Prognostic potential of automated Ki67 evaluation in breast cancer: different hot spot definitions versus true global score

Proliferation scores in canine anal sac adenocarcinomas: Ki67 global score is superior to Ki67 hotspot indices and mitotic count for prognosis

Canine anal sac adenocarcinoma (ASAC) is an aggressive malignancy with high metastatic potential. Histologic and proliferation parameters such as mitotic count and Ki67 scores have limited prognostic value according to the published literature. Using pathologist-supervised digital image analysis methods with the image analysis software QuPath, we analyzed 58 cases of ASAC to evaluate mitotic count (MC) and Ki67 indices, explore relationships between different Ki67 indices [semi-automatic Ki67 digital hotspot score (Ki67-saHDS), Ki67 global digital score (Ki67-GDS), and fully automatic Ki67 digital hotspot score (Ki67-faHDS)] and MC, and to verify which method carries the most significant prognostic value. The MC did not impact median tumor-related survival (TRS) time. Although high correlation coefficients were observed between the 3 Ki67 scores, Ki67-GDS had more prognostic relevance than hotspot-based scores (Ki67-saHDS and Ki67-faHDS). Dogs with Ki67-GDS ≥ 26 had significantly shorter survival times (175, days 95%, confidence interval (95% CI) = 123-540) compared to dogs with Ki67-GDS< 26 (median survival time (MST) 650 days, 95% CI = 503->1579). No association was observed between TRS and Ki67-faHDS or Ki67-saHDS. On multivariate analysis, anisokaryosis and Ki67-GDS, but not tumor size, lymphovascular invasion, or MC, were independent prognostic markers for survival. These results demonstrate the advantage of Ki67 GDS over hotspot-based scores; however, these data need to be validated in a larger cohort of cases before clinical implementation.

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.

Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence

Breast cancer is a common malignant tumor of women. Ki67 is an important biomarker of cell proliferation. With the quantitative analysis, it is an important indicator of malignancy for breast cancer diagnosis. However, it is difficult to accurately and quantitatively evaluate the count of positive nucleus during the diagnosis process of pathologists, and the process is time-consuming and labor-intensive. In this work, we employed a quantitative analysis method of Ki67 in breast cancer based on deep learning approach. For the diagnosis of breast cancer, according to breast cancer diagnosis guideline, we first identified the tumor region of Ki67 pathological image, neglecting the non-tumor region in the image. Then, we detect the nucleus in the tumor region to determine the nucleus location information. After that, we classify the detected nucleuses as positive and negative according to the expression level of Ki67. According to the results of quantitative analysis, the proportion of positive cells is counted. Combining the above process, we design a breast Ki67 quantitative analysis pipeline. The Ki67 quantitative analysis system was assessed on the validation set. The Dice coefficient of the tumor region segmentation model was 0.848, the Average Precision index of the nucleus detection model was 0.817, and the accuracy of the nucleus classification model was 96.66%. Besides, in clinical independent sample experiment, the results show that the proposed breast Ki67 quantitative analysis system achieve excellent correlation with the diagnosis efficiency of doctors improved more than ten times and the overall consistency of diagnosis is intra-group correlation coefficient: 0.964. The research indicates that our quantitative analysis method of Ki67 in breast cancer has high clinical application value.

Risk stratification scheme based on the TNM staging system for dogs with oral malignant melanoma centered on clinicopathologic presentation

Introduction

Oral malignant melanoma (OMM) is the most common malignant oral neoplasm in dogs. Tumor recurrence, progression, and regional and distant metastasis remain major obstacles despite advanced therapy. Tumor size has been a consistent, key independent prognostic factor; however, other clinical and histopathologic features impact prognosis and likely influence optimal treatment strategies. Adoption of a risk stratification scheme for canine OMM that stratifies groups of dogs on defined clinicopathologic features may improve reproducible and comparable studies by improving homogeneity within groups of dogs. Moreover, it would aid in the generation of multidisciplinary prospective studies that seek to define optimal treatment paradigms based on defined clinicopathologic features.

