Breast cancer is the most common malignancy and leading cause of cancer-related fatalities among women worldwide [1] . In addition to histological grading and staging, immunohistochemical markers, including hormone receptors HER2 and Ki67, are essential in breast cancer diagnosis [2] . These biomarkers not only serve as treatment indicators but also offer valuable prognostic insights. Ki67, a nuclear protein expressed in proliferative cells, serves as the foundation for determining the Ki67 index (KI), denoting the percentage of immunoreactive tumor cells among all tumor cells, and thereby serving as a measure of tumor proliferation [3] . The KI has been used for subtyping hormone-positive breast carcinomas into luminal type A (low KI) and luminal type B (high KI), which predict prognosis and indicate suitability for chemoradiotherapy [4] [5] [6] . Typically, a threshold of 14% is adopted to demarcate low and high proliferation categories. Therefore, it is critical to precisely quantify the KI. However, in daily clinical practice, KI assessment predominantly relies on the subjective estimation of pathologists. Despite meticulous adherence to the standard international protocol during counting, substantial intra- and inter-observer variations persist [6] [7] .

Many factors contribute to observer variation [6] , including pre-analytical considerations (specimen type and cold ischemic time), analytical aspects (antibodies and autostainers), and interpretation factors (scoring method and area chosen). Pathologists often choose different areas for KI counting due to intratumoral heterogeneity. Ki67 intratumoral heterogeneity presents a challenge for assessment and quantification and is a daily phenomenon encountered by pathologists. In most breast cancers, Ki67-positive cells exhibit non-uniform distribution throughout the tumor, with peripheral tumor cells often displaying greater activity and higher KI. Some studies reported that hotspots tend to occur most frequently at tumor edges [8] . Conversely, to investigate the predictive value of Ki67 heterogeneity on prognosis, researchers have measured the KI gaps between hotspots and the average from sampled fields [9] [10] [11] . However, evaluating this heterogeneity has proven challenging due to the inability to manually obtain comprehensive KI informationacross the entire tumor face.

In recent decades, due to advancements in deep learning and convolutional neural networks, digital image analysis (DIA) has found applications in automatic KI assessment. Both commercial and open-source image analysis software solutions have become readily accessible [12] [13] . Compared to human evaluation, these algorithms are more efficient and reliable in quantification [14] [15] . Numerous pathologists and researchers have used these tools to study KI in breast cancer patients. However, most studies choose to evaluate KI in limited regions (tissue microarray or hotspots), serving as proxies for the whole tumor [16] [17] . This approach is favored because a full-face tumor whole-slide image (WSI) typically includes thousands of high-power fields (HPF). The use of WSIs would consume substantial computational power and time for analysis. In 2016, researchers applied DIA to breast cancer Ki67 WSIs to obtain a comprehensive Ki67 index. They also introduced sophisticated indicators to describe Ki67 heterogeneity [18] [19] . Retrieving complete Ki67 profiles within WSIs, as opposed to obtaining a single Ki67 index number, is the first step in decoding heterogeneity.

In order to survey how Ki67 heterogeneity affects breast cancer subtyping and how many areas are adequate for Ki67 index estimation, in this study, we used DIA to assess comprehensive Ki67 profiles in breast cancer at the WSI level and to visualize intratumoral heterogeneity. In addition, we utilized this data to simulate the impact of sample size and heterogeneity on KI estimation.

The Ki67 index has been used in the field of breast pathology for several decades, and Ki67 intratumoral heterogeneity is a well-known issue. Nevertheless, numerous unresolved questions stem from this heterogeneity, including whether global KI or hotspot KI should be reported. Where should we define hotspots, and how do we locate them? Which threshold should be used to divide tumors into low- or high-proliferative groups? Does the degree of statistical or spatial heterogeneity matter, and how should we measure it? How many HPF or tumor cells should pathologists count? These questions pose a considerable challenge for resolution, primarily due to the limitations faced by human pathologists in quantifying millions of cells or conducting a comprehensive evaluation of a single Ki67 slide. Due to the advancements made in deep learning and computational capabilities, researchers are now able to apply DIA to WSI to address these questions. In this study, we used image analysis software to infer full-face tumor Ki67 slides and retrieved Ki67 profiles to generate boxplots and histograms that statistically visualize heterogeneity. Heat maps were employed to reveal the spatial information and different Ki67 distribution patterns. Based on sampling simulations, our findings suggest that more than 10 HPFs (approximately 1.24 mm²) are required for global KI estimation.

