TY - GEN
T1 - Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks
AU - Sobhani, Faranak
AU - Hamidinekoo, Azam
AU - Hall, Allison H.
AU - King, Lorraine
AU - Marks, Jeffrey R.
AU - Maley, Carlo
AU - Horlings, Hugo M.
AU - Shelley Hwang, E.
AU - Yuan, Yinyin
N1 - Funding Information:
This project was funded by NIH R01 CA185138 and CDMRP Breast Cancer Research Program Award BC132057 and Breast Cancer Now (2015NovPR638).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Ductal Carcinoma In Situ (DCIS) is a non-obligatory precursor of Invasive Breast Cancer. It is the most common mammographically detected breast cancer. Predicting DCIS progression to invasive ductal carcinoma is a major clinical challenge due to the lack of a uniform classification system in the diagnosis and prognostication of this disease. To characterise the tissue microecology of DCIS, we proposed and tested the model "DCIS-Identification model"based on Generative Adversarial Networks (GAN) for detection and segmentation of DCIS ducts from multiplex immunohistochemistry (IHC) staining samples. We also trained a Spatially Constrained Convolutional Neural Network (SC-CNN) to detect and classify single cells based on their CA9 and FOXP3 expression. The DCIS-Identification model was evaluated on 8 whole slide images, resulting in an average Dice score of 0.95 for the segmentation performance. The single cell identification framework was tested on 10 randomly selected whole slide sections, achieving the average accuracy of 88.6% in a 5 fold cross validation scheme. With the proposed pipeline, we efficiently integrated deep learning, computational pathology and spatial statistics to report distinct differences in the microenvironments of DCIS and IDC/DCIS samples. The proposed pipeline provides a tool for a better understanding of the mechanism of tumours in DCIS and IDC/DCIS cases.
AB - Ductal Carcinoma In Situ (DCIS) is a non-obligatory precursor of Invasive Breast Cancer. It is the most common mammographically detected breast cancer. Predicting DCIS progression to invasive ductal carcinoma is a major clinical challenge due to the lack of a uniform classification system in the diagnosis and prognostication of this disease. To characterise the tissue microecology of DCIS, we proposed and tested the model "DCIS-Identification model"based on Generative Adversarial Networks (GAN) for detection and segmentation of DCIS ducts from multiplex immunohistochemistry (IHC) staining samples. We also trained a Spatially Constrained Convolutional Neural Network (SC-CNN) to detect and classify single cells based on their CA9 and FOXP3 expression. The DCIS-Identification model was evaluated on 8 whole slide images, resulting in an average Dice score of 0.95 for the segmentation performance. The single cell identification framework was tested on 10 randomly selected whole slide sections, achieving the average accuracy of 88.6% in a 5 fold cross validation scheme. With the proposed pipeline, we efficiently integrated deep learning, computational pathology and spatial statistics to report distinct differences in the microenvironments of DCIS and IDC/DCIS samples. The proposed pipeline provides a tool for a better understanding of the mechanism of tumours in DCIS and IDC/DCIS cases.
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U2 - 10.1109/ISBI52829.2022.9761413
DO - 10.1109/ISBI52829.2022.9761413
M3 - Conference contribution
AN - SCOPUS:85129651971
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - ISBI 2022 - Proceedings
PB - IEEE Computer Society
T2 - 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Y2 - 28 March 2022 through 31 March 2022
ER -