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Article Dans Une Revue Annals of Surgical Oncology Année : 2020

Prospective Multicenter Study Validate a Prediction Model for Surgery Uptake Among Women with Atypical Breast Lesions

Veronique Boussion
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Joelle Mollard
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Corinne Balleyguier
Suzette Delaloge
Stefan Michiels

Résumé

Background: Diagnosis of atypical breast lesions (ABLs) leads to unnecessary surgery in 75-90% of women. We have previously developed a model including age, complete radiological target excision after biopsy, and focus size that predicts the probability of cancer at surgery. The present study aimed to validate this model in a prospective multicenter setting. - methods: Women with a recently diagnosed ABL on image-guided biopsy were recruited in 18 centers, before wire-guided localized excisional lumpectomy. Primary outcome was the negative predictive value (NPV) of the model. Results: The NOMAT model could be used in 287 of the 300 patients included (195 with ADH). At surgery, 12 invasive (all grade 1), and 43 in situ carcinomas were identified (all ABL: 55/287, 19%; ADH only: 49/195, 25%). The area under the receiving operating characteristics curve of the model was 0.64 (95% CI 0.58-0.69) for all ABL, and 0.63 for ADH only (95% CI 0.56-0.70). For the pre-specified threshold of 20% predicted probability of cancer, NPV was 82% (77-87%) for all ABL, and 77% (95% CI 71-83%) for patients with ADH. At a 10% threshold, NPV was 89% (84-94%) for all ABL, and 85% (95% CI 78--92%) for the ADH. At this threshold, 58% of the whole ABL population (and 54% of ADH patients) could have avoided surgery with only 2 missed invasive cancers. Conclusion: The NOMAT model could be useful to avoid unnecessary surgery among women with ABL, including for patients with ADH.
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Dates et versions

hal-03013819 , version 1 (19-11-2020)

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Catherine Uzan, Chafika Mazouni, Caroline Rossoni, Brigitte de Korvin, Christine Tunon de Lara, et al.. Prospective Multicenter Study Validate a Prediction Model for Surgery Uptake Among Women with Atypical Breast Lesions. Annals of Surgical Oncology, 2020, ⟨10.1245/s10434-020-09107-z⟩. ⟨hal-03013819⟩
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