Digital Image Analysis Algorithms Research Articles

Prognostic impact of HER2 biomarker levels in trastuzumab-treated early HER2-positive breast cancer

BackgroundOverexpression of human epidermal growth factor receptor 2 (HER2) caused by HER2 gene amplification is a driver in breast cancer tumorigenesis. We aimed to investigate the prognostic significance of manual scoring and digital image analysis (DIA) algorithm assessment of HER2 copy numbers and HER2/CEP17 ratios, along with ERBB2 mRNA levels among early-stage HER2-positive breast cancer patients treated with trastuzumab.MethodsThis retrospective study comprised 371 early HER2-positive breast cancer patients treated with adjuvant trastuzumab, with HER2 re-testing performed on whole tumor sections. Digitized tumor tissue slides were manually scored and assessed with uPath HER2 Dual ISH image analysis, breast algorithm. Targeted ERBB2 mRNA levels were assessed by the Xpert® Breast Cancer STRAT4 Assay. HER2 copy number and HER2/CEP17 ratio from in situ hybridization assessment, along with ERBB2 mRNA levels, were explored in relation to recurrence-free survival (RFS).ResultsThe analysis showed that patients with tumors with the highest and lowest manually counted HER2 copy number levels had worse RFS than those with intermediate levels (HR = 2.7, CI 1.4–5.3, p = 0.003 and HR = 2.1, CI 1.1–3.9, p = 0.03, respectively). A similar trend was observed for HER2/CEP17 ratio, and the DIA algorithm confirmed the results. Moreover, patients with tumors with the highest and the lowest values of ERBB2 mRNA had a significantly worse prognosis (HR = 2.7, CI 1.4–5.1, p = 0.003 and HR = 2.8, CI 1.4–5.5, p = 0.004, respectively) compared to those with intermediate levels.ConclusionsOur findings suggest that the association between any of the three HER2 biomarkers and RFS was nonlinear. Patients with tumors with the highest levels of HER2 gene amplification or ERBB2 mRNA were associated with a worse prognosis than those with intermediate levels, which is of importance to investigate in future clinical trials studying HER2-targeted therapy.

Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm

Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view.

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated aDelphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i)scanners, quality assurance, and validation of scans; ii)integration of scanners and systems into the pathology laboratory information system; iii)the digital workflow; and iv)digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices.

Improved concordance of challenging human epidermal growth factor receptor 2 dual in-situ hybridisation cases with the use of a digital image analysis algorithm in breast cancer.

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression by HER2 immunohistochemistry and in-situ hybridisation (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines define 5 groups based on HER2 expression and copy number. Manual pathologist quantification by light microscopy of equivocal and less common HER2 ISH groups (groups 2-4) can be challenging, and there are no data on interobserver variability in reporting of these cases. We sought to determine whether a digital algorithm could improve interobserver variability in the assessment of difficult HER2 ISH cases. HER2 ISH was evaluated in a cohort enriched for less common HER2 patterns using standard light microscopy versus analysis of whole slide images using the Roche uPath HER2 dual ISH image analysis algorithm. Standard microscopy demonstrated significant interobserver variability with a Fleiss's kappa value of 0.471 (fair-moderate agreement) improving to 0.666 (moderate-good) with the use of the algorithm. For HER2 group designation (groups 1-5), there waspoor-moderate reliability between pathologists bymicroscopy [intraclass correlation coefficient (ICC) = 0.526], improving to moderate-good agreement (ICC = 0.763) with the use of the algorithm. In subgroup analysis, the algorithm improved concordance particularly in groups 2, 4 and 5. Time to enumerate cases was also significantly reduced. This work demonstrates the potential of a digital image analysis algorithm to improve the concordance of pathologist HER2 amplification status reporting in less common HER2 groups. This has the potential to improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.

