A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification

Feng, Ruiwei; Liu, Xuechen; Chen, Jintai; Chen, Danny Z.; Gao, Honghao; Wu, Jian

doi:10.1109/jbhi.2020.3040269

Cited by 82 publications

(48 citation statements)

References 26 publications

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“…Currently, DL has enabled rapid advances in computational pathology ( 11 , 12 ). For example, DL methods have been applied to segment and classify glomeruli with different staining and various pathologic changes, thus achieving the automatic analysis of renal biopsies ( 13 , 14 ); meanwhile, DL-based automatic colonoscopy tissue segmentation and classification have shown promise for colorectal cancer detection ( 15 , 16 ); besides, the analysis of gastric carcinoma and precancerous status can also benefit from DL schemes ( 17 , 18 ). More recently, for the ALN metastasis detection, it is reported that DL algorithms on digital lymph node pathology images achieved better diagnostic efficiency of ALN metastasis than pathologists ( 19 , 20 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Zhu

Tang

et al. 2021

Front. Oncol.

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ObjectivesTo develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.MethodsA total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.ResultsThe best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).ConclusionOur study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

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Section: Introductionmentioning

confidence: 99%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Zhu

Tang

et al. 2021

Front. Oncol.

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“…Now, the well-known loss function is cross-entropy loss. A new loss function, class-wise DSC loss, for training the segmentation network of colonoscopy pathology images was presented by Feng et al (2020) .…”

Section: Head and Neck Tumor Multiomics Analysismentioning

confidence: 99%

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Wang

2021

Front. Genet.

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Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.

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“…Currently, DL has enabled rapid advances in computational pathology (11, 12). For example, DL methods have been applied to segment and classify glomeruli with different staining and various pathologic changes, thus achieving the automatic analysis of renal biopsies (13, 14); meanwhile, there has shown promise for colorectal cancer detection (15, 16) by DL based automatic colonoscopy tissue segmentation and classification; besides, the analysis of gastric carcinoma and precancerous status can also benefit from DL schemes (17, 18). More recently, for the ALN metastasis detection, it is reported that DL algorithms on digital lymph node pathology images achieved better diagnostic efficiency of ALN metastasis than pathologists (19, 20).…”

Section: Introductionmentioning

confidence: 99%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Zhu

Tang

et al. 2021

Preprint

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Objectives: To develop and validate a deep learning (DL) based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A deep learning core-needle biopsy (DL-CNB) model was built on the attention based multiple instance learning (AMIL) framework to predict ALN status utilizing the deep learning features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the receiver operating characteristic curve (AUCs) were analyzed to evaluate our model. Results: The best performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nuclei features including density (p=0.015), circumference (p=0.009), circularity (p=0.010), and orientation (p=0.012). Conclusion: Our study provides a novel deep learning-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with early breast cancer.

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A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification

Cited by 82 publications

References 26 publications

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

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