RNA Oncoimmune Phenotyping of HPV-Positive p16-Positive Oropharyngeal Squamous Cell Carcinomas by Nodal Status

AI Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records

OBJECTIVE

This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders.

METHODS

We used a primary dataset containing 2-3 s of vowel "ah", demographics, and 26 items of structured medical records (e.g., symptoms, comorbidity, smoking and alcohol consumption, vocal demand) from 60 patients with pathology-proved glottic neoplasm (i.e., squamous cell carcinoma, carcinoma in situ, and dysplasia) and 1940 patients with benign voice disorders. The validation dataset comprised data from 23 patients with glottic neoplasm and 1331 patients with benign disorders. The AI model combined convolutional neural networks, gated recurrent units, and attention layers. We used 10-fold cross-validation (training-validation-testing: 8-1-1) and preserved the percentage between neoplasm and benign disorders in each fold.

RESULTS

Results from the AI model using voice signals reached an area under the ROC curve (AUC) value of 0.631, and additional demographics increased this to 0.807. The highest AUC of 0.878 was achieved when combining voice, demographics, and medical records (sensitivity: 0.783, specificity: 0.816, accuracy: 0.815). External validation yielded an AUC value of 0.785 (voice plus demographics; sensitivity: 0.739, specificity: 0.745, accuracy: 0.745). Subanalysis showed that AI had higher sensitivity but lower specificity than human assessment (p < 0.01). The accuracy of AI detection with additional medical records was comparable with human assessment (82% vs. 83%, p = 0.78).

CONCLUSIONS

Voice signal alone was insufficient for AI differentiation between glottic neoplasm and benign voice disorders, but additional demographics and medical records notably improved AI performance and approximated the prediction accuracy of humans.

LEVEL OF EVIDENCE

NA Laryngoscope, 2024.

De-escalated radiation for human papillomavirus virus-related oropharyngeal cancer: evolving paradigms and future strategies

The incidence of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma has increased dramatically in recent years reaching epidemic-like proportions. Data has emerged not only showing that these cancers are a unique entity with distinct molecular characteristics but that they also have a significantly improved prognosis as a result of their exquisite radiosensitivity compared to their HPV-negative counterparts. This, it has been increasingly suggested that these tumors can be targeted with de-escalated approaches using reduced doses of radiation. The overriding goal of de-escalation is to maintain the high cure and survival rates associated with traditional approaches while reducing the incidence of both short- and long-term toxicity. Although the exact reason for the improved radiosensitivity of HPV-positive oropharyngeal carcinoma is unclear, prospective studies have now been published demonstrating that de-escalated radiation can successfully maintain the high rates of cure and preserve quality of life for appropriately selected patients with this disease. However, these studies have been complicated by such factors as the relatively limited sample sizes, as well as the variability in treatment, inclusion criteria, and follow-up. As the data continues to mature on de-escalation, it is unquestionable that treatment paradigms for this disease will evolve. The ongoing quest to define a standard regimen comprises the subject of this review.

Development of a Novel Molecular Test for Determining HPV Integration Status in HPV-Positive Oropharynx Cancers

This diagnostic study describes the development of an assay for human papillomavirus–driven cancers of the oropharynx and the role viral integration could play in the process.

High-Throughput NanoString Analysis of Oncogenic Human Papillomavirus and Tumor Microenvironment Transcription in Head and Neck Squamous Cell Carcinoma

Human papillomaviruses (HPVs), specifically high-risk HPVs, are responsible for up to 3% of all cancers in women and up to 2% of all cancers in men. They have been identified as the etiological agent of cervical cancer and have been increasingly found to be the driver behind head and neck cancers of the oropharynx. A system in which we can simultaneously observe transcriptional changes to both a host's tumor microenvironment and its associated oncogenic driver (e.g., HPV) would be highly valuable for understanding HPV's role in tumorigenesis. This article describes a detailed methodology for utilizing high-throughput RNA analysis to study viral transcription in formalin-fixed, paraffin-embedded clinical tumor samples. Although our lab utilizes these methods for the study of head and neck cancer, the principles contained within are widely applicable to all fields of HPV study. © 2021 Wiley Periodicals LLC. Basic Protocol: HPV16 transcript analysis using NanoString Support Protocol 1: Preparation of RNA from formalin-fixed, paraffin-embedded slides Support Protocol 2: Preparation of RNA from cell lysates Support Protocol 3: Fluorometric RNA concentration and RNA integrity analysis Support Protocol 4: Determination of input RNA based on DV₃₀₀ calculation.

