Relationship between the Ki67 index and its area based approximation in breast cancer

Initial interactions with the FDA on developing a validation dataset as a medical device development tool

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

One label is all you need: Interpretable AI-enhanced histopathology for oncology

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Metaplastic Breast Cancer: Current Understanding and Future Directions

Metaplastic breast cancers (MBC) encompass a group of highly heterogeneous tumors which share the ability to differentiate into squamous, mesenchymal or neuroectodermal components. While often termed rare breast tumors, given the relatively high prevalence of breast cancer, they are seen with some frequency. Depending upon the definition applied, MBC represents 0.2% to 1% of breast cancers diagnosed in the United States. Less is known about the epidemiology of MBC globally, though a growing number of reports are providing information on this. These tumors are often more advanced at presentation relative to breast cancer broadly. While more indolent subtypes exist, the majority of MBC subtypes are associated with inferior survival. MBC is most commonly of triple-negative phenotype. In less common hormone receptor positive MBCs, hormone receptor status appears not to be prognostic. In contrast, relatively rare HER2-positive MBCs are associated with superior outcomes. Multiple potentially targetable molecular features are overrepresented in MBC including DNA repair deficiency signatures and PIK3/AKT/mTOR and WNT pathways alterations. Data on the prevalence of targets for novel antibody-drug conjugates is also emerging. While chemotherapy appears to be less active in MBC than in other breast cancer subtypes, efficacy is seen in some MBCs. Disease-specific trials, as well as reports of exceptional responses, may provide clues for novel approaches to this often hard-to-treat breast cancer. Strategies which harness newer research tools, such as large data and artificial intelligence hold the promise of overcoming historic barriers to the study of uncommon tumors and could markedly advance disease-specific understanding in MBC.

Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation

Purpose

The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used.

Approach

We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network.

Results

The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An F -score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation.

Conclusions

An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.

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.

Expression site agnostic histopathology image segmentation framework by self supervised domain adaption

MOTIVATION

With the sites of antigen expression different, the segmentation of immunohistochemical (IHC) histopathology images is challenging, due to the visual variances. With H&E images highlighting the tissue structure and cell distribution more broadly, transferring more salient features from H&E images can achieve considerable performance on expression site agnostic IHC images segmentation.

METHODS

To the best of our knowledge, this is the first work that focuses on domain adaptive segmentation for different expression sites. We propose an expression site agnostic domain adaptive histopathology image semantic segmentation framework (ESASeg). In ESASeg, multi-level feature alignment encodes expression site invariance by learning generic representations of global and multi-scale local features. Moreover, self-supervision enhances domain adaptation to perceive high-level semantics by predicting pseudo-labels.

RESULTS

We construct a dataset with three IHCs (Her2 with membrane stained, Ki67 with nucleus stained, GPC3 with cytoplasm stained) with different expression sites from two diseases (breast and liver cancer). Intensive experiments on tumor region segmentation illustrate that ESASeg performs best across all metrics, and the implementation of each module proves to achieve impressive improvements.

CONCLUSION

The performance of ESASeg on the tumor region segmentation demonstrates the efficiency of the proposed framework, which provides a novel solution on expression site agnostic IHC related tasks. Moreover, the proposed domain adaption and self-supervision module can improve feature domain adaption and extraction without labels. In addition, ESASeg lays the foundation to perform joint analysis and information interaction for IHCs with different expression sites.

Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ)

The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.

