An international Ki67 reproducibility study

Analysis of Tumor Proliferation Markers in Early-Stage Luminal Breast Cancer: A Comprehensive Study Using Mitotic Activity Index, Ki-67, and Phosphohistone H3 Expression

Introduction and Aim: Routinely used proliferation markers such as mitotic activity index (MAI) and Ki-67 index show limited reproducibility due to high interobserver variability in breast cancer assessment. Phosphohistone H3 (PhH3), a novel proliferation marker, is gaining attention in breast cancer research. This study aimed to evaluate the inter-rater agreement among MAI, Ki-67, and PhH3 expressions in early-stage luminal breast cancer and assess the impact of replacing MAI with PhH3 index on tumor histological grading. Materials and Methods: Three pathologists assessed MAI, Ki-67, and PhH3 expressions in 66 early-stage luminal breast cancer specimens. Mitotic Activity Index was scored based on mitotic figures in an area of 2 mm2 while Ki-67 index utilized a 14% threshold for positively stained nuclei. Phosphohistone H3 expression cutoff was set at 13 positive cells per 2 mm2. The inter-rater agreement for the 3 variables was analyzed using Cohen kappa statistics. Results: Among the 3 parameters, the kappa score of the PhH3 expression reflected very strong agreement between the 3 observers (κ = 0.991, 0.907, and 0.916). Only moderate agreement was noted for MAI (κ = 0.898, 0.562, and 0.592) and substantial agreement for Ki-67 index (κ = 0.869, 0.673, and 0.678). Moreover, replacing MAI with PhH3 index led to upgrade of histological grade in 15% to 16% of patients. Conclusion: Our study demonstrated that PhH3 is a more reproducible proliferation marker than MAI and Ki-67. Incorporation of PhH3-based mitotic index in breast cancer grading might reduce the variation in the assessment of histological grade.

Comparative analysis of Ki-67 labeling index morphometry using deep learning, conventional image analysis, and manual counting

The Ki-67 labeling index is essential for predicting the prognosis of breast cancer and for diagnosing neuroendocrine and gastrointestinal stromal tumors. However, current manual counting and digital image analysis (DIA)-based methods are limited in terms of accurate estimation. This study aimed to assess and compare the capabilities of different DIA systems for Ki-67 counting using the conventional manual counting method. A total of 239 tissue microarray cores from patients with stomach cancer were immunohistochemically stained for Ki-67 and digitally scanned. For the analysis, we employed three different annotation methods: whole TMA core, box selection of the epithelium, and hand-free selection of the epithelium. We used DIA system of 3DHistech, Roche, aetherAI, and manual counting by the pathologists. The annotation methods showed different Ki-67 positivity but were lower than the pathologist manual counting. The results demonstrate that the Roche system is the preferred method for analyzing the entire TMA, whereas aetherAI outperforms the box selection method. Furthermore, 3DHistech is the most accurate method for hands-free selection of the epithelium. The manual counting results showed good agreement among pathologists, with an average intraclass correlation coefficient of 0.93. These results emphasize the importance of carefully selecting annotation methods to determine Ki-67 positivity. To determine the most suitable method for individual laboratories, multiple approaches should be assessed before implementing a DIA system in routine practice.

Correlation analysis of Ki67 changes with survival outcomes in breast cancer before and after neoadjuvant therapy based on residual cancer Burden grade

PURPOSE

This study aims to investigate the change of Ki67 value pre- and post-neoadjuvant therapy (NAT) and evaluate its potential value in predicting survival outcomes in different molecular subtypes of breast cancer.

METHODS

A total of 257 breast cancer patients who underwent NAT at Renmin Hospital of Wuhan University from July 2019 to Sep 2023 were included in this study. The Ki67 index of the patients was re-interpreted by two attending physicians, and the changes of Ki67 value pre- and post-NAT were compared. Chi-square test (χ2) and logistic regression were conducted to examine the correlation between various characteristics and the efficacy of NAT. Disease-free survival (DFS) was calculated using the Kaplan-Meier curve and compared using the log-rank test.

RESULTS

Patients with higher histological grade, negative expression of estrogen receptor (ER) or progesterone receptor (PR), positive expression of human epidermal growth receptor 2 (HER2), higher pretreatment Ki67 index, absence of lymph node metastasis, and those with HER2 positive and triple-negative breast cancer were associated with improved efficacy of NAT. Our study identified that the optimal cut-off value for the changes in Ki67 index pre- and post-NAT related to the effectiveness of NAT was "-88.19 %" in whole chort, which was related to the aforementioned clinical characteristics. Besides, the optimal cut-off values for the luminal, HER2-enriched and triple-negative subtypes were "-91.83 %", "-46.12 %" and "-81.67 %", respectively. Survival analysis demonstrated that the changes in Ki67 value were significantly associated with DFS in the HER2-enriched and triple-negative subtype, but not in the luminal subtype.

CONCLUSIONS

Preoperative clinicopathological features and changes in Ki67 value pre-and post-NAT can contribute to providing patients with a more accurate prognosis.

A chemically defined, mechanically tunable, and bioactive hyaluronic acid/alginate double-network hydrogel for liver cancer organoid construction

Liver cancer organoids replicate the pathophysiology of primary tumors, making them ideal for drug screening and efficacy evaluation. However, their growth in complex, variable, animal-derived matrices hinders practical application. Here, we designed an easily accessible, chemically defined, biocompatible double-network hydrogel (HADR) using methacrylated hyaluronic acid (HAMA), sodium alginate (SA), methacrylamide dopamine (DMA), and c(RGDFC) for liver cancer organoid culture. By optimizing critical extracellular matrix (ECM) parameters, the HADR hydrogel achieves compatibility with the physiological mechanics of the human liver and fosters the adhesion and proliferation of multiple cell types. In vitro drug efficacy tests showed that HepG2 cell line-derived liver cancer organoids exhibited higher IC50 values than 2D cultures, indicating greater drug resistance. Subcutaneous tumor models in nude mice revealed that HADR hydrogels created a microenvironment for HepG2 cells mirroring the natural tumor ECM, leading to increased tumor volume, denser cell arrangement, and concurrent microvascular development. In vivo drug efficacy evaluations indicated that DOX treatment downregulated Ki-67 and MMP-9 expression, inhibiting HepG2 cell proliferation, invasion, and metastasis. These findings demonstrate the potential of HADR hydrogels for liver cancer organoid culture, offering new strategies for personalized drug screening and efficacy evaluation.

The crucial role of bioimage analysts in scientific research and publication

Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective - the result of the recent The Company of Biologists Workshop 'Effectively Communicating Bioimage Analysis', which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions - proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions. We also advocate for increased funding, standardized practices and enhanced collaboration, and we conclude with a call to action for all stakeholders to join efforts in advancing BIA.

Survival Risk Score for Invasive Nonmetastatic Breast Cancer: A Real-World Analysis

PURPOSE

This study aimed to develop a multivariable, weighted overall survival (OS) risk score (SRS) for nonmetastatic (M0) invasive breast cancer (M0-BC, SRSM0-BC).

MATERIALS AND METHODS

This study included a training (1,890 patients) and a validation cohort (850 patients) from the Reggio Emilia Cancer Registry (RE-CR). Ten traditional prognostic variables were evaluated.