Methods

To build a platform upon which to develop a risk stratification scheme, we performed a systematic review of clinicopathologic features of OMM, with particular attention to levels of evidence of published research and the quantitative prognostic effect of clinicopathologic features.

Results

Tumor size and presence of bone lysis were repeatable features with the highest level of evidence for prognostic effects on survival. Overall, with strict inclusion criteria for paper review, the levels of evidence in support of other, previously proposed risk factors were low. Factors contributing to the challenge of defining clear prognostic features including inconsistencies in staging and reporting of prognostic variables, incomplete clinical outcome data, inhomogeneous treatment, and absence of randomized controlled studies.

Discussion

To overcome this in the future, we propose a risk stratification scheme that expands the TNM system to incorporate specific designations that highlight possible prognostic variables. The ability to capture key data simply from an expanded TNM description will aid in future efforts to form strong conclusions regarding prognostic variables and their influence (or lack thereof) on therapeutic decision-making and outcomes.

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.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

PURPOSE

To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.

METHODS

In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.

RESULTS

Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumors as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumors as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumors, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups (N = 124) was 71.0%, with a Cohen's kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups.

CONCLUSION

The results from this clinical evaluation of image-based risk stratification shows a considerable agreement to an established gene expression assay in routine breast pathology.

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.

Clinico-Pathological and Prognostic Significance of a Combination of Tumor Biomarkers in Iranian Patients With Breast Cancer

PURPOSE

Breast cancer is one of the most common cancers in the world. It is a multifaceted malignancy with different histopathological and biological features. Molecular biomarkers play an essential role in accurate diagnosis, classification, prognosis, prediction of treatment response, and cancer surveillance. This study investigated the clinico-pathological and prognostic significance of HER3 and ROR1 in breast cancer samples.

METHODS

Tissue microarrays (TMA) were constructed using tissue blocks of 444 Iranian breast cancer patients diagnosed with breast cancer. Overall survival (OS) and disease-free survival (DFS) were assessed after 5 years follow-up. TMA slides were stained with monoclonal antibodies against ROR1, HER3, ER, PR, Ki67, P53, HER2 and CK5/6 using IHC and correlation between the investigated tumor markers and the clinico-pathological parameters of patients were analyzed.

RESULTS

Our results showed a significant correlation between ROR1 and ER, PR, HER3, and CK5/6 expression. Additionally, there was a significant correlation between HER3 and ER, PR, HER2, and Ki67 expression. Ki67 was also correlated with HER2 and P53 expression. HER3 expression was significantly correlated with tumor stage, lymph node metastasis, perineural invasion, and multifocal tumors. Furthermore, ROR1 expression was significantly associated with tumor metastasis, lympho-vascular invasion, and perineural invasion. While HER2-HER3 coexpression was significantly associated with poor OS, HER3-ROR1 coexpression was associated with lymph node invasion, lymph node metastasis, and distant metastasis.

CONCLUSION

ROR1 and HER3 displayed significant association with different clinic-pathological features and in addition to the other tumor biomarkers could be considered as diagnostic and prognostic biomarkers in breast cancer patients.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

One bout of endurance exercise does not change gene expression or proliferation in a C26 colon carcinoma in immunocompetent mice

PURPOSE

Exercise typically reduces tumour growth, proliferation and improves outcomes. Many of these effects require exercise to change gene expression within a tumour, but whether exercise  actually affects gene expression within a tumour has not been investigated yet. The aim of this study was, therefore, to find out whether one bout of endurance exercise alters gene expression and proliferation in a C26 carcinoma in immunocompetent mice.

METHODS

BALB/c were injected with C26 colon carcinoma cells. Once the tumours had formed, the mice either ran for 65 min with increasing intensity or rested before the tumour was dissected. The tumours were then analysed by RNA-Seq and stained for the proliferation marker KI67.

