Precision immunoprofiling by image analysis and artificial intelligence

Koelzer, Viktor H.; Sirinukunwattana, Korsuk; Rittscher, Jens; Mertz, Kirsten D.

doi:10.1007/s00428-018-2485-z

Cited by 112 publications

(95 citation statements)

References 83 publications

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“…Another potential approach is machine learning using artificial intelligence (AI). AI can digitize whole‐slide images of tissue samples and enable an accurate and reproducible means for the unbiased assessment of regularities in the expression of immunohistochemical markers, tumor morphology, and TILs 71 . The ability of machine learning tools to detect key features in complex immunophenotypic datasets underlines their potential importance for the development of novel predictive models in cancer immunotherapy.…”

Section: Future Perspectivesmentioning

confidence: 99%

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

et al. 2020

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Esophageal squamous cell carcinoma (ESCC) is the main prevalent histological type of esophageal cancer, predominantly constituting 90% of cases worldwide. Despite the development of multidisciplinary therapeutic approaches, its prognosis remains unfavorable. Recently, the development of monoclonal antibodies inhibiting programmed death 1 (PD‐1) or programmed death‐ligand 1 (PD‐L1) has led to marked therapeutic responses among multiple malignancies including ESCC. However, only a few patients achieved clinical benefits due to resistance. Therefore, precise and accurate predictive biomarkers should be identified for personalized immunotherapy in clinical settings. Because the tumor immune microenvironment can potentially influence the patient's response to immune checkpoint inhibitors, tumor immunity, such as PD‐L1 expression on tumors, tumor‐infiltrating lymphocytes, tumor‐associated macrophages, and myeloid‐derived suppressor cells, in ESCC should be further investigated. In this review, accumulated evidence regarding the tumor immune microenvironment and immune checkpoint inhibitors in ESCC are summarized.

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Section: Future Perspectivesmentioning

confidence: 99%

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

et al. 2020

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“…Advances in digital pathology and widely available scanner have set the stage for the clinical application of machine learning (ML), and make it possible to develop an assistive tools of Artificial Intelligences (AI) to improve the pathologic practice [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for automated cancer detection and tumor proportion score estimation of PD-L1 expression in lung adenocarcinoma

Liu

et al. 2020

Preprint

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BackgroundThis study proposed a computational method to detect the cancer areas and calculate the tumor proportion score (TPS) of PD-L1 immunohistochemistry (IHC) expression for lung adenocarcinoma based on deep learning and transfer learning. Patients and methodsPD-L1 22C3 and SP142 IHC slides of lung adenocarcinoma samples on digitized whole-slide images (WSI) database were employed. We build a deep convolutional neural network (DCNN) to automatically segment cancer regions. TPS was calculated based on segmented areas and then compared with the interpretations of pathologists. ResultsWe trained a DCNN model based on 22C3 dataset and fine-tuned it with SP142 dataset. We obtain a robust performance on cancer region detection on both datasets, with a sensitivity of 93.36% (22C3) and 92.80% (SP142) and a specificity of 93.97% (22C3) and 89.25% (SP142). With all the coefficient of determinations larger than 0.9 and Fleiss' and Cohen's Kappa larger than 0.8 (between mean or median of pathologists and TPS calculated by our method), we also found out the strong correlation between the TPS estimated by our computational method and estimation from multiple pathologists' interpretations of 22C3 and SP142 respectively. ConclusionWe provide an AI method to efficiently predict cancer region and calculate TPS in PD-L1 IHC slide of lung adenocarcinoma on two different antibodies. It demonstrates the potential of using deep learning methods to conveniently access PD-L1 IHC status. In the future, we will further validate the AI tool for automated scoring PD-L1 in large volume samples.3

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“…Companies like Microsoft are focused to get antigen-specific binding data for several diseases along with ovarian and pancreatic cancers ( 65 ). ML algorithms have an immense scope of application in immune-oncology, specifically in pattern recognition in histopathological images and in survival analysis ( 66 ). The immune response to cancer cells varies widely among people.…”

Section: Ai and Secondary Omics Datamentioning

confidence: 99%

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Biswas

Chakrabarti

2020

Front. Oncol.

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Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

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Precision immunoprofiling by image analysis and artificial intelligence

Cited by 112 publications

References 83 publications

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

Deep learning approach for automated cancer detection and tumor proportion score estimation of PD-L1 expression in lung adenocarcinoma

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

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