RESULTS

In the training set, all the variables but the human epidermal growth factor receptor were significantly associated with OS at univariable analysis. A multivariable model identified an increased death risk for estrogen receptor (hazard ratio [HR], 2.0 [95% CI, 1.1 to 3.1]; P = .021), tumor stages T2-T3 (HR, 2.4 [95% CI, 1.3 to 4.7]; P = .009) and T4 (HR, 5.1 [95% CI, 2.0 to 13.0]; P < .001), and age >74 years (HR, 5.7 [95% CI, 4.0 to 8.2]; P < .001). By assigning scores according to HRs, four risk categories were generated (P for trend <.001). The HRs of death in the high- (282 patients, 15.6%), intermediate-high (275 patients, 15.2%), and intermediate-risk (349 patients, 19.2%) categories patients were, respectively, 27.3, 12.9, and 3.5 times higher, compared with the low-risk (909 patients, 50%) group. Harrell'C index was 81.1%, and the explained variation in mortality was 66.6. Internal cross-validation performed on the accrual index dates yielded a Harrell'C index ranging from 79.5% to 82.3% and an explained variation in mortality ranging from 60.3% to 69.4%. In the validation set, the same risk categories (P for trend <.001) were devised. The Harrell'C index and the explained variation in mortality were 76.1% and 53.7%, respectively, in the whole cohort, maintaining an elevated percentage according to the two accrual index dates.

CONCLUSION

SRSM0-BC using the real-world RE-CR data set may represent a low-cost, accessible, globally applicable model in daily clinical practice, helping to prognostically stratify patients with invasive M0-BC.

Peer-to-peer validation of Ki-67 scoring in a pathology quality circle as a tool to assess interobserver variability: are we better than we thought?

Ki-67, a nuclear protein expressed in all stages of cellular proliferation, is a valuable tool to assess tumor proliferation and has been linked to more aggressive tumor behavior. However, interlaboratory staining heterogeneity and inter-observer variability challenge its reproducibility. Round Robin tests are a suitable tool to standardize and harmonize immunohistochemical and molecular analyses in histopathology. The study investigates the interrater and interlaboratory reproducibility of Ki-67-scoring using both manual and automated approaches. Unstained TMA slides comprising diverse tumor types (breast cancer, neuroendocrine tumors, lymphomas, and head and neck squamous cell carcinoma) were distributed to six pathology laboratories, each employing their routine staining protocols. Manual and automated scoring methods were applied, and interrater and interlaboratory agreement assessed using intraclass correlation coefficients (ICC). The results highlight good-to-excellent reliability overall, with automated scoring demonstrating higher consistency (ICC 0.955) than manual scoring (ICC 0.871). Results were more variable when looking at the individual entities. Reliability remained good for lymphomas (ICC 0.878) and breast cancer (ICC 0.784) and was poor in well-differentiated neuroendocrine tumors (ICC 0.354). This study clearly advocates standardized practices and training to ensure consistency in Ki-67-assessment, and it demonstrates that this can be achieved in a peer-to-peer approach in local quality-circles.

[Quantification of Ki-67 in PitNET (pituitary neuroendocrine tumors)/adenomas]

This study is the first to compare the determination of the Ki-67 index in pituitary neuroendocrine tumors (PitNET)/pituitary adenomas by pathologists with a computerized method (Cognition MasterSuite from VMScope, Berlin, Germany). PitNET/pituitary adenomas often show a low proliferation index. Observer variability is high, especially when estimating in this low percentage range. A more reliable determination would be possible using the four-eyes principle, but this cannot be realized continuously; thus, digital image analysis is a promising solution. In the study, there was clear agreement between the Ki-67 estimate by two experienced pathologists and the determination with the aid of digital image analysis. The digital image analysis system is excellent for determining the proliferation rate of PitNET/pituitary adenomas and can therefore be used to determine the "third" and "fourth eye".

The REMAR (Rhein-Main-Registry) real-world study: prospective evaluation of the 21-gene breast recurrence score® assay in addition to Ki-67 for adjuvant treatment decisions in early-stage breast cancer

PURPOSE

Ki-67 is recommended by international/national guidelines for risk stratification in early breast cancer (EBC), particularly for defining "intermediate risk," despite inter-laboratory/inter-observer variability and cutoff uncertainty. We investigated Ki-67 (> 10%- < 40%, determined locally) as a prognostic marker for intermediate/high risk in EBC, pN0-1 patients.

METHODS

This prospective, non-interventional, real-world study included females ≥ 18 years, with pN0/pN1mi/pN1, HR+ , HER2-negative EBC, and locally determined Ki-67 ranging 10%-40%. The primary outcome was changes in treatment recommendations after disclosing the Oncotype DX Breast Recurrence Score®(RS) assay result.

RESULTS

The analysis included 567 patients (median age, 57 [range, 29-83] years; 70%/1%/29%/ with pN0/pN1mi/pN1 disease; 81% and 19% with RS results 0-25 and 26-100, respectively). The correlations between local and central Ki-67, local Ki-67, and the RS, and central Ki-67 and the RS results were weak (r = 0.35, r = 0.3, and r = 0.46, respectively), and discrepancies were noted in both directions (e.g., local Ki-67 was lower or higher than central Ki-67). After disclosing the RS, treatment recommendations changed for 190 patients (34%). Changes were observed in pN0 and pN1mi/pN1 patients and in patients with centrally determined Ki-67 ≤ 10% and > 10%. Treatment changes were aligned with RS results (adding chemotherapy for patients with higher RS results, omitting it for lower RS results), and their net result was 8% reduction in adjuvant chemotherapy use (from 32% pre-RS results to 24% post-RS results).

CONCLUSION

The Oncotype DX® assay is a tool for individualizing treatments that adds to classic treatment decision factors. The RS result and Ki-67 are not interchangeable, and Ki-67, as well as nodal status, should not be used as gatekeepers for testing eligibility, to avoid under and overtreatment.

Flow cytometric analysis for Ki67 assessment in formalin-fixed paraffin-embedded breast cancer tissue

BACKGROUND

Pathologists commonly employ the Ki67 immunohistochemistry labelling index (LI) when deciding appropriate therapeutic strategies for patients with breast cancer. However, despite several attempts at standardizing the Ki67 LI, inter-observer and inter-laboratory bias remain problematic. We developed a flow cytometric assay that employed tissue dissociation, enzymatic treatment and a gating process to analyse Ki67 in formalin-fixed paraffin-embedded (FFPE) breast cancer tissue.

RESULTS

We demonstrated that mechanical homogenizations combined with thrombin treatment can be used to recover efficiently intact single-cell nuclei from FFPE breast cancer tissue. Ki67 in the recovered cell nuclei retained reactivity against the MIB-1 antibody, which has been widely used in clinical settings. Additionally, since the method did not alter the nucleoskeletal structure of tissues, the nuclei of cancer cells can be enriched in data analysis based on differences in size and complexity of nuclei of lymphocytes and normal mammary cells. In a clinical study using the developed protocol, Ki67 positivity was correlated with the Ki67 LI obtained by hot spot analysis by a pathologist in Japan (rho = 0.756, P < 0.0001). The number of cancer cell nuclei subjected to the analysis in our assay was more than twice the number routinely checked by pathologists in clinical settings.

CONCLUSIONS

The findings of this study showed the application of this new flow cytometry method could potentially be used to standardize Ki67 assessments in breast cancer.

Advancing Ki67 hotspot detection in breast cancer: a comparative analysis of automated digital image analysis algorithms

AIM

Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment.

METHODS

Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring protocol.

RESULTS

Automated hotspot detection and assessment by VDS and DL could be performed in 73% and 100% of the cases, respectively. Automated hotspot detection by VDS and DL led to higher Ki67 hotspot PIs (mean 39.6% and 38.3%, respectively) compared to manual consensus Ki67 PIs (mean 28.8%). Comparing manual consensus Ki67 PIs with VDS Ki67 PIs revealed substantial correlation (r = 0.90), while manual consensus versus DL Ki67 PIs demonstrated high correlation (r = 0.95).

CONCLUSION

Automated Ki67 hotspot detection and analysis correlated strongly with manual Ki67 assessment and provided higher PIs compared to manual assessment. The DL-based algorithm outperformed the VDS-based algorithm in clinical applicability, because it did not depend on virtual alignment of slides and correlated stronger with manual scores. Use of a DL-based algorithm may allow clearer Ki67 PI cutoff values, thereby improving the clinical usability of Ki67.

The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer

PURPOSE

Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods.

METHODS

The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival.

RESULTS

The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives).

