RESUMEN
BACKGROUND/AIMS: To evaluate the safety and efficacy of high intensity focused ultrasound (HIFU) therapy in patients with local advanced pancreatic cancer. METHODOLOGY: 39 patients with local advanced pancreatic cancer were treated with HIFU, including 26 male and 13 female patients. The locations of the tumours were as follows: head of pancreas in 7 patients, body and/or tail of pancreas in 32 patients. Pain relief, time to progression (TTP), median survival and complications were analysed after HIFU treatment. RESULTS: There were no severe complications or adverse events related to HIFU therapy in any of the patients treated. Pain relief was achieved in 79.5% of patients. Median TTP was 5.0 months. The median overall survival time was 11 months. 6-month and 1-year survival rate for patients were 82.1% and 30.8% respectively. CONCLUSIONS: Although this study may have limitations, preliminary results demonstrate the safetyof clinical application of HIFU for pancreatic cancer and reveal it to be a promising mode of treatment for local advanced pancreatic cancers.
Asunto(s)
Ultrasonido Enfocado de Alta Intensidad de Ablación , Neoplasias Pancreáticas/cirugía , Adulto , Anciano , Progresión de la Enfermedad , Femenino , Ultrasonido Enfocado de Alta Intensidad de Ablación/efectos adversos , Ultrasonido Enfocado de Alta Intensidad de Ablación/mortalidad , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Dolor/etiología , Dolor/prevención & control , Neoplasias Pancreáticas/complicaciones , Neoplasias Pancreáticas/mortalidad , Neoplasias Pancreáticas/patología , Estudios Prospectivos , Análisis de Supervivencia , Factores de Tiempo , Tomografía Computarizada por Rayos X , Resultado del TratamientoRESUMEN
Due to the high data dimensionality of a hyperspectral image, dimensionality reduction algorithm has attracted much attention in hyperspectral image analysis. Band selection algorithm, which selects appropriate bands from the original set of spectral bands, can preserve original information from the data and is useful for image classification and recognition. In the present paper, a novel band selection algorithm based on orthogonal projection divergence (OPD) is proposed, it aims to discriminate the interesting objects from background and noise information, maximize the spectral similarity between different spectral vectors by projecting the original data to feature space. Two HYDICE Washington DC Mall images and an HYMAP Purdue campus image data were experimented, and support vector machine (SVM) classifier was used for classification. The selected band number varies from 5 to 40 in order to study the impacts of different band selection algorithms on different features. For the computation complex, the sequential floating forward search (SFFS) was used to get the appropriate bands. The experiments have proved that our proposed OPD algorithm can outperform other traditional band selection methods such as SAM, ED, SID, and LCMV-BCC for hyperspectral image analysis. It is proven that OPD band selection is effective and robust in hyperspectral remote sensing dimensionality reduction