A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Park, Jeonghyuk; Jang, Bo Gun; Kim, Yeong Won; Park, Soo Young; Kim, Baek Hui; Kim, Myeung Ju; Ko, Hyungsuk; Gwak, Jae Moon; Lee, Eun Ji; Chung, Yul Ri; Kim, Kyungdoc; Myung, Jae Kyung; Park, Jeong Hwan; Choi, Dong-Hwan; Jung, Chang Won; Park, Bong Hee; Jung, Kihyo; Kim, Dong Il

doi:10.1158/1078-0432.ccr-20-3159

Cited by 31 publications

(20 citation statements)

References 30 publications

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“…Lastly, it can be used to develop an algorithm for rare cancers for which there does not exist a large number of histologic images, using datasets from more well-known cancer types that bear histologic resemblance. Previous DL algorithms in digital pathology developed models without considering morphological similarities or dissimilarities among cohorts [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . While several studies already demonstrated the morphological similarities using cross-cohort classification 22,25 and pan-cancer/tissue classification 18,23 , our work takes an additional step Sampled positive and negative patches of all cohorts are shown below.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, data-driven algorithms including deep learning (DL) have been devised to detect cancer using H&E morphology 2 . While a larger dataset size used to train DL algorithms directly translates to better performance, previous cancer detection models have been trained on cancer-specific datasets such as breast cancer 2-5 , skin cancer 6-8 , lung cancer 9 , bladder cancer 10 , prostate cancer 8,11,12 , stomach cancer [13][14][15] , colon cancer 14 and lymph node metastases 8,16,17 , with restricted capacity from limited data. Another approach is to develop a universal model with the hope that increasing the dataset size outweighs the drawbacks brought by introducing irrelevant information or features 18 .…”

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confidence: 99%

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Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Park¹,

Chung²,

Kong³

et al. 2021

Sci Rep

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There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Park¹,

Chung²,

Kong³

et al. 2021

Sci Rep

Self Cite

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“…In recent years, AI has been used in the eld of pathologic image diagnosis, and many studies have shown promising results in detecting and diagnosing cancers in a variety of organs, including the stomach, breast, skin, prostate, brain, and lung [13,[22][23][24]. As for cervical cancer, with the advancement in the management of preinvasive lesions, the increasing diagnostic workload of cervical biopsy calls for the development of high-performance algorithms with high sensitivity and speci city.…”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Cho

Kim

Park

et al. 2021

Preprint

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Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1,106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the 4-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, and 0.971 in the non-neoplasm, CIN3, CIN1 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the 3-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%) and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.

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“…Park et al, trained a DL algorithm to identify gastric cancers in endoscopy biopsy specimens and showed that the system can increase time to diagnosis and may be potentially applied in countries with a lack of specialized pathologists [ 169 ]. Similarly, a recent multicentric study built a DL-based algorithm to aid in the diagnosis of gastric cancer and applied this using data from different scanners and different hospitals showing its generalization [ 170 ].…”

Section: Machine Learning—basic Concepts Specific Applications and Future Directions In Geamentioning

confidence: 99%

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Klein

Duda

2021

Cancers

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Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

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A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Cited by 31 publications

References 30 publications

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

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