Assessment Of Ki67 In Breast Cancer Research Articles

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

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

Assessment of Predictive Biomarkers in Breast Cancer: Challenges and Updates.

The management of patients with breast cancer (BC) relies on the assessment of a defined set of well-established prognostic and predictive markers. Despite overlap, prognostic markers are used to assess the risk of recurrence and the likely benefit of systemic therapy, whereas predictive markers are used to determine the type of systemic therapy to be offered to an individual patient. In this review, we provide an update and present some challenges in the assessment of the main BC-specific molecular predictive markers, namely hormone receptors (oestrogen receptor [ER] and progesterone receptor [PR]), human epidermal growth factor receptor 2 (HER2), and KI67. As the main platform for assessing these markers in BC is immunohistochemistry (IHC), we address the cut-off values used to define positivity, the ER-low subgroup, the existence and significance of the ER-/PR+ phenotype, the use of PR in routine practice, and the role of hormone receptors in ductal carcinoma in situ. We discuss the newly introduced HER2-low class of BC and the clinical/biological difference between different HER2 groups (e.g., HER2 IHC score 3+ BCs vs. those with a HER2 IHC score 2+ with HER2 gene amplification). The review concludes with an update on the applications of KI67 assessment in BC and observations on the role of immune checkpoint identification in BC.

Ki67 Assessment in Breast Cancer: Are We There Yet?

Ki67 Assessment in Breast Cancer: Are We There Yet?

An interobserver reproducibility analysis of size-set semiautomatic counting for Ki67 assessment in breast cancer

PurposeThe proliferation marker Ki67 has prognostic and predictive values in breast cancer, and the cutoff of the Ki67 label index (LI) is a key index for chemotherapy. However, poor interobserver consistency in Ki67 assessment has limited the clinical use of Ki67, especially in luminal cancers. Here, we reported a modified Ki67 assessment method, size-set semiautomatic counting (SSSAC) and investigated its interobserver reproducibility. MethodsOne hundred invasive breast cancer tissues were set immunostained for Ki67 in one laboratory, scanned as digital slides, and sent to 41 pathologists at the laboratories of 16 hospitals for Ki67 LI assessment using size-set semiautomatic counting (SSSAC), size-set visual assessment (SSVA) and size-set digital image analysis (SSDIA) with a specific image viewing software (Aperio Image Scope, Leica, Germany). The intraclass correlation coefficient (ICC) and Bland-Altman plot were used to evaluate interobserver reproducibility. The Wilcoxon signed-rank test was used to analyze the difference in the Ki67 values assessed by SSSAC and SSDIA. ResultsSSSAC demonstrated better interobserver reproducibility (ICC = 0.942) than SSVA (ICC = 0.802). The interobserver reproducibility was better in Ki67 homogeneously stained slides and centralized hot-spot slides than in scattered hot-spot slides. The Ki67 value assessed with SSSAC was obviously higher than that assessed with SSDIA (negative ranks (SSDIA < SSSAC): N = 80, sum of ranks = 4274.50; positive ranks (SSDIA > SSSAC): N = 17, sum of ranks = 478.50; Z = −6.837; P < 0.001). ConclusionSSSAC shows satisfactory interobserver reproducibility in the Ki67 assessment of breast cancer and may be a candidate standard method for Ki67 LI assessment in breast cancer and other malignancies.

Ki67 assessment in breast cancer: an update

Although immunohistochemical detection of the Ki67 antigen has been used for many years to assess cancer proliferation, this marker is still not recommended for routine use in clinical management of breast cancer. The major reason for this situation is a lack of a standardised procedure for Ki67 assessment as well as persistence of several issues of debate with regards to the Ki67 assay interpretation and the marker's clinical utility. Nowadays Ki67 assessment is principally used for estimation of prognosis and guiding the decision on adjuvant treatment choice, as well as for prediction of response to neoadjuvant treatment in ER+/HER2- breast cancer. In ER-/HER2+ and ER-/HER2- tumours, high post-neoadjuvant Ki67 index is associated with unfavourable prognosis. We review here the elements impacting analytical validity of the Ki67 immunohistochemical assay, the evidence of its clinical utility and the current recommendations for its use in breast cancer management.

Quantum dots-based quantitative and in situ multiple imaging on ki67 and cytokeratin to improve ki67 assessment in breast cancer.

