Proliferation Activity in Canine Gastrointestinal Lymphoma

Gastrointestinal lymphomas are uncommon in dogs and little is known about their distinct subtypes or proliferation rate. The aim of this study was to stratify 33 canine gastrointestinal lymphoma samples according to the latest World Health Organization classification and to determine the Ki67 proliferation index by manual counting, digital image analysis and visual estimation. The Ki67 index was then correlated with subtype, immunophenotype, mitotic index, grade and tumour location. The mitotic index correlated positively with the Ki67 index. A significantly higher number of Ki67-positive cells was found in enteropathy-associated T-cell lymphoma type I and in diffuse large B-cell lymphoma compared with enteropathy-associated T-cell lymphoma type II. There was also a significant difference in Ki67 immunolabelled cells between grade 1 and grade 2 lymphomas. Moderate agreement was found between the Ki67 index as obtained by manual counting and visual estimation, but there was strong agreement between manual counting and digital image analysis. The user-friendly digital imaging system used in this study could have potential for future determination of the Ki67 index in lymphoid neoplasms.

Ki-67 index of 5% could better predict the clinical prognosis of well-differentiated pancreatic neuroendocrine tumours

BACKGROUND

The pathological classification of well-differentiated pancreatic neuroendocrine tumour (pNET) is based largely upon Ki-67 index. However, current controversies abound about the classification of pNETG1/pNETG2.

PATIENTS AND METHODS

Clinicopathological data were retrospectively analysed for 153 pNETG1/pNETG2 patients hospitalized at China-Japan Friendship Hospital. The critical values of pNETG1/pNETG2 were examined by using the area under the receiver operating characteristic curve and survival analysis was used to compare the clinical prognosis of pNETG1/G2.

RESULTS

Among them, 52.3% were males. The median age was 49 (18-81) years and the clinical types were pNETG1 (n = 38) and pNETG2 (n = 115). According to the receiver operating characteristic curve, the optimal cut-off value was 5.5% for classifying pNETG1/pNETG2. Significant differences between pNETG1 (n = 101) and pNETG2 (n = 52) existed in overall survival (P = 0.001) and disease-free survival (P = 0.013) when Ki-67 index was 5%. Yet no significant differences existed in overall survival (P = 0.378) or disease-free survival (P = 0.091) between pNETG1 and pNETG2 when Ki-67 index was 3%. Furthermore, multivariate analysis indicated that the revised pathological grade was an independent risk factor for mortality and post-operative recurrence of pNET patients (P = 0.003 and 0.014; hazard ratio (HR) = 4.005 and 2.553).

CONCLUSION

Thus, differentiating pNETG1/pNETG2 with Ki-67 index (5%) is proposed as the cut-off value and a new Ki-67 index (5%) is a better predictor of pNET mortality and post-operative recurrence than Ki-67 index (3%).

Ki-67 Proliferation Index Assessment in Gastroenteropancreatic Neuroendocrine Tumors by Digital Image Analysis With Stringent Case and Hotspot Level Concordance Requirements

OBJECTIVES

The Ki-67 proliferation index is integral to gastroenteropancreatic neuroendocrine tumor (GEP-NET) assessment. Automated Ki-67 measurement would aid clinical workflows, but adoption has lagged owing to concerns of nonequivalency. We sought to address this concern by comparing 2 digital image analysis (DIA) platforms to manual counting with same-case/different-hotspot and same-hotspot/different-methodology concordance assessment.

METHODS

We assembled a cohort of GEP-NETs (n = 20) from 16 patients. Two sets of Ki-67 hotspots were manually counted by three observers and by two DIA platforms, QuantCenter and HALO. Concordance between methods and observers was assessed using intraclass correlation coefficient (ICC) measures. For each comparison pair, the number of cases within ±0.2xKi-67 of its comparator was assessed.

RESULTS

DIA Ki-67 showed excellent correlation with manual counting, and ICC was excellent in both within-hotspot and case-level assessments. In expert-vs-DIA, DIA-vs-DIA, or expert-vs-expert comparisons, the best-performing was DIA Ki-67 by QuantCenter, which showed 65% cases within ±0.2xKi-67 of manual counting.

CONCLUSIONS

Ki-67 measurement by DIA is highly correlated with expert-assessed values. However, close concordance by strict criteria (>80% within ±0.2xKi-67) is not seen with DIA-vs-expert or expert-vs-expert comparisons. The results show analytic noninferiority and support widespread adoption of carefully optimized and validated DIA Ki-67.

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%.

Ki-67 proliferation index in neuroendocrine tumors: Can augmented reality microscopy with image analysis improve scoring?

BACKGROUND

The Ki-67 index is important for grading neuroendocrine tumors (NETs) in cytology. However, different counting methods exist. Recently, augmented reality microscopy (ARM) has enabled real-time image analysis using glass slides. The objective of the current study was to compare different traditional Ki-67 scoring methods in cell block material with newer methods such as ARM.

METHODS

Ki-67 immunostained slides from 50 NETs of varying grades were retrieved (39 from the pancreas and 11 metastases). Methods with which to quantify the Ki-67 index in up to 3 hot spots included: 1) "eyeball" estimation (EE); 2) printed image manual counting (PIMC); 3) ARM with live image analysis; and 4) image analysis using whole-slide images (WSI) (field of view [FOV] and the entire slide).

RESULTS

The Ki-67 index obtained using the different methods varied. The pairwise kappa results varied from no agreement for image analysis using digital image analysis WSI (FOV) and histology to near-perfect agreement for ARM and PIMC. Using surgical pathology as the gold standard, the EE method was found to have the highest concordance rate (84.2%), followed by WSI analysis of the entire slide (73.7%) and then both the ARM and PIMC methods (63.2% for both). The PIMC method was the most time-consuming whereas image analysis using WSI (FOV) was the fastest method followed by ARM.

CONCLUSIONS

The Ki-67 index for NETs in cell block material varied by the method used for scoring, which may affect grade. PIMC was the most time-consuming method, and EE had the highest concordance rate. Although real-time automated counting using image analysis demonstrated inaccuracies, ARM streamlined and hastened the task of Ki-67 quantification in NETs.

Comparison of Monitor-Image and Printout-Image Methods in Ki-67 Scoring of Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NET) are classified according to tumor grade. Ki-67 and mitotic count are the two determinants of this classification. Therefore, Ki-67 scoring becomes very important in classifying the patients accurately. Eye-balling, counting of cells through the microscope, automated image analysis systems, and manual counting of printed image are the four major scoring methods in use. The aim of this study is to show the agreement between monitor-image method (MIM) and printout-image method (PIM) of Ki-67 scoring. In our study, 120 GEP-NETs from 85 patients diagnosed between January 2005 and July 2017 were evaluated. Thirty-seven cases with either polypectomy or resection material were selected. Seven different scoring methods using either a monitor-image or a printout-image were applied for Ki-67 scoring. They are as follows: whole-PIM, 1/9-PIM, whole-MIM, 1/4-MIM, 1/6-MIM, 1/9-MIM, and 1/12-MIM. In the comparison of Ki-67 scoring methods, intraclass correlation coefficients ranging from 0.951 to 0.999 were found. The Bland-Altman analysis showed near-perfect agreement between whole-MIM and whole-PIM as well as 1/9-MIM and 1/9-PIM. The level of agreements among the other methods were sufficient too, but there was a relative decrease in the level of agreement as the area of counting becomes smaller. The average application time decreased from 373.7 to 41.7 s gradually as the scoring area becomes smaller. Our study shows that there is a remarkable agreement between the MIM and PIM used in Ki-67 scoring.