The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
Open accessAbstract
During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.
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Deep learning in histopathology: the path to the clinic
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Emerging role of deep learning‐based artificial intelligence in tumor pathology
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Development and Validation of a Deep Learning Model for Non–Small Cell Lung Cancer Survival
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Deep learning models in medical image analysis
Tsuneki, M. (2022) · Journal of Oral Biosciences · doi:10.1016/j.job.2022.03.003 · Cited by 257
Weakly-supervised learning for lung carcinoma classification using deep learning
Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O., & Tsuneki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-66333-x · Cited by 250
Metadata
- Authors
- Gertych, A., Swiderska-Chadaj, Z., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., Salemi, H., Guzman, S., Walts, A. E., & Knudsen, B. S.
- Venue
- Scientific Reports
- Published
- 2019-02-06
- Volume
- 9
- Issue
- 1
- Pages
- 1483-1483
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2922268597
- Cited by
- 188
- References
- 9
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- gold
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- Gertych, A., Swiderska-Chadaj, Z., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., Salemi, H., Guzman, S., Walts, A. E., & Knudsen, B. S. (2019). Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Scientific Reports. https://doi.org/10.1038/s41598-018-37638-9
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