Liquid biopsy of cancers,detecting tumor-related information from liquid samples,has attracted wide attentions as an emerging technology.Our previously reported large-area PERFECT(Precise-Efficient-Robust-Flexible-Eas...Liquid biopsy of cancers,detecting tumor-related information from liquid samples,has attracted wide attentions as an emerging technology.Our previously reported large-area PERFECT(Precise-Efficient-Robust-Flexible-Easy-ControllableThin)filter has demonstrated competitive sensitivity in recovering rare tumor cells from clinical samples.However,it is time-consuming and easily biased to manually inspect rare target cells among numerous background cells distributed in a large area(Φ≥13 mm).This puts forward an urgent demand for rapid and bias-free inspection.Hereby,this paper implemented deep learning-based object detection for the inspection of rare tumor cells from large-field images of PERFECT filters with hematoxylin-eosin(HE)-stained cells recovered from bronchoalveolar lavage fluid(BALF).CenterNet,EfficientDet,and YOLOv5 were trained and validated with 240 and 60 image blocks containing tumor and/or background cells,respectively.YOLOv5 was selected as the basic network given the highest mAP@0.5 of 92.1%,compared to those of CenterNet and EfficientDet at 85.2%and 91.6%,respectively.Then,tricks including CIoU loss,image flip,mosaic,HSV augmentation and TTA were applied to enhance the performance of the YOLOv5 network,improving mAP@0.5 to 96.2%.This enhanced YOLOv5 network-based object detection,named as BALFilter Reader,was tested and cross-validated on 24 clinical cases.The overall diagnosis performance(~2 min)with sensitivity@66.7%±16.7%,specificity@100.0%±0.0%and accuracy@75.0%±12.5%was superior to that from two experienced pathologists(10–30 min)with sensitivity@61.1%,specificity@16.7%and accuracy@50.0%,with the histopathological result as the gold standard.The AUC of the BALFilter Reader is 0.84±0.08.Moreover,a customized Web was developed for a user-friendly interface and the promotion of wide applications.The current results revealed that the developed BALFilter Reader is a rapid,bias-free and easily accessible AI-enabled tool to promote the transplantation of the BALFilter technique.This work can easily expand to other cytopathological diagnoses and improve the application value of micro/nanotechnology-based liquid biopsy in the era of intelligent pathology.展开更多
基金supported by the National Key R&D Program of China(Grant No.2020YFC2005405)the National Natural Science Foundation of China(Grant No.61904004 and Grant No.82027805)+2 种基金the Seeding Grant for Medicine and Information on Sciences awarded by Peking University(Grant No.BMU2018MI003)Dr.Yaoping Liu thanks the Postdoctoral Science Foundation of China(Grant Nos.2018M631261 and 2019T20018)supported by the 111 Project(B18001).
文摘Liquid biopsy of cancers,detecting tumor-related information from liquid samples,has attracted wide attentions as an emerging technology.Our previously reported large-area PERFECT(Precise-Efficient-Robust-Flexible-Easy-ControllableThin)filter has demonstrated competitive sensitivity in recovering rare tumor cells from clinical samples.However,it is time-consuming and easily biased to manually inspect rare target cells among numerous background cells distributed in a large area(Φ≥13 mm).This puts forward an urgent demand for rapid and bias-free inspection.Hereby,this paper implemented deep learning-based object detection for the inspection of rare tumor cells from large-field images of PERFECT filters with hematoxylin-eosin(HE)-stained cells recovered from bronchoalveolar lavage fluid(BALF).CenterNet,EfficientDet,and YOLOv5 were trained and validated with 240 and 60 image blocks containing tumor and/or background cells,respectively.YOLOv5 was selected as the basic network given the highest mAP@0.5 of 92.1%,compared to those of CenterNet and EfficientDet at 85.2%and 91.6%,respectively.Then,tricks including CIoU loss,image flip,mosaic,HSV augmentation and TTA were applied to enhance the performance of the YOLOv5 network,improving mAP@0.5 to 96.2%.This enhanced YOLOv5 network-based object detection,named as BALFilter Reader,was tested and cross-validated on 24 clinical cases.The overall diagnosis performance(~2 min)with sensitivity@66.7%±16.7%,specificity@100.0%±0.0%and accuracy@75.0%±12.5%was superior to that from two experienced pathologists(10–30 min)with sensitivity@61.1%,specificity@16.7%and accuracy@50.0%,with the histopathological result as the gold standard.The AUC of the BALFilter Reader is 0.84±0.08.Moreover,a customized Web was developed for a user-friendly interface and the promotion of wide applications.The current results revealed that the developed BALFilter Reader is a rapid,bias-free and easily accessible AI-enabled tool to promote the transplantation of the BALFilter technique.This work can easily expand to other cytopathological diagnoses and improve the application value of micro/nanotechnology-based liquid biopsy in the era of intelligent pathology.
