PurposeThe prediction of breast cancer recurrence is a crucial factor for successful treatment and follow-up planning. The principal objective of this study was to construct a novel prognostic model based on support vector machine (SVM) for the prediction of breast cancer recurrence within 5 years after breast cancer surgery in the Korean population, and to compare the predictive performance of the model with the previously established models.MethodsData on 679 patients, who underwent breast cancer surgery between 1994 and 2002, were collected retrospectively from a Korean tertiary teaching hospital. The following variables were selected as independent variables for the prognostic model, by using the established medical knowledge and univariate analysis: histological grade, tumor size, number of metastatic lymph node, estrogen receptor, lymphovascular invasion, local invasion of tumor, and number of tumors. Three prediction algorithms, with each using SVM, artificial neural network and Cox-proportional hazard regression model, were constructed and compared with one another. The resultant and most effective model based on SVM was compared with previously established prognostic models, which included Adjuvant! Online, Nottingham prognostic index (NPI), and St. Gallen guidelines.ResultsThe SVM-based prediction model, named 'breast cancer recurrence prediction based on SVM (BCRSVM),' proposed herein outperformed other prognostic models (area under the curve=0.85, 0.71, 0.70, respectively for the BCRSVM, Adjuvant! Online, and NPI). The BCRSVM evidenced substantially high sensitivity (0.89), specificity (0.73), positive predictive values (0.75), and negative predictive values (0.89).ConclusionAs the selected prognostic factors can be easily obtained in clinical practice, the proposed model might prove useful in the prediction of breast cancer recurrence. The prediction model is freely available in the website (http://ami.ajou.ac.kr/bcr/).

PurposeIdiopathic granulomatous mastitis (IGM) is a rare chronic inflammatory disease of unknown etiology. The diagnosis of IGM requires that other granulomatous lesions in the breast be excluded. Tuberculous mastitis (TM) is also an uncommon disease that is often difficult to differentiate from IGM. The purpose of this study is to develop a new algorithm for the differential diagnosis and treatment of IGM and TM.MethodsMedical records of 68 patients (58 with IGM and 10 with TM) between July 1999 and February 2009 were retrospectively reviewed.ResultsThe mean age of the patients was 33.5 (IGM) and 40 (TM) years (p=0.018). The median follow-up was 84 months. Of the total 10 patients with TM, 5 patients had a history of pulmonary tuberculosis. The most common symptoms of the diseases were breast lump and pain. However, axillary lymphadenopathy was more seen in TM (50%) compared to IGM (20.6%) (p=0.048). TM showed more cancer-mimicking findings on radiologic study (p=0.028). In IGM, 48 patients (82.7%) underwent surgical wide excision and 21 patients (36.2%) were managed with corticosteroid therapy and antibiotics. All of the TM patients received anti-tuberculosis medications and 9 patients (90%) underwent wide excision. The mean treatment duration was 2.8 months in IGM and 8.4 months in TM. Recurrence developed in 5 patients (8.6%) in IGM and 1 patient (10%) in TM.ConclusionThis study shows different characteristics between IGM and TM. The IGM patients were younger and had more mastalgia symptoms than the TM patients. Axillary lymphadenopathy was seen more often in TM patients. Half of the TM patients had pulmonary tuberculosis or tuberculosis lymphadenitis. Surgical wide excision might be both therapeutic and useful for providing an exact diagnosis.

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In conjunction with the standard BI-RADS MRI lexicon, the adjacent vessel sign and increased ipsilateral whole-breast vascularity may serve as additional predictors of a poor prognosis.