CONCLUSION

This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

Fit-for-Purpose Ki-67 Immunohistochemistry Assays for Breast Cancer

New therapies are being developed for breast cancer, and in this process, some "old" biomarkers are reutilized and given a new purpose. It is not always recognized that by changing a biomarker's intended use, a new biomarker assay is created. The Ki-67 biomarker is typically assessed by immunohistochemistry (IHC) to provide a proliferative index in breast cancer. Canadian laboratories assessed the analytical performance and diagnostic accuracy of their Ki-67 IHC laboratory-developed tests (LDTs) of relevance for the LDTs' clinical utility. Canadian clinical IHC laboratories enrolled in the Canadian Biomarker Quality Assurance Pilot Run for Ki-67 in breast cancer by invitation. The Dako Ki-67 IHC pharmDx assay was employed as a study reference assay. The Dako central laboratory was the reference laboratory. Participants received unstained slides of breast cancer tissue microarrays with 32 cases and performed their in-house Ki-67 assays. The results were assessed using QuPath, an open-source software application for bioimage analysis. Positive percent agreement (PPA, sensitivity) and negative percent agreement (NPA, specificity) were calculated against the Dako Ki-67 IHC pharmDx assay for 5%, 10%, 20%, and 30% cutoffs. Overall, PPA and NPA varied depending on the selected cutoff; participants were more successful with 5% and 10%, than with 20% and 30% cutoffs. Only 4 of 16 laboratories had robust IHC protocols with acceptable PPA for all cutoffs. The lowest PPA for the 5% cutoff was 85%, for 10% was 63%, for 20% was 14%, and for 30% was 13%. The lowest NPA for the 5% cutoff was 50%, for 10% was 33%, for 20% was 50%, and for 30% was 57%. Despite many years of international efforts to standardize IHC testing for Ki-67 in breast cancer, our results indicate that Canadian clinical LDTs have a wide analytical sensitivity range and poor agreement for 20% and 30% cutoffs. The poor agreement was not due to the readout but rather due to IHC protocol conditions. International Ki-67 in Breast Cancer Working Group (IKWG) recommendations related to Ki-67 IHC standardization cannot take full effect without reliable fit-for-purpose reference materials that are required for the initial assay calibration, assay performance monitoring, and proficiency testing.

Prognosis and influencing factors of ER-positive, HER2-low breast cancer patients with residual disease after neoadjuvant chemotherapy: a retrospective study

Previously, we found that patients with estrogen receptor (ER)-positive, HER2-low breast cancer are resistant to neoadjuvant chemotherapy (NACT) and have worse outcomes than those who achieve pathological complete response (pCR) after NACT. This study aimed to investigate the prognosis and influencing factors in these patients. A total of 618 patients with ER-positive breast cancer who received standard thrice-weekly NACT were enrolled, including 411 patients with ER-positive, HER2-low breast cancer. Data on the clinicopathological features of these patients before and after NACT were collected. Univariate and multivariate Cox regression analyses were used to identify the independent factors affecting 5-year disease-free survival (DFS). Among the ER-positive, HER2-low patients, 49 (11.9%) achieved a pCR after NACT. A significant difference in survival was observed between patients with and without residual disease after NACT. Additionally, changes in immunohistochemical markers and tumor stages before and after NACT were found to be significant. According to univariate and multivariate analyses, cN_stage (P = 0.002), ER (P = 0.002) and Ki67 (P = 0.023) expression before NACT were significantly associated with 5-year DFS, while pT_stage (P = 0.015), pN_stage (P = 0.029), ER (P = 0.020) and Ki67 (P < 0.001) levels after NACT were related to 5-year DFS in ER-positive, HER2-low patients with residual disease. Our study suggested that high proliferation, low ER expression and advanced stage before and after NACT are associated with a poor prognosis, providing useful information for developing long-term treatment strategies for ER-positive, HER2-low breast cancer in patients with residual disease in the future.

Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

Clinical and Analytical Validation of Two Methods for Ki-67 Scoring in Formalin Fixed and Paraffin Embedded Tissue Sections of Early Breast Cancer

Proliferation determined by Ki-67 immunohistochemistry has been proposed as a useful prognostic and predictive marker in breast cancer. However, the clinical validity of Ki-67 is questionable. In this study, Ki-67 was retrospectively evaluated by three pathologists using two methods: a visual assessment of the entire slide and a quantitative assessment of the tumour margin in 411 early-stage breast cancer patients with a median follow-up of 26.8 years. We found excellent agreement between the three pathologists for both methods. The risk of recurrence for Ki-67 was time-dependent, as the high proliferation group (Ki-67 ≥ 30%) had a higher risk of recurrence initially, but after 4.5 years the risk was higher in the low proliferation group. In estrogen receptor (ER)-positive patients, the intermediate Ki-67 group initially followed the high Ki-67 group, but eventually followed the low Ki-67 group. ER-positive pN0-1 patients with intermediate Ki-67 treated with endocrine therapy alone had a similar outcome to patients treated with chemotherapy. A cut-off value of 20% appeared to be most appropriate for distinguishing between the high and low Ki-67 groups. To summarize, a simple visual whole slide Ki-67 assessment turned out to be a reliable method for clinical decision-making in early breast cancer patients. We confirmed Ki-67 as an important prognostic and predictive biomarker.

Subspecialized breast pathologists have suboptimal interobserver agreement in Ki-67 evaluation using 20% as the cutoff

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.

Gastric intestinal metaplasia: progress and remaining challenges

Most gastric cancers arise in the setting of chronic inflammation which alters gland organization, such that acid-pumping parietal cells are lost, and remaining cells undergo metaplastic change in differentiation patterns. From a basic science perspective, recent progress has been made in understanding how atrophy and initial pyloric metaplasia occur. However, pathologists and cancer biologists have long been focused on the development of intestinal metaplasia patterns in this setting. Arguably, much less progress has been made in understanding the mechanisms that lead to the intestinalization seen in chronic atrophic gastritis and pyloric metaplasia. One plausible explanation for this disparity lies in the notable absence of reliable and reproducible small animal models within the field, which would facilitate the investigation of the mechanisms underlying the development of gastric intestinal metaplasia (GIM). This review offers an in-depth exploration of the current state of research in GIM, shedding light on its pivotal role in tumorigenesis. We delve into the histological subtypes of GIM and explore their respective associations with tumor formation. We present the current repertoire of biomarkers utilized to delineate the origins and progression of GIM and provide a comprehensive survey of the available, albeit limited, mouse lines employed for modeling GIM and engage in a discussion regarding potential cell lineages that serve as the origins of GIM. Finally, we expound upon the myriad signaling pathways recognized for their activity in GIM and posit on their potential overlap and interactions that contribute to the ultimate manifestation of the disease phenotype. Through our exhaustive review of the progression from gastric disease to GIM, we aim to establish the groundwork for future research endeavors dedicated to elucidating the etiology of GIM and developing strategies for its prevention and treatment, considering its potential precancerous nature.

The Role of Ki-67 in HR+/HER2- Breast Cancer: A Real-World Study of 956 Patients

Objective

This study determined the cut-off value of Ki-67 expression and discussed the interaction between Ki-67 and histological grade, further explored the prognostic role of Ki-67 in hormone receptor-positive and human epidermal growth factor receptor 2 negative (HR+/HER2-) breast cancer;.

Materials and Methods

We assessed the Ki-67 expression of 956 patients with HR+/HER2 breast cancer diagnosed in the General Hospital of Ningxia Medical University from 2015 to 2019 by immunohistochemistry (IHC), The disease-free survival (DFS) was defined as the time from postoperative to the first local recurrence, distant metastasis or death of the disease. The follow-up by means of inpatient or outpatient medical records and telephone.