RESULTS

One bout of running for 65 min did not systematically change gene expression in C26 carcinomas of BALB/c mice when compared to BALB/c mice that were rested. However, when analysed for sex, the expression of 17, mostly skeletal muscle-related genes was higher in the samples of the female mice taken post-exercise. Further histological analysis showed that this signal likely comes from the presence of muscle fibres from the panniculus carnosus muscle inside the tumours. Also, we found no differences in the positivity for the proliferation marker KI67 in the control and exercise C26 carcinomas.

CONCLUSION

A bout of exercise did not systematically affect gene expression or proliferation in C26 carcinomas in immunocompetent BALB/c mice.

Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.

Unsupervised domain adaptive tumor region recognition for Ki67 automated assisted quantification

PURPOSE

Ki67 is a protein associated with tumor proliferation and metastasis in breast cancer and acts as an essential prognostic factor. Clinical work requires recognizing tumor regions on Ki67-stained whole-slide images (WSIs) before quantitation. Deep learning has the potential to provide assistance but largely relies on massive annotations and consumes a huge amount of time and energy. Hence, a novel tumor region recognition approach is proposed for more precise Ki67 quantification.

METHODS

An unsupervised domain adaptive method is proposed, which combines adversarial and self-training. The model trained on labeled hematoxylin and eosin (H&E) data and unlabeled Ki67 data can recognize tumor regions in Ki67 WSIs. Based on the UDA method, a Ki67 automated assisted quantification system is developed, which contains foreground segmentation, tumor region recognition, cell counting, and WSI-level score calculation.

RESULTS

The proposed UDA method achieves high performance in tumor region recognition and Ki67 quantification. The AUC reached 0.9915, 0.9352, and 0.9689 on the validation set and internal and external test sets, respectively, substantially exceeding baseline (0.9334, 0.9167, 0.9408) and rivaling the fully supervised method (0.9950, 0.9284, 0.9652). The evaluation of automated quantification on 148 WSIs illustrated statistical agreement with pathological reports.

CONCLUSION

The model trained by the proposed method is capable of accurately recognizing Ki67 tumor regions. The proposed UDA method can be readily extended to other types of immunohistochemical staining images. The results of automated assisted quantification are accurate and interpretable to provide assistance to both junior and senior pathologists in their interpretation.

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.

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.

Comparison Between Manual and Automated Assessment of Ki-67 in Breast Carcinoma: Test of a Simple Method in Daily Practice

BACKGROUND

In the era of "precision medicine," the availability of high-quality tumor biomarker tests is critical and tumor proliferation evaluated by Ki-67 antibody is one of the most important prognostic factors in breast cancer. But the evaluation of Ki-67 index has been shown to suffer from some interobserver variability. The goal of the study is to develop an easy, automated, and reliable Ki-67 assessment approach for invasive breast carcinoma in routine practice.

PATIENTS AND METHODS

A total of 151 biopsies of invasive breast carcinoma were analyzed. The Ki-67 index was evaluated by 2 pathologists with MIB-1 antibody as a global tumor index and also in a hotspot. These 2 areas were also analyzed by digital image analysis (DIA).

RESULTS

For Ki-67 index assessment, in the global and hotspot tumor area, the concordances were very good between DIA and pathologists when DIA focused on the annotations made by pathologist (0.73 and 0.83, respectively). However, this was definitely not the case when DIA was not constrained within the pathologist's annotations and automatically established its global or hotspot area in the whole tissue sample (concordance correlation coefficients between 0.28 and 0.58).

CONCLUSIONS

The DIA technique demonstrated a meaningful concordance with the indices evaluated by pathologists when the tumor area is previously identified by a pathologist. In contrast, basing Ki-67 assessment on automatic tissue detection was not satisfactory and provided bad concordance results. A representative tumoral zone must therefore be manually selected prior to the measurement made by the DIA.

