Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images

Abhishek Vahadane,Shreya Sharma,Devraj Mandal, Madan Dabbeeru, Josephine Jakthong,Miguel Garcia-Guzman,Shantanu Majumdar, Chung-Wein Lee

Computers in Biology and Medicine(2022)

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Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%–40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image. Multiplex immunofluorescence (mIF) imaging reveals the expression of an increased number of immune markers in tumour biopsies and has been used extensively in immunotherapy research. Recent rapid progress of Artificial Intelligence (AI)-based imaging analysis, particularly Deep Learning, provides cost effective and high-quality solutions to healthcare. In this article, we propose an imaging pipeline that utilizes three-colour mIF images (DAPI, PD-L1, and Pan-cytokeratin) as input and predicts the CPS using AI techniques. Our novel pipeline is composed of three modules employing algorithms of image processing, machine learning, and deep learning techniques. The first module of quality check (QC) detects and removes the image regions contaminated with sectioning and staining artefacts. The QC module ensures that only image regions free of the three common artefacts are used for downstream analysis. The second module of nuclear segmentation uses deep learning to segment and count nuclei in the DAPI images wherein our specialized method can accurately separate touching nuclei. The third module of cell phenotyping calculates CPS by identifying and counting PD-L1 positive cells and tumour cells. These modules are data-efficient and require only few manual annotations for training purposes. Using tumour biopsies from a clinical trial, we found that the CPS from the AI-based models shows a high Spearman correlation (78%, p = 0.003) to the pathologist-scored CPS.
CPS,Combined positive score,mIF,Multiplex immunofluorescence,IHC,Immunohistochemistry,HNSCC,Head and neck squamous cell carcinoma,PD1,Program cell death protein 1,PD-L1,Programmed cell death ligand 1,ICI,Immune checkpoint inhibitors,FFPE,Formalin-fixed, paraffin-embedded,AI,Artificial intelligence,Deep learning
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