Due to the intratumoral heterogeneity of Ki67 in breast cancers, global KI and hotspot KI are correlated but significantly different. As noted in this study, setting the threshold at 14% resulted in a reclassification of one-third of cases, upgrading them to a highly proliferative category based on hotspot KI instead of global KI. For most pathologists, the Ki67 index has a similar mitotic count; therefore, we tended to evaluate focal hotspots rather than the whole tumor. Some researchers posit that the most proliferative areas represent tumor behavior and predict prognosis more accurately [8] . However, most early studies on KI in breast cancer primarily relied on tissue microarrays (random samples) or genetic profiling rather than a comprehensive examination of full-face tumor sections [16] [21] [22] . Therefore, the use of global KI may align more consistently with the results of these studies. Regardless of whether global KI or hotspot KI is employed, the primary challenge lies in the manual impossibility of conducting a thorough, full-face tumor Ki67 evaluation; pathologists can only sample some areas and count a limited number of cells (up to 1000). These comprehensive Ki67 profiles extracted from WSI can provide more information than a single percentage value, whether global a hotspot-derived.

Hotspot identification is another often overlooked problem in pathology due to the absence of a well-defined practical framework. Intuitively, pathologists rely on subjective judgments to identify areas with the highest Ki67 positivity when observing low-power fields, designating these regions as hotspots [11] [23] . However, there is no consensus regarding the optimal size of such areas and the minimum number of cells required to qualify as a hotspot. In our study, we established an image tile, similar to an HPF, as the fundamental unit for dividing the WSI and chose 100 tumor cells as the minimal requirement. Statistically, we could identify the tiles with the highest KI from the Ki67 profile, but these might be a few outliers that do not represent tumor behavior. Therefore, the concept of “Pareto hotspots” was adopted [18] , and we considered that these areas might offer a more accurate reflection of tumor proliferation compared to sporadic, extreme hotspots. Spatially, we easily identified hotspots within the heat map, which clearly visualizes the Ki67 density.

The next question pertained to determining the most appropriate threshold for clinical use. Many studies have proposed the separation of low- and high-proliferation groups as follows: 10%, 14%, or 20% [4] [5] [24] . Recently, the Gallen/Vienna Consensus Conference on Early Breast Cancer Treatment Standards has recommended three categories: low (<5%), intermediate (5% - 30%), and high (30%) [25] . In addition to pre-analytical factors, inter-laboratory, inter-observer, and even intra-observer variations hinder the threshold setup. Therefore, a more precise and reliable quantification method is required. Using DIA, we can not only obtain a comprehensive Ki67 profile, but the data also exhibit greater accuracy and reproducibility compared to human estimates [8] [14] . The precise quantification of KI can solidify the Ki67 study, enabling the establishment of a threshold for patient classification and even considering KI as a continuous variable [2] .

Some researchers have suggested that Ki67 intratumoral heterogeneity could be a prognostic factor, and various parameters have been used to indicate the degree of heterogeneity. The most commonly used parameter is known as the “heterogenicity score”, typically defined as the difference between the hotspot KI and the global KI (or lowest KI). Two studies found that heterogeneity scores were independently associated with prognosis and lymph node metastasis [10] [23] . However, the KI data in these two studies were obtained through manual counting in the selected fields. Plancoulaine et al. [18] used the comprehensive Ki67 profile obtained through DIA and proposed parameters to quantify heterogeneity, including entropy and bimodality. Their findings revealed that Ashman’s D, an indicator of bimodality, was an independent predictor of overall survival [19] . Furthermore, Ki67 expression was quantified using the hexagonal tiling approach, and heat maps were generated to depict spatial heterogeneity. Taking inspiration from these trailblazing studies, we employed heat maps to visualize Ki67 density and initially classified the spatial distribution into three patterns: randomly distributed, peripheral, and central. The peripheral pattern conforms to the general assumption held by pathologists: the most proliferative cells are situated at the tumor edges, where they actively search for mitotic activity. A classic example of this pattern can be observed in tumors with central necrosis, as they are so large that only peripheral tumor cells have sufficient blood supply to grow. However, nearly half of the tumors exhibited a randomly distributed pattern, indicating that the hotspots were evenly distributed within the tumor. Only a minority of patients displayed proliferative centers. These different patterns could be the result of differences in factors such as blood supply, immune responses, or the tumor microenvironment (hormones or growth factors). This spatial information provides a novel perspective for interpreting Ki67 expression and understanding tumor behaviors.