Colorimetric histology using plasmonically active microscope slides

The human eye can distinguish as many as 10,000 different colours but is far less sensitive to variations in intensity¹, meaning that colour is highly desirable when interpreting images. However, most biological samples are essentially transparent, and nearly invisible when viewed using a standard optical microscope². It is therefore highly desirable to be able to produce coloured images without needing to add any stains or dyes, which can alter the sample properties. Here we demonstrate that colorimetric histology images can be generated using full-sized plasmonically active microscope slides. These slides translate subtle changes in the dielectric constant into striking colour contrast when samples are placed upon them. We demonstrate the biomedical potential of this technique, which we term histoplasmonics, by distinguishing neoplastic cells from normal breast epithelium during the earliest stages of tumorigenesis in the mouse MMTV-PyMT mammary tumour model. We then apply this method to human diagnostic tissue and validate its utility in distinguishing normal epithelium, usual ductal hyperplasia, and early-stage breast cancer (ductal carcinoma in situ). The colorimetric output of the image pixels is compared to conventional histopathology. The results we report here support the hypothesis that histoplasmonics can be used as a novel alternative or adjunct to general staining. The widespread availability of this technique and its incorporation into standard laboratory workflows may prove transformative for applications extending well beyond tissue diagnostics. This work also highlights opportunities for improvements to digital pathology that have yet to be explored.

Capsaicin Inhibits Proliferation and Induces Apoptosis in Breast Cancer by Down-Regulating FBI-1-Mediated NF-κB Pathway

Background

As a natural compound extracted from a variety of hot peppers, capsaicin has drawn increasing attention to its anti-cancer effects against multiple human cancers including breast cancer. FBI-1 is a major proto-oncogene negatively regulating the transcription of many tumor suppressor genes, and plays a vital role in tumorigenesis and progression. However, whether FBI-1 is involved in capsaicin-induced breast cancer suppression has yet to be ascertained. This study aimed to investigate the effects of capsaicin on proliferation and apoptosis and its association with FBI-1 expression in breast cancer.

Methods

CCK-8 and morphological observation assay were employed to detect cell proliferation. Flow cytometry and TUNEL assay were conducted to detect cell apoptosis. RNA interference technique was used to overexpress or silence FBI-1 expression. qRT-PCR and/or Western blot analysis were applied to detect the protein expression of FBI-1, Ki-67, Bcl-2, Bax, cleaved-Caspase 3, Survivin and NF-κB p65. Xenograft model in nude mice was established to assess the in vivo effects.

Results

Capsaicin significantly inhibited proliferation and induced apoptosis in breast cancer in vitro and in vivo, along with decreased FBI-1, Ki-67, Bcl-2 and Survivin protein expression, increased Bax protein expression and activated Caspase 3. Furthermore, FBI-1 overexpression obviously attenuated the capsaicin-induced anti-proliferation and pro-apoptosis effect, accompanied with the above-mentioned proteins reversed, whereas FBI-1 silencing generated exactly the opposite response. In addition, as a target gene of FBI-1, NF-κB was inactivated by p65 nuclear translocation suppressed with capsaicin treatment, which was perceptibly weakened with FBI-1 overexpression or enhanced with FBI-1 silencing.

Conclusion

This study reveals that FBI-1 is closely involved in capsaicin-induced anti-proliferation and pro-apoptosis of breast cancer. The underlying mechanism may be related to down-regulation of FBI-1-mediated NF-κB pathway. Targeting FBI-1 with capsaicin may be a promising therapeutic strategy in patients with breast cancer.

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology

Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI has proved to be capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in the practice of pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.

Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data

Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is subject to inter pathologist variability. With the development of image processing and machine learning, many methods have been introduced for automatic Ki-67 estimation. But most of them require manual annotations and are restricted to one type of cancer. In this work, we propose a pooled Otsu's method to generate labels and train a semantic segmentation deep neural network (DNN). The output is postprocessed to find the Ki-67 index. Evaluation of two different types of cancer (bladder and breast cancer) results in a mean absolute error of 3.52%. The performance of the DNN trained with automatic labels is better than DNN trained with ground truth by an absolute value of 1.25%.