Results

22.5% was used as the cut-off for low/high Ki-67 expression in HR+/HER2- breast cancer. Compared with the value of 14%, which is commonly used in clinic at present, the consistency of the two values is moderate (Kappa = 0.484, P<0.001). The expression of Ki-67 was increased with the grade. (Median: G1:10%; G2:20%; G3:40%. Mean: G1:13%; G2:23%; G3:39%, P <0.001). Survival analysis was based on all patients for a median of 51 months (24-89 months), 63 cases had recurrence or metastasis during the follow-up, which 21 cases had low expression of Ki-67 and 42 cases had high expression. The patients with Ki-67 ≥ 22.5% had a 2.969 higher risk of early recurrence and metastasis than the patients with Ki-67 < 22.5%. There were 4 cases of local recurrence, 7 cases of regional lymph node metastasis, and 52 cases of distant metastasis in all patients, the common distant metastases were bone, liver, and lung, and rare metastases were adrenal gland, bone marrow, and pericardium.

Conclusion

In HR+/HER2- breast cancer, patients with Ki-67 > 22.5% have a worse prognosis and are more likely to have early recurrence and metastasis.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis

BACKGROUND

Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression.

METHODS

The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5 years, range: 29-90 years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer.

RESULTS

The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively.

CONCLUSIONS

Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Is Ki-67 Really Useful as a Predictor for Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer?

Neoadjuvant chemotherapy (NACT) is routinely offered to operable locally advanced breast cancer (LABC) patients desirous of breast conservation surgery and inoperable LABC patients. Pathological complete response (pCR) following chemotherapy is recognized as a surrogate for survival outcomes in high grade tumour subtypes. Many biological and tumor characters have been shown to predict pCR. The current study was performed with the aim of investigating the ability of Ki-67 in predicting pCR with NACT in breast cancer patients. A total of 105 patients with locally advanced breast cancer who completed NACT followed by surgery were included in this study from January 2020 till December 2022. Patients with advanced metastatic breast carcinoma, who did not give consent for NACT, who did not complete NACT and who did not undergo surgery were excluded. All patients were assessed for Ki-67 score on core-needle biopsy samples and response rate was assessed clinically and by histopathological examination of resected specimen. Quantitative variables were compared using unpaired t-test or Mann-Whitney 'U' test and for categorical variables Chi-square or Fisher's exact test were used. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive potential of Ki-67 expression levels in predicting pCR. To identify the predictive factors associated with pCR, univariate analysis was performed. The P value < 0.05 was considered as statistically significant. Mean age was 51.57 ± 10.8 years. 51 patients achieved clinical complete response (cCR) and 33 achieved pCR after NACT. Mean Ki-67 index in overall study population, in pCR group and no pCR group was 46.44 ± 22.92%, 51.60 ± 22.3% and 44.06 ± 22.7%, respectively. On univariate analysis, ER negativity, PR negativity and Her 2neu positivity were found predictive of pCR. On subgroup analysis, TNBC and Her 2neu positive sub groups were associated with higher cCR and pCR rate. We found no significant association between Ki-67 and pCR. This result may be confounded by the fact that a significant duration of the study was in the COVID-19 pandemic. Validation of this data is required in a large prospective study.

Breast Cancer Molecular Subtyping in Practice: A Real-World Study of the APIS Breast Cancer Subtyping Assay in a Consecutive Series of Breast Core Biopsies

The APIS Breast Cancer Subtyping Kit is an mRNA-based assessment of the seven parameters including three biomarkers routinely assessed in all the newly diagnosed breast cancers (BC), oestrogen receptor (ER), progesterone receptor (PR) and HER-2 and an additional four genes that create a novel proliferation signature, MKI67, PCNA, CCNA2 and KIF23. Taken together, the data are used to produce a molecular subtype for every sample. The kit was evaluated against the current standard protocol of immunohistochemistry (IHC) and/or in situ hybridisation (ISH) in breast cancer patients. The data were presented at the weekly breast multidisciplinary team (MDT) meeting. A total of 98 consecutive cases of pre-operative breast cancer core biopsies and two core biopsies of nodal metastases yielding 100 cases were assessed. IHC and APIS results were available for 100 and 99 cases. ER was concordant in 97% cases, PR was concordant in 89% and HER-2 results were concordant with IHC/ISH in 100% of the cases. Ki-67 IHC was discordant in 3% of cases when compared with MK167 alone but discordant in 24% when compared with the four-gene proliferation signature. In conclusion, our study indicates that the APIS Breast Cancer Subtyping Kit is highly concordant when compared to the results produced for ER/PR/HER-2 by IHC and/or ISH. The assay could play a role in the routine assessment of newly diagnosed breast cancer (BC) specimens.

Prediction of the MammaPrint Risk Group Using MRI Features in Women With Estrogen Receptor-Positive, HER2-Negative, and 1 to 3 Node-Positive Invasive Breast Cancer

BACKGROUND

MammaPrint assigns chemotherapeutic benefits to patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and 1 to 3 node-positive invasive breast cancer. However, its cost and time-consuming nature limit its use in certain clinical settings. We aimed to develop and validate the prediction models for the low MammaPrint risk group using clinicopathologic and MRI features.

PATIENTS AND METHODS

Overall, 352 women with ER-positive, HER2-negative, and 1 to 3 node-positive invasive breast cancer were retrospectively reviewed and assigned to development (n = 235) and validation sets (n = 117). Univariate and multivariate analyses identified features associated with the low MammaPrint risk group. The area under the receiver operating characteristic curves (AUROCs) of models based on clinicopathologic, MRI, and combined features were evaluated.

RESULTS

Development set multivariate analysis showed that clinicopathologic features including low histologic grade (odds ratio [OR], 5.29; P = .02), progesterone receptor-positivity (OR, 3.23; P = .01), and low Ki-67 (OR, 6.05; P < .001) and MRI features, including peritumoral edema absence (OR, 2.24; P = .04) and a high proportion of persistent components (OR, 1.15; P = .004) were significantly associated with the low MammaPrint risk group. The AUROCs of models based on clinicopathologic, MRI, and combined features were 0.77, 0.64, and 0.80 in the development and 0.66, 0.60, and 0.70 in the validation sets, respectively.

CONCLUSION

The combined model incorporating clinicopathologic and MRI features showed potential in predicting the low MammaPrint risk group, and may support decision-making in clinical settings with limited access to MammaPrint.

Association of physical weight statuses defined by body mass index (BMI) with molecular subtypes of premenopausal breast cancer: a systematic review and meta-analysis

BACKGROUND AND PURPOSE

The association between overweight/obesity and postmenopausal breast cancer has been proven. However, uncertainty exists regarding the association between physical weight statuses and premenopausal breast cancer subtypes. This study aimed to explore the association of body weight statuses with molecular subtypes of premenopausal breast cancer.

METHOD

A systematic search of Medline, PubMed, Embase, and Web of Science was performed. The Newcastle-Ottawa Scale (NOS) and the Joanna Briggs Institute (JBI) Critical Appraisal tools were used to evaluate the quality of the literature. STATA and R software were used to analyze the extracted data.

RESULT

The meta-analysis included 35 observational studies with a total of 41,049 premenopausal breast cancer patients. The study showed that the proportion of underweight patients was 4.8% (95% CI = 3.9-5.8%, P = 0.01), overweight was 29% (95%CI = 27.1-30.9%, P < 0.01), obesity was 17.8% (95% CI = 14.9-21.2%, P < 0.0001), and normal weight was 51.6% (95% CI = 46.7-56.5%, P < 0.0001). The pooled results showed that in comparison to the normal weight group, being physically underweight is related to a 1.44-fold risk (OR = 1.44, 95%CI = 1.28-1.63, P < 0.0001) of HER2 + breast cancer. Overweight is related to a 1.16-fold risk (OR = 1.16, 95%CI = 1.06-1.26, P = 0.002) of TNBC and a 16% lower risk (OR = 0.84, 95%CI = 0.75-0.93, P = 0.001) of ER + breast cancer. When compared to underweight/normal weight populations, both overweight (OR = 0.74, 95%CI = 0.56-0.97, P = 0.032) and obesity (OR = 0.70, 95%CI = 0.50-0.98, P = 0.037) can reduce the risk of ER + PR + breast cancer.

