What is Radiomics?
Turning medical images into data
Core Services
How can
radiomics.bio support
your clinical trial?
Disease Characterisation
Enