Variability in measuring the Ki-67 labeling index in patients with 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.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

Predictive Value of K i -67 Index in Evaluating Sporadic Vestibular Schwannoma Recurrence: Systematic Review and Meta-analysis

Introduction  K _i -67 is often used as a proliferation index to evaluate how aggressive a tumor is and its likelihood of recurrence. Vestibular schwannomas (VS) are a unique benign pathology that lends itself well to evaluation with K _i -67 as a potential marker for disease recurrence or progression following surgical resection. Methods  All English language studies of VSs and K _i -67 indices were screened. Studies were considered eligible for inclusion if they reported series of VSs undergoing primary resection without prior irradiation, with outcomes including both recurrence/progression and K _i -67 for individual patients. For published studies reporting pooled K _i -67 index data without detailed by-patient values, we contacted the authors to request data sharing for the current meta-analysis. Studies reporting a relationship between K _i -67 index and clinical outcomes in VS for which detailed patients' outcomes or K _i -67 indices could not be obtained were incorporated into the descriptive analysis, but excluded from the formal (i.e., quantitative) meta-analysis. Results  A systematic review identified 104 candidate citations of which 12 met inclusion criteria. Six of these studies had accessible patient-specific data. Individual patient data were collected from these studies for calculation of discrete study effect sizes, pooling via random-effects modeling with restricted maximum likelihood, and meta-analysis. The standardized mean difference in K _i -67 indices between those with and without recurrence was calculated as 0.79% (95% confidence interval [CI]: 0.28-1.30; p  = 0.0026). Conclusion  K _i -67 index may be higher in VSs that demonstrate recurrence/progression following surgical resection. This may represent a promising means of evaluating tumor recurrence and potential need for early adjuvant therapy for VSs.

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.

Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer

Background

Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers.

Methods

A total of 300 invasive breast cancer specimens were randomly divided into training and validation sets, with each set including 150 cases. Breast cancer Ki-67 standard reference card ranging from 5 to 90% were created. The training set was interpreted by nine pathologists of different ages through microscopic visual assessment (VA), SRC, microscopic manual counting (MC), and AI. The validation set was interpreted by three randomly selected pathologists using SRC and AI. The intra-group correlation coefficient (ICC) were used for consistency analysis.

Results

In the homogeneous and heterogeneous groups of validation sets, the consistency among the pathologists that used SRC and AI was very good, with an ICC of>0.905. In the validation set, using SRC and AI, three pathologists obtained results that were very consistent with the gold standard, having an ICC above 0.95, and the inter-observer agreement was also very good, with an ICC of>0.9.

Conclusions

AI has satisfactory inter-observer repeatability, and the true value was closer to the gold standard, which is the preferred method for Ki-67LI reproducibility; While AI software has not been popularized, SRC may be interpreted as breast cancer Ki-67LI’s standard candidate method.

Concordance between core needle biopsy and surgical excision specimens for Ki-67 in breast cancer - a systematic review of the literature

AIMS

The biomarkers oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are routinely measured in patients with breast cancer with international consensus on how they should be interpreted. There is evidence to support use of other biomarkers to give more detailed predictive and prognostic information. Ki-67 is one example, and measures the proliferative activity of cancer cells. It is important that this can be performed at diagnosis of breast cancer for patients who do not have initial surgical treatment (mainly older women) and those receiving neoadjuvant therapies.

METHODS AND RESULTS

A systematic review was performed to assess concordance of measurement of Ki-67 between core needle biopsy (CNB) samples and surgical excision (SE) samples in patients with invasive breast cancer. MEDLINE and Embase databases were searched. Studies were eligible if performed within the last 10 years; included quantitative measurement of Ki-67 in both CNB and SE samples with no prior breast cancer treatment; measured concordance between two samples; and had full text available. A total of 22 studies, including 5982 paired CNB and SE samples on which Ki-67 was measured, were appraised. Overall, there appeared to be concordance; however, reliability was unclear. Where given, the Cohen's kappa coefficient (κ) of correlation between samples ranged from 0.261 to 0.712. The concordance rate between CNB and SE where measured as a percentage had a range from 70.3 to 92.7% CONCLUSIONS: Assessment of level of concordance of Ki-67 between CNB and SE samples is hampered by different methodologies. International consensus on Ki-67 measurement is urgently needed.

Study of Ki-67 index in the molecular subtypes of breast cancer: Inter-observer variability and automated scoring

Background

Ki-67 index is an important prognostic marker in breast cancer and is also used to differentiate luminal A subtype from luminal B. Inter-observer variations in determining the index and the cut-off value to be considered in distinguishing the two subtypes remain problems in clinical practice.

Methods

MIB-1 immunohistochemistry was done on 200 cases of breast cancer with 50 cases in each molecular subtype. The Ki-67 scoring was done manually by two observers and automated method (using the software ImmunoRatio). The mean value of Ki-67 was calculated in each molecular group and in the entire estrogen receptor and progesterone receptor (ER/PR) positive group. The inter-observer variability between the two observers and the automated method was also assessed.

Results

The mean and median values of Ki-67 of all the 200 cases obtained by manual scoring was 31.13% and 29.65% by observer 1, 28.48% and 27.90% by observer 2, and 38.27% and 35.45% by the automated method. The mean Ki-67 value obtained by manual scoring, in luminal A, luminal B, HER2 enriched and triple negative was 21.07%, 37.19%, 33.72% and 27.27%, respectively. There was significant correlation between the two observers and with the automated scoring.. The mean value of the Ki-67 index in the ER/PR positive group was 29.1%.

Conclusion

The inter-observer correlation and the correlation with the automated scoring system of the Ki-67 index was good. 29.1% was the mean Ki-67 index in the ER/PR positive group and this value was within the acceptable range as per St Galen's recommendation.

Comparative evaluation of three proliferation markers, Ki-67, TOP2A, and RacGAP1, in bronchopulmonary neuroendocrine neoplasms: Issues and prospects

The classification of bronchopulmonary neuroendocrine neoplasms (BP-NEN) into four tumor entities (typical carcinoids (TC), atypical carcinoids (AC), small cell lung cancers (SCLC), large cell neuroendocrine lung carcinomas (LCNEC)) is difficult to perform accurately, but important for prognostic statements and therapeutic management decisions. In this regard, we compared the expression of three proliferation markers, Ki-67, Topoisomerase II alpha (TOP2A), and RacGAP1, in a series of tumor samples from 104 BP-NEN patients (24 TC, 21 AC, 52 SCLC, 7 LCNEC) using different evaluation methods (immunohistochemistry (IHC): Average evaluation, Hotspot evaluation, digital image analysis; RT-qPCR).

The results indicated that all three markers had increased protein and mRNA expression with poorer differentiation and correlated well with each other, as well as with grading, staging, and poor survival. Compared with Ki-67 and TOP2A, RacGAP1 allowed for a clearer prognostic statement. The cut-off limits obtained for Ki-67-Average (IHC) were TC-AC 1.5, AC-SCLC 19, and AC-LCNEC 23.5. The Hotspot evaluation generated equal to higher, the digital image analysis generally lower between-entity cut-off limits.

All three markers enabled a clear-cut differentiation between the BP-NEN entities, and all methods evaluated were suitable for marker assessment. However, to define optimal cut-off limits, the Ki-67 evaluation methods should be standardized. RacGAP1 appeared to be a new marker with great potential.

A comparison of Ki-67 counting methods in luminal Breast Cancer: The Average Method vs. the Hot Spot Method