CONCLUSION

In the premenopausal breast cancer population, the distribution of patients' numbers with different weight statuses was significantly distinct among the various breast cancer subtypes. Additionally, the associations between physical weight statuses and the risk of premenopausal breast cancer subtypes are divergent.

Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer

In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer

The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.

Implementation of Digital Image Analysis in Assessment of Ki67 Index in Breast Cancer

The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.

Relationship of Ki-67 index in biopsies of metastatic breast cancer tissue and circulating tumor cells (CTCs) at the time of biopsy collection

BACKGROUND

The proliferation marker Ki-67 is a major pathological feature for the description of the state of disease in breast cancer. It helps to define the molecular subtype and to stratify between therapy regimens in early breast cancer and helps to assess the therapy response. Circulating tumor cells (CTCs) are a negative prognostic biomarker for progression free (PFS) and overall survival (OS) in patients with metastatic breast cancer. Therefore, the CTC count is often described as surrogate for the tumor burden. Both, decrease of Ki-67 and CTC count are considered as evidence for therapy response. The presented work analyzed the correlation between the Ki-67 indices of metastatic tissue biopsies and CTC counts in biopsy time-adjacent peripheral blood samples.

PATIENTS AND METHODS

Blood samples from 70 metastatic breast cancer patients were obtained before the start of a new line of systemic therapy. CTCs were enumerated using CellSearch® (Menarini Silicon Biosystems, Bologna, Italy) whereas intact CTCs (iCTCs) and non-intact or apoptotic CTCs (aCTCs) were distinguished using morphologic criteria. The proportion of cells expressing Ki-67 was evaluated using immunohistochemistry on biopsies of metastases obtained concurrently with CTC sampling before the start of a new line of systemic therapy.

RESULTS

65.7% of patients had a Ki-67 index of > 25%. 28.6% of patients had ≥ 5, 47.1% ≥ 1 iCTCs. 37.1% had ≥ 5, 51.4% ≥ 1 aCTCs. No correlation was shown between Ki-67 index and iCTC and aCTC count (r = 0.05 resp. r = 0.05, Spearman's correlation index). High CTC-counts did not coincide with high Ki-67 index. High Ki-67, ≥ 5 iCTCs and aCTCs are associated with poor progression free (PFS) and overall survival (OS).

CONCLUSION

CTCs and Ki-67 are independent prognostic markers in metastatic breast cancer. High Ki-67 in metastatic tumor tissue is not correlated to high iCTC or aCTC counts in peripheral blood.

Combined utility of Ki-67 index and tumor grade to stratify patients with pancreatic ductal adenocarcinoma who underwent upfront surgery

OBJECTIVE

To investigate the prognostic prediction of a new indicator, combined by tumor grade and Ki-67, in patients with resected pancreatic ductal adenocarcinoma (PDAC).

METHODS

Data were retrospectively collected from consecutive patients who underwent primary resection of pancreas from December 2012 to December 2017. Tumor grade and Ki-67 were reviewed from routine pathological reports. G-Ki67 was classified as three categories as I (G1/2 and Ki-67 < 40%), II (G1/2 and Ki-67 ≥ 40%), and III(G3/4 and all Ki-67).

RESULTS

Cox regression analyses revealed that tumor stage (II vs. I: hazard ratio (HR), 3.781; 95% confidence index (CI), 2.844-5.025; P < 0.001; III vs. I: HR, 7.476; 95% CI, 5.481-10.20; P < 0.001) and G-Ki67 (II vs. I: HR, 1.299; 95% CI, 1.038-1.624; P = 0.022; III vs. I: HR, 1.942; 95% CI, 1.477-2.554; P < 0.001) were independent prognostic factors in the developing cohort. The result was rectified in the validation cohort. In subgroups analysis, G-Ki67 (II vs. I: HR, 1.866 ; 95% CI, 1.045-3.334; P = 0.035; III vs. I: HR, 2.333 ; 95% CI, 1.156-4.705; P = 0.018) also had a high differentiation for survival prediction.

CONCLUSION

Our findings indicate that three-categories of G-Ki67 in resectable PDAC according to the routine pathological descriptions provided additional prognostic information complementary to the TNM staging system.

Ki67 assessment protocol as an integral biomarker for avoiding radiotherapy in the LUMINA breast cancer trial

AIMS

The LUMINA trial demonstrated a very low local recurrence rate in women ≥55 years with low-risk luminal A breast cancer (defined as grade I-II, T1N0, hormone receptor positive, HER2 negative and Ki67 index ≤13.25%) treated with breast-conserving surgery and endocrine therapy (but no other systemic therapy), supporting the safe omission of radiation in these women. Here we describe the protocol for Ki67 assessment, the companion diagnostic used to guide omission of adjuvant radiotherapy.

METHODS

Ki67 immunohistochemistry was performed on full-face sections at one of three regional labs. Pathologists trained in the International Ki67 in Breast Cancer Working Group (IKWG) method demarcated tumour areas on scanned slides and scored 100 nuclei from each of at least five randomly selected 1-mm fields. For cases with high Ki67 heterogeneity, further virtual cores were selected and scored in order to confidently assign a case as luminal A (≤13.25%) or B (>13.25%). Interlaboratory variability was assessed through an annual quality assurance programme during the study period.

RESULTS

From the quality assurance programme, the mean Ki67 index across all cases/labs was 13%. The observed intraclass correlation coefficient (ICC) and kappa statistics were ≥0.9 and ≥0.7, respectively, indicating a substantial level of agreement. Median scoring time was 4 min per case. The IKWG-recommended scoring method, performed directly from slides, requiring up to four scored fields, is concordant with the LUMINA scoring method (ICC ≥ 0.9).

CONCLUSION

Ki67 is a practical, reproducible, and inexpensive biomarker that can identify low-risk luminal A breast cancers as potential candidates for radiation de-escalation.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01791829.

A risk-based subgroup analysis of the effect of adjuvant S-1 in estrogen receptor-positive, HER2-negative early breast cancer

PURPOSE

The Phase III POTENT trial demonstrated the efficacy of adding S-1 to adjuvant endocrine therapy for estrogen receptor-positive, HER2-negative early breast cancer. We investigated the efficacy of S-1 across different recurrence risk subgroups.

METHODS

This was a post-hoc exploratory analysis of the POTENT trial. Patients in the endocrine-therapy-only arm were divided into three groups based on composite risk values calculated from multiple prognostic factors. The effects of S-1 were estimated using the Cox model in each risk group. The treatment effects of S-1 in patients meeting the eligibility criteria of the monarchE trial were also estimated.

RESULTS

A total of 1,897 patients were divided into three groups: group 1 (≤ lower quartile of the composite values) (N = 677), group 2 (interquartile range) (N = 767), and group 3 (> upper quartile) (N = 453). The addition of S-1 to endocrine therapy resulted in 49% (HR: 0.51, 95% CI: 0.33-0.78) and 29% (HR: 0.71, 95% CI 0.49-1.02) reductions in invasive disease-free survival (iDFS) events in groups 2 and 3, respectively. We could not identify any benefit from the addition of S-1 in group 1. The addition of S-1 showed an improvement in iDFS in patients with one to three positive nodes meeting the monarchE cohort 1 criteria (N = 290) (HR: 0.47, 95% CI: 0.29-0.74).

CONCLUSIONS

The benefit of adding adjuvant S-1 was particularly marked in group 2. Further investigations are warranted to explore the optimal usage of adjuvant S-1.

Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method

Medical image processing has become increasingly important in recent years, particularly in the field of microscopic cell imaging. However, accurately counting the number of cells in an image can be a challenging task due to the significant variations in cell size and shape. To tackle this problem, many existing methods rely on deep learning techniques, such as convolutional neural networks (CNNs), to count cells in an image or use regression counting methods to learn the similarities between an input image and a predicted cell image density map. In this paper, we propose a novel approach to monitor the cell counting process by optimizing the loss function using the optimal transport method, a rigorous measure to calculate the difference between the predicted count map and the dot annotation map generated by the CNN. We evaluated our algorithm on three publicly available cell count benchmarks: the synthetic fluorescence microscopy (VGG) dataset, the modified bone marrow (MBM) dataset, and the human subcutaneous adipose tissue (ADI) dataset. Our method outperforms other state-of-the-art methods, achieving a mean absolute error (MAE) of 2.3, 4.8, and 13.1 on the VGG, MBM, and ADI datasets, respectively, with smaller standard deviations. By using the optimal transport method, our approach provides a more accurate and reliable cell counting method for medical image processing.

Ki-67 index after neoadjuvant endocrine therapy as a prognostic biomarker in patients with ER-positive/HER2-negative early breast cancer: a systematic review and meta-analysis

BACKGROUND

Neoadjuvant treatment discriminates responders, but pathologic complete response is uncommon in oestrogen receptor (ER)-positive/HER2-negative early breast cancer. We aimed to assess the prognostic value of Ki-67 index after neoadjuvant endocrine therapy (NET).

METHODS

We conducted a systematic literature search of PubMed, Embase, CENTRAL, and conference proceedings up to 22nd August 2023 to identify studies reporting the association of Ki-67 index after NET with recurrence-free survival (RFS) and/or overall survival (OS) in women with ER-positive/HER2-negative early breast cancer. We combined RFS and OS hazard ratios (HRs) with 95% confidence intervals (CIs).

RESULTS

Twelve studies including 7897 patients were analysed. Most studies were clinical trials (n = 7547) including only postmenopausal women (n = 3953) treated with aromatase inhibitor (n = 3359). Three studies evaluated Ki-67 in a preplanned core biopsy at 2-4 weeks of NET (n = 3348), while nine evaluated Ki-67 in the surgical specimen (n = 4549) after 2-24 weeks of NET. Median follow-up ranged between 37 and 95 months for RFS and 62-84 months for OS. High Ki-67 index after NET was significantly associated with worse RFS (HR 2.48, 95% CI 1.86-3.30) and OS (HR 2.66, 95% CI 1.65-4.28). A sensitivity analysis including three studies that measured Ki-67 in a preplanned core biopsy showed similar association with RFS (HR 2.41, 95% CI 1.77-3.30).

CONCLUSIONS

High Ki-67 after NET is associated with worse survival outcomes, even after a short course of NET, emphasising the prognostic value of this biomarker in women with ER-positive/HER2-negative early breast cancer.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Differential distribution of actual and surrogate oncotype DX recurrence scores in breast cancer patients by age, menopausal status, race, and body mass index

PURPOSE

The Oncotype DX Recurrence Score (RS) is a widely used prognostic tool for estrogen receptor-positive breast cancer patients. Multiple surrogate models can predict RS with good accuracy. In this study we aimed to determine whether the RS and two surrogate indices were differentially distributed by age, menopausal status, race, and body mass index (BMI).

METHODS

516 breast cancer cases treated at a single institution were analyzed. Epidemiologic data, RS, tumor size, grade, and biomarker data were abstracted. Breast Cancer Prognostic Score (BCPS) and modified Magee equation 2 were used to calculate surrogate RS. Patients were stratified into different groups based on age, menopausal status, race, BMI, or a combination of strata. Mean and standard deviation were calculated for each group/subgroup.

RESULTS

Age below median (< 63) was associated with higher RS, especially in obese and Black patients. RS was also higher in obese and Black patients in the premenopausal subgroup. Black patients had a higher RS compared to White women in the premenopausal and non-obese subgroups. BMI < 30 was associated with higher RS, especially in older, postmenopausal, and Black patients. Some of these observations were replicated by the two surrogate models. The surrogate recurrence scores were higher in the younger age group, in non-obese older/postmenopausal women, and in younger/premenopausal obese individuals.

CONCLUSIONS

Higher RS was observed in younger and premenopausal breast cancer patients, especially among the Black and obese subgroups, and in non-obese patients, especially among Black and older/postmenopausal women, suggesting more aggressive disease in these subgroups. Some statistical differences could be replicated by both surrogate models, suggesting that they may have utility in breast cancer epidemiology studies that do not have access to Oncotype DX RS or patient outcome data.

Ki-67, 21-Gene Recurrence Score, Endocrine Resistance, and Survival in Patients With Breast Cancer

Importance

Both high 21-gene recurrence score (RS) and high Ki-67 level are poor prognostic factors in patients with estrogen receptor (ER)-positive ERBB2-negative (ER+/ERBB-) breast cancer; however, a discrepancy between the 2 has been noted. Survival differences according to these 2 biomarkers are not well known.

Objective

To assess the associations between RS and Ki-67 expression and between Ki-67 expression and recurrence-free survival in patients with ER+/ERBB- breast cancer with low RS.

Design, Setting, and Participants

This cohort study included women treated for ER+/ERBB2- breast cancer who underwent the 21-gene RS test from March 2010 to December 2020 in 2 hospitals in Korea.

Exposures

Recurrence score and Ki-67 level.

Main Outcomes and Measures

A Cox proportional hazards regression model was used to examine the association of Ki-67 with recurrence-free survival (RFS), while a binary logistic regression model was used to examine the association between Ki-67 and secondary endocrine resistance. High Ki-67 expression was defined as 20% or greater, and low genomic risk as an RS of 25 or less. Secondary endocrine resistance was defined as breast cancer recurrence that occurred after at least 2 years of endocrine therapy and during or within the first year after completing 5 years of adjuvant endocrine therapy.

Results

A total of 2295 female patients were included (mean [SD] age, 49.8 [9.3] years), of whom 1948 (84.9%) were in the low genomic risk group and 1425 (62.1%) had low Ki-67 level. The median follow-up period was 40 months (range, 0-140 months). The RS and Ki-67 level had a moderate correlation (R = 0.455; P < .001). Of the patients with low Ki-67 level, 1341 (94.1%) had low RS, whereas 607 of 870 patients with high Ki-67 level (69.8%) had low RS. In patients with low RS, the RFS differed significantly according to Ki-67 level (low Ki-67, 98.5% vs high Ki-67, 96.5%; P = .002). Among the 1807 patients with low genomic risk who did not receive chemotherapy, high Ki-67 level was independently associated with recurrence (hazard ratio, 2.51; 95% CI, 1.27-4.96; P = .008). Recurrence after 3 years differed significantly according to Ki-67 level (low Ki-67, 98.7% vs high Ki-67, 95.7%; P = .003), whereas recurrence within 3 years did not differ (low Ki-67, 99.3% vs high Ki-67, 99.3%; P = .90). In addition, Ki-67 was associated with secondary endocrine resistance in patients with low RS who did not receive chemotherapy (odds ratio, 2.49; 95% CI, 1.13-5.50; P = .02).

Conclusions and Relevance

In this cohort study of patients with ER+/ERBB2- breast cancer, a moderate correlation was observed between Ki-67 and RS, and high Ki-67 level in patients with low genomic risk was associated with increased risk of secondary endocrine resistance.