The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer

Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high rates of disease-related morbidity and mortality due to advanced loco-regional stage at diagnosis. Early detection and prompt treatment offer the best outcomes to patients, yet the majority of OC lesions are detected at late stages with 45% survival rate for 2 years. The primary cause of poor OC outcomes is unavailable or ineffective screening and surveillance at the local point-of-care level, leading to delays in specialist referral and subsequent treatment. Lack of adequate awareness of OC among the public and professionals, and barriers to accessing health care services in a timely manner also contribute to delayed diagnosis. As image analysis and diagnostic technologies are evolving, various artificial intelligence (AI) approaches, specific algorithms and predictive models are beginning to have a considerable impact in improving diagnostic accuracy for OC. AI based technologies combined with intraoral photographic images or optical imaging methods are under investigation for automated detection and classification of OC. These new methods and technologies have great potential to improve outcomes, especially in low-resource settings. Such approaches can be used to predict oral cancer risk as an adjunct to population screening by providing real-time risk assessment. The objective of this study is to (1) provide an overview of components of delayed OC diagnosis and (2) evaluate novel AI based approaches with respect to their utility and implications for improving oral cancer detection.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Novel induction therapy transoral surgery treatment paradigm with risk-adapted adjuvant therapy for squamous cell carcinoma of the head and neck - Mature clinical and functional outcomes

OBJECTIVE

Induction chemotherapy in head and neck squamous cell carcinoma (HNSCCA) has principally been studied prior to radiation therapy. We evaluated pre-operative induction therapy followed by surgery followed by risk-adapted adjuvant therapy. This report details the mature 5-year survival statistics, clinical and functional outcomes.

METHODS

An IRB-approved single institution prospective phase II clinical trial from October 2012 to November 2016 was conducted for patients with transorally-resectable American Joint Committee on Cancer 7th ed. stage III/IV HNSCCA. Patients were treated once weekly for six weeks with a multi-drug induction regimen of carboplatin, paclitaxel and daily lapatinib followed by transoral surgery and neck dissection. Patients were then stratified based on pathologic response to either observation or adjuvant therapy. Survival statistics and functional patient outcomes were analyzed. Specifically, peri-operative outcomes were analyzed and compared to a matched surgical cohort.

RESULTS

38/40 enrolled patients completed trial therapy. Median hospital stay was 3 days with 9/38 patients receiving a PEG (median 46 days). Median NPO status was 1 day, with a median return to a regular diet in 16 days. Mean patient weight was well preserved from pretreatment to 1 year after surgery (85.1 kg (95% CI 79.6-90.7) vs 83.1 kg (95% CI 77.7-88.6 kg) respectively). Of the 38 patients who completed trial therapy; DSS, PFS and OS were 100%, 97% and 97% respectively with median follow up of 4.9 years (3.33-7.25).

CONCLUSION

Transoral surgery was feasible following this novel induction regimen with excellent peri-operative, functional and longterm survival outcomes.

High risk HPV detection by RNAscope in situ hybridization combined with Cdc2 protein expression by immunohistochemistry for prognosis of oropharyngeal squamous cell carcinoma

