Unknown

Dataset Information

0

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


ABSTRACT: 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-Controllable-Thin) 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.

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10541878 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Liu Zheng Z   Zhang Jixin J   Wang Ningyu N   Feng Yun'ai Y   Tang Fei F   Li Tingyu T   Lv Liping L   Li Haichao H   Wang Wei W   Liu Yaoping Y  

Microsystems & nanoengineering 20230929


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 (<b>P</b>recise-<b>E</b>fficient-<b>R</b>obust-<b>F</b>lexible-<b>E</b>asy-<b>C</b>ontrollable-<b>T</b>hin) 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 backgr  ...[more]

Similar Datasets

| S-EPMC8339831 | biostudies-literature
| S-EPMC5357681 | biostudies-literature
| S-EPMC10068177 | biostudies-literature
| S-EPMC6576254 | biostudies-literature
2019-06-24 | PXD008273 | Pride
| S-EPMC10413015 | biostudies-literature
| S-EPMC4099252 | biostudies-literature
| S-EPMC10909222 | biostudies-literature
2022-09-03 | GSE212402 | GEO
| S-EPMC7346432 | biostudies-literature