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Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

Wang, K S ; Yu, G ; Xu, C ; Meng, X H ; Zhou, J ; Zheng, C ; Deng, Z ; Shang, L ; Liu, R ; Su, S ; Zhou, X ; Li, Q ; Li, J ; Wang, J ; Ma, K ; Qi, J ; Hu, Z ; Tang, P ; Deng, J ; Qiu, X ; Li, B Y ; Shen, W D ; Quan, R P ; Yang, J T ; Huang, L Y ; Xiao, Y ; Yang, Z C ; Li, Z ; Wang, S C ; Ren, H ; Liang, C ; Guo, W ; Li, Y ; Xiao, H ; Gu, Y ; Yun, J P ; Huang, D ; Song, Z ; Fan, X ; Chen, L ; Yan, X ; Li, Z ; Huang, Z C ; Huang, J ; Luttrell, J ; Zhang, C Y ; Zhou, W ; Zhang, K ; Yi, C ; Wu, C ; Shen, H ; Wang, Y P ; Xiao, H M ; Deng, H W

BMC medicine, 2021-03, Vol.19 (1), p.76-76, Article 76 [Periódico revisado por pares]

England: BioMed Central

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Título:
Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence
Autor: Wang, K S ; Yu, G ; Xu, C ; Meng, X H ; Zhou, J ; Zheng, C ; Deng, Z ; Shang, L ; Liu, R ; Su, S ; Zhou, X ; Li, Q ; Li, J ; Wang, J ; Ma, K ; Qi, J ; Hu, Z ; Tang, P ; Deng, J ; Qiu, X ; Li, B Y ; Shen, W D ; Quan, R P ; Yang, J T ; Huang, L Y ; Xiao, Y ; Yang, Z C ; Li, Z ; Wang, S C ; Ren, H ; Liang, C ; Guo, W ; Li, Y ; Xiao, H ; Gu, Y ; Yun, J P ; Huang, D ; Song, Z ; Fan, X ; Chen, L ; Yan, X ; Li, Z ; Huang, Z C ; Huang, J ; Luttrell, J ; Zhang, C Y ; Zhou, W ; Zhang, K ; Yi, C ; Wu, C ; Shen, H ; Wang, Y P ; Xiao, H M ; Deng, H W
Assuntos: Accuracy ; Artificial Intelligence ; Artificial neural networks ; Cancer ; Cancer diagnosis ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms - diagnosis ; Datasets ; Deep Learning ; Diagnosis ; Health services ; Histopathology ; Histopathology image ; Humans ; Image analysis ; Image processing ; Laboratories ; Medical diagnosis ; Medical imaging ; Neural networks ; Neural Networks, Computer ; Object recognition ; Pathology ; Patients ; ROC Curve
É parte de: BMC medicine, 2021-03, Vol.19 (1), p.76-76, Article 76
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.
Editor: England: BioMed Central
Idioma: Inglês

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Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

England: BioMed Central

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