Digital Image Analysis Platform Research Articles

Aramchol improves hepatic fibrosis in metabolic dysfunction-associated steatohepatitis: Results of multimodality assessment using both conventional and digital pathology.

Antifibrotic trials rely on conventional pathology despite recognized limitations. We compared single-fiber digital image analysis with conventional pathology to quantify the antifibrotic effect of Aramchol, a stearoyl-CoA desaturase 1 inhibitor in development for metabolic dysfunction-associated steatohepatitis. Fifty-one patients with metabolic dysfunction-associated steatohepatitis enrolled in the open-label part of the ARMOR trial received Aramchol 300mg BID and had paired pre-post treatment liver biopsies scored by consensus among 3 hepatopathologists, and separately assessed by a digital image analysis platform (PharmaNest) that generates a continuous phenotypic Fibrosis Composite Severity (Ph-FCS) score. Fibrosis improvement was defined as: ≥1 NASH Clinical Research Network (NASH-CRN) stage reduction; "improved" by ranked pair assessment; reduction in Ph-FCS ("any" for ≥0.3 absolute reduction and "substantial" for ≥25% relative reduction). Fibrosis improved in 31% of patients (NASH-CRN), 51% (ranked pair assessment), 74.5% (any Ph-FCS reduction), and 41% (substantial Ph-FCS reduction). Most patients with stable fibrosis by NASH-CRN or ranked pair assessment had a Ph-FCS reduction (a third with substantial reduction). Fibrosis improvement increased with treatment duration: 25% for <48 weeks versus 39% for ≥48 weeks by NASH-CRN; 43% versus 61% by ranked pair assessment, mean Ph-FCS reduction -0.54 (SD: 1.22) versus -1.72 (SD: 1.02); Ph-FCS reduction (any in 54% vs. 100%, substantial in 21% vs. 65%). The antifibrotic effect of Aramchol was corroborated by reductions in liver stiffness, Pro-C3, and enhanced liver fibrosis. Changes in Ph-FCS were positively correlated with changes in liver stiffness. Continuous fibrosis scores generated in antifibrotic trials by digital image analysis quantify antifibrotic effects with greater sensitivity and a larger dynamic range than conventional pathology.

Exploration of Digital Image Analysis for Ki67 Quantification in the Grading of Medullary Thyroid Carcinoma: A Pilot Study with 85 Cases.

Although uncommon, medullary thyroid carcinoma (MTC) accounts for a significant proportion of thyroid cancer deaths. Recent studies have validated the two-tier International Medullary Thyroid Carcinoma Grading System (IMTCGS) to predict clinical outcomes. A 5% Ki67 proliferative index (Ki67PI) cut-off separates low-grade from high-grade MTC. In this study, we compared digital image analysis (DIA) to manual counting (MC) for determining the Ki67PI in a MTC cohort, and explored the challenges encountered. Available slides from 85 MTCs were reviewed by two pathologists. The Ki67PI was documented by immunohistochemistry for each case, scanned with the Aperio® slide scanner at 40× magnification, and quantified using the QuPath® DIA platform. The same hotspots were screenshot, printed in color, and blindly counted. For each case, over 500 MTC cells were counted. Each MTC was graded using IMTCGS criteria. In our MTC cohort (n = 85), 84.7 and 15.3% were low- and high-grade with the IMTCGS. In the entire cohort, QuPath® DIA performed well (R2 = 0.9891) but appeared to undercall compared to MC. QuPath® performed better in high-grade cases (R2 = 0.99) compared to low-grade cases (R2 = 0.7071). Overall, Ki67PI determined with either MC or DIA did not affect IMTCGS grade. Encountered DIA challenges include optimizing cell detection, overlapping nuclei, and tissue artifacts. Encountered MC challenges include background staining, morphologic overlap with normal elements, and counting time. Our study highlights the utility of DIA in quantifying Ki67PI for MTC and can serve as an adjunct for grading in conjunction with the other criteria of mitotic activity and necrosis.

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

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. 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. 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. 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.

Abstract P5-02-01: Analytical validation and prognostic potential of an automated digital scoring protocol for Ki67: An International Ki67 Working Group study