HER2 in situ hybridization testing in breast cancer: Applying algorithm-assisted assessment to reduce interobserver variability in difficult cases.

e13561 Background: Accurate assessment of HER2 expression by HER2 immunohistochemistry and in-situ hybridization (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines defined 5 subgroups based on HER2 expression and copy number. Few reports have investigated interobserver variability when applying the revised guidelines. Digital algorithms may have a role in assisting pathologists in assessing cases with equivocal and uncommon patterns of HER2 amplification. This study was designed to evaluate interobserver variability in the gold standard manual assessment of HER2 ISH, particularly in less common HER2 ISH groups. Subsequently, we evaluated whether a new digital image analysis algorithm (Roche uPath HER2 Dual ISH image analysis) could reduce interobserver variability and potentially improve patient selection for targeted anti-HER2 therapy. Methods: Of 2982 breast cancer cases, we derived an enriched cohort of 99 patient cases with HER2 IHC 2+ or 3+ and selective ISH results (groups 2-4, evidence of HER2 heterogeneity, borderline negative group 5 and borderline positive group 1). As per guidelines, a minimum of 20 tumour cells per slide were manually and independently counted by 4 anatomical pathologists. After a washout period and second round of de-identification, the slides were scanned (DP200; Roche) and re-scored using the digital algorithm. Results: Pathologist reporting of patient HER2 status (HER2 positive vs. HER2 negative) using manual microscopy demonstrated significant interobserver variability, with a Fleiss’s kappa value of 0.471 (95% CI: 0.378-0.564; P= 1.24E-21) indicating fair-moderate agreement. Interobserver variability in manual scoring of HER2 ISH at the level of HER2 group designation (groups 1-5) showed poor-moderate reliability between pathologists, with an intraclass correlation coefficient (ICC) of 0.526 (95% CI: 0.407-0.641; P= 9.28E-21). Repeat analysis of the cohort using algorithm-assisted quantification resulted in moderate-substantial concordance in HER2 patient status reporting, with a Fleiss’s kappa value of 0.666 (95% CI: 0.572-0.759; P= 1.70E-40). Algorithm-assisted quantification resulted in moderate-good agreement of HER2 group designation, with an ICC of 0.763 (95% CI: 0.683-0.832; P= 8.76E-48). In sub-group analysis, use of the algorithm improved concordance particularly in groups 2,4 and 5. Time to report cases was also significantly reduced with the use of the algorithm. Conclusions: Our study demonstrates the potential to improve the concordance of HER2 amplification status reporting in less common HER2 subgroups using a digital algorithm and potentially improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.

Abstract 6634: Digital and spacial characterization of PD-L1 expression and IVD assay performance in the immune landscape of head and neck squamous cell carcinoma: A multimodal approach

Abstract The anti-PD-L1 antibody (22C3) is used as a companion diagnostic for successful checkpoint inhibitor therapy in head and neck squamous cell carcinoma. The positive predictive value of this assay, however, is less than ideal as a high PD-L1 score does not necessarily associate with a good response, and a good response may be seen with a low or negative PD-L1 score. In principle, this could be due to lack of accuracy and/or precision of the assay, as well as biological factors, such as variable expression across tumor cells and interacting immune cells. In this study, we describe a digital image analysis algorithm to address assay-related problems and improve both accuracy and precision, as well as improve identification of PD-L1 expression in differing cell populations of the tumor microenvironment. In addition, we propose a panel of assays that in aggregate might reveal inherent heterogeneity among cases with similar PD-L1 score and thus provide vital context underlying the efficacy of checkpoint inhibitors in the clinic. Here we show that applying novel machine-learning based digital image analysis to multiplex assays together with digital scoring of PD-L1 provides an accurate and precise assessment of the real-world tumor milieu that we hypothesize will help establish the immunophenotypes that inform therapeutic efficacy of checkpoint inhibitors. We have additionally interrogated whether expression of PD-L1 in these groups are consistent with known molecular patterns assayed using novel methodologies, such as high-plex digital spatial transcriptomics through the nanoString GeoMx Digital Spatial Profiler (DSP) platform and assessed the ability of the different digital platforms to provide concordant data relating to real-world expression patterns of PD-L1 and associated biomarkers. Thus, our results point to the importance of robust methodologies, used in combination, to evaluate complex tumor immune landscapes and the advantages of digital analyses to provide accurate and precise clinical contexts for better patient outcomes. Citation Format: Clara Troccoli, Lauren Matelski, Adam Beharry, Vanessa Ly, Morgan Wambaugh, Melanie Amen, Will Paces, Geoffrey Metcalf, Roberto Gianani, Tom Turi. Digital and spacial characterization of PD-L1 expression and IVD assay performance in the immune landscape of head and neck squamous cell carcinoma: A multimodal approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6634.

