Abstract
BackgroundAccurate 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.MethodsBased 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.ResultsOur 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.ConclusionsThis 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.
Highlights
Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is timeconsuming and knowledge-intensive, but is essential for CRC patients’ treatment
Wang et al BMC Medicine (2021) 19:76 (Continued from previous page). This first-ever generalizable artificial intelligence (AI) system can handle large amounts of whole-slide image (WSI) consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists
This results in overworked pathologists, which can lead to higher chances of deficiencies in their routine work and dysfunctions of the pathology laboratories with more laboratory errors [4]
Summary
Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is timeconsuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current trends indicate a shortage of pathologists around the world, including USA [5] and low- to middle-income countries [6]. This results in overworked pathologists, which can lead to higher chances of deficiencies in their routine work and dysfunctions of the pathology laboratories with more laboratory errors [4]. It is imperative to develop reliable tools for pathological image analysis and CRC detection that can improve clinical efficiency and efficacy without unintended human bias during diagnosis
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