Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images

Ba, Wei; Rui, Wang; Yin, Guang; Song, Zhigang; Zou, Jinyi; Zhong, Cheng; Yang, Jingrun; Yu, Guanzhen; Yang, Hongyu; Zhang, Litao; Li, Chang-Jiu

doi:10.1016/j.tranon.2021.101161

Cited by 27 publications

(19 citation statements)

References 23 publications

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“…Other reasons for false results included poor image quality, such as section folds, knife marks, and overstaining, as have demonstrated in previous study. [7,11,16] This study had several limitations. The gastritis pathological subtypes were complex.…”

Section: Resultsmentioning

confidence: 97%

“…One of the deep learning algorithms, Convolutional Neural Network (CNN), has been proven to have great advantages in whole-slide pathological images (WSIs) recognition [7,8] . Many studies have demonstrated that CNN achieved high sensitivity and specificity in WSI classification, including lung cancer [9] , prostate cancer [10] , cutaneous melanoma [11] , bladder cancer [10] , and metastatic carcinoma of lymph node [12,13] .…”

Section: Introductionmentioning

confidence: 99%

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Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Wang

Liu

et al. 2021

Chinese Medical Sciences Journal

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Objective To developed a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide pathological images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG) and chronic atrophic gastritis (CAtG) in the test set, respectively. The areas under ROC (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.910 and 0.905, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG and CAtG were 0.790, 1.000 (accuracy 0.880); 0.985, 0.829 (accuracy 0.901) and 0.952, 0.992 (accuracy 0.986) respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. ConclusionThe deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, It might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Wang

Liu

et al. 2021

Chinese Medical Sciences Journal

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“…Studies have demonstrated that deep learning could achieve high accuracy in different pathological diagnostic tasks 19 – 22 . It is notable that deep learning with full automation with no human pathologist backup is not the objective 10 , 23 , 24 , and even the best algorithm needs to be integrated into existing clinical workflows to improve patient care.…”

Section: Discussionmentioning

confidence: 99%

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Wang

Shang

et al. 2022

Modern Pathology

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Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists’ diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists’ accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

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“…In recent years, artificial intelligence has achieved unprecedented development, and the application of this frontier technology in the field of medicine has gradually become a new trend. Recent studies have demonstrated promising results of deep learning algorithms in recognizing various lesions using WSIs ( 12 , 14 , 15 , 21 , 22 ). As for EC, the increasing diagnostic workload of endometrial biopsy specimens calls for the development of new models with high sensitivity and specificity.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, artificial intelligence has seen tremendous growth, and the application of this cutting-edge technology to the area of pathology has gradually become a new trend. The latest studies have demonstrated that deep learning can be applied in the pathological diagnosis of a variety of organs, such as the prostate ( 9 , 10 ), stomach ( 11 – 13 ), melanoma ( 14 ), lymph node metastasis ( 15 ), etc. In these studies, deep learning models can be used as a screening tool to flag the suspected malignant area in advance, prompting pathologists to thoroughly examine the ROIs, thus improving the diagnostic accuracy and shortening diagnostic time.…”

Section: Introductionmentioning

confidence: 99%

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

Zhang

Ba²,

Zhao³

et al. 2022

Front. Oncol.

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The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists’ confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

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Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images

Cited by 27 publications

References 23 publications

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

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