Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu, Jingjin; Liu, Mei; Li, Xiru

doi:10.21037/gs-22-11

Cited by 15 publications

(7 citation statements)

References 162 publications

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“…Third, our experimental task was to recognize nuclear grade and molecular marker information based on a single imaging pattern, which is inherently challenging. The combination of DLR and pathology data will enable a deeper exploration of image information ( 63 ).…”

Section: Discussionmentioning

confidence: 99%

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ

Zhu,

Kuang,

Jiang

et al. 2024

Gland Surg

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Background Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5 th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+...

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Section: Discussionmentioning

confidence: 99%

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ

Zhu,

Kuang,

Jiang

et al. 2024

Gland Surg

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“…Reference 7 proposes the use of super pixels and CNNs for cervical cancer cell segmentation, achieving an accuracy of 94.50% in detecting core regions. Reference 8 combines deep learning architecture and derivative based retrieval methods to segment the left ventricle of cardiac ultrasound images.…”

Section: Related Workmentioning

confidence: 99%

CT and MRI fusion diagnosis technology based on recurrent neural network

Liu,

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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With the continuous progress of digital medicine and smart medical technology, the application of computer vision technology in medical image processing is also constantly developing and advancing. Image fusion technology in computer vision technology can complement the advantages of medical imaging through effective algorithms, discover useful and valuable information in medical diagnosis, and it is very effective in compensating for the shortcomings of medical image presentation technology and the lack of image information. The fusion of CT medical images and MRI medical images using computer vision technology can combine the advantages of CT images displaying clear bone information and high image resolution with the advantages of MRI images displaying clear soft structures, forming complementary advantages, thus making the display information of medical images more abundant. This article mainly utilizes wavelet transform algorithm to fuse CT and MRI anatomical imaging and proposes a medical image classification and diagnosis method based on local spatial sequence recurrent neural network (RNN), extracting higherlevel semantic features of images with better representativeness. This method extracts the local spatial sequence features of the samples and utilizes a temporal RNN to highly integrate and abstract the local spatial information. While obtaining high-level semantic features, it enhances the role of useful pixels in the local space and suppresses the influence of useless pixels. The experimental results show that the proposed method improves classification accuracy.

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“…A review of such approaches is beyond the scope of this paper. We refer the reader to recent comprehensive reviews for more insights on AI and deep learning in computational pathology [26]- [29], with specific reviews for histology [30]- [34] and cytology [35]- [37]. If some ethical issues have appeared in the use of computational pathology in clinical routine [38], most pathologists are in favor of their use [39].…”

Section: Computational Pathologymentioning

confidence: 99%

“…Standardizing the sample preparation in laboratories is of course a solution to such issues. The CAP NSH WSI Quality Improvement Program 34 is an initiative toward this purpose. Labs can have the quality of their histological H&E WSIs estimated and feedback is provided to help the lab in preventing the appearance of preparation or digitization artifacts.…”

Section: Quality Driven By Diagnosismentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

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Progress on deep learning in digital pathology of breast cancer: a narrative review

Cited by 15 publications

References 162 publications

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ

CT and MRI fusion diagnosis technology based on recurrent neural network

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

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