Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study

Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system's performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist's report.

Association Between Ki-67 Proliferative Index and Oncotype-Dx Recurrence Score in Hormone Receptor-Positive, HER2-Negative Early Breast Cancers. A Systematic Review of the Literature

Background

Oncotype-Dx (ODx) is a 21-gene assay used as a prognostic and predictive tool for hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative, node-negative, or 1 to 3 lymph node-positive early breast cancers (EBCs). The cost of the test, which is not available in low-middle income countries (LMICs), is not within the means of most individuals. The Ki-67 index is a marker of tumor proliferation that is cost-effective and easily performed and has been substituted in many cases to obtain prognostic information.

Objective

We aimed to identify the correlation between the ODx recurrence score (RS) and the Ki-67 index in HR-positive EBCs and to determine whether Ki-67, like the ODx, can help facilitate clinical decision-making.

Design

Systematic review correlating Ki-67 index and ODx in HR-positive and HER2-negative EBCs as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Data sources and methods

We searched different databases between January 2010 and May 2023 and included retrospective/prospective cohorts, clinical trials, case-control, and cross-sectional studies involving HR-positive and HER2-negative EBCs correlating the Ki-67 index and ODx RS categories.

Results

Of the 18 studies included, 16 indicated a positive or weakly positive correlation between ODx and the Ki-67 index. The combined P value of the included studies is <0.05 (P = .000), which shows a statistical significance between the 2. Our review also discusses the potential of machine learning and artificial intelligence (AI) in Ki-67 assessment, offering a cost-effective and reproducible alternative.

Conclusion

Even although there are limitations, studies indicate a favorable association between ODx and the Ki-67 index in specific situations. This implies that Ki-67 can offer important predictive details, especially regarding the likelihood of relapse in HR-positive EBC. This is particularly significant in LMICs where financial constraints often hinder the availability of costly diagnostic tests.

Artificial intelligence in breast cancer: application and future perspectives

Breast cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in women worldwide. Early diagnosis and treatment are the key for a favorable prognosis. The application of artificial intelligence technology in the medical field is increasingly extensive, including image analysis, automated diagnosis, intelligent pharmaceutical system, personalized treatment and so on. AI-based breast cancer imaging, pathology and adjuvant therapy technology cannot only reduce the workload of clinicians, but also continuously improve the accuracy and sensitivity of breast cancer diagnosis and treatment. This paper reviews the application of AI in breast cancer, as well as looks ahead and poses challenges to the future development of AI for breast cancer detection and therapeutic, so as to provide ideas for future research.

Dermatopathology - Current status and development in German-speaking dermatology

Dermatopathology has been an integral part of dermatology for more than 100 years and is essential for high quality patient care. In German-speaking countries, dermatologists can acquire an additional qualification in dermatopathology after appropriate further training. For many years, dermatopathological diagnostics has advanced far beyond morphology. Immunohistochemistry and molecular pathology are nowadays an essential part and a prerequisite for the preservation of our discipline. Due to the increasing implementation of digitalization and artificial intelligence, dermatopathology is forward-looking and offers an attractive working environment for young colleagues. Dermatopathology is also indispensable for research, and this fact should also be taken into account by creating academic positions and professorships in the future.