Malignant external otitis and squamous cell carcinoma of the external auditory meatus are both characteristically seen in the older population. While some of the signs and symptoms of these two diseases are similar (otalgia, otorrhea, bone destruction on computed tomographic scan), they have rarely been reported in the same patient. We present an unusual case of temporal bone cancer and malignant external otitis presenting concurrently in an elderly, nondiabetic host. Precipitating factors and diagnostic criteria are discussed. This case illustrates the importance of an adequate biopsy specimen in all patients presenting with presumed "classic" malignant external otitis. Consideration must also be given for the existence of malignant external otitis in the presence of carcinoma. Finally, evolution of malignant external otitis in this patient following aural irrigation supports previous suggestions that this disease may be induced iatrogenically.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1001/archotol.1993.01880180107021 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFThe choice of adjuvant radiotherapy in pancreatic cancer patients after up-front radical surgery.
PLoS One
January 2025
Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
Background: The role of adjuvant radiotherapy in pancreatic cancer following radical surgery remains a subject of of controversy. This study aimed to more accurately screen pancreatic patients who benefit from adjuvant radiotherapy.
Methods: Clinicopathologic characteristics of patients with resectable pancreatic cancer were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015).
Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.
Tomography
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.
Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.
Towards Personalized Radiotherapy in Pelvic Cancer: Patient-Related Risk Factors for Late Radiation Toxicity.
Curr Oncol
January 2025
Department of Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Meibergdreef, 1105 AZ Amsterdam, The Netherlands.
Normal tissue reactions vary significantly among patients receiving the same radiation treatment regimen, reflecting the multifactorial etiology of late radiation toxicity. Predicting late radiation toxicity is crucial, as it aids in the initial decision-making process regarding the treatment modalities. For patients undergoing radiotherapy, anticipating late toxicity allows for planning adjustments to optimize individualized care.
View Article and Find Full Text PDFAccurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!