Unsuccessful external validation of the MAC-score for predicting increased MIB-1 index in patients with spinal meningiomas

OBJECTIVE

Recently, the MAC-spinal meningioma score (MAC-score) was proposed to preoperatively identify spinal meningioma patients with high MIB-1 indices. Risk factors were age ≥ 65 years, a modified McCormick score (mMCs) ≥ 2, and absence of tumor calcification. The aim of this study was to externally validate the MAC-score in an independent cohort.

METHODS

Using the same inclusion and exclusion criteria as in the original study, we performed a retrospective, single-center, population-based, cohort study that included patients who had undergone surgical treatment for spinal meningiomas between 2005 - 2017. Data was collected from patient charts and radiographic images. Validation was performed by applying the MAC-score to our cohort and evaluating the area under the receiver operating characteristic curve (AUC).

RESULTS

In total, 108 patients were included. Baseline and outcome data were comparable to the original development study. An increased MIB-1 index (≥5%) was observed in 56 (52%) patients. AUC of the MAC-score in our validation cohort was 0.61 (95% CI: 0.51 - 0.71), which corresponds to a poor discriminative ability.

CONCLUSION

The MAC-score showed poor discriminative ability for MIB-1 index prediction in patients with spinal meningiomas. Moreover, the MAC-score rests on a weak theoretical and statistical foundation. Consequently, we argue against its clinical implementation.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Predictive Power of MIB-1 vs. Mitotic Count on Progression-Free Survival in Skull-Base Meningioma

Simple Summary

Meningiomas are mainly benign intracranial tumors. Nevertheless, risk of recurrence exists in long-term follow-up, so new prognostic markers are still need to be identified. MIB-1 is no diagnostic criterion in WHO classification of meningiomas by now. This retrospective study shows that MIB-1 as well as mitotic count are good predictors for progression-free survival in skull-base meningiomas. The implantation of MIB-1 may enable an improved classification of meningiomas regarding progression-free survival. Moreover, this analysis of skull-base meningiomas shows that current cut-offs may have to be adjusted for meningioma location.

Abstract

Although meningiomas are mainly non-aggressive and slow-growing tumors, there is a remarkable recurrence rate in a long-term follow-up. Proliferative activity and progression-free survival (PFS) differs significantly among the anatomic location of meningiomas. The aim of the present study was to investigate the predictive power of MIB-1 labeling index and mitotic count (MC) regarding the probability of PFS in the subgroup of skull-base meningiomas. A total of 145 patients were included in this retrospective study. Histopathological examinations and follow-up data were collected. Ideal cut-off values for MIB-1 and MC were ≥4.75 and ≥6.5, respectively. MIB-1 as well as MC were good predictors for PFS in skull-base meningiomas. Time-dependent analysis of MIB-1 and MC in prediction of recurrence of skull-base meningioma showed that their prognostic values were comparable, but different cut-offs for MC should be considered regarding the meningioma’s location. As the achievement of a gross total resection can be more challenging in skull-base meningiomas and second surgery implies a higher risk profile, the recurrence risk could be stratified according to these findings and guide decision-making for follow-ups vs. adjuvant therapies.

Immunohistochemical Profile of Parathyroid Tumours: A Comprehensive Review

Immunohistochemistry remains an indispensable tool in diagnostic surgical pathology. In parathyroid tumours, it has four main applications: to detect (1) loss of parafibromin; (2) other manifestations of an aberrant immunophenotype hinting towards carcinoma; (3) histogenesis of a neck mass and (4) pathogenetic events, including features of tumour microenvironment and immune landscape. Parafibromin stain is mandatory to identify the new entity of parafibromin-deficient parathyroid neoplasm, defined in the WHO classification (2022). Loss of parafibromin indicates a greater probability of malignant course and should trigger the search for inherited or somatic CDC73 mutations. Aberrant immunophenotype is characterised by a set of markers that are lost (parafibromin), down-regulated (e.g., APC protein, p27 protein, calcium-sensing receptor) or up-regulated (e.g., proliferation activity by Ki-67 exceeding 5%) in parathyroid carcinoma compared to benign parathyroid disease. Aberrant immunophenotype is not the final proof of malignancy but should prompt the search for the definitive criteria for carcinoma. Histogenetic studies can be necessary for differential diagnosis between thyroid vs. parathyroid origin of cervical or intrathyroidal mass; detection of parathyroid hormone (PTH), chromogranin A, TTF-1, calcitonin or CD56 can be helpful. Finally, immunohistochemistry is useful in pathogenetic studies due to its ability to highlight both the presence and the tissue location of certain proteins. The main markers and challenges (technological variations, heterogeneity) are discussed here in the light of the current WHO classification (2022) of parathyroid tumours.