Immune Microenvironment in Sporadic Early-Onset versus Average-Onset Colorectal Cancer

The incidence of left-sided colon and rectal cancer in young people are increasing worldwide, but its causes are poorly understood. It is not clear if the tumor microenvironment is dependent on age of onset, and little is known about the composition of tumor-infiltrating T cells in early-onset colorectal cancer (EOCRC). To address this, we investigated T-cell subsets and performed gene expression immune profiling in sporadic EOCRC tumors and matched average-onset colorectal cancer (AOCRC) tumors. Left-sided colon and rectal tumors from 40 cases were analyzed; 20 EOCRC (<45 years) patients were matched 1:1 to AOCRC (70-75 years) patients by gender, tumor location, and stage. Cases with germline pathogenic variants, inflammatory bowel disease or neoadjuvant-treated tumors were excluded. For T cells in tumors and stroma, a multiplex immunofluorescence assay combined with digital image analysis and machine learning algorithms was used. Immunological mediators in the tumor microenvironment were assessed by NanoString gene expression profiling of mRNA. Immunofluorescence revealed no significant difference between EOCRC and AOCRC with regard to infiltration of total T cells, conventional CD4+ and CD8+ T cells, regulatory T cells, or γδ T cells. Most T cells were located in the stroma in both EOCRC and AOCRC. Immune profiling by gene expression revealed higher expression in AOCRC of the immunoregulatory cytokine IL-10, the inhibitory NK cell receptors KIR3DL3 and KLRB1 (CD161), and IFN-a7 (IFNA7). In contrast, the interferon-induced gene IFIT2 was more highly expressed in EOCRC. However, in a global analysis of 770 tumor immunity genes, no significant differences could be detected. T-cell infiltration and expression of inflammatory mediators are similar in EOCRC and AOCRC. This may indicate that the immune response to cancer in left colon and rectum is not related to age of onset and that EOCRC is likely not driven by immune response deficiency.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R2 = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

Digital image analysis of intraepithelial B-lymphocytes to assess lymphoepithelial lesions in salivary glands of Sjögren's syndrome patients.

Salivary glands of primary SS (pSS) patients characteristically harbour periductal infiltrates, in which lymphoepithelial lesions (LELs) can develop. LELs are composed of hyperplastic ductal epithelium with infiltrating lymphocytes and may assist in the challenging diagnostic process of pSS. As manual identification of LELs remains difficult, we aimed to identify LELs by using an objective digital image analysis (DIA) algorithm that detects intraepithelial lymphocytes. A virtual triple-staining technique developed for this study was used to count intraepithelial lymphocytes in consecutive slides stained for CD3 (T-lymphocytes), high-molecular-weight cytokeratin (hmwCK) (striated ducts) and CD20 (B-lymphocytes) in labial and parotid gland biopsies in a diagnostic cohort of 109 sicca patients. Patients were classified as having pSS or non-SS according to the ACR-EULAR classification criteria. T-lymphocytes were detected in almost all analysed ducts of pSS and non-SS sicca patients, whereas intraepithelial B-lymphocytes were present in 59-68% of labial and parotid gland biopsies of pSS patients, against only 2-3% of patients classified as non-SS. Intraepithelial B-lymphocytes were found in almost all striated ducts with hyperplasia (LELs). Remarkably, ∼25% of analysed striated ducts without hyperplasia of pSS patients also contained B-lymphocytes (precursor-LELs). Furthermore, presence of intraepithelial B-lymphocytes was associated with clinical parameters of pSS (i.e. serology). The presence of intraepithelial B-lymphocytes in salivary gland biopsies of sicca patients is a clear indicator of pSS and can be used as an objective alternative to LEL scoring. Therefore, identification of B-lymphocyte-containing ducts should be added to the diagnostic histopathological work-up of patients suspected of pSS.

Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group.

Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920–0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593–4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.

Validation of a DKK1 RNAscope chromogenic in situ hybridization assay for gastric and gastroesophageal junction adenocarcinoma tumors

Dickkopf-1 (DKK1) is a secreted modulator of Wnt signaling that is frequently overexpressed in tumors and associated with poor clinical outcomes. DKN-01 is a humanized monoclonal therapeutic antibody that binds DKK1 with high affinity and has demonstrated clinical activity in gastric/gastroesophageal junction (G/GEJ) patients with elevated tumoral expression of DKK1. Here we report on the validation of a DKK1 RNAscope chromogenic in situ hybridization assay to assess DKK1 expression in G/GEJ tumor tissue. To reduce pathologist time, potential pathologist variability from manual scoring and support pathologist decision making, a digital image analysis algorithm that identifies tumor cells and quantifies the DKK1 signal was developed. Following CLIA guidelines the DKK1 RNAscope chromogenic in situ hybridization assay and digital image analysis algorithm were successfully validated for sensitivity, specificity, accuracy, and precision. The DKK1 RNAscope assay in conjunction with the digital image analysis solution is acceptable for prospective screening of G/GEJ adenocarcinoma patients. The work described here will further advance the companion diagnostic development of our DKK1 RNAscope assay and could generally be used as a guide for the validation of RNAscope assays with digital image quantification.

Association of Fusobacterium nucleatum with Specific T-cell Subsets in the Colorectal Carcinoma Microenvironment.

While evidence indicates that Fusobacterium nucleatum (F. nucleatum) may promote colorectal carcinogenesis through its suppressive effect on T-cell-mediated antitumor immunity, the specific T-cell subsets involved remain uncertain. We measured F. nucleatum DNA within tumor tissue by quantitative PCR on 933 cases (including 128 F. nucleatum-positive cases) among 4,465 incident colorectal carcinoma cases in two prospective cohorts. Multiplex immunofluorescence combined with digital image analysis and machine learning algorithms for CD3, CD4, CD8, CD45RO (PTPRC isoform), and FOXP3 measured various T-cell subsets. We leveraged data on Bifidobacterium, microsatellite instability (MSI), tumor whole-exome sequencing, and M1/M2-type tumor-associated macrophages [TAM; by CD68, CD86, IRF5, MAF, and MRC1 (CD206) multimarker assay]. Using the 4,465 cancer cases and inverse probability weighting method to control for selection bias due to tissue availability, multivariable-adjusted logistic regression analysis assessed the association between F. nucleatum and T-cell subsets. The amount of F. nucleatum was inversely associated with tumor stromal CD3+ lymphocytes [multivariable OR, 0.47; 95% confidence interval (CI), 0.28-0.79, for F. nucleatum-high vs. -negative category; P trend = 0.0004] and specifically stromal CD3+CD4+CD45RO+ cells (corresponding multivariable OR, 0.52; 95% CI, 0.32-0.85; P trend = 0.003). These relationships did not substantially differ by MSI status, neoantigen load, or exome-wide tumor mutational burden. F. nucleatum was not significantly associated with tumor intraepithelial T cells or with M1 or M2 TAMs. The amount of tissue F. nucleatum is associated with lower density of stromal memory helper T cells. Our findings provide evidence for the interactive pathogenic roles of microbiota and specific immune cells.

Tumour Stroma Ratio Assessment Using Digital Image Analysis Predicts Survival in Triple Negative and Luminal Breast Cancer

Simple SummaryTumour stroma is known to predict outcome and play an important role in the growth and spread of solid tumours and their response to therapy. In breast cancer, there is evidence that the tumour stroma ratio (TSR) can predict outcome in aggressive triple negative breast cancer, but its value for the more common hormone receptor positive breast cancer is unclear. Using computerised image analysis and machine learning algorithms, we show that TSR is an important factor in predicting outcome for triple negative disease and hormone receptor positive cancer. However, its influence on good or poor outcome appears to depend on tumour type and the relative predominance of the stromal component. By better understanding the role of the tumour stroma in cancer growth, and its response to treatment, this study may help support the role of TSR as a new prognostic marker for breast cancer to guide clinical decision making.We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan–Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1–3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3–5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4–0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.

Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer.

The improvement of tumor biomarkers prepared for clinical use is a long process. A good biomarker should predict not only prognosis but also the response to therapies. In this review, we describe the biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer, considering different breast cancer subtypes. In hormone receptor (HR)-positive/human epidermal growth factor 2 (HER2)-negative breast cancers, various genomic markers highly associated with proliferation have been tested. Among them, only two genomic signatures, the 21-gene recurrence score and 70-gene signature, have been reported in prospective randomized clinical trials and met the primary endpoint. However, these genomic markers did not suffice in HER2-positive and triple-negative (TN) breast cancers, which present only classical clinical and pathological information (tumor size, nodal or distant metastatic status) for decision making in the adjuvant setting in daily clinic. Recently, patients with residual invasive cancer after neoadjuvant chemotherapy are at a high-risk of recurrence for metastasis, which, in turn, make these patients best applicants for clinical trials. Two clinical trials have shown improved outcomes with post-operative capecitabine and ado-trastuzumab emtansine treatment in patients with either TN or HER2-positive breast cancer, respectively, who had residual disease after neoadjuvant chemotherapy. Furthermore, tumor-infiltrating lymphocytes (TILs) have been reported to have a predictive value for prognosis and response to chemotherapy from the retrospective analyses. So far, TILs have to not be used to either withhold or prescribe chemotherapy based on the absence of standardized evaluation guidelines and confirmed information. To overcome the low reproducibility of evaluations of TILs, gene signatures or digital image analysis and machine learning algorithms with artificial intelligence may be useful for standardization of assessment for TILs in the future.

Ki-67 Proliferation Index in Breast Cancer as a Function of Assessment Method: A NordiQC Experience.

Immunohistochemical staining for Ki-67 is used to calculate a Ki-67 proliferation index (PI) that carries prognostic and predictive information in various cancers including breast carcinomas. Studies have documented challenges for observers to reproducibly estimate the Ki-67 PI. At present, no international consensus exists concerning scoring method (eg, hotspots vs. overall average, digital vs. manual counting) or even the definition of a Ki-67-positive cell. To clarify the approach to Ki-67 scoring and evaluation of the interobserver agreement among participants in the Nordic Immunohistochemical Quality Control (NordiQC) Breast Cancer Module, a study was set up on the basis of an online web module containing 15 digitized tissue microarray cores of breast carcinomas stained for Ki-67 in the NordiQC reference laboratory. All participants were invited to attend the study. In addition to Ki-67 scoring, they were asked to disclose their preferred method for Ki-67 estimation and their job title. For comparison, slides were analyzed using a Digital Image Analysis algorithm based on Virtual Double Staining. In total, 199 participants enrolled for the study. Overall, there was a good correlation in Ki-67 PIs among the participants, although results for some cores varied significantly. However, when applying a cutoff of 20%, a relatively low κ value of 0.52 was observed. Participants scoring in hotspots reported higher Ki-67 PIs than participants estimating an overall average, and, not surprisingly, participants who considered weak Ki-67 nuclear staining positive obtained higher Ki-67 PIs than those who did not. Ki-67 PI was not correlated to job title. The Virtual Double Staining algorithm obtained Ki-67 values close to the mean value of the human observers. Our study underlines the need for international standardization and guidelines in estimation of Ki-67 PI. Digital Image Analysis may be a useful tool in this process.

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.

Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning

Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided tumor bud selection. Development of computer algorithms for this purpose requires unequivocal examples of individual tumor buds. As such, we undertook a large-scale, international, and digital observer study on individual tumor bud assessment. From a pool of 46 colorectal cancer cases with tumor budding, 3000 tumor bud candidates were selected, largely based on digital image analysis algorithms. For each candidate bud, an image patch (size 256 × 256 µm) was extracted from a pan cytokeratin-stained whole-slide image. Members of an International Tumor Budding Consortium (n = 7) were asked to categorize each candidate as either (1) tumor bud, (2) poorly differentiated cluster, or (3) neither, based on current definitions. Agreement was assessed with Cohen’s and Fleiss Kappa statistics. Fleiss Kappa showed moderate overall agreement between observers (0.42 and 0.51), while Cohen’s Kappas ranged from 0.25 to 0.63. Complete agreement by all seven observers was present for only 34% of the 3000 tumor bud candidates, while 59% of the candidates were agreed on by at least five of the seven observers. Despite reports of moderate-to-substantial agreement with respect to tumor budding grade, agreement with respect to individual pan cytokeratin-stained tumor buds is moderate at most. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.