BackgroundAs a marker for tumor cell proliferation, Ki67 has important impacts on breast cancer (BC) prognosis. Although immunohistochemical staining is the current standard method, variations in analytical practice make it difficult for pathologists to manually measure Ki67 index. This study was to develop a fluorescent spectrum-based quantitative analysis of Ki67 expression by quantum-dots (QDs) multiple imaging technique.MethodsA QDs-based in situ multiple fluorescent imaging method was developed, which stained nuclear Ki67 as red signal and cytoplasmic cytokeratin (CK) as green signal. Both Ki67 and CK signals were automatically separated and quantified by professional spectrum analysis software. This technique was applied to tissue microarrays from 240 BC patients. Both Ki67 and CK values, and Ki67/CK ratio were obtained for each patient, and their prognostic value on 5-year disease free survival was assessed.ResultsThis method simultaneously stains nuclear Ki67 and cytoplasmic CK with clear signal contrast, making it easy for signal separation and quantification. The total fluorescent signal intensities of both Ki67 sum and CK sum were obtained, and Ki67/CK ratio calculated. Ki67 sum and Ki67/CK ratio were each attributed into two grades by X-tile software based on the best P value principle. Multivariate analysis showed Ki67 grade (P = 0.047) and Ki67/CK grade (P = 0.004) were independent prognostic factors. Furthermore, area under curve (AUC) of ROC analysis for Ki67/CK grade (AUC: 0.683, 95%CI: 0.613–0.752) was higher than Ki67 grade (AUC: 0.665, 95%CI: 0.596–0.734) and HER-2 gene (AUC: 0.586, 95%CI: 0.510–0.661), but lower than N stage (AUC: 0.760, 95%CI: 0.696–0.823) and histological grade (AUC: 0.756, 95%CI: 0.692–0.820) on predicting the risk for recurrence.ConclusionsA QDs-based quantitative and in situ multiple imaging on Ki67 and CK was developed to improve Ki67 assessment in BC, and Ki67/CK grade had better performance than Ki67 grade in predicting prognosis.

P1-07-16: Number Needed To Count: A Novel Model for Ki67 Assessment in Breast Cancer.

Abstract Background: Tumor proliferation assessed by Ki67 is frequently used as a prognostic and above all treatment predictive marker i breast cancer where a decrease in Ki67 expression, following neo-adjuvant therapy, may be interpreted as a reliable treatment effect. Previous studies have indicated that the optimal number of tumor cells needed to count may vary from case to case. The standard error of the estimated proportion will decrease with increasing number of tumor cells counted (n), but a dilution bias will affect the estimate in cases with few tumor cells within a hotspot area. Hence, a large n is appropriate in Ki67-homogeneous hotspot areas and inappropriate, at least for small hotspots, in case of heterogeneity. Further, a chosen cut-off value has implications for the choice of n. A low n may be sufficient for extreme proportions, but the closer the true unknown proportion is to the cutoff, the larger n will be required. The lack of consensus concerning Ki67 assessment may jeopardize the comparison of research results. Hence, a standardized counting model is warranted. Material and Methods: Exact two-sided confidence intervals for proportions based on the binomial distribution were used to derive rejection regions for sequential testing of the null hypothesis that the fraction of Ki67-positive cells is equal to the chosen cut-off (20% in this study). A lower limit of 50 counted tumor cells to get a reasonably stable estimate and an upper limit of 400 tumor cells to prevent extreme dilution bias for small hotspots, were applied. Briefly, the counting strategy can be explained as follows: Locate a hotspot and count 50 tumor cells. If the Ki67 estimate belongs to the upper or lower rejection region, stop counting. If not, count another 10 tumor cells and perform a new hypothesis test. Proceed until either the null hypothesis has been rejected or the upper limit of 400 has been reached. Simulation was used to determine that a nominal significance level of α=0.01 for each test will keep the overall probability of falsely rejecting the null hypothesis fixed at 0.05. The novel counting strategy was compared to static counting of 200 tumor cells using 100 Ki67-stained breast cancer samples. Results: The median number of tumor cells needed to count to determine Ki67-status was 100 and the average 175. The rejection region was reached immediately after 50 tumor cells counted for 32 samples with Ki67-levels far from the cut-off, whereas counting 400 tumor cells was insufficient for classification in another 18 samples. In samples classified as highly proliferative (&amp;gt;20%), the mean Ki67-estimate was 49% using the counting model compared to 42% using a fixed denominator of 200 tumor cells. The largest absolute difference between the two estimates for these 32 samples was 23% — from 42% (model) to 19% (static), thus more than a factor two and a dilution effect leading to changed Ki67-status. Conclusions: Estimation of the fraction of Ki67-positive tumor cells using a fixed denominator may be inadequate — especially for small hotspots. We hereby propose a strategy for tumor cell count optimization that hopefully will contribute to standardization of the counting practice for tumor proliferation assessed by Ki67. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-16.