磁共振成像
卷积神经网络
乳腺癌
医学
新辅助治疗
计算机科学
放射科
病态的
人工智能
癌症
内科学
作者
Maria Colomba Comes,Annarita Fanizzi,Samantha Bove,Vittorio Didonna,Sergio Diotiaiuti,Federico Fadda,Daniele La Forgia,Francesco Giotta,Agnese Latorre,Annalisa Nardone,Gennaro Palmiotti,Cosmo Maurizio Ressa,L. Rinaldi,Alessandro Rizzo,Tiziana Talienti,Pasquale Tamborra,Alfredo Zito,Vito Lorusso,Raffaella Massafra
标识
DOI:10.1016/j.compbiomed.2024.108132
摘要
So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information.
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