Visual and digital assessment of Ki-67 in breast cancer tissue - a comparison of methods

Background

In breast cancer (BC) Ki-67 cut-off levels, counting methods and inter- and intraobserver variation are still unresolved. To reduce inter-laboratory differences, it has been proposed that cut-off levels for Ki-67 should be determined based on the in-house median of 500 counted tumour cell nuclei. Digital image analysis (DIA) has been proposed as a means to standardize assessment of Ki-67 staining in tumour tissue. In this study we compared digital and visual assessment (VA) of Ki-67 protein expression levels in full-face sections from a consecutive series of BCs. The aim was to identify the number of tumour cells necessary to count in order to reflect the growth potential of a given tumour in both methods, as measured by tumour grade, mitotic count and patient outcome.

Methods

A series of whole sections from 248 invasive carcinomas of no special type were immunohistochemically stained for Ki-67 and then assessed by VA and DIA. Five 100-cell increments were counted in hot spot areas using both VA and DIA. The median numbers of Ki-67 positive tumour cells were used to calculate cut-off levels for Low, Intermediate and High Ki-67 protein expression in both methods.

Results

We found that the percentage of Ki-67 positive tumour cells was higher in DIA compared to VA (medians after 500 tumour cells counted were 22.3% for VA and 30% for DIA). While the median Ki-67% values remained largely unchanged across the 100-cell increments for VA, median values were highest in the first 1-200 cells counted using DIA. We also found that the DIA100 High group identified the largest proportion of histopathological grade 3 tumours 70/101 (69.3%).

Conclusions

We show that assessment of Ki-67 in breast tumours using DIA identifies a greater proportion of cases with high Ki-67 levels compared to VA of the same tumours. Furthermore, we show that diagnostic cut-off levels should be calibrated appropriately on the introduction of new methodology.

The Effect of Dynamic, In Vivo-like Oxaliplatin on HCT116 Spheroids in a Cancer-on-Chip Model Is Representative of the Response in Xenografts

The cancer xenograft model in which human cancer cells are implanted in a mouse is one of the most used preclinical models to test the efficacy of novel cancer drugs. However, the model is imperfect; animal models are ethically burdened, and the imperfect efficacy predictions contribute to high clinical attrition of novel drugs. If microfluidic cancer-on-chip models could recapitulate key elements of the xenograft model, then these models could substitute the xenograft model and subsequently surpass the xenograft model by reducing variation, increasing sensitivity and scale, and adding human factors. Here, we exposed HCT116 colorectal cancer spheroids to dynamic, in vivo-like, concentrations of oxaliplatin, including a 5 day drug-free period, on-chip. Growth inhibition on-chip was comparable to existing xenograft studies. Furthermore, immunohistochemistry showed a similar response in proliferation and apoptosis markers. While small volume changes in xenografts are hard to detect, in the chip-system, we could observe a temporary growth delay. Lastly, histopathology and a pharmacodynamic model showed that the cancer spheroid-on-chip was representative of the proliferating outer part of a HCT116 xenograft, thereby capturing the major driver of the drug response of the xenograft. Hence, the cancer-on-chip model recapitulated the response of HCT116 xenografts to oxaliplatin and provided additional drug efficacy information.