Finding the (biomarker-defined) subgroup of patients who benefit from a novel therapy: No time for a game of hide and seek

An important element of precision medicine is the ability to identify, for a specific therapy, those patients for whom benefits of that therapy meaningfully exceed the risks. To achieve this goal, treatment effect usually is examined across subgroups defined by a variety of factors, including demographic, clinical, or pathologic characteristics or by molecular attributes of patients or their disease. Frequently such subgroups are defined by the measurement of biomarkers. Even though such examination is necessary when pursuing this goal, the evaluation of treatment effect across a variety of subgroups is statistically fraught due to both the danger of inflated false-positive error rate from multiple testing and the inherent insensitivity to how treatment effects differ across subgroups.Pre-specification of subgroup analyses with appropriate control of false-positive (i.e. type I) error is recommended when possible. However, when subgroups are specified by biomarkers, which could be measured by different assays and might lack established interpretation criteria, such as cut-offs, it might not be possible to fully specify those subgroups at the time a new therapy is ready for definitive evaluation in a Phase 3 trial. In these situations, further refinement and evaluation of treatment effect in biomarker-defined subgroups might have to take place within the trial. A common scenario is that evidence suggests that treatment effect is a monotone function of a biomarker value, but optimal cut-offs for therapy decisions are not known. In this setting, hierarchical testing strategies are widely used, where testing is first conducted in a particular biomarker-positive subgroup and then is conducted in the expanded pool of biomarker-positive and biomarker-negative patients, with control for multiple testing. A serious limitation of this approach is the logical inconsistency of excluding the biomarker-negatives when evaluating effects in the biomarker-positives, yet allowing the biomarker-positives to drive the assessment of whether a conclusion of benefit could be extrapolated to the biomarker-negative subgroup.Examples from oncology and cardiology are described to illustrate the challenges and pitfalls. Recommendations are provided for statistically valid and logically consistent subgroup testing in these scenarios as alternatives to reliance on hierarchical testing alone, and approaches for exploratory assessment of continuous biomarkers as treatment effect modifiers are discussed.

The Biomarker Ki-67: Promise, Potential, and Problems in Breast Cancer

Ki-67 is a nuclear protein serendipitously discovered by monoclonal antibody selection in the early 1980s. While it has been applied for decades in the context of breast cancer as a putative prognostic and, more recently, predictive, biomarker, even after all this time there is incomplete agreement as to the validity of the immunohistochemical assays employed for Ki-67 assessment, given possible effects of the disparate methodologies employed and possible confounding preanalytical, analytical, and interpretive variables. In this brief review, the history of Ki-67 and the problems, particularly with the analytical and interpretive variables, are highlighted through a selective review of the published literature. The contributions of the International Ki-67 Breast Cancer Working Group are highlighted, and in particular, the recommendations made by this group are reviewed. The potential of Ki-67 as a biomarker for breast cancer has not yet been fully realized, but an understanding of the power as well as the limitations of the methods of Ki-67 assessment are important if this biomarker can realize its potential.

The Story of the Magee Equations: The Ultimate in Applied Immunohistochemistry

Magee equations (MEs) are a set of multivariable models that were developed to estimate the actual Onco type DX (ODX) recurrence score in invasive breast cancer. The equations were derived from standard histopathologic factors and semiquantitative immunohistochemical scores of routinely used biomarkers. The 3 equations use slightly different parameters but provide similar results. ME1 uses Nottingham score, tumor size, and semiquantitative results for estrogen receptor (ER), progesterone receptor, HER2, and Ki-67. ME2 is similar to ME1 but does not require Ki-67. ME3 includes only semiquantitative immunohistochemical expression levels for ER, progesterone receptor, HER2, and Ki-67. Several studies have validated the clinical usefulness of MEs in routine clinical practice. The new cut-off for ODX recurrence score, as reported in the Trial Assigning IndividuaLized Options for Treatment trial, necessitated the development of Magee Decision Algorithm (MDA). MEs, along with mitotic activity score can now be used algorithmically to safely forgo ODX testing. MDA can be used to triage cases for molecular testing and has the potential to save an estimated $300,000 per 100 clinical requests. Another potential use of MEs is in the neoadjuvant setting to appropriately select patients for chemotherapy. Both single and multi-institutional studies have shown that the rate of pathologic complete response (pCR) to neoadjuvant chemotherapy in ER+/HER2-negative patients can be predicted by ME3 scores. The estimated pCR rates are 0%, <5%, 14%, and 35 to 40% for ME3 score <18, 18 to 25, >25 to <31, and 31 or higher, respectively. This information is similar to or better than currently available molecular tests. MEs and MDA provide valuable information in a time-efficient manner and are available free of cost for anyone to use. The latter is certainly important for institutions in resource-poor settings but is also valuable for large institutions and integrated health systems.

Subtypes of high-grade breast ductal carcinoma in situ (DCIS): incidence and potential clinical impact

OBJECTIVE

The purpose of this study was to investigate and classify the molecular subtypes of high-grade ductal carcinoma in situ (DCIS) and identify possible high-risk subtypes. The heterogenicity of DCIS with variable clinical and histopathological presentations has been recognized. Nevertheless, only histopathological grading and diameter are currently implemented in clinical decision-making following the diagnosis of DCIS. The molecular subtypes of DCIS and their IHC surrogate markers have not been defined in conventional treatment guidelines and recommendations. We applied the definitions of molecular subtypes according to the IHC surrogate markers defined for IBC and subclassified high-grade DCIS, accordingly.

METHODS

Histopathological specimens were collected, revised, and regraded from 494 patients diagnosed with DCIS between 1996 and 2018. Other in situ and papillary lesions observed in breast biopsies were excluded from this study. 357 high-grade DCIS cases were submitted to IHC analysis. The markers investigated were ER, PR, HER2, and Ki67.

RESULTS

45 cases were classified as grade 1, 19 as grade 2, and 430 as grade 3. Sixty patients with high-grade DCIS had an additional invasive component in the surgical specimen. Thirty-three patients were diagnosed with recurrent DCIS or invasive cancer (minimum one year after their primary DCIS diagnosis). The proportions of luminal A and luminal B HER2-negative subtypes varied depending on whether 2011 or 2013 St. Gallen Consensus Conference guidelines were adopted. Luminal A was the most prevalent subtype, according to both classifications. The luminal B HER2-positive subtype was found in 22.1% of cases, HER2-enriched subtype in 21.8%, and TPN subtype in 5.6%. There were strong indications that HER2-enriched subtype was significantly more frequent among DCIS with invasive component (p = 0.0169).

CONCLUSIONS

High-grade DCIS exhibits all the molecular subtypes previously identified in IBC, but with a somewhat different distribution in our cohort. HER2-enriched subtype is substantially related to the presence of an invasive component in DCIS; consequently, it is regarded as a high-risk entity.

Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

The usefulness of CanAssist Breast over Ki67 in breast cancer recurrence risk assessment

BACKGROUND

Assessment of Ki67 by immunohistochemistry (IHC) has limited utility in clinical practice owing to analytical validity issues. According to International Ki67 Working Group (IKWG) guidelines, treatment should be guided by a prognostic test in patients expressing intermediate Ki67 range, >5%-<30%. The objective of the study is to compare the prognostic performance of CanAssist Breast (CAB) with that of Ki67 across various Ki67 prognostic groups.

METHODS

The cohort had 1701 patients. Various risk groups were compared for the distant relapse-free interval (DRFi) derived from Kaplan-Meier survival analysis. As per IKWG, patients are categorized into three risk groups: low-risk (<5%), intermediate risk (>5%-<30%), and high-risk (>30%). CAB generates two risk groups, low and high risk based on a predefined cutoff.

RESULTS

In the total cohort, 76% of the patients were low risk (LR) by CAB as against 46% by Ki67 with a similar DRFi of 94%. In the node-negative sub-cohort, 87% were LR by CAB with a DRFi of 97% against 49% by Ki67 with a DRFi of 96%. In subgroups of patients with T1 or N1 or G2 tumors, Ki67-based risk stratification was not significant while it was significant by CAB. In the intermediate Ki67 (>5%-<30%) category up to 89% (N0 sub-cohort) were LR by CAB and the percentage of LR patients was 25% (p < 0.0001) higher compared to NPI or mAOL. In the low Ki67 (≤5%) group, up to 19% were segregated as high-risk by CAB with 86% DRFi suggesting the requirement of chemotherapy in these low Ki67 patients.

CONCLUSION

CAB provided superior prognostic information in various Ki67 subgroups, especially in the intermediate Ki67 group.

