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

Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system's performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist's report.

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.

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.

The synthetic molecule stauprimide impairs cell growth and migration in triple-negative breast cancer

Stauprimide, a semi-synthetic derivative of staurosporine, is known mainly for its potent differentiation-enhancing properties in embryonic stem cells. Here, we studied the effects of stauprimide in cell growth and migration of triple-negative breast cancer cells in vitro, evaluating its potential antitumoral activity in an orthotopic mouse model of breast cancer in vivo. Our results from survival curves, EdU incorporation, cell cycle analysis and annexin-V detection in MDA-MB-231 cells indicated that stauprimide inhibited cell proliferation, arresting cell cycle in G₂/M without induction of apoptosis. A decrease in the migratory capability of MDA-MB-231 was also assessed in response to stauprimide. In this work we pointed to a mechanism of action of stauprimide involving the modulation of ERK1/2, Akt and p38 MAPK signalling pathways, and the downregulation of MYC in MDA-MB-231 cells. In addition, orthotopic MDA-MB-231 xenograft and 4T1 syngeneic models suggested an effect of stauprimide in vivo, increasing the necrotic core of tumors and reducing metastasis in lung and liver of mice. Together, our results point to the promising role of stauprimide as a putative therapeutic agent in triple-negative breast cancer.

Visual Counting and Automated Image-analytic Assessment of Ki-67 and their Prognostic Value in Synovial Sarcoma

BACKGROUND

Ki-67 is a widely used proliferation marker reflecting prognosis in various tumors. However, visual assessment and scoring of Ki-67 suffers from marked inter-observer and intra-observer variability. We aimed to assess the concordance of manual counting and automated image-analytic scoring methods for Ki-67 in synovial sarcoma.

PATIENTS AND METHODS

Tissue microarrays from 34 patients with synovial sarcoma were immunostained for Ki-67 and scored both visually and with 3DHistech QuantCenter.

RESULTS

The automated assessment of Ki-67 expression was in good agreement with the visually counted Ki-67 (r _Pearson =0.96, p<0.001). In a Cox regression model automated [hazard ratio (HR)=1.047, p=0.024], but not visual (HR=1.063, p=0.053) assessment method associated high Ki-67 scores with worse overall survival.

CONCLUSION

The automated Ki-67 assessment method appears to be comparable to the visual method in synovial sarcoma and had a significant association to overall survival.

Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images

MOTIVATION

For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation.

RESULTS

This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006.

AVAILABILITY AND IMPLEMENTATION

The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.