Diagnosis and Treatment of a Neck Node Swelling Suspicious for a Malignancy: An Algorithmic Approach

Aim. To present an up-to-date algorithm incorporating recent advances regarding its diagnosis and treatment. Method. A Medline/Pubmed search was performed to identify relevant studies published in English from 1990 until 2008. Only clinical studies were identified and were used as basis for the diagnostic algorithm. Results. The eligible literature provided only observational evidence. The vast majority of neck nodes from occult primaries (>90%) represent SCC with a high incidence among middle aged man. Smoking and alcohol abuse are important risk factors. Asiatic and North African patients with neck node metastases are at risk of harbouring an occult nasopharyngeal carcinoma. The remainder are adenocarcinoma, undifferentiated carcinoma, melanoma, thyroid carcinoma and Merkel cell carcinoma. Fine needle aspiration cytology (FNAC) reaches sensitivity and specificity percentages of 81% and 100%, respectively and plays an important role as the second diagnostic step after routine ENT mirror and/or endoscopic examination. FDG-PET/CT has proven to be helpful in identifying occult primary carcinomas of the head and neck, especially when applied as a guiding tool prior to panendoscopy, and may induce treatment related clinical decisions in up to 60% of cases. Conclusion. Although reports on the diagnostic process offer mainly descriptive studies, current information seems sufficient to formulate a diagnostic algorithm to contribute to a more systematic diagnostic approach preventing unnecessary steps

Implementation of a rapid learning platform: predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Background and Purpose To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset. Materials and Methods Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre\u27s (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort). Results Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so. Conclusions The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort

TCT-446 Long-Term Clinical Outcomes after Percutaneous Coronary Intervention for Chronic Total Occlusions in Elderly Patients (≥75 years): Five-Year Outcomes from a 1,791 Patient Multi-National Registry

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