Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk

Texto Completo
AutomatedDetectionBreast.pdf embargoed access
Solicita copia
Al rellenar este formulario estáis solicitando una copia del artículo, depositado en el repositorio institucional (DUGiDocs), a su autor o al autor principal del artículo. Será el mismo autor quien decidirá enviar una copia del documento a quien lo solicite si lo considera oportuno. En todo caso, la Biblioteca de la UdG no interviene en este proceso ya que no está autorizada a facilitar artículos cuando éstos son de acceso restringido.
Purpose To evaluate the performance of an automated computer-aided detection (CAD) system to detect breast cancers that were overlooked or misinterpreted in a breast MRI screening program for women at increased risk. Methods We identified 40 patients that were diagnosed with breast cancer in MRI and had a prior MRI examination reported as negative available. In these prior examinations, 24 lesions could retrospectively be identified by two breast radiologists in consensus: 11 were scored as visible and 13 as minimally visible. Additionally, 120 normal scans were collected from 120 women without history of breast cancer or breast surgery participating in the same MRI screening program. A fully automated CAD system was applied to this dataset to detect malignant lesions. Results At 4 false-positives per normal case, the sensitivity for the detection of cancer lesions that were visible or minimally visible in retrospect in prior-negative examinations was 0.71 (95% CI = 0.38-1.00) and 0.31 (0.07-0.59), respectively. Conclusions A substantial proportion of cancers that were misinterpreted or overlooked in an MRI screening program was detected by a CAD system in prior-negative examinations. It has to be clarified with further studies if such a CAD system has an influence on the number of misinterpreted and overlooked cancers in clinical practice when results are given to a radiologist ​
​Tots els drets reservats