Molecular Investigation on a Triple Negative Breast Cancer Xenograft Model Exposed to Proton Beams

Specific breast cancer (BC) subtypes are associated with bad prognoses due to the absence of successful treatment plans. The triple-negative breast cancer (TNBC) subtype, with estrogen (ER), progesterone (PR) and human epidermal growth factor-2 (HER2) negative receptor status, is a clinical challenge for oncologists, because of its aggressiveness and the absence of effective therapies. In addition, proton therapy (PT) represents an effective treatment against both inaccessible area located or conventional radiotherapy (RT)-resistant cancers, becoming a promising therapeutic choice for TNBC. Our study aimed to analyze the in vivo molecular response to PT and its efficacy in a MDA-MB-231 TNBC xenograft model. TNBC xenograft models were irradiated with 2, 6 and 9 Gy of PT. Gene expression profile (GEP) analyses and immunohistochemical assay (IHC) were performed to highlight specific pathways and key molecules involved in cell response to the radiation. GEP analysis revealed in depth the molecular response to PT, showing a considerable immune response, cell cycle and stem cell process regulation. Only the dose of 9 Gy shifted the balance toward pro-death signaling as a dose escalation which can be easily performed using proton beams, which permit targeting tumors while avoiding damage to the surrounding healthy tissue.

Is Ki-67 index overexpression in IDH wild type glioblastoma a predictor of shorter Progression Free survival? A clinical and Molecular analytic investigation

BACKGROUND

Ki-67 proliferation index is widely used for differentiating between high and low-grade gliomas, but differentiating between the same grade IV appears to be more problematic, and the point about its prognostic value for GBM patients remains unclear. To reduce the possibility to find a marked histological heterogeneity, and may contain areas that could be diagnosed as lower grade, in this study we considered a large group of patients with IDH wild-type Glioblastoma (IDH-WT GBM) and we have analyzed previously reported prognostic factors, in regards to their relationship with the Ki-67 expression index.

METHODS

We explore the prognostic impact of ki-67 index status in 127 patients affected by IDH-WT GBM. We therefore analyzed clinical characteristics, tumor genetics, dimension and clinical outcomes. We selected a total of 127 patients affected by newly diagnosed IDH-WT GBM who underwent surgery, radiation, and chemotherapy in our Institution in the period ranging between January 2014 and December 2016 RESULTS: The volume of the lesion had a strong association with the Ki67 overexpression. In particular lesions whose volume was greater than 45  cm³, presented a higher percentage of Ki67 expression demonstrating that greater tumors are more likely associated to higher values of Ki67 percentages. On a multivariate analysis, it was possible to outline that Ki67 was significant a predictor of shorter PFS independently from the age of the patients, the volume of the lesion and preoperative KPS.

CONCLUSIONS

There is a correlation between percentage staining of Ki-67 and OS in our cohort of patients with IDH-WT GBM. This is only the third observational study documenting a positive correlation between Ki-67 and overall survival in GBM and the first one demonstrates that percentage Ki-67 staining >20 % predicts poorer progression free survival in IDH-WT GBM.

Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma

RATIONALE AND OBJECTIVES

The purpose of this study was to establish and validate radiomics signatures based on ultrasound (US) medicine images to assess the biological behaviors of intrahepatic cholangiocarcinoma (ICC) in a noninvasive manner.

MATERIALS AND METHODS

This study consisted of 128 ICC patients. We focused on evaluating six pathological features: microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7. Region of interest (ROI) of ICC was identified by manually plotting the tumor contour on the grayscale US image. We extracted radiomics features from medical US imaging. Then, dimensionality reduction methods and classifiers were used to develop radiomic signatures for evaluating six pathological features in ICC. Finally, independent validation datasets were used to assess the radiomic signatures performance.

RESULTS

We extracted 1076 quantitative characteristic parameters on the US medicine images. Based on extracted radiomics features, the best performing radiomic signatures for evaluating microvascular invasion features were produced by hypothetical test + support vector machine (SVM), perineural invasion subgroup were least absolute shrinkage and selection operator + principal component analysis + support vector machine, differentiation subgroup were hypothetical test + decision tree, Ki-67 subgroup were hypothetical test + logistic regression, vascular endothelial growth factor subgroup were hypothetical test + Gradient Boosting Decision Tree (GBDT), and cytokeratin 7 subgroup were hypothetical test + bagging, respectively.

CONCLUSION

Through the high-throughput radiomics analysis based on US medicine images, we proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of ICC noninvasively.

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

Background

The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning.

Methods

We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining.

Results

The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

Conclusions

Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.