Abstract Background: The nuclear proliferation biomarker Ki67 has multiple potential roles in breast cancer, including aiding decisions based on prognosis, but has unacceptable between-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) Assess inter-laboratory reproducibility of automated Ki67 measurement among 17 participating labs and compare those with standardized pathologist-based visual scoring. (ii) Investigate the comparability of Ki67 measurement across corresponding core biopsy and whole section cases. (iii) Test prognostic potential of the built Ki67 scoring algorithms on an independent cohort. Methods: Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding whole tumor sections from 30 ER+ breast cancer cases were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and stained for Ki67 using the Mib-1 antibody. The QuPath (open-source software) DIA platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed in our previous study (Acs et al, Lab Invest 2019). A detailed guideline for building an automated Ki67 scoring algorithm was sent to the participating labs. Visual scoring of average Ki67 expression was performed by pathologists according to published standardized methods (Leung et al, NPJ Br Cancer 2016; Leung et al, Histopath 2019). Locked down DIA Ki67 scoring algorithms were applied to a validation cohort: 222 breast cancer cases from the Karolinska University Hospital in whole section format. Sufficient reproducibility to declare analytical validity was defined as an Intra Class Correlation (ICC) with lower limit of 95% credible interval (CI) &amp;gt;0.80. Markov Chain Monte Carlo routines for generalized linear mixed models were used to estimate ICCs and calculate corresponding CIs. Results: The same-section ICC was 0.902 (CI: 0.852-0.949) across 17 labs using calibrated DIA platform on core biopsy slides and 0.845 (CI: 0.778-0.912) on whole sections. The different-section ICC across the 17 labs was 0.873 (CI: 0.806-0.932) scoring on core biopsy slides and 0.777 (CI: 0.670-0.874) on whole sections. The pathologist-based visual Ki67 scoring showed ICC of 0.860 for all comparisons, respectively (CI: 0.795-0.927). Similar to what was observed for visual Ki67 scoring, the DIA scores are higher for core biopsy slides compared to paired whole sections (p≤0.001; median difference: 5.31%; IQR: 11.50%). Ki67 scores of all locked down DIA algorithms correlates significantly (p≤0.023) with outcome on the validation cohort (observed hazard ratios range: 2.518-2.922). Conclusions: Automated Ki67 evaluation using a calibrated, open-source DIA platform (QuPath) met the pre-specified criterion of success on core biopsies but not on whole sections in the multi-institutional setting. The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation) and intratumor heterogeneity. We found that different algorithms built according to calibrated DIA methods had similar prognostic potential. Assessment of clinical utility is planned. Citation Format: Balazs Acs, Samuel C.Y. Leung, Kelley M. Kidwell, Indu Arun, Renaldas Augulis, Sunil S. Badve, Yalai Bai, Anita L. Bane, John M.S. Bartlett, Jane Bayani, Gilbert Bigras, Annika Blank, Signe Borgquist, Henk Buikema, Martin C. Chang, Robin L. Dietz, Andrew Dodson, Anna Ehinger, Susan Fineberg, Cornelia M. Focke, Dongxia Gao, Allen M. Gown, Carolina Gutierrez, Johan Hartman, Judith C. Hugh, Zuzana Kos, Anne-Vibeke Lænkholm, Arvydas Laurinavicius, Richard M. Levenson, Rustin Mahboubi-Ardakani, Mauro G. Mastropasqua, Takuya Moriya, Sharon Nofech-Mozes, C. Kent Osborne, Liron Pantanowitz, Frédérique M. Penault-Llorca, Tammy Piper, Mary Anne Quintayo, Tilman T. Rau, Stefan Reinhard, Stephanie Robertson, Takashi Sakatani, Roberto Salgado, Melanie Spears, Jane Starczynski, Tomoharu Sugie, Bert van der Vegt, Giuseppe Viale, Shakeel Virk, Lila A. Zabaglo, Daniel F. Hayes, Mitch Dowsett, Torsten O. Nielsen, David L. Rimm, International Ki67 in Breast Cancer Working Group, BIG-NABCG. Analytical validation and prognostic potential of an automated digital scoring protocol for Ki67: An International Ki67 Working Group study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-01.

Potential Pitfalls in Diagnostic Digital Image Analysis: Experience with Ki-67 and PHH3 in Gastrointestinal Neuroendocrine Tumors

Gastrointestinal neuroendocrine tumors, or GI-NETs are a highly diverse group of tumors derived from neuroendocrine cells of the GI tract. In GI-NET, a spectrum of histological and molecular parameters exists to predict prognosis and survival. Immunohistochemistry for Ki67, a nuclear antigen that is present in all but the G0 phase of the cell cycle with specificity for proliferating cells, can be used to determine a tumors proliferation index. With this in mind, grading of gastrointestinal neuroendocrine tumors is critical for prognosis and can impact clinical decision making. Recently, digital image analysis (DIA) has been shown in studies to be a superior and less time-consuming alternative to the manual scoring of Ki-67 in breast cancer, secondary to its theoretical diagnostic reproducibility. In DIA, the correct identification of tumor cells and non-tumor is paramount to avoid over or under calculation of biomarker expression. Additionally, DIA requires a pathologist to manually outline a tumor in large tissue areas of hematoxylin and eosin (H&E) sections, which is impractical. The findings in our study showed that ventana virtuoso software computer analyzed Ki-67 only correlated well with Neuroendocrine carcinomas while manual analysis of mitotic index and Ki67 were found to be gold standard. The performance of DIA in our study was plagued by software issues. In future, the advent of new digital imaging technologies such as virtual dual staining will hopefully improve diagnostic accuracy and reproducibility across different DIA platforms. Ultimately, determination of therapeutic strategies should be guided by an amalgamation of clinicopathologic characteristics not limited to mitotic index and Ki-67. As well, A visual check of the results should always be performed and correlated with other findings.

