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Life Sciences
An increasing number of biopharmaceutical companies are looking for an AI-imaging partner. AI-powered imaging-based solutions can predict which patients will respond to a drug, find the right candidates for clinical studies, stratify patients with different risks and accelerate drug development programs.
Applications
Oncology
Our tissue-agnostic AI methods are applied at each stage of drug development programs. First we apply our AI techniques to harmonize image quality across hospitals and scanner models, then we perform automated organ and lesion detection. A tissue characterization follows to extract a signature based on the most relevant radiomics-based and deep features. These features are then used to develop predictive models of tumor growth, treatment response, overall survival, disease free survival, among many other patient outcomes.
Immunology
Our AI-based tools provide patients with an early diagnosis for rheumatic diseases in the main imaging modalities. We support the digital transformation of therapy indication either during drug-development phases or in post-approval scenarios, deploying an integrated offering to healthcare providers.
Neuroscience
We apply our AI-based methods for automated segmentation and quantification of brain tissues and abnormal lesions. We assess differences in the volume of brain regions in neurodegenerative and inflammatory diseases and in metabolic disorders, extracting surrogate imaging biomarkers.
Hematology
We provide automated segmentation methods for lesions and organs in whole body PET/CT examinations. The most frequent clinical scenarios are lymphoma and multiple myeloma, extracting lesion-level features, which, combined at the patient level, can help in predicting treatment response in standard or advanced therapies as well as other phenomena like cytokine release syndrome (CRS) or Immune effector cell-associated neurotoxicity syndrome (ICANS) in cell therapies such as Chimeric Antigen Receptor T-cell therapies (CAR-T).
Case Studies
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Hematology
Oncology
Predicting survival, neurotoxicity, and response in B-Cell lymphoma patients treated with CAR-T therapy using an imaging features-based model
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Inmunology
Empowering DIPG and Neuroblastoma through advanced imaging biomarkers
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Hematology
Oncology
Prognostic value of genetic alterations and 18F-FDG PET/CT imaging features in diffuse large B cell lymphoma
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Oncology
Development of CT-based radiomic model to predict 5-year progression free survival in locally advanced head and neck squamous cell carcinoma treated with definitive chemoradiation
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Oncology
Prediction of metastatic relapse in intermediate/high risk localized prostate cancer patients from staging medical images and clinical variables
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Inmunology
Post-approval: Identifying patients suffering from axial spondyloarthritis (axSpA) at an early stage, candidates for an approved drug
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Inmunology
Oncology
Drug revival: Prediction of treatment response to immunotherapy in NSCLC
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Oncology
Managing, storing and analyzing large-scale imaging and multi-omics datasets through QP-Insights® platform
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Oncology
Early detection of localized colon cancer relapse using an AI-based predictive model
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