Browsing by Author "Lirng, Jiing-Feng"

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Brain morphological changes associated with cyclic menstrual pain
(2010-09-01)
Tu, Cheng-Hao
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Niddam, David M.
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Chao, Hsiang-Tai
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Chen, Li-Fen
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Chen, Yong-Sheng
Primary dysmenorrhea (PDM) is the most prevalent gynecological disorder for women in the reproductive age. PDM patients suffer from lower abdominal pain that starts with the onset of the menstrual flow. Prolonged nociceptive input to the central nervous system can induce functional and structural alterations throughout the nervous system. In PDM, a chronic viscero-nociceptive drive of cyclic nature, indications of central sensitization and altered brain metabolism suggest a substantial central reorganization. Previously, we hypothesized that disinhibition of orbitofrontal networks could be responsible for increased pain and negative affect in PDM. Here, we further tested this hypothesis. We used an optimized voxel-based morphometry (VBM) approach to compare total and regional gray matter (GM) increases and decreases in 32 PDM patients with 32 healthy age and menstrual cycle matched (peri-ovulatory phase) controls. Abnormal decreases were found in regions involved in pain transmission, higher level sensory processing, and affect regulation while increases were found in regions involved in pain modulation and in regulation of endocrine function. Moreover. GM changes in regions involved in top-down pain modulation and in generation of negative affect were related to the severity of the experienced PDM pain. Our results demonstrate that abnormal GM volume changes are present in PDM patients even in the absence of pain. These changes may underpin a combination of impaired pain inhibition, increased pain facilitation and increased affect. Our findings highlight that longer lasting central changes may occur not only in sustained chronic pain conditions but also in cyclic occurring pain conditions. (C) 2010 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
Prediction of Metastasis in Head and Neck Cancer from Computed Tomography Images
(2018-01-01)
Lo, Tzu-Yun
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Wei, Pei-Yin
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Yen, Chia-Heng
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Lirng, Jiing-Feng
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Yang, Muh-Hwa
The current medical method for determining whether the malignant tumor of the head and neck metastasizes to the lymph is to interpret the pathological section of the patient's lymph. This study proposes a support vector machine (SVM) based method Pred-Meta to predict metastasis of a malignant tumor from a patient's computed tomography (CT) image. Pred-Meta utilizes three feature types, including texture, morphology, and grayscale, and an optimal feature selection method cooperated with SVM. The data set consists of 75 samples from 70 patients in head and neck cancer provided by Taipei Veterans General Hospital of Taiwan with a record of lymphatic metastasis. Pred-Meta using leave-one-out cross-validation achieved 100% in predicting metastasis. The merit of the Pred-Meta method is its non-invasiveness and low cost. Auxiliary physicians screen out patients with high risk of diversion in the early stages to help plan treatment guidelines. The limitation of Pred-Meta suffers from the small number of training samples. It is expected that Pred-Meta would perform better in testing independent cohort when the number of training samples significantly increases.