Neoadjuvant Metformin Added to Systemic Therapy Decreases the Proliferative Capacity of Residual Breast Cancer.

The proliferative capacity of residual breast cancer (BC) disease indicates the existence of partial treatment resistance and higher probability of tumor recurrence. We explored the therapeutic potential of adding neoadjuvant metformin as an innovative strategy to decrease the proliferative potential of residual BC cells in patients failing to achieve pathological complete response (pCR) after pre-operative therapy. We performed a prospective analysis involving the intention-to-treat population of the (Metformin and Trastuzumab in Neoadjuvancy) METTEN study, a randomized multicenter phase II trial of women with primary, non-metastatic (human epidermal growth factor receptor 2) HER2-positive BC evaluating the efficacy, tolerability, and safety of oral metformin (850 mg twice-daily) for 24 weeks combined with anthracycline/taxane-based chemotherapy and trastuzumab (arm A) or equivalent regimen without metformin (arm B), before surgery. We centrally evaluated the proliferation marker Ki67 on sequential core biopsies using visual assessment (VA) and an (Food and Drug Administration) FDA-cleared automated digital image analysis (ADIA) algorithm. ADIA-based pre-operative values of high Ki67 (≥20%), but not those from VA, significantly predicted the occurrence of pCR in both arms irrespective of the hormone receptor status (p = 0.024 and 0.120, respectively). Changes in Ki67 in residual tumors of non-pCR patients were significantly higher in the metformin-containing arm (p = 0.025), with half of all patients exhibiting high Ki67 at baseline moving into the low-Ki67 (<20%) category after neoadjuvant treatment. By contrast, no statistically significant changes in Ki67 occurred in residual tumors of the control treatment arm (p = 0.293). There is an urgent need for innovative therapeutic strategies aiming to provide the protective effects of decreasing Ki67 after neoadjuvant treatment even if pCR is not achieved. Metformin would be evaluated as a safe candidate to decrease the aggressiveness of residual disease after neoadjuvant (pre-operative) systemic therapy of BC patients.

Invasive lobular carcinoma of the breast: assessment of proliferative activity using automated Ki-67 immunostaining

Invasive lobular carcinoma (ILC) is almost always classified as Nottingham histological grade 2. Despite this, prognosis is markedly varied, with some ILCs behaving more akin to grade 3 invasive ductal carcinoma (IDC). Methods to separate these aggressive ILCs are needed. Digital image analysis (DIA) of the Ki-67 biomarker has potential in this regard; thus, we sought to determine the feasibility of its use for automated evaluation of ILC. An initial pilot study demonstrated no ILC specific changes were required to our Ki-67 DIA algorithm for reproducible results. Subsequently, 42 consecutive cases of ILC were evaluated by visual mitosis counting in H&E stained sections and by DIA on Ki-67 stained sections. Ki-67 proliferative index (PI) DIA showed significant correlation with visual mitosis counting on H&E stained sections (rs=0.63; p<0.05), significant strong correlation (rs=0.78; p<0.05) and substantial agreement (κ=0.62) with manual/visual Ki-67 assessment and significant positive associations with grade, nodal status and 'pleomorphic' ILC subtype, and a wide stratification of values in classical/grade 2 ILC. In conclusion, DIA of Ki-67 PI in ILC is feasible, correlates with mitotic index, manual/visual Ki-67 PI and clinico-pathological variables. The broad stratification of Ki-67 PI in classical/grade 2 ILC supports its practicability as a biomarker with prognostic and predictive potential, although large studies with outcome data are required for validation.

Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction.

Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.