Convolutional Neural Network for Histopathological Analysis of Osteosarcoma

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick J.; Rakheja, Dinesh; Sengupta, Anita

doi:10.1089/cmb.2017.0153

Cited by 64 publications

(57 citation statements)

References 14 publications

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“…For resection samples, pathologists should provide as much information possible for TNM staging that usually determine therapeutic decisions. In osteosarcoma, a CNN‐based model distinguished three types of region of interests (ROIs), namely the tumor, necrotic and non‐tumor component (e.g., bone, cartilage), on a patch level (64,000 patches from 82 WSIs) with an accuracy of 92.4% [43]. In addition, the proportion of necrosis, a variable factor for prognosis, could be calculated.…”

Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

238

147

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The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

238

147

View full text Add to dashboard Cite

show abstract

“…9,10 Here, artificial intelligence has been used to quantitate the degree of necrosis to assess clinical correlations over standard methods in estimating treatment response. 11…”

Section: Neoadjuvant Therapy In Other Tumor Typesmentioning

confidence: 99%

Current Status and Future Perspectives on Neoadjuvant Therapy in Lung Cancer

Blumenthal

Bunn

Chaft

et al. 2018

Journal of Thoracic Oncology

152

124

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This Review Article provides a multi-stakeholder view on the current status of neoadjuvant therapy in lung cancer. Given the success of oncogene-targeted therapy and immunotherapy for patients with advanced lung cancer, there is a renewed interest in studying these agents in earlier disease settings with the opportunity to have an even greater impact on patient outcomes. There are unique opportunities and challenges with the neoadjuvant approach to drug development. To achieve more rapid knowledge turns, study designs, endpoints, and definitions of pathologic response should be standardized and harmonized. Continued dialogue with all stakeholders will be critical to design and test novel induction strategies, which could expedite drug development for patients with early lung cancer who are at high risk for metastatic recurrence. Published by Elsevier Inc. on behalf of International Association for the Study of Lung Cancer.

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“…To overcome the problem caused by small datasets, in the paper, a deep model with Siamese network (DS-Net) was designed to automatically classify osteosarcoma images from TCIA. 32 In recent years, some research literatures [33][34][35][36][37] have proposed some methods for histological classification in osteosarcoma using deep learning methods. It should be noted that the method in Ref.…”

Section: Introductionmentioning

confidence: 99%

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

Fu¹

2020

Medical Physics

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To achieve automatic classification of viable and necrotic tumor regions in osteosarcoma, most of the existing deep learning methods can only design a simple model to prevent overfitting on small datasets, which leads to the weak ability of extracting image features and low accuracy of the models. In order to solve the above problem, a deep model with Siamese network (DS-Net) was designed in this paper. Methods: The DS-Net constructed on the basis of full convolutional networks is composed of an auxiliary supervision network (ASN) and a classification network. The construction of the ASN based on the Siamese network aims to solve the problem of a small training set (the main bottleneck of deep learning in medical images). It uses paired data as the input and updates the network through combined labels. The classification network uses the features extracted by the ASN to perform accurate classification. Results: Pathological diagnosis is the most accurate method to identify osteosarcoma. However, due to intraclass variation and interclass similarity, it is challenging for pathologists to accurately identify osteosarcoma. Through the experiments on hematoxylin and eosin (H&E)-stained osteosarcoma histology slides, the DS-Net we constructed can achieve an average accuracy of 95.1%. Compared with existing methods, the DS-Net performs best in the test dataset. Conclusions: The DS-Net we constructed can not only effectively realize the histological classification of osteosarcoma, but also be applicable to many other medical image classification tasks affected by small datasets.

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Convolutional Neural Network for Histopathological Analysis of Osteosarcoma

Cited by 64 publications

References 14 publications

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Current Status and Future Perspectives on Neoadjuvant Therapy in Lung Cancer

Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma

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