In spite of the usefulness of the Ki-67 labeling index (LI) as a prognostic and predictive marker in breast cancer, its clinical application remains limited due to variability in its measurement and the absence of a standard method of interpretation. This study was designed to compare the two methods of assessing Ki-67 LI: the average method vs. the hot spot method and thus to determine which method is more appropriate in predicting prognosis of luminal/HER2-negative breast cancers. Ki-67 LIs were calculated by direct counting of three representative areas of 493 luminal/HER2-negative breast cancers using the two methods. We calculated the differences in the Ki-67 LIs (ΔKi-67) between the two methods and the ratio of the Ki-67 LIs (H/A ratio) of the two methods. In addition, we compared the performance of the Ki-67 LIs obtained by the two methods as prognostic markers. ΔKi-67 ranged from 0.01% to 33.3% and the H/A ratio ranged from 1.0 to 2.6. Based on the receiver operating characteristic curve method, the predictive powers of the KI-67 LI measured by the two methods were similar (Area under curve: hot spot method, 0.711; average method, 0.700). In multivariate analysis, high Ki-67 LI based on either method was an independent poor prognostic factor, along with high T stage and node metastasis. However, in repeated counts, the hot spot method did not consistently classify tumors into high vs. low Ki-67 LI groups. In conclusion, both the average and hot spot method of evaluating Ki-67 LI have good predictive performances for tumor recurrence in luminal/HER2-negative breast cancers. However, we recommend using the average method for the present because of its greater reproducibility.

Breast cancer under age 40: a different approach

Breast cancer (BC) under age 40 is a complex disease to manage due to the additionally fertility-related factors to be taken in consideration. More than 90% of young patients with BC are symptomatic. Women<40 years are more likely to develop BC with worse clinicopathological features and more aggressive subtype. This has been frequently associated with inferior outcomes. Recently, the prognostic significance of age<40 has been shown to differ according to the BC subtype, being associated with worst recurrence-free survival (RFS) and overall survival (OS) for luminal BC. The biology of BC<40 has also been explored through analysis of large genomic data set, and specific pathways overexpressed in these tumors have been identified which can lead to the development of targeted therapy in the future. A multidisciplinary tumor board should determine the optimal locoregional and systemic management strategies for every individual patient with BC before the start of any therapy including surgery. This applies to both early (early breast cancer (EBC)) and advanced (advanced breast cancer (ABC)) disease, before the start of any therapy. Mastectomy even in young patients confers no overall survival advantage when compared to breast-conserving treatment (BCT), followed by radiotherapy. Regarding axillary approach, indications are identical to other age groups. Young age is one of the most important risk factors for local recurrence after both breast-conserving surgery (BCS) and mastectomy, associated with a higher risk of distant metastasis and death. Radiation after BCS reduces local recurrence from 19.5 to 10.2% in BC patients 40 years and younger. The indications for and the choice of systemic treatment for invasive BC (both early and advanced disease) should not be based on age alone but driven by the biological characteristics of the individual tumor (including hormone receptor status, human epidermal growth factor receptor 2 (HER-2) status, grade, and proliferative activity), disease stage, and patient's comorbidities. Recommendations regarding the use of genomic profiles such as MammaPrint, Oncotype Dx, and Genomic grade index in young women are similar to the general BC population. Especially in the metastatic setting, patient preferences should always be taken into account, as the disease is incurable. The best strategy for these patients is the inclusion into well-designed, independent, prospective randomized clinical trials. Metastatic disease should always be biopsied whenever feasible for histological confirmation and reassessment of biology. Endocrine therapy is the preferred option for hormone receptor-positive disease (HR+ve), even in presence of visceral metastases, unless there is concern or proof of endocrine resistance or there is a need for rapid disease response and/or symptom control. Recommendations for chemotherapy (CT) should not differ from those for older patients with the same characteristics of the metastatic disease and its extent. Young age by itself should not be an indication to prescribe more intensive and combination CT regimens over the sequential use of monotherapy. Poly(ADP-ribose) polymerase inhibitors (PARP inhibitors) represent an important group of promising drugs in managing patients with breast cancer susceptibility gene (BRCA)-1- or BRCA-2-associated BC. Specific age-related side effects of systemic treatment (e.g., menopausal symptoms, change in body image, bone morbidity, cognitive function impairment, fertility damage, sexual dysfunction) and the social impact of diagnosis and treatment (job discrimination, taking care for children) should also be carefully addressed when planning systemic long-lasting therapy, such as endocrine therapy. Survivorship concerns for young women are different compared to older women, including issues of fertility, preservation, and pregnancy.