High risk human papillomavirus (HPV) infection is related to the development of head and neck squamous cell carcinoma (HNSCC). Oropharyngeal squamous cell carcinoma (OPSCC) is a common type of HNSCC, and its incidence has increased significantly in recent years. In this study, high risk HPV, the expression of P53, P21, and Cdc2 in OPSCC tissues was detected and the prognostic factors and clinical value of OPSCC were discussed. According to the WHO classification and diagnosis standard for head and neck tumors (2017 Edition), 49 OPSCC cases with complete clinical data were collected from Tangshan Head and Neck Disease Pathology Research Base from January 1, 2012 to December 31, 2018. The E6 and E7 mRNA of HPV 16 and HPV 18 were detected by RNAscope in situ hybridization. The expression of P53, P21, and Cdc2 protein was observed by SP immunohistochemical method and all cases were followed up for survival. Median survival time was analyzed by Kaplan-Meier method. The Log-rank test was used for single factor analysis and Cox regression model was used to analyze multiple prognostic factors. In 49 OPSCC cases the median age was 53 years; 14 were HPV-DNA positive (14/49, 28.6%) while 35 were negative (35/49, 71.4%). E6, E7 mRNA test showed that 20 cases (20/49, 40.8%) were positive for HPV-16. Among them 11 cases were positive for HPV-16 DNA. 2 cases were positive for HPV-18 mRNA (2/49, 4.08%). 27 cases were negative for mRNA16 and 18 (27/49, 55.1%). The prevalence of HPV was 68.8% (11/16) in the non-smoking group, which was higher than that of the smoking group (10/33, 33.3%), (χ²=5.463, P=0.019). There was no significant correlation between HPV detection and gender, age, drinking, tumor differentiation degree, and clinical stage (P > 0.05). The expression rates of P53, P21, and Cdc2 in OPSCC tissues were 63.3% (31/49), 65.3% (32/49), and 67.3% (33/49), respectively. There was no significant correlation between expression of all the three proteins and gender, age, HPV, smoking, drinking, tumor differentiation, and clinical stage (P > 0.05). Cox multifactor regression analysis showed that HPV (HR=0.275, 95% CI: 0.146-0.517), tumor differentiation (HR=1.751, 95% CI: 1.231-2.492), stage (HR=3.268, 95% CI: 1.758-6.074) and expression of Cdc2 protein (HR=1.804, 95% CI: 0.990-3.286) were related to the survival time of patients (P < 0.05). Our findings support that most of the HPV-positive OPSSC patients were non-smokers. The patients with negative HPV, low differentiation, late stage, and Cdc2 positive expression have poor prognosis and need to be followed up.

Machine learning and its potential applications to the genomic study of head and neck cancer-A systematic review

BACKGROUND

Machine learning (ML) is powerful tool that can identify and classify patterns from large quantities of cancer genomic data that may lead to the discovery of new biomarkers, new drug targets, and a better understanding of important cancer genes. The aim of this systematic review was to evaluate the existing literature and assess the application of machine learning of genomic data in head and neck cancer (HNC).

MATERIALS AND METHODS

The addressed focused question was "Does machine learning of genomic data play a role in prognostic prediction of HNC?" PubMed, EMBASE, Scopus, Web of Science, and gray literature from January 1990 up to and including May 2018 were searched. Two independent reviewers performed the study selection according to eligibility criteria.

RESULTS

A total of seven studies that met the eligibility criteria were included. The majority of studies were cohort studies, one a case-control study and one a randomized controlled trial. Two studies each evaluated oral cancer and laryngeal cancer, while other one study each evaluated nasopharyngeal cancer and oropharyngeal cancer. The majority of studies employed support vector machine (SVM) as a ML technique. Among the included studies, the accuracy rates for ML techniques ranged from 56.7% to 99.4%.

CONCLUSION

Our findings showed that ML techniques for the analysis of genomic data can play a role in the prognostic prediction of HNC.

Artificial Intelligence for the Otolaryngologist: A State of the Art Review

Objective

To provide a state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies.

Data Sources

PubMed and Medline search engines.

Review Methods

A structured search of the current literature was performed (up to and including September 2018). Search terms related to topics of AI in otolaryngology were identified and queried to identify relevant articles.

Conclusions

AI is at the forefront of conversation in academic research and popular culture. In recent years, it has been touted for its potential to revolutionize health care delivery. Yet, to date, it has made few contributions to actual medical practice or patient care. Future adoption of AI technologies in otolaryngology practice may be hindered by misconceptions of what AI is and a fear that machine errors may compromise patient care. However, with potential clinical and economic benefits, it is vital for otolaryngologists to understand the principles and scope of AI.

Implications for Practice

In the coming years, AI is likely to have a major impact on biomedical research and the practice of medicine. Otolaryngologists are key stakeholders in the development and clinical integration of meaningful AI technologies that will improve patient care. High-quality data collection is essential for the development of AI technologies, and otolaryngologists should seek opportunities to collaborate with data scientists to guide them toward the most impactful clinical questions.