4.4 Article

Comparison Between Manual and Automated Assessment of Ki-67 in Breast Carcinoma: Test of a Simple Method in Daily Practice

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biology

SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Si-Yuan Lu et al.

Summary: Breast cancer is a deadly disease that poses a serious threat to women, and an accurate early diagnosis is crucial for treatment. SAFNet, a novel breast cancer detection model based on ultrasound images and deep learning, combines multiple network models through majority voting to provide more accurate results. Simulation experiments demonstrate that SAFNet outperforms existing methods in breast cancer classification, making it a reliable tool for clinical diagnosis.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Oncology

Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group

Torsten O. Nielsen et al.

Summary: Ki67 immunohistochemistry (IHC) is commonly used in breast cancer as a proliferation marker, but its analytical validity has been questioned. The International Ki67 in Breast Cancer Working Group (IKWG) recommends careful preanalytical handling, adoption of a standardized visual scoring method, and participation in quality assurance and quality control programs. Clinical utility of Ki67 IHC in breast cancer care is currently limited to prognosis assessment in stage I or II breast cancer.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2021)

Article Computer Science, Information Systems

Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

Yu-Dong Zhang et al.

Summary: The study developed a new method called BDR-CNN-GCN, combining CNN, BN, DO, and RSP, which effectively improved the detection accuracy of malignant lesions. In experiments, the method showed high sensitivity, specificity, and accuracy on the breast miniMIAS dataset.

INFORMATION PROCESSING & MANAGEMENT (2021)

Article Biochemistry & Molecular Biology

Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group

Ceren Boyaci et al.

Summary: The study demonstrates high reproducibility and independent prognostic potential in using IKWG guidelines to score Ki67 in breast cancer. Machine-read scores showed similar and significant hazard ratios for relapse-free survival in an ER+ breast cancer cohort.

BIOMOLECULES (2021)

Article Multidisciplinary Sciences

PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer

Farzin Negahbani et al.

Summary: The study highlights the importance of Ki-67 index and TILs in predicting tumor progression and chemotherapy response, while emphasizing the potential of deep learning methods for automated cell detection. The proposed dataset and method for estimating Ki-67 expression and TILs score in breast cancer cells show promising results compared to existing state-of-the-art methods.

SCIENTIFIC REPORTS (2021)

Article Medicine, Research & Experimental

Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study

Balazs Acs et al.

LABORATORY INVESTIGATION (2019)

Article Multidisciplinary Sciences

QuPath: Open source software for digital pathology image analysis

Peter Bankhead et al.

SCIENTIFIC REPORTS (2017)

Article Pathology

An international study to increase concordance in Ki67 scoring

Mei-Yin C. Polley et al.

MODERN PATHOLOGY (2015)

Editorial Material Oncology

Assessment of Ki67 in Breast Cancer: Recommendations from the International Ki67 in Breast Cancer Working Group

Mitch Dowsett et al.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2011)

Article Oncology

Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer

Maggie C. U. Cheang et al.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2009)