Deep learning-based framework for quantification and prognostic of polyploid giant cancer cells

Jingying Yang; Chengye Zhang; Ruidian Gong; Qiming He; Yonghong He

doi:10.1117/12.2685073

19 October 2023 Deep learning-based framework for quantification and prognostic of polyploid giant cancer cells

Jingying Yang, Chengye Zhang, Ruidian Gong, Qiming He, Yonghong He

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090E (2023) https://doi.org/10.1117/12.2685073
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Primary liver cancer is one of the most lethal cancers worldwide. Polyploid giant cancer cells seen on pathological examination are associated with prognosis of it. However, its quantification requires doctors to interpret region by region on pixel-level images, which is time-intensive and subjective, not been applied clinically yet. For the first time, we proposed the PGCCI index based on the quantity of polyploid giant cancer cells and the size of the tumor region, developed a deep learning based PGCCdet framework for detection and calculation, and proved the validity of PGCCI by survival analysis. In our framework, U-Net and Resnet are used for foreground segmentation and tissue classification respectively. For polyploid giant cancer cells detection, we proposed YOLOCN, a detection network combining multi-scale feature fusion and multi-dimensional attention mechanism, to achieve accurate quantification. For survival analysis, Kaplan-Meier method is implemented. Experiments show that on the self-owned HCCP dataset, the F1 score of detection is 0.814 and the map@0.5 is 0.871, better than other methods. Survival analysis on the TCGA dataset with 323 cases shows that the median survival time of the low PGCCI group was 84.4 months, while the high PGCCI group was 38.3 months. The Pvalue was smaller than 0.0001, indicating a significant survival difference. The results show our framework can accurately detect polyploid giant cancer cells and the PGCCI can effectively predict survival. This framework can change the way of diagnosis of liver cancer with great application potential in the clinical diagnosis and treatment in the future.

Citation Download Citation

Jingying Yang, Chengye Zhang, Ruidian Gong, Qiming He, and Yonghong He "Deep learning-based framework for quantification and prognostic of polyploid giant cancer cells", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090E (19 October 2023); https://doi.org/10.1117/12.2685073

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KEYWORDS

Object detection

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Education and training

Cancer detection

Cancer

Tissues

Deep learning

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