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Purpose: Ki-67 expression levels in breast cancer have prognostic and predictive significance. Therefore, accurate Ki-67 evaluation is important for optimal patient care. Although an algorithm developed by the International Ki-67 in Breast Cancer Working Group (IKWG) improves interobserver variability, it is tedious and time-consuming. In this study, we simplify IKWG algorithm and evaluate its interobserver agreement among breast pathologists in Ki-67 evaluation.
Methods: Six subspecialized breast pathologists (4 juniors, 2 seniors) assessed the percentage of positive cells in 5% increments in 57 immunostained Ki-67 slides. The time spent on each slide was recorded. Two rounds of ring study (R1, R2) were performed before and after training with the modified IKWG algorithm (eyeballing method at 400× instead of counting 100 tumor nuclei per area). Concordance was assessed using Kendall's and Kappa coefficients.
Results: Analysis of ordinal scale ratings for all categories with 5% increments showed almost perfect agreement in R1 (0.821) and substantial in R2 (0.793); Seniors and juniors had substantial agreement in R1 (0.718 vs. 0.649) and R2 (0.756 vs. 0.658). In dichotomous scale analysis using 20% as the cutoff, the overall agreement was moderate in R1 (0.437) and R2 (0.479), among seniors (R1: 0.436; R2: 0.437) and juniors (R1: 0.445; R2: 0.505). Average scoring time per case was higher in R2 (71 vs. 37 s).
Conclusion: The modified IKWG algorithm does not significantly improve interobserver agreement. A better algorithm or assistance from digital image analysis is needed to improve interobserver variability in Ki-67 evaluation.
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http://dx.doi.org/10.1007/s10549-023-07197-3 | DOI Listing |
Anal Chim Acta
November 2025
Department of Breast Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, PR China. Electronic address:
Background: Breast-conserving surgery (BCS) is the primary surgical approach for patients with breast cancer. The accurate determination of surgical margins during BCS is critical for patient prognosis; however, time constraints and limitations in current pathological techniques often prevent pathologists from performing this assessment intraoperatively. The inability to reliably assess margins during surgery can lead to incomplete tumor removal and the need for additional surgeries.
View Article and Find Full Text PDFDeep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago.
View Article and Find Full Text PDFBackground: Cancer morbidity disproportionately affects patients in low- and middle-income countries (LMICs), where timely and accurate tumor profiling is often nonexistent. Immunohistochemistry-based assessment of estrogen receptor (ER) status, a critical step to guide use of endocrine therapy (ET) in breast cancer, is often delayed or unavailable. As a result, ET is often prescribed empirically, leading to ineffective and toxic treatment for ER-negative patients.
View Article and Find Full Text PDFDent Res J (Isfahan)
August 2025
Departments of Oral and Maxillofacial Pathology, Cancer Preclinical Imaging Group, Preclinical Core Facility, Tehran University of Medical Sciences, Tehran, Iran.
Malignant tumors are able to grow at sites distant from the primary site of origin. Breast, prostate, renal, thyroid, and lung carcinomas commonly metastasize to bone. Jaw metastasis is rare but may occur more often than generally estimated.
View Article and Find Full Text PDFBreast Cancer Res
September 2025
Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
Ki67 is a broadly available biomarker of proliferation with various approaches to its evaluation in breast cancer. The International Ki67 in Breast Cancer Working Group (IKWG) recommends calculating Ki67 globally across the tumor area, as this method offers high interobserver concordance. These recommendations have been integrated into many international breast cancer guidelines (ASCO, ESMO), yet there is no real-world data on if it improved inter-pathologists and inter-laboratory variability.
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