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- W2314950916 abstract "Background: Population-based mammography screening has resulted in the increased detection of suspicious, non-palpable lesions that require histopathological assessment. When a “pre-malignant” lesion, such as lobular carcinoma in situ (LCIS), atypical ductal or lobular hyperplasia (ADH and ALH) or flat epithelial atypia (FEA), is discovered on the primary biopsy, a surgical excision is recommended due to the risk of underestimation of the lesion. DCIS or invasive carcinoma is finally retrieved in 10–30% of the patients, meaning a large part receive unnecessary surgery. The aim of this study was to define a nomogram integrating clinical, imaging and histological data to predict for the risk of underestimation of the lesion. Intentionally, we decided to include all the premalignant lesions which implied the same management, whereas most of the previous models of the literature are focused in only one or another type of atypia. Patients and Methods: We collected complete clinical, radiological and double-reading histological data on all patients with a diagnosis of pure atypical lesion (CLIS, ADH, ALH, FEA) on image-guided biopsy performed at the One-Stop Unjt of our breast care center from 2004 to 2011. Univariate and multivariate logistic regression analyses were used to develop a model predicting for the presence of DCIS or invasive carcinoma on the final surgical sample, and build a nomogram. This nomogram was evaluated on a training set of 205 patients treated at IGR. Results: 205 patients were eligible for the study. Of these 205 patients, 50 cancers (24.4%) were diagnosed at definitive surgery (21 DCIS, 20 ductal and 9 lobular invasive carcinoma). Univariate analysis retrieved age (p = 0.03), number of biopsy cores (p = 0.02) and type of radiological anomalies (p = 0.02) as factors associated with cancer at surgery. The presence of microcalcifications on biopsies of limit significance was included in the multivariate analysis (p = 0.09). The final most informative nomogram included information on patient age (p = 0.03), type of radiological anomalies (p = 0.02) and number of biopsy cores (p = 0.04). The predictive accuracy of the nomogram was 0.71 and 0.68 in the training set before and after bootstrapping, respectively. The calibration of the nomogram was good. The sensitivity, specificity, positive predictive value, and negative predictive values were 68%, 73%, 45%, and 88%, respectively. Conclusion: This nomogram could help identify a subset of patients with premalignant disease for whom surgery could be spared and who would be eligible for exclusive clinical follow-up. A validation study is currently undergoing in an external dataset. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-12-01." @default.
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- W2314950916 date "2012-12-15" @default.
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- W2314950916 title "Abstract P4-12-01: A nomogram based on clinical, imaging and histological data to predict the risk of upgrades to malignancy at surgery in biopsy-diagnosed premalignant lesions of the breast" @default.
- W2314950916 doi "https://doi.org/10.1158/0008-5472.sabcs12-p4-12-01" @default.
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