Turmeric ethanol extract (Curcuma longa L.) reduces apoptosis and promotes canine osteosarcoma cell proliferation

Abstract Curcuma longa L., also known as turmeric, has been widely studied for its various therapeutic properties, including antineoplastic action. The ethanolic extract of the plant contains several phenolic compounds, especially curcumin. Osteosarcoma is a predominant bone tumor in dogs and humans, characterized by high metastatic potential and an unfavorable prognosis. The aim of this study was to investigate the effects of turmeric ethanol extract on canine osteosarcoma cells from established culture. The cells were cultured and treated with different curcumin concentrations (0, 10 μM, 20 μM, 50 μM, 100 μM, and 1000 μM) and exposure times (24h, 48h, and 72h). We first performed tetrazolium reduction technique (MTT) assay and calculated IC50. An immunocytochemistry assay was performed after extract treatment to verify the expression of mutated p53 and therefore study the proliferative potential of malignant cells; Bcl-2 and Ki-67 were used to assess apoptosis and the degree of malignancy, respectively. The extract enhanced the proliferation of canine osteosarcoma cells, reaching 3,819.74% at 50 μM of curcumin. The extract also significantly altered the expression of mutated p53 and Ki-67 proteins but not that of Bcl-2, suggesting that it did not induce this antiapoptotic pathway. Overall, these results are prerequisite to better understanding how natural compounds such as turmeric ethanolic extract affect cell proliferation and could be used to treat various diseases.

Extrato etanólico de açafrão (Curcuma longa L.) reduz apoptose e promove proliferação de células de osteossarcoma canino

Resumo A Curcuma longa L., planta conhecida popularmente como açafrão, tem sido amplamente estudada por suas diversas propriedades terapêuticas, incluindo a ação antineoplásica. O extrato etanólico da planta contém diversos compostos fenólicos, com destaque para a curcumina. O osteossarcoma é um tumor ósseo predominante em cães e humanos, caracterizado por apresentar alto potencial metastático e prognóstico desfavorável. Procurou-se investigar os efeitos de diferentes concentrações de curcumina do extrato etanólico de açafrão sobre células de osteossarcoma canino de cultura estabelecida. As células foram cultivadas e submetidas ao tratamento com extrato com diferentes concentrações de curcumina (0, 10 μM, 20 μM, 50 μM, 100 μM e 1000 μM) e tempos de exposição (24h, 48h e 72h) pelo EEA. Inicialmente, foram realizados: técnica de redução do tetrazólio (MTT) e cálculo da IC50. Posteriormente, após o tratamento com o extrato, realizou-se o ensaio de imunocitoquímica para verificar a expressão de p53 mutada e estudar o potencial proliferativo das células malignas; Bcl-2, com intuito de averiguar o estímulo de via antiapoptótica; e o marcador Ki-67, que sinaliza aumento no grau de malignidade. O extrato promoveu proliferação de células de osteossarcoma canino, com incremento de até 3819,74% na concentração de 50μM de curcumina. O composto também alterou a expressão das proteínas p53 mutante e Ki-67 significativamente, mas não alterou a expressão de Bcl-2, mostrando que não induziu a via antiapoptótica mediada por esta. Estes resultados demonstram que o extrato etanólico do açafrão apresenta potencial proliferativo sobre células de osteossarcoma canino, sugerindo a necessidade de conscientização e conhecimento dos reais efeitos de determinados compostos naturais, considerados seguros ao serem utilizados como tratamento de diversas enfermidades.

Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group

Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920-0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593-4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.

Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

Superpixel image segmentation of VISTA expression in colorectal cancer and its relationship to the tumoral microenvironment

Colorectal cancer (CRC) is the third most common cause of cancer related death in the United States (Jasperson et al. in Gastroenterology 138:2044–2058, 10.1053/j.gastro.2010.01.054, 2010). Many studies have explored prognostic factors in CRC. Today, much focus has been placed on the tumor microenvironment, including different immune cells and the extracellular matrix (ECM). The present study aims to evaluate the role of V-domain immunoglobulin suppressor of T cell activation (VISTA). We utilized QuPath for whole slides image analysis, performing superpixel image segmentation (SIS) on a 226 patient-cohort. High VISTA expression correlated with better disease-free survival (DFS), high tumor infiltrative lymphocyte, microsatellite instability, BRAF mutational status as well as lower tumor stage. High VISTA expression was also associated with mature stromal differentiation (SD). When cohorts were separated based on SD and MMR, only patients with immature SD and microsatellite stability were found to correlate VISTA expression with DFS. Considering raised VISTA expression is associated with improved survival, TILs, mature SD, and MMR in CRC; careful, well-designed clinical trials should be pursued which incorporate the underlying tumoral microenvironment.

Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma

BACKGROUND

Ki-67 immunohistochemistry (IHC) staining is a widely used cancer proliferation assay; however, its limitations could be improved with automated scoring. The OncotypeDXTM Recurrence Score (ORS), which primarily evaluates cancer proliferation genes, is a prognostic indicator for breast cancer chemotherapy response; however, it is more expensive and slower than Ki-67.

OBJECTIVE

To compare manual Ki-67 (mKi-67) with automated Ki-67 (aKi-67) algorithm results based on manually selected Ki-67 "hot spots" in breast cancer, and correlate both with ORS.

METHODS

105 invasive breast carcinoma cases from 100 patients at our institution (2011-2013) with available ORS were evaluated. Concordance was assessed via Cohen's Kappa (κ).

RESULTS

57/105 cases showed agreement between mKi-67 and aKi-67 (κ 0.31, 95% CI 0.18-0.45), with 41 cases overestimated by aKi-67. Concordance was higher when estimated on the same image (κ 0.53, 95% CI 0.37-0.69). Concordance between mKi-67 score and ORS was fair (κ 0.27, 95% CI 0.11-0.42), and concordance between aKi-67 and ORS was poor (κ 0.10, 95% CI -0.03-0.23).

CONCLUSIONS

These results highlight the limits of Ki-67 algorithms that use manual "hot spot" selection. Due to suboptimal concordance, Ki-67 is likely most useful as a complement to, rather than a surrogate for ORS, regardless of scoring method.

Reliability of Ki67 visual scoring app compared to eyeball estimate and digital image analysis and its prognostic significance in hormone receptor-positive breast cancer

We analysed the reproducibility of Ki67 labelling index (LI) between two scorers using the International Ki67 Working Group (IKWG) global methods on an Android application (APP), correlated the APP and eyeball estimate (EBE) with digital image analysis (DIA) scores and determined the prognostic significance of Ki67LI. Global weighted (GW) and global unweighted (GUW) Ki67 app scores of hormone receptor-positive and HER2 (human epidermal growth factor receptor 2)-negative breast cancer patients were obtained. Reproducibility of Ki67LI between 2 scorers and correlation of APP and EBE scores with DIA scores were performed. The prognostic significance of APP scores and its correlation with other clinico-pathologic variables were evaluated. The intra-class correlation coefficient (ICC) between 2 scorers showed excellent reliability with both GW and GUW methods. ICC between DIA and APP scores was significantly greater than DIA versus EBE. The three categories of APP scores based on median value and cut points of 10%, 18% and 38% were significantly associated with poor DFS. On multivariate analysis, significant association between Ki67LI, tumour size, nodal involvement and DFS was noted. Our study shows that the visual Ki67 scoring app is effective in bringing consistency to KI67LI and APP scores showed significant correlation with DFS.

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study

AIMS

The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance.

METHODS AND RESULTS

We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope.

CONCLUSION

The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

Background

Emerging data support the use of thymidine kinase 1 (TK1) activity as a prognostic marker and for monitoring of response in breast cancer (BC). The long-term prognostic value of TK1 kinetics during neoadjuvant chemotherapy is unclear, which this study aimed to elucidate.

