ArticleScientific reports2019
Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Tumour-infiltrating lymphocytes: from prognosis to treatment selection.British journal of cancer · 2023Pooled it
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.Nature communications · 2025Article
- Biological and clinical significance of tumour-infiltrating lymphocytes in the era of immunotherapy: a multidimensional approach.Nature reviews. Clinical oncology · 2025Review
- Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.Tissue engineering. Part A · 2024Review
- Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial.ArXiv · 2024Article
- Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.Nature communications · 2024Article
- WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.Molecular cancer research : MCR · 2024Article
- Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024Review
- Review
- Tumor-infiltrating lymphocytes in melanoma: from prognostic assessment to therapeutic applications.Frontiers in immunology · 2024Review
- End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.Scientific reports · 2023Article
- Comparison of consecutive and restained sections for image registration in histopathology.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors.International journal of molecular sciences · 2023Review
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.Nature cancer · 2022Review
- Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.