Enhanced YOLOv5 network-based object detection(BALFilter Reader)promotes PERFECT filter-enabled liquid biopsy of lung cancer from bronchoalveolar lavage fluid(BALF)

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.