and Eastern Cooperative Oncology Group status 0 to 1. An initial cohort of 7 patients underwent staging and endoscopic procedures for benign disease. The remaining 40 patients all had malignant tumors of the oropharynx. MAIN OUTCOMES AND MEASURES: Safety was measured by the incidence of device-related serious adverse events. Feasibility and performance were measured by the conversion rate from the use of the single-port robotic surgical system to either open surgery or the use of any other transoral technology required to complete the planned procedure. Secondary end points of swallowing function and surgical margins were also measured. RESULTS: All 47 patients (8 women and 39 men; mean [SD] age, 61 [8] years) safely underwent transoral resection with the single-port robotic surgical system without conversion to open surgery, laser surgery, or multiport robotic surgery. There were no intraoperative complications or device-related serious adverse events. Mean (SD) estimated intraoperative blood loss per procedure was 15.4 (23.9) mL; no patients received a transfusion. Two patients underwent a planned tracheotomy owing to medical comorbidity (previous chemoradiotherapy; obesity and severe sleep apnea). Two patients (4%) had grade III or IV postoperative hemorrhage, requiring a return to the operating room; however, both patients had medical comorbidities requiring the use of antithrombotic medication. The incidence of positive margins for patients with oropharyngeal malignancy was 3% (1 of 40). Within 30 days, 45 patients (96%) were eating by mouth and without the need for a percutaneous endoscopic gastrostomy tube. CONCLUSIONS AND RELEVANCE: This study describes the results of phase 2 clinical testing of a next-generation, robotic surgical system using a single-port architecture. The use of the device appears to be feasible, safe, and effective for transoral robotic surgery of oropharyngeal tumors. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT03010813 and NCT03049280.