Low expression of Ki-67/MIB-1 labeling index in IDH wild type glioblastoma predicts prolonged survival independently by MGMT methylation status

PURPOSE

The Ki-67/MIB-1 labeling index (LI) is clinically used to differentiate between high and low-grade gliomas, while its prognostic value remains questionable. Glioblastoma (GBM) expressing wild-type isocitrate dehydrogenase IDH^wt, a relatively common malignant brain tumor in adults, is characterized by a dismal prognosis. Herein, we have retrospectively investigated the prognostic role of Ki-67/MIB-1-LI in a large group of IDH^wt GBM.

METHODS

One hundred nineteen IDH^wt GBM patients treated with surgery followed by Stupp's protocol in our Institution between January 2016 and December 2021 were selected. A cut-off value for Ki-67/MIB-1-LI was used with minimal p-value based approach.

RESULTS

A multivariate analysis showed that Ki-67/MIB-1-LI expression < 15% significantly correlated with a longer overall survival (OS), independently from the age of the patients, Karnofsky performance status scale, extent of surgery and O⁶-methylguanine (O6-MeG)-DNA methyltransferase promoter methylation status.

CONCLUSIONS

Among other studies focused on Ki-67/MIB-1-LI, this is the first observational study showing a positive correlation between OS of IDH^wt GBM patients and Ki-67/MIB-1-LI that we propose as a new predictive marker in this subtype of GBM.

Machine Learning Approach for the Determination of the Best Cut-off Points for Ki67 Proliferation Index in Adjuvant and Neo-adjuvant Therapy Breast Cancer Patients

BACKGROUND

This study aims to evaluate Ki67 cut-off points for differentiating low and high-risk patients based on survival and recurrence and find the best Ki67 cut-off points in breast cancer patients undergoing adjuvant and neoadjuvant therapy using machine learning methods.

PATIENTS AND METHODS

Patients with breast cancer treated at 2 referral hospitals between December 2000 and March 2021 who had invasive breast cancer entered this study. There were 257 patients in the neoadjuvant group and 2139 in the adjuvant group. A decision tree method was used to predict the likelihood of survival and recurrence. The 2-ensemble technique of RUSboost and bagged tree were imposed on the decision tree method to increase the accuracy of the determination. 80 percent of the data was used to train and validate the model, and 20% was used as a test.

RESULTS

In adjuvant therapy breast cancer patients with Invasive ductal carcinoma (IDC) and Invasive lobular carcinoma (ILC) the cutoff points for survival were 20 and 10, respectively. For luminal A, luminal B, Her2 neu, and triple-negative adjuvant therapy patients' the cutoff points for survival were 25, 15, 20, and 20, respectively. For neoadjuvant therapy luminal A and luminal B group, survival cutoff points were 25 and 20, respectively.

CONCLUSION

Despite variability in measurement and cut-off points, the Ki-67 proliferation index is still helpful in the clinic. Further investigation is needed to determine the best cut-off points for different patients. The sensitivity and specificity of Ki-67 cutoff point prediction models in this study could further prove its significance as a prognostic factor.

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

Consensus based recommendations for the diagnosis of serous tubal intraepithelial carcinoma: an international Delphi study

AIM

Reliably diagnosing or safely excluding serous tubal intraepithelial carcinoma (STIC), a precursor lesion of tubo-ovarian high-grade serous carcinoma (HGSC), is crucial for individual patient care, for better understanding the oncogenesis of HGSC, and for safely investigating novel strategies to prevent tubo-ovarian carcinoma. To optimize STIC diagnosis and increase its reproducibility, we set up a three-round Delphi study.

METHODS AND RESULTS

In round 1, an international expert panel of 34 gynecologic pathologists, from 11 countries, was assembled to provide input regarding STIC diagnosis, which was used to develop a set of statements. In round 2, the panel rated their level of agreement with those statements on a 9-point Likert scale. In round 3, statements without previous consensus were rated again by the panel while anonymously disclosing the responses of the other panel members. Finally, each expert was asked to approve or disapprove the complete set of consensus statements. The panel indicated their level of agreement with 64 statements. A total of 27 statements (42%) reached consensus after three rounds. These statements reflect the entire diagnostic work-up for pathologists, regarding processing and macroscopy (three statements); microscopy (eight statements); immunohistochemistry (nine statements); interpretation and reporting (four statements); and miscellaneous (three statements). The final set of consensus statements was approved by 85%.

CONCLUSION

This study provides an overview of current clinical practice regarding STIC diagnosis amongst expert gynecopathologists. The experts' consensus statements form the basis for a set of recommendations, which may help towards more consistent STIC diagnosis.

Ki67 in Breast Cancer Assay: An Ad Hoc Testing Recommendation from the Canadian Association of Pathologists Task Force

Ki67, a marker of cellular proliferation, is commonly assessed in surgical pathology laboratories. In breast cancer, Ki67 is an established prognostic factor with higher levels associated with worse long-term survival. However, Ki67 IHC is considered of limited clinical use in breast cancer management largely due to issues related to standardization and reproducibility of scoring across laboratories. Recently, both the American Food and Drug Administration (FDA) and Health Canada have approved the use of abemaciclib (CDK4/6 inhibitor) for patients with HR+/HER2: high-risk early breast cancers in the adjuvant setting. Health Canada and the FDA have included a Ki67 proliferation index of ≥20% in the drug monograph. The approval was based on the results from monarchE, a phase III clinical trial in early-stage chemotherapy-naïve, HR+, HER2 negative patients at high risk of early recurrence. The study has shown significant improvement in invasive disease-free survival (IDFS) with abemaciclib when combined with adjuvant endocrine therapy at two years. Therefore, there is an urgent need by the breast pathology and medical oncology community in Canada to establish national guideline recommendations for Ki67 testing as a predictive marker in the context of abemaciclib therapy consideration. The following recommendations are based on previous IKWG publications, available guidance from the monarchE trial and expert opinions. The current recommendations are by no means final or comprehensive, and their goal is to focus on its role in the selection of patients for abemaciclib therapy. The aim of this document is to guide Canadian pathologists on how to test and report Ki67 in invasive breast cancer. Testing should be performed upon a medical oncologist’s request only. Testing must be performed on treatment-naïve tumor tissue. Testing on the core biopsy is preferred; however, a well-fixed resection specimen is an acceptable alternative. Adhering to ASCO/CAP fixation guidelines for breast biomarkers is advised. Readout training is strongly recommended. Visual counting methods, other than eyeballing, should be used, with global rather than hot spot assessment preferred. Counting 100 cells in at least four areas of the tumor is recommended. The Ki67 scoring app developed to assist pathologists with scoring Ki67 proposed by the IKWG, available for free download, may be used. Automated image analysis is very promising, and laboratories with such technology are encouraged to use it as an adjunct to visual counting. A score of <5 or >30 is more robust. The task force recommends that the results are best expressed as a continuous variable. The appropriate antibody clone and staining protocols to be used may take time to address. For the time being, the task force recommends having tonsils/+pancreas on-slide control and enrollment in at least one national/international EQA program. Analytical validation remains a pending goal. Until the data become available, using local ki67 protocols is acceptable. The task force recommends participation in upcoming calibration and technical validation initiatives.

Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics

Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.

Ki 67: a Promising Prognostic Marker in Early Breast Cancer-a Review Article

Ki67 index is considered to be a reliable indicator of the proliferative activity of breast cancer. Additionally, the Ki67 proliferative marker may play a role in assessing response to systemic therapeutic strategies and can act as a prognostic biomarker. But its limited reproducibility which stems from a lack of standardization of procedures, inter-observer variability, and preanalytical and analytical variabilities all have hampered the use of the Ki67 index in clinical practice. Currently, clinical trials have been evaluating Ki67 as a predictive marker for needing adjuvant chemotherapy in luminal early breast cancer patients receiving neoadjuvant endocrine therapy. But the inconsistencies existing in the estimation of the Ki67 index limit the utility of Ki67 in standard clinical practice. The purpose of this review is to evaluate the benefits and drawbacks of utilizing Ki-67 in early-stage breast cancer to prognosticate the disease and predict the risk of recurrence.