Visible- and near-infrared hyperspectral imaging for the quantitative analysis of PD-L1+ cells in human lymphomas: Comparison with fluorescent multiplex immunohistochemistry.

INTRODUCTION: We analyzed the expression of PD-L1 in human lymphomas using hyperspectral imaging (HSI) compared to visual assessment (VA) and conventional digital image analysis (DIA) to strengthen further the value of HSI as a tool for the evaluation of brightfield-based immunohistochemistry (IHC). In addition, fluorescent multiplex immunohistochemistry (mIHC) was used as a second detection method to analyze the impact of a different detection method. MATERIAL AND METHODS: 18 cases (6 follicular lymphomas and 12 diffuse large B-cell lymphomas) were stained for PD-L1 by IHC and for PD-L1, CD3, and CD8 by fluorescent mIHC. The percentage of positively stained cells was evaluated with VA, HSI, and DIA for IHC and VA and DIA for mIHC. Results were compared between the different methods of detection and analysis. RESULTS: An overall high concordance was found between VA, HSI, and DIA in IHC (Cohens Kappa = 0.810VA/HSI, 0.710 VA/DIA, and 0.516 HSI/DIA) and for VAmIHCversus DIAmIHC (Cohens Kappa = 0.894). Comparing IHC and mIHC general agreement differed depending on the methods compared but reached at most a moderate agreement (Cohens Kappa between 0.250 and 0.483). This is reflected by the significantly higher percentage of PD-L1+ cells found with mIHC (pFriedman = 0.014). CONCLUSION: Our study shows a good concordance for the different analysis methods. Compared to VA and DIA, HSI proved to be a reliable tool for assessing IHC. Understanding the regulation of PD-L1 expression will further enlighten the role of PD-L1 as a biomarker. Therefore it is necessary to develop an instrument, such as HSI, which can offer a reliable and objective evaluation of PD-L1 expression.

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas.

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.

Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example.

PURPOSE: The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). METHODS: We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). RESULTS: Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. CONCLUSION: Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making.

Reduction of complement factor H binding to CLL cells improves the induction of rituximab-mediated complement-dependent cytotoxicity.

A main effector mechanism of rituximab (RTX) is the induction of complement-dependent cytotoxicity (CDC). However, this effector function is limited, because CLL cells are protected from complement-induced damage by regulators of complement activation (RCAs). A prominent RCA in fluid phase is factor H (fH), which has not been investigated in this context yet. Here, we show that fH binds to CLL cells and that human recombinant fH-derived short-consensus repeat 18-20 (hSCR18-20) interferes with this binding. In complement-based lysis assays, CLL cells from therapy-naive patients were differently susceptible to RTX-induced CDC and were defined as CDC responder or CDC non-responder, respectively. In CDC responders, but notably also in non-responders, hSCR18-20 significantly boosted RTX-induced CDC. Killing of the cells was specific for CD20(+) cells, whereas CD20(-) cells were poorly affected. CDC resistance was independent of expression of the membrane-anchored RCAs CD55 and CD59, although blocking of these RCAs further boosted CDC. Thus, inhibition of fH binding by hSCR18-20 sensitizes CLL cells to CDC and may provide a novel strategy for improving RTX-containing immunochemotherapy of CLL patients.