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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Yang, Huan Chen, Lili Cheng, Zhiqiang Yang, Minglei Wang, Jianbo Lin, Chenghao Wang, Yuefeng Huang, Leilei Chen, Yangshan Peng, Sui Ke, Zunfu Li, Weizhong |
| Abstract | Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios. |
| Related Links | https://bmcmedicine.biomedcentral.com/counter/pdf/10.1186/s12916-021-01953-2.pdf |
| Ending Page | 14 |
| Page Count | 14 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17417015 |
| DOI | 10.1186/s12916-021-01953-2 |
| Journal | BMC Medicine |
| Issue Number | 1 |
| Volume Number | 19 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2021-03-29 |
| Access Restriction | Open |
| Subject Keyword | Medicine Public Health Biomedicine Deep learning Lung cancer Cancer mimic Whole slide image Histopathological classification Medicine/Public Health |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine |
| Journal Impact Factor | 7.1/2023 |
| 5-Year Journal Impact Factor | 8.8/2023 |
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