Faculty Bibliography

Background/UNASSIGNED:In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. Methods/UNASSIGNED:A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. Results/UNASSIGNED:Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. Conclusions/UNASSIGNED:A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Introduction Oral mucositis (OM) is one of the most debilitating adverse effects in patients undergoing cancer treatment. Physiologically, chemotherapy (CT) and radiotherapy (RT) evoke a profound inflammatory response, resulting in mucosal injury, which can result in an increased susceptibility to infection. Objectives The objective of this pilot study was to asses the effects of a novel oral care protocol on OM severity and to evaluate salivary cytokines in head and neck cancer patients undergoing RT or CT/RT at the NYU Langone Laura and Isaac Perlmutter Cancer Center. Methods A total of ten participants were included in this study, and randomized to an InterventionGroup (IG), or ControlGroup (CG). Subjects assigned to the CG received a standard of care oral hygiene on a bi-weekly basis. Subjects assigned to the IG received the Oral Mucosal Deterging and Dental Prophylaxis (OMDP) protocol weekly, which consisted of a periodontal surface debridement and cleansing and deterging of the oral mucosa surfaces. Results Salivary inflammatory biomarkers, noted in levels of IFN-gamma, IL10, IL12p70, IL13, TNFalpha and IL-6 had a significant increase in the CG and reduced or stayed the same under the IG. Although not statistically significant, a tendency of pain decrease was observed in the IG and difficulty in swallowing was statistically significant when both groups were compared (p = 0,016). Conclusions These results suggest that overall inflammation was consistently higher as compared to baseline in the CG, providing encouragement for the effectiveness of the oral care protocol as a coadjutant treatment for this population

Carcinoma of unknown primary (CUP) comprises approximately 1%-5% of all head and neck malignancies. Primary site detection rates for metastatic cervical squamous cell carcinoma (SCC) remain variable, with current diagnostic imaging unable to identify all tumours. Prevailing evidence suggests that most head and neck CUP are located in the oropharynx. Diagnostic surgical efforts have been directed at sampling the entire oropharynx. Present techniques that make this possible include transoral robotic surgery (TORS). TORS Lingual tonsillectomy or tongue base mucosectomy performed in the setting of head and neck CUP increases the detection rate of occult tumour. The indication, surgical technique, evidence base, and controversies of performing TORS in the context of the unknown primary are presented.

Oxytocin is a hypothalamic neuropeptide first recognized as a regulator of parturition and lactation which has recently gained attention for its ability to modulate social behaviors. In this chapter, we review several aspects of the oxytocinergic system, focusing on evidence for release of oxytocin and its receptor distribution in the cortex as the foundation for important networks that control social behavior. We examine the developmental timeline of the cortical oxytocin system as demonstrated by RNA, autoradiographic binding, and protein immunohistochemical studies, and describe how that might shape brain development and behavior. Many recent studies have implicated oxytocin in cognitive processes such as processing of sensory stimuli, social recognition, social memory, and fear. We review these studies and discuss the function of oxytocin in the young and adult cortex as a neuromodulator of central synaptic transmission and mediator of plasticity.

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

The incidence of oral cancer in the United States is increasing, especially in young people and women. Patients with oral cancer report severe functional pain. Using a patient cohort accrued through the New York University Oral Cancer Center and immune-competent mouse models, we identify a sex difference in the prevalence and severity of oral cancer pain. A neutrophil-mediated endogenous analgesic mechanism is present in male mice with oral cancer. Local naloxone treatment potentiates cancer mediator-induced orofacial nociceptive behavior in male mice only. Tongues from male mice with oral cancer have significantly more infiltrating neutrophils compared to female mice with oral cancer. Neutrophils isolated from the cancer-induced inflammatory microenvironment express beta-endorphin and met-enkephalin. Furthermore, neutrophil depletion results in nociceptive behavior in male mice. These data suggest a role for sex-specific, immune cell-mediated endogenous analgesia in the treatment of oral cancer pain.

BACKGROUND:Amyloidosis encompasses a group of disorders sharing the common feature of intercellular deposition of amyloid protein by several different pathogenetic mechanisms. Primary solitary amyloidosis, or amyloidoma, is a rare subset of amyloidosis in which amyloid deposition is focal and not secondary to a systemic process or plasma cell dyscrasia. CASE DESCRIPTION/METHODS:This 84-year-old female presented with history of multiple syncopal episodes, dysphagia, and ataxia. Motor strength was 3+/5 in the right upper extremity. Rheumatoid factor, cyclic citrullinated peptide (CCP), and anti-nuclear antibody (ANA) were normal. Serum and urine immune-electrophoresis detected no abnormal bands. Computed tomography (CT) and magnetic resonance imaging (MRI) demonstrated a non-enhancing soft-tissue mass extending from the retro-clivus to C2 posteriorly, eccentric to the right with severe mass effect on the upper cervical medullary junction. Endoscopic trans-nasal debulking of the retro-clival mass was performed with occiput to C5 posterior instrumentation for spinal stabilization. CONCLUSIONS:Primary solitary amyloidosis, unlike other forms of amyloidosis, has an excellent prognosis with local resection. Diagnosis requires special stains and a degree of suspicion for the disease. This is the first report to document an endoscopic trans-nasal approach for removal of a primary solitary amyloidosis of the retro-clivus. Management of vertebral amyloidoma involves aggressive local resection of the tumor when feasible and spine stabilization as the degree of tumor involvement mandates. Complete evaluation for the diagnosis of systemic amyloidosis is essential for the management and prognostication. Surgeons encountering such lesions must maintain high suspicion for this rare disease and advise pathologists accordingly to establish the correct diagnosis.