Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection

Digital image analysis of Ki67 hotspot detection and index counting in gastroenteropancreatic neuroendocrine neoplasms

The Ki-67 proliferative index plays a pivotal role in the subclassification of neuroendocrine neoplasm (NEN) according to the WHO Classification of Digestive System Tumors (5th edition), which designates neuroendocrine tumor (NET) grades 1, 2, and 3 for Ki-67 proliferative index of <3 %, 3-20 %, and >20 %, respectively. Proliferative index calculation must be performed in the hotspot, traditionally selected by visual scanning at low-power magnification. Recently, gradient map visualization has emerged as a tool for various purposes, including hotspot selection. This study includes 97 cases of gastrointestinal neuroendocrine neoplasms, with hotspots selected by bare eye and gradient map visualization (GM). Each hotspot was analyzed using three methods: eye estimation (EE), digital image analysis (DIA), and manual counting. Of the NENs studied, 91 % were NETs (26 % for G1, 55 % for G2, and 10 % for G3). Only 9 cases were neuroendocrine carcinoma (NEC). Between two hotspot selection methods, GM resulted in a higher grade in 14.77 % of cases, primarily upgrading from NET G1 to G2. Among the counting methods, DIA demonstrated substantial agreement with manual counting, both for pathologist and resident. Grading by other methods tended to result in a higher grade than MC (26.99 % with EE and 8.52 % with DIA). Given its clinical and statistical significance, this study advocates for the application of GM in hotspot selection to identify higher-grade tumors. Furthermore, DIA provides accurate grading, offering time efficiency over MC.

Association between Ki67 expression and therapeutic outcome in colon cancer

Ki67 is a commonly used proliferation marker in pathological diagnosis of tumors; however, its prognostic value in colon cancer is controversial. A total of 312 consecutive patients with stage I-III colon cancer who underwent radical surgery with or without adjuvant chemotherapy were included in the present study. Ki67 expression was assessed using immunohistochemistry and was classified according to 25% intervals. The association between Ki67 expression and clinicopathological features was analyzed. Long-term postoperative survival, including disease-free survival (DFS) and overall survival, was calculated, and its association with Ki67 was analyzed. High Ki67 expression (>50%) was associated with improved DFS in patients treated with adjuvant chemotherapy postoperatively, but not in patients who received surgery alone (P=0.138). Ki67 expression was significantly associated with histological differentiation of the tumor (P=0.01), while it was not associated with other clinicopathological factors. Multivariate analysis demonstrated that pathological T and N stage were independent prognostic factors. In conclusion, high Ki67 expression was associated with a good therapeutic outcome in patients receiving adjuvant chemotherapy in colon cancer.

T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

OBJECTIVE

To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.

METHODS

This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.

RESULTS

High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).

CONCLUSION

T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

KEY POINTS

• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

J, Eriksson F, Ugleholdt H, Broholm H, Mathiesen T. Mitotic and Proliferative Indices in WHO Grade III Meningioma

Simple Summary

Malignant meningiomas are rare primary intracranial tumors associated with considerable morbidity and mortality. The diagnosis is based on the number of mitotic figures (mitotic index, MI). Consequently, the quantification of mitotic figures is prone to inter- and intraobserver variability. The mitotic marker, phosphohistone-H3 (PHH3), has been shown to be a more robust mitotic marker. Despite the prognostic value of MI across all meningioma grades, little is known of the prognostic value of the MI within malignant meningioma. Therefore, this study investigates the MI in a series of malignant meningiomas to analyze the association to progression-free survival and mitotic and proliferative indices. Furthermore, we investigated the precision (repeatability) of mitotic counts and the agreement between MI and PHH3 MI.

Abstract

Meningiomas with inherently high mitotic indices and poor prognosis, such as WHO grade III meningiomas, have not been investigated separately to establish interchangeability between conventional mitotic index counted on H&E stained slides (MI) and mitotic index counted on phosphohistone-H3 stained slides (PHH3 MI). This study investigates the agreement of MI and PHH3 MI and to analyze the association of progression-free survival (PFS) and MI, PHH3 MI, and the proliferative index (PI, Ki-67) in WHO grade III meningioma. Tumor specimens from 24 consecutive patients were analyzed for expression of Ki-67, PHH3 MI, and MI. Quantification was performed independently by two observers who made replicate counts in hot spots and overall tumor staining. Repeatability in replicate counts from MI and PHH3 MI was low in both observers. Consequently, we could not report the agreement. MI, PHH3 MI and hot spot counts of Ki-67 were associated with PFS (MI hot spot HR = 1.61, 95% CI 1.12–2.31, p = 0.010; PHH3 MI hot spot HR = 1.59, 95% CI 1.15–2.21, p = 0.006; Ki-67 hot spot HR = 1.06, 95% CI 1.02–1.11. p = 0.004). We found markedly low repeatability of manually counted MI and PHH3 MI in WHO grade III meningioma, and we could not conclude that the two methods agreed. Subsequently, quantification with better repeatability should be sought. All three biomarkers were associated with PFS.