Abstract P4-02-01: Analytical validation of an automated digital scoring protocol for Ki67: International multicenter collaboration study

Abstract Background/Goal: Ki67 expression has been a valuable prognostic marker in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we tested 3 automated digital image analysis (DIA) platforms including an open source platform to: (i) Investigate the reproducibility of Ki67 measurement across platforms with supervised classifiers performed by the same operator and by multiple operators. (ii) Compare accuracy of the 3 DIA platforms against outcome (prognostic potential). (iii) Assess inter-laboratory reproducibility of a calibrated DIA tool to evaluate Ki67 in breast cancer among 10 participating labs of the International Ki67 in Breast Cancer Working Group (IKWG). Methods: The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (H) (IndicaLabs), QuantCenter (QC) (3DHistech), QuPath (QP) (open-source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. As a ground truth, we evaluated Ki67 LI with meticulous manual tissue segmentation using the Spectrum Webscope (SW) (Aperio). Calibration was performed using 30 ER+ breast cancer cases from phase 3 of the IKWG initiative where blocks were centrally cut and stained for Ki67. The inter-laboratory analysis was done with 10 participating laboratories divided into 2 groups where members within the same group were given the same set of images. The outcome cohort consisted of 149 breast cancer cases from the Yale Pathology archives in tissue microarray format. Intra-class correlation coefficient (ICC) was used to measure reproducibility with the pre-specified criterion for success being to exceed 0.80. Kaplan-Meier analysis supported with log-rank test was performed to assess prognostic potential. Results: All 3 DIA platforms showed excellent inter-platform reproducibility (ICC: 0.933, CI: 0.879-0.966). Also, excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of SW (QP ICC: 0.970, CI: 0.936-0.986; H ICC: 0.968, CI: 0.933-0.985; QC ICC: 0.964, CI: 0.919-0.983). The intra-DIA reproducibility was also excellent for all platforms (QP ICC: 0.992, CI: 0.986-0.996; H ICC: 0.972, CI: 0.924-0.988; QC ICC: 0.978, CI: 0.932-0.991). Comparing each DIA against outcome, the hazard ratios were similar (QP=3.309, H=3.077, QC=3.731). The inter-operator reproducibility was particularly high (ICC: 0.962-0.995). As QP is open source software and also showed the lowest intra-DIA platform variability, we selected the QP platform to investigate inter-laboratory reproducibility among 10 IKWG labs. The different-section ICC across the 10 labs was 0.974 (CI: 0.954 - 0.986). The same-section ICC estimate was 0.984 (CI: 0.971-0.992) for group 1 and 0.978 (CI: 0.956-0.989) for group 2. Conclusions: Our results showed outstanding reproducibility both within and between DIA platforms. We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Automated Ki67 evaluation using a calibrated, open-source DIA platform (QuPath) met the pre-specified criterion of success in the multi-institutional setting. Assessment of clinical utility is planned. Citation Format: Acs B, Leung SC, Pelekanou V, Bai Y, Martinez-Morilla S, Toki M, Chang MC, Gholap A, Jadhav A, Hugh JC, Bigras G, Laurinavicius A, Augulis R, Levenson R, Todd A, Piper T, Virk S, van der Vegt B, Hayes DF, Dowsett M, Nielsen TO, Rimm DL. Analytical validation of an automated digital scoring protocol for Ki67: International multicenter collaboration study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-02-01.

Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study

Ki67 expression has been a valuable prognostic variable in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we investigate the reproducibility of Ki67 measurement between three image analysis platforms with supervised classifiers performed by the same operator, by multiple operators, and finally we compare their accuracy in prognostic potential. Two breast cancer patient cohorts were used for this study. The standardization was done with the 30 cases of ER+ breast cancer that were used in phase 3 of International Ki67 in Breast Cancer Working Group initiatives where blocks were centrally cut and stained for Ki67. The outcome cohort was from 149 breast cancer cases from the Yale Pathology archives. A tissue microarray was built from representative tissue blocks with median follow-up of 120 months. The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (IndicaLab), QuantCenter (3DHistech), and QuPath (open source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. Intraclass correlation coefficient (ICC) was used to measure reproducibility. Between-DIA platform reproducibility was excellent (ICC: 0.933, CI: 0.879–0.966). Excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of Spectrum Webscope (QuPath-Spectrum Webscope ICC: 0.970, CI: 0.936–0.986; HALO-Spectrum Webscope ICC: 0.968, CI: 0.933–0.985; QuantCenter-Spectrum Webscope ICC: 0.964, CI: 0.919–0.983). All platforms showed excellent intra-DIA reproducibility (QuPath ICC: 0.992, CI: 0.986–0.996; HALO ICC: 0.972, CI: 0.924–0.988; QuantCenter ICC: 0.978, CI: 0.932–0.991). Comparing each DIA against outcome, the hazard ratios were similar. The inter-operator reproducibility was particularly high (ICC: 0.962–0.995). Our results showed outstanding reproducibility both within and between-DIA platforms, including one freely available DIA platform (QuPath). We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Results justify multi-institutional DIA studies to assess clinical utility.This study shows excellent reproducibility among 3 different digital image (DIA) analysis platforms in Ki67 scoring. An outstanding concordance was found among four operators using the same calibrated DIA platform suggesting a highly reproducible Ki67 scoring method. Finally, the authors also found that all three DIA platforms were essentially indistinguishable with respect to prediction of breast cancer patient outcome.

Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement

PurposeThe Ki67 proliferation index is a prognostic and predictive marker in breast cancer. Manual scoring is prone to inter- and intra-observer variability. The aims of this study were to clinically validate digital image analysis (DIA) of Ki67 using virtual dual staining (VDS) on whole tissue sections and to assess inter-platform agreement between two independent DIA platforms.MethodsSerial whole tissue sections of 154 consecutive invasive breast carcinomas were stained for Ki67 and cytokeratin 8/18 with immunohistochemistry in a clinical setting. Ki67 proliferation index was determined using two independent DIA platforms, implementing VDS to identify tumor tissue. Manual Ki67 score was determined using a standardized manual counting protocol. Inter-observer agreement between manual and DIA scores and inter-platform agreement between both DIA platforms were determined and calculated using Spearman’s correlation coefficients. Correlations and agreement were assessed with scatterplots and Bland–Altman plots.ResultsSpearman’s correlation coefficients were 0.94 (p < 0.001) for inter-observer agreement between manual counting and platform A, 0.93 (p < 0.001) between manual counting and platform B, and 0.96 (p < 0.001) for inter-platform agreement. Scatterplots and Bland–Altman plots revealed no skewness within specific data ranges. In the few cases with ≥ 10% difference between manual counting and DIA, results by both platforms were similar.ConclusionsDIA using VDS is an accurate method to determine the Ki67 proliferation index in breast cancer, as an alternative to manual scoring of whole sections in clinical practice. Inter-platform agreement between two different DIA platforms was excellent, suggesting vendor-independent clinical implementability.

Automated Detection and Quantification of Prostate Cancer in Needle Biopsies by Digital Image Analysis

Introduction: Triple immunohistochemical (IHC) stains including antibodies specific for alpha-methylacyl-CoA-racemase and basal cell markers have been a valuable aid in accurate identification of prostate carcinoma. However, accurate quantification of minuscule areas of prostate carcinoma in biopsy specimens can often be a challenge. Here we assessed the diagnostic value and quantitative use of automated digital image analysis on triple IHC stained prostate needle biopsies. Methods: Twelve cases of prostate needle biopsy material including 75 needle cores were stained with triple-antibody cocktail (P504S + 34βE12 + p63). Slides were digitally scanned with the APERIO digital image analyzer and evaluated with the GENIE pattern and color recognition digital image analysis that we developed. A slide with known areas of adenocarcinoma, high grade prostatic intraepithelial neoplasia (PIN), benign glands and stroma was used as a training set for the automated digital image analysis platform. Results: Among 75 needle biopsy cores, 19 (25.33%) contained adenocarcinoma by histology. Digital image analysis recognized adenocarcinoma in 95% of these needle biopsies. The average area of the needle biopsy was 7.63 mm2 and overall the average area of tumor was 0.196 mm2. The smallest area of tumor recognized by the program was 0.0022 mm2 (0.0363% of the core) and the largest was 0.62 mm2 (8.17% of the core) among needle core biopsies. False positives resulted from areas of high grade PIN with patchy basal cells. The false negative was caused by uneven AMACR staining in one area of adenocarcinoma. Digital recognition of areas of interest was improved by three successive image analysis training which increased the sensitivity and specificity from 83% and 89% to 90% and 93%, respectively. Conclusions: Digital image analysis in concert with IHC triple staining may be useful for accurate detection and quantitative analysis of small foci of prostatic adenocarcinoma. Defining methods to increase the sensitivity and specificity of quantitative automated digital image analysis will likely evolve as an area of investigation. Future automated digital scanning and innovative pattern and color recognition technologies may open avenues for classifying a variety of prostate lesions.