In the assessment of KI, the matter of sample size is another long-standing question. Early studies suggested a requirement of 500 - 1000 cells [21] . According to the protocol established by the International Ki67 Breast Cancer Working Group, a minimum of three HPFs (including more than 100 cells) from different Ki67 density areas is required for estimation. In our study, we conducted simulations with various sample sizes to estimate the global KI, and most cases required more than 10 HPFs. Reasonably, a higher global KI value corresponds to increased heterogeneity and a greater number of tiles (HPF) to be examined [26] . In clinical practice, it could be imprecise to use KI from core biopsy to represent KI in whole tumors, especially in cases of tumors with a high global KI. Therefore, it is necessary to repeat the Ki67 evaluation when excised specimens are available [27] . Nevertheless, it is imperative to establish a comprehensive Ki67 profile, given that sampled KI values from limited areas cannot represent tumors with high heterogeneity.

There are several limitations to this study, the first of which is the absence of a correlation test between Ki67 and genetic profiles. The question of which parameters within the Ki67 profile play a more decisive role in predicting genetic subtypes remains unanswered. A technological challenge we encountered was outlier data owing to an imperfect algorithm. The software incorrectly identified lymphocytes or red blood cells as tumor cells. However, our team is working on enhancing and accelerating KI assessment by applying tumor masks [12] [28] . Another concern is the coexistence of ductal carcinoma in situ within the invasive components. In theory, KI should only be counted in the invasive regions; however, we did not exclude in situ lesions. This issue could potentially be addressed through area-segmentation algorithms capable of distinguishing invasive lesions from in situ lesions.

In next step, we are planning to solidify the result of study in advance. Firstly, epithelial masks will be applied to KI quantification for more accurate evaluation. Also, the statistic analysis and heat map generation will be performed automatically and packaged into the software. Therefore, data generation will be more efficient. Secondly, more breast cancer cases are required and subtypes, such as luminal type or triple-negative type, should be separated into different group. This is because clinical utility of KI might work differently in subtypes. For example, in hormone-positive early breast cancer, KI is an indicator for adjuvant chemotherapy, but for triple-negative breast cancer, it could be a prognostic factor. Lastly, overall or disease-free survival rate should be correlated with their comprehensive KI profiles and KI heat map patterns. Therefore, it is possible to figure out which parameters and thresholds have the most important clinical utility.

In conclusion, using DIA, we are now capable of addressing Ki67 heterogeneity and revealing distribution patterns that were previously deemed implausible. Future studies should focus on more robust software to consistently generate spatial information in a wider range of cases, enabling the determination of parameters that accurately represent tumor behavior. The comprehensive Ki67 profile holds the potential to revolutionize the approach to conducting proliferative activity surveys in breast cancers.

The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.

The authors thank Dr. Chu Hsiu-Yi for her support and assistance in writing.

Chien-Hui Wu: Methodology, Investigation, Writing—Original Draft. Min-Hsiang Chang: Methodology, Investigation, Software, Formal analysis, Writing—Original Draft. Hsin-Hsiu Tsai: Software. Yi-Ting Peng: Software.

The authors declare no conflicts of interest regarding the publication of this paper.

Wu, C.-H., Chang, M.-H., Tsai, H.-H. and Peng, Y.-T. (2024) Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis. Advances in Breast Cancer Research, 13, 11-26. https://doi.org/10.4236/abcr.2024.132002

DIA, digital image analysis;

HPF, high-power fields;

IQR, interquartile range;

KI, Ki67 index;

WSI, whole-slide image.