The highest Fuhrman and WHO/ISUP grade influences the Ki-67 labeling index of those of grades 1 and 2 in clear cell renal cell carcinoma

Nuclear grade is one of the most important prognostic factors in clear cell renal cell carcinoma (CCRCC). Although CCRCCs usually have intratumoral heterogeneity with various nuclear atypia including nucleolar prominence, it is unclear whether a similar degree of nuclear grade component demonstrates the same proliferative activity. We aimed to reveal whether the presence of a higher nuclear grade has an effect on proliferative activity among each assigned nuclear grade in CCRCCs. We enrolled 129 CCRCC patients containing at least two different nuclear grades. We separately assessed nuclear grade using the Fuhrman and World Health Organization and International Society of Urologic Pathologists (WHO/ISUP) grading systems. In addition, we selected blocks containing different nuclear grade and assessed the Ki-67 labeling index (LI) for each using a computer-based analysis system. Ki-67 LIs significantly correlated with both Fuhrman and WHO/ISUP grades (P < 0.001 and P < 0.001). Of note, the LIs among Fuhrman and WHO/ISUP grades 1 and 2 were also statistically significant according to the highest nuclear grade (P < 0.01 for both grades 1 and 2). Our data suggests that the highest nuclear grade influences the proliferative activity in tumor components regardless of the morphologically assigned nuclear grades. The exact evaluation of Ki-67 LI in CCRCC can provide a more precise information of the malignant potential.

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen’s kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice.

Correlation Between Digital and Manual Determinations of Ki-67/MIB-1 Proliferative Indices in Human Meningiomas

Objective. Proliferative activity in tumor tissues is assessed as the percentage of Ki-67/MIB-1-positive cells, or the proliferative index (PI). The PI is routinely assessed manually. However, the subjectivity of manual assessments might result in poor reproducibility. We hypothesized that digital assessments might reduce the error. Method. In our study, we assessed Ki-67/MIB-1 PIs, both manually and digitally, with tissue microarrays constructed from 141 human meningioma samples. Spearman-rank correlation and κ statistics were applied for correlation and agreement analyses, respectively. Mann-Whitney U tests were used to compare MIB-1 PIs between groups. Prognostic ability was assessed with Kaplan-Meier and Cox regression analyses. Results. We found a significant, high correlation (Spearman ρ = 0.832, P < .01) and moderate agreement (κ coefficient = 0.617, observed agreement = 80.9%) between the 2 methods. Both methods found significantly different Ki-67/MIB-1 PIs for different World Health Organization grades (P < .05). Neither method showed significant prognostic value. Conclusion. Digital determinations of Ki-67/MIB-1 PIs in human meningiomas are feasible for the daily routine.

Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

OBJECTIVES

To determine the utility of the amide proton transfer-weighted MR imaging in differentiating the WHO grade and predict proliferative activity of meningioma.

METHODS

Fifty-three patients with WHO grade I meningiomas and 26 patients with WHO grade II meningiomas underwent conventional and APT-weighted sequences on a 3.0 Tesla MR before clinical intervention. The APT-weighted (APTw) parameters in the solid tumor region were obtained and compared between two grades using the t test; the receiver operating characteristic (ROC) curve was used to assess the best parameter for predicting the grade of meningiomas. Pearson's correlation coefficient was calculated between the APTw_max and Ki-67 labeling index in meningiomas.

RESULTS

The APTw_max and APTw_mean values were not significantly different between WHO grade I and grade II meningiomas (p = 0.103 and p = 0.318). The APTw_min value was higher and the APTw_max-min value was lower in WHO grade II meningiomas than in WHO grade I tumors (p = 0.027 and p = 0.019). But the APTw_min was higher and the APTw_max-min was lower in microcystic meningiomas than in WHO grade II meningiomas (p = 0.001 and p = 0.006). The APTw_min combined with APTw_max-min showed the best diagnostic performance in predicting the grade of meningiomas with an AUC of 0.772. The APTw_max value was positively correlated with Ki-67 labeling index (r = 0.817, p < 0.001) in meningiomas; the regression equation for the Ki-67 labeling index (%) (Y) and APTw_max (%) (X) was Y = 4.9 × X - 12.4 (R² = 0.667, p < 0.001).

CONCLUSION

As a noninvasive imaging method, the ability of APTw-MR imaging in differentiating the grade of meningiomas is limited, but the technology can be used to predict the proliferative activity of meningioma.

KEY POINTS

• The APTw _min value was higher and the APTw _max-min value was lower in WHO grade II meningioma than in grade I tumors. • The APTw _min value was higher and the APTw _max-min value was lower in microcystic meningiomas than in WHO grade II meningiomas. • The APTw _max value was positively correlated with meningioma proliferation index.

Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support

Background

Determining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography.

Materials and Methods

Relatively low-resolution WSI scans (0.50 μm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography.

Results

The automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation (R ² = 0.9916) and by agreement with a pathologist (R ² = 0.8605) as well as through assessment of counting function using an independently tuned object counting algorithm (OCA) (R ² = 0.9482). Limits of agreement analysis support method interchangeability. MF counts obtained led to accurate patient survival prediction in all (n = 30) except one case. By contrast, more variable performance was documented when several pathologists examined similar cases using microscopy (pair-wise correlations, rho range = 0.7597-0.9286).

Conclusions

Automated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.