Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy - Azam, Abu Bakr; Wee, Felicia; Väyrynen, Juha P.; Yim, Willa Wen-You; Xue, Yue Zhen; Chua, Bok Leong; Lim, Jeffrey Chun Tatt; Somasundaram, Aditya Chidambaram; Tan, Daniel Shao Weng; Takano, Angela; Chow, Chun Yuen; Khor, Li Yan; et al. (Frontiers in Immunology, 2024)
Immunophenotyping, utilizing multi-marker assays such as multiplex immunohistochemistry (mIHC), plays an important role in therapeutic monitoring, biomarker discovery, and personalized treatments. Deep learning (DL) models which predict biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues circumvent the cost and labor limitations of conventional IHC and mIHC, thereby increasing accessibility to multi-marker immunophenotyping. Conventionally, cell labels for training the DL models are derived from IHCstained tissue sections adjacent to H&E-stained sections. This spatial separation between tissue sections might diminish the accuracy of cell labels, potentially limiting the model performance.Although many such DL models have been developed, the effect of ground truth cell label derivation methods on model prediction performance has not been studied. In this study, we focused on CD3 + T-cells in lung cancer tissues as a proof-of-concept to assess the impact on model prediction performance. We showed that a Pix2Pix generative adversarial network (P2P-GAN)based virtual staining model exhibited significantly improved prediction performance when trained with ground truth cell labels obtained from the same tissue section as the H&E-stained section (termed the 'same-section' model), compared to a model trained on cell labels from an adjacent tissue section (termed the 'serial-section' model). Moreover, the same-section model 4 outperformed the serial-section model in stratifying lung cancer patients within a public cohort into groups with better and worse survival outcomes, highlighting its clinical significance.Collectively, our findings suggest that employing ground truth cell labels obtained through the same-section approach could revolutionize immunophenotyping DL solutions.