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1.  Solid neuroendocrine breast carcinoma: mammographic and sonographic features in thirteen cases 
Chinese Journal of Cancer  2012;31(11):549-556.
This study aimed to determine and quantitate the mammographic and sonographic characteristics in 13 cases of solid neuroendocrine breast carcinoma (NEBC) and to analyze the association of radiological findings with the clinical and histopathologic findings. The clinical data and imaging findings of 13 female patients with histologically confirmed solid NEBC were reviewed. Imaging data were evaluated by two radiologists for a consensual diagnosis. All patients presented with one palpable mass; only 1 experienced occasional breast pain, and 5 complained of fluid discharge. In 7 patients, the masses were firm and mobile. Regional lymph node metastasis was noted in only 1 patient. For the 10 patients who underwent mammography, 6 had a mass, 1 had clustered small nodules with clustered punctuate microcalcifications, 2 had asymmetric focal density, and 1 had solitary punctuate calcification. Most of the masses had irregular shape with indistinct or microlobulated margins. For the 9 patients who underwent ultrasonography (US), 9 masses were depicted, all of which were hypoechoic, mostly with irregular shape and without acoustic phenomena. Different types of acoustic phenomena were also identified. One patient had developed distant metastases during follow-up. NEBC has a variety of presentations, but it is mostly observed on mammograms as a dense, irregular mass with indistinct or microlobulated margins. Sonographically, it typically presents as an irregular, heterogeneously hypoechoic mass with normal sound transmission. Histories of nipple discharge and calcification observed using imaging are not rare.
doi:10.5732/cjc.011.10370
PMCID: PMC3777518  PMID: 22640624
Solid neuroendocrine carcinoma of the breast; mammography; sonography
2.  Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach 
Journal of Digital Imaging  2011;24(5):764-771.
In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n = 12) and fibroadenomas (n = 20); grade 1, well-differentiated infiltrating ductal carcinoma (n = 12); grade 2, moderately differentiated infiltrating ductal carcinoma (n = 38); grade 3, poorly differentiated infiltrating ductal carcinoma (n = 27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G =  6.438 + 1.186 ×  Ln <θ>. The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.
doi:10.1007/s10278-011-9381-2
PMCID: PMC3180539  PMID: 21512853
Algorithms; computer-aided diagnosis (CAD); mammography CAD; breast diseases; clustered microcalcification detection

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