21 research outputs found

    Detection of the abnormal GIST in the prior mammograms even with no overt sign of breast cancer

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    Can radiologists distinguish prior mammograms with no overt signs of cancer from women who were later diagnosed with breast cancer from the prior mammograms of women reported as normal and subsequently confirmed to be cancerfree? Twenty-three radiologists and breast physicians viewed 200 craniocaudial mammograms for a half-second and rated whether the woman would be recalled on a scale of 0 (clearly normal) to 100 (clearly abnormal). The dataset included five categories of mammograms, with each category containing 40 cases. The categories were Cancer (current cancer-containing mammograms), Prior-Vis (prior mammograms with visible cancer signs), Contra (current âñormal' mammograms contralateral to the cancer), Prior-Invis (priors without visible cancer signs), and Normal (priors of normal cases). For each radiologist, four pairs of analyses were performed to evaluate whether the radiologists could distinguish mammograms in each category from the normal mammograms: Cancer vs Normal, Prior-Vis vs Normal, Contra vs Normal, and Prior-Invis vs Normal. The Area under Receiver Operating Characteristic curves (AUC) was calculated for each paired grouping and each radiologist. Wilcoxon Signed Rank test showed the AUC values were above-chance for all comparisons: Cancer (z=4.20, P<0.001); Prior-Vis (z=4.11, P<0.001); Contra (z=4.17, P<0.001); Prior-Invis (z=3.71, P<0.001). The results suggest that radiologists can distinguish patients who were diagnosed with cancer from individuals without breast cancer at an above-chance level based on a half-second glimpse of mammogram even before the lesion becomes apparently visible (Prior-Invis). Apparently, something about the breast parenchyma can look abnormal before the appearance of a localized lesion

    CAMELLIA SINENSIS VAR. MADOENSIS (SECT. THEA, THEACEAE), A NEW TAXON FROM VIETNAM

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    Camellia sinensis var. madoensis is described and illustrated as a new variety of Camellia sinensis (section Thea, Theaceae) from Xuan Loc Commune, Song Cau District, Phu Yen Province. The new variety is easily distinguishable from C. sinensis var. sinensis by style free ½ to the base. The ITS sequence of this variety is also different from that of Camellia sinensis and its other varieties, while the matK gene sequences are nearly identical among Camellia taxa

    CAMELLIA SINENSIS VAR. MADOENSIS (SECT. THEA, THEACEAE), A NEW TAXON FROM VIETNAM

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    Camellia sinensis var. madoensis is described and illustrated as a new variety of Camellia sinensis (section Thea, Theaceae) from Xuan Loc Commune, Song Cau District, Phu Yen Province. The new variety is easily distinguishable from C. sinensis var. sinensis by style free ½ to the base. The ITS sequence of this variety is also different from that of Camellia sinensis and its other varieties, while the matK gene sequences are nearly identical among Camellia taxa.Camellia sinensis var. madoensis được mô tả và minh họa với vai trò là một thứ mới của Camellia sinensis (section Thea, Theaceae) ghi nhận tại xã Xuân Lộc, huyện Sông Cầu, tỉnh Phú Yên. Thứ mới này có thể dễ dàng phân biệt với C. sinensis var. sinensis bởi vòi nhụy rời ½ tính từ đế. Trình tự ITS của thứ này cũng khác với Camellia sinensis và các thứ khác của nó

    The Impact of Prior Mammograms on the Diagnostic Performance of Radiologists in Early Breast Cancer Detection: A Focus on Breast Density, Lesion Features and Vendors Using Wholly Digital Screening Cases

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    Background: This study aims to investigate the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images, and with the presence of prior images from the same and different vendors. Methods: 612 radiologists’ readings across 9 test sets, consisting of 540 screening mammograms (361-normal and 179-cancer) with 245 cases having prior images obtained from same vendor as current images, 129 from a different vendor and 166 cases having no prior images, were retrospectively analysed. True positive (sensitivity), true negative (specificity) and area under ROC curve (AUC) values of radiologists were calculated for three groups of cases (without prior images (NP), with prior images from same vendor (SP), and with prior images from different vendor (DP)). Logistic regression was used to estimate the odds ratio (OR) of true positive, true negative and true cancer localization among case groups with different levels of breast density and lesion characteristics. Results: Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP (0.803-and-0.785 vs. 0.712; p p p p p p < 0.0001). Conclusions: Cases without previous mammograms or with prior mammograms obtained from different vendors were more likely to benefit radiologists in cancer detection, whilst prior mammograms undertaken from the same vendor were more useful for radiologists in evaluating normal cases

    Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback

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    It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers’ weakness and strength are identified
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