Dataset Information

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

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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 (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 backgr ...[more]

PMID: 37786899

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Project description:BackgroundDifferentiating malignant lung tumors from benign pulmonary nodules is a great challenge. While the analysis of bronchoalveolar lavage fluid (BALF) is used for diagnosing infections and interstitial lung diseases, there is limited evidence to support its use for lung cancer diagnosis. This study aimed to interrogate the potential of using BALF cell-free DNA (cfDNA) to discriminate malignant lesions from benign nodules.MethodsFifty-three patients with solid pulmonary nodules (≤2 cm) were prospectively enrolled, including 21 confirmed with benign disease and 32 with malignant tumors. Mutations were profiled for 30 tumor tissues and 40 BALFs. Paired BALFs and plasma from 48 patients underwent DNA methylation profiling. A methylome-based classification model was developed for BALF and plasma separately.ResultsAmong the 30 patients with paired tissues and BALFs, 96.7% and 70% had alterations detected from their tissues (79 alterations) and BALFs (53 alterations), respectively. Using tissues as references, BALFs revealed 14 new alterations and missed 41. BALF mutation displayed a sensitivity of 71%, specificity of 77.8%, and accuracy of 72.5% in detecting lung cancer. BALF methylation achieved an accuracy of 81.3%, with both sensitivity and specificity being 81%. Plasma methylation showed a 66.7% sensitivity, 71.4% specificity, and 68.8% accuracy. BALF methylation also demonstrated 82.4% sensitivity in stage I patients. Parallel bronchoscopy, lavage cytology, and bronchial brushing demonstrated an inferior sensitivity of 23%, 3.1%, and 9.7%, respectively, compared with BALF methylation and mutation (P<0.0001).ConclusionsBALF cfDNA can serve as a liquid biopsy media for both mutation and methylation profiling, demonstrating better sensitivities in distinguishing small malignant tumors from benign nodules than conventional methods.KeywordsLung cancer diagnosis; pulmonary nodule; bronchoalveolar lavage fluid (BALF); methylation; genomic mutation.

Project description:Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

Dataset Information

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

Publications

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

Similar Datasets

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