Methods

Material from patients enrolled to the single-arm prospective PROMIX trial of neoadjuvant epirubicin, docetaxel and bevacizumab for early BC was used. Ki67 in baseline biopsies was assessed both centrally and by automated digital imaging analysis. TK1 activity was measured from blood samples obtained at baseline and following two cycles of chemotherapy. The associations of TK1 and its kinetics as well as Ki67 with event-free survival and overall survival (OS) were evaluated using multivariable Cox regression models.

Results

Central Ki67 counting had excellent correlation with the results of digital image analysis (r = 0.814), but not with the diagnostic samples (r = 0.234), while it was independently prognostic for worse OS [adjusted hazard ratio (HR_adj) = 2.72, 95% confidence interval (CI) 1.19-6.21, P = 0.02]. Greater increase in TK1 activity after two cycles of chemotherapy resulted in improved event-free survival (HR_adj = 0.50, 95% CI 0.26-0.97, P = 0.04) and OS (HR_adj = 0.46, 95% CI 0.95, P = 0.04). There was significant interaction between the prognostic value of TK1 kinetics and Ki67 (p_interaction 0.04).

Conclusion

Serial measurement of serum TK1 activity during neoadjuvant chemotherapy provides long-term prognostic information in BC patients. The ease of obtaining serial samples for TK1 assessment motivates further evaluation in larger studies.

•

This is a correlative analysis of a prospective phase II study on neoadjuvant chemotherapy for breast cancer.

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Serial measurement of serum TK1 activity during treatment provides independent long-term prognostic information.

•

We demonstrate the validity and clinical utility of both central and automated image analysis-based Ki67 assessment.

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Finally, we explore the biologic correlations between TK1 and Ki67.

A Holistic Appraisal of Stromal Differentiation in Colorectal Cancer: Biology, Histopathology, Computation, and Genomics

Cancer comprises epithelial tumor cells and associated stroma, often times referred to as the "tumoral microenvironment". Cancer-associated fibroblasts (CAFs) are the most notable components of the tumor mesenchyme. CAFs promote the initiation of cancer through angiogenesis, invasion and metastasis. Histologically, the differentiation of stroma has been reported to correlate with prognostic outcomes in patients with colorectal cancer. This review summarizes our current understanding of the extracellular matrix (ECM) in colorectal carcinoma (CRC), showcasing the functions of CAFs and its role in stromal differentiation (SD). We also review current state-of-the-art biology, histopathology, computation, and genomics in the setting of the stroma. SD is distinctive morphologically, and is easily recognized by a surgical pathologist; we offer a lexicon and guide for discovering the essence of stroma, as well as an incipient vision of the future for computation and molecular genomics. We propose that the mesenchymal phenotype, which encompasses a cancer migratory/metastatic capacity, could occur through the process of SD. Looking forward, pathologists will need to invest time and energy into SD, embracing the concept and propagating its use. For patients with colorectal cancer, stroma is a brave new frontier, one not only rich in biologic diversity, but also potentially critical for therapeutic decision making.

Detection of Ki67 Hot-Spots of Invasive Breast Cancer Based on Convolutional Neural Networks Applied to Mutual Information of H&E and Ki67 Whole Slide Images

Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established prognostic and predictive biomarker that determines the choice of therapeutic protocols. In this paper, we present three deep learning-based approaches to automatically detect and quantify Ki67 hot-spot areas by means of the Ki67 labeling index. To this end, a dataset composed of 100 whole slide images (WSIs) belonging to 50 breast cancer cases (Ki67 and H&E WSI pairs) was used. Three methods based on CNN classification were proposed and compared to create the tumor proliferation map. The best results were obtained by applying the CNN to the mutual information acquired from the color deconvolution of both the Ki67 marker and the H&E WSIs. The overall accuracy of this approach was 95%. The agreement between the automatic Ki67 scoring and the manual analysis is promising with a Spearman’s ρ correlation of 0.92. The results illustrate the suitability of this CNN-based approach for detecting hot-spots areas of invasive breast cancer in WSI.