Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method

Zehra, Talat; Shams, Mahin; Ahmad, Zubair; Chundriger, Qurratulain; Ahmed, Arsalan; Jaffar, Nazish

doi:10.29271/jcpsp.2023.05.544

Cited by 5 publications

(2 citation statements)

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“…38 , 39 Xie et al, 39 in a multicentric observational study involving 771 pairs of stained slides employed a scale-invariant feature transform (SIFT)-based AI system, achieving a remarkable 93% accuracy in identifying cancer tissues and 91.5% accuracy in calculating the Ki-67 index, showcasing the potential of AI to enhance accuracy and repeatability in Ki-67 assessment. In addition, studies by Li et al, 40 Fulawka et al, 41 Cai et al, 42 and Zehra et al 43 further highlight the consistent and reproducible results achieved through AI assistance in Ki-67 labeling, offering improved diagnostic accuracy and efficiency in BC evaluation.…”

Section: Discussionmentioning

confidence: 83%

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

Mooghal,

Khan,

Samar

et al. 2024

Breast Cancer�(Auckl)

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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.

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Section: Discussionmentioning

confidence: 83%

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

Mooghal,

Khan,

Samar

et al. 2024

Breast Cancer�(Auckl)

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“…30,31 It provides standardised data capture, precise image analysis, predictive models, and quality control, streamlining tasks and improving patient outcomes when integrated into clinical workflows. [30][31][32][33][34][35][36][37][38] [33][34] For classifying Ki67 status classification, Chen et al employed a retrospective observational study with large datasets, achieving remarkable accuracy and area under the curve (AUC) using a Convolutional Neural Network (CNN) model for Ki67 status classification. 31 Another study found that an AI-empowered microscope improved Ki-67 assessment reproducibility and accuracy.…”

Section: S-113mentioning

confidence: 99%

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review

Mehwish Mooghal,

Saba Anjum,

Wajiha Khan

et al. 2024

J Pak Med Assoc

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Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial intelligence (AI) can optimize Ki-67 assessment, improving precision and workflow efficiency in BC management. The study presents a critical analysis of the current state of AI-powered Ki-67 assessment. Results demonstrate high agreement between AI and standard Ki-67 assessment methods highlighting AI's potential as an auxiliary tool for pathologists. Despite these advancements, the review acknowledges limitations such as the restricted timeframe and diverse study designs, emphasizing the need for further research to address these concerns. In conclusion, AI holds promise in enhancing Ki-67 assessment's precision and workflow efficiency in BC diagnosis. While challenges persist, the integration of AI can revolutionize BC care, making it more accessible and precise, even in resource-limited settings. Keywords: Artificial Intelligence, Ki-67 Antigen, Prognosis, Pathologists, Breast Cancer

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Pros and cons of artificial intelligence implementation in diagnostic pathology

van Diest,

Flach,

van Dooijeweert

et al. 2024

Histopathology

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The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.

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Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method

Cited by 5 publications

References 0 publications

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

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

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review

Pros and cons of artificial intelligence implementation in diagnostic pathology

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