An Overview of the Antioxidant Effects of Ascorbic Acid and Alpha Lipoic Acid (in Liposomal Forms) as Adjuvant in Cancer Treatment

Antioxidants are known to minimize oxidative stress by interacting with free radicals produced as a result of cell aerobic reactions. Oxidative stress has long been linked to many diseases, especially tumours. Therefore, antioxidants play a crucial role in the prevention or management of free radical-related diseases. However, most of these antioxidants have anticancer effects only if taken in large doses. Others show inadequate bioavailability due to their instability in the blood or having a hydrophilic nature that limits their permeation through the cell membrane. Therefore, entrapping antioxidants in liposomes may overcome these drawbacks as liposomes have the capability to accommodate both hydrophilic and hydrophobic compounds with a considerable stability. Additionally, liposomes have the capability to accumulate at the cancer tissue passively, due to their small sizes, with enhanced drug delivery. Additionally, liposomes can be engineered with targeting moieties to increase the delivery of chemotherapeutic agents to specific tumour cells with decreased accumulation in healthy tissues. Therefore, combined use of liposomes and antioxidants, with or without chemotherapeutic agents, is an attractive strategy to combat varies tumours. This mini review focuses on the liposomal delivery of selected antioxidants, namely ascorbic acid (AA) and alpha-lipoic acid (ALA). The contribution of these nanocarriers in enhancing the antioxidant effect of AA and ALA and consequently their anticancer potentials will be demonstrated.

Current immunoassay methods and their applications to clinically used biomarkers of breast cancer

Breast cancer is the leading cause of cancer-related mortality worldwide, with a higher incidence in developed countries. The biomarkers for breast cancer such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, CA (cancer antigen) 15-3, CA 27.29, and carcinoembryonic antigen have been recommended for use in the laboratory based on the guidelines of American and European societies. Immunoassays have been frequently and consistently used to detect these clinically established biomarkers of breast cancer. Despite the higher accessibility of serum biomarkers, including CA 15-3, CA 27.29, and CEA, compared to tissue markers, variations in immunoassays affect their standardization and clinical utility. When reviewing the immunoassays used to detect these serum markers, we found that the most frequently used immunoassay was enzyme-linked immunosorbent assay, followed by electrochemiluminescent immunoassay, and then chemiluminescence immunoassay for CA 15-3 and CEA. Meanwhile, the chemiluminescence immunoassay was the most common technique for CA27.29. The electrochemiluminescent immunoassay and monoclonal fluorometric assay have become the preferred methods in 2010-2019 compared to 2000-2009. Analytical and clinical performance factors such as sensitivity, specificity, detection limit, hazard risk to laboratory personnel, speed, and economic feasibility influenced these changes in user preference. When using the immunoassays, there should be a comprehensive understanding of the principles, advantages, vulnerability, and precautions for interpretation. In the future, a combination of immunological biomarkers and genetic platforms will benefit patients with breast cancer by facilitating prognosis prediction and guiding therapeutic intervention.

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling

BACKGROUND

The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open.

RESULTS

In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions.

CONCLUSIONS

The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

Clinical features and their effect on outcomes of patients with triple negative breast cancer with or without lymph node involvement

Objective

Clinical and pathological characteristics of triple negative breast cancer (TNBC) treatment are required for escalation or de-escalation of treatment because of a lack of druggable targets. This study aimed to identify the factors affecting the risk of disease recurrence and disease-related death in patients with TNBC.

Methods

Patients with TNBC who were treated at the University Medical Centre Maribor between January 2010 and December 2017 were studied. Clinical and pathological data were analyzed using multivariate analysis and non-parametric tests. Subgroup analysis was performed to examine additional factors that affect 5-year overall survival (OS) and recurrence-free survival.

Results

Multivariate analysis showed that tumor size and the lymph node ratio (LNR) were significant risks in our population. Better discrimination of patients at risk of a shorter recurrence-free survival and OS was achieved by using the LNR. Only lymphovascular invasion was significant for predicting 5-year OS.

Conclusion

For risk-based decision-making systems, the LNR is useful for discriminating between high- and low-risk patients with TNBC.