ABSTRACT
Objective To explore the feasibility of using digital imaging processing (DIP)to extract EUS image parameters for the differential diagnosis of autoimmune pancreatitis (AIP)and chronic pancreati-tis (CP).Methods A total of 81 patients with AIP and 100 patients with CP diagnosed from May 2005 to January 2013 were recruited to this study.A total of 105 parameters of 9 categories were extracted from the region of interest by using computer-based techniques.Then the distance between class algorithm and se-quential forward selection (SFS)algorithm were used for a better combination of features.A support vector machine (SVM)predictive model was built,trained,and validated.Results Overall,25 parameters of 5 categories were selected as a better combination of features when the incidence of accurate category was max (90.08%).A total of 181 sample sets were randomly divided into a training set and a testing set by using two different algorithms and 200 random tests were performed.The average accuracy,sensitivity,specificity, the positive and negative predictive values of AIP based on the half-and-half method were (86.04 ± 3.15)%,(83.66 ±6.57)%,(88.54 ±4.37)%,(85.96 ±4.44)% and (87.12 ±4.39)%,respective-ly.Conclusion Computer-aided diagnosis of EUS images is objective and non-invasive,which can improve the accuracy in differentiating AIP from CP.This technology provides a new valuable diagnostic tool for the clinical determination of AIP.
ABSTRACT
Objective To extract the texture features of endoscopic ultrasonography (EUS) by digital imaging processing(DIP) and pattern recognition,and then to investigate its value for differential diagnosis between pancreatic cancer and chronic pancreatitis.Methods Two hundred and two patients with pathologicaly confirmed pancreatic malignancy,who underwent EUS from Feb 2005 to Mar 2011,and 104 patients with chronic pancreatitis (including 34 cases of autoimmune pancreatitis),who underwent EUS from May 2002 to Aug 2011,were randomly recruited in this study.The optimal texture features of EUS images in this study were selected by the sequence forward search (SFS) algorithm.With the optimal feature combination,cases were automatically divided into pancreatic cancer and chronic pancreatitis based on the findings of support vector machine (SVM),which were compared with the real results.the sensitivity,specificity,accuracy,positive predictive value and negative predictive value were calculated.Results Nine categories and 105 texture features were extracted based on all EUS images,and 13 features were chosen as optimal combination.Images of 306 cases were randomly divided into training set ( 153 cases,101 cases of cancer,52 cases of chronic pancreatitis) and testing set ( 153 cases,101 cases of cancer,52 cases of chronic pancreatitis).The classifier was trained with the training set and tested with testing set.We proceeded 200 times randomly.the average accuracy,sensitivity,specificity,positive predictive value and negative predictive value were ( 86.08 ± 0.14) ％,(79.47 ± 0.32) ％,( 89.71 ± 0.18 ) ％,( 81.21 ± 0.26 ) ％,( 88.93 ± 0.14 ) ％,respectively.Conclusion Differential diagnosis of pancreatic cancer from chronic pancreatitis by Computer-assisted EUS image analysis,highly accurate,convenient,non-invasive and less costly,is a novel and valuable method of early diagnosis.
ABSTRACT
Objective To process the image of endoscopic uhrasonography(EUS)by digital imaging processing(DIP)and pattem recognition,and to evaluate its efficacy in diagnosis of pancreatic adenocarcinoma.Methods Two hundreds and sixteen patients,who underwent EUS between Feb 2005 and Feb 2007,were randomly recruited to the study.The cohort jncluded 153 cases of pancreatic cancer,which were confirmed by cytological findings after fine-needle aspiration,and 63 cases of non-pancreatic cancer(normal pancreas and chronic panereatitis).The texture features of the EUS image were selected and extracted,and cases were automatically divided into cancer and non-cancer based on findings of support vector machine (SVM).Sensitivity,specificity and accuracy of the technique were calculated.Results From each region of interest(ROI),a total of69 texture features vest in 9 sets were extracted,and 25 features with most set interval were taken as initial.The images of 216 cases were divided randomly into training set(108 eases,76 cancer and 32 non cancer)and testing set(108 cases,77 cancer and 31 non cancer).After 50 times of random tests,the average accuracy,sensitivity and specificity of the diagnosis of pancreatic cancer were (97.98±1.237)%,(94.32±0.0354)%,and(99.45±0.0102)%respectively.Conclusion DIP,combined with computer aided EUS imaging,is an accurate and noninvasive technique in diagnosis of pancreatic cancer.which warrants novel and further researches.
ABSTRACT
We have recently seen an increasing number of patients with osteoporosis of the type that occurs as a chronic illness in the elderly, and particularly in elderly female patients. It is important not only to treat pain but to follow-up with treatments to prevent further bone mass loss. To measure bone mass in patients with osteoporosis, we employed Digital Imaging Processing (DIP). In this study, the authors examined changes in the bone mass of patients in long-term therapy with Keishi-ka-Jutsubu-to and Gosha-Jinki-Gan. As a comparative-control group, or non-treatment group, we selected 11 patients who had been diagnosed as having osteporosis in an outpatient clinic, and whose bone mass had been measured with DIP. These patients discontinued treatment, but returned to the outpatient clinic six months to one year later. The average duration of non-treatment in the control group was 9.8 months. Metacarpal index (MCI) and metacarpal bone mineral density (m-BMD) at the first visit were 0.40±0.07 and 2.22±0.38, but 10 months later they were 0.36±0.05 and 1.97±0.38, which represents a significant decrease.<br>In 20 cases given Keishi-ka-Jutsubu-to, the initial bone mass data were: MCI, 0.39±0.08; m-BMD, 2.07±0.32. Measurements performed after three, six, and nine months of treatment showed no difference or increase from the initial values.<br>In 12 cases given Gosha-Jinki-Gan, the initial data were: MCI, 0.40±0.07; m-BMD, 2.06±0.27. Measurements performed after three, six and nine months of treatment showed no difference from the initial values.<br>The severity of pain was equally reduced by treatment with Kampo formulation or NSAIDs (non-steroidal anti-inflammatory drugs) by four weeks, but after eight weeks low back pain in patients treated with the Kampo formulation was significantly reduced compared with low back pain in the group treated with NSAIDs.