Assuntos
Adenocarcinoma Mucinoso , Proteínas de Ligação a DNA , Mutação , Infecções por Papillomavirus , Proteína Smad4 , Fatores de Transcrição , Neoplasias do Colo do Útero , Humanos , Feminino , Proteína Smad4/genética , Adenocarcinoma Mucinoso/patologia , Adenocarcinoma Mucinoso/genética , Adenocarcinoma Mucinoso/virologia , Neoplasias do Colo do Útero/patologia , Neoplasias do Colo do Útero/virologia , Neoplasias do Colo do Útero/genética , Proteínas de Ligação a DNA/genética , Fatores de Transcrição/genética , Infecções por Papillomavirus/patologia , Infecções por Papillomavirus/virologia , Infecções por Papillomavirus/complicações , Infecções por Papillomavirus/genética , Tumores Neuroendócrinos/patologia , Tumores Neuroendócrinos/genética , Tumores Neuroendócrinos/virologia , Papillomaviridae/genética , Papillomaviridae/isolamento & purificação , Colo do Útero/patologia , Colo do Útero/virologia , Pessoa de Meia-Idade , Papillomavirus HumanoRESUMO
Artificial intelligence (AI)-systems can improve cancer diagnosis, yet their development often relies on subjective histological features as ground truth for training. Here, we developed an AI-model applied to histological whole-slide images (WSIs) using CDH1 bi-allelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 bi-allelic mutations (accuracy=0.95) and diagnosed ILC (accuracy=0.96). A total of 74% of samples classified by the AI-model as having CDH1 bi-allelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and non-coding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI-model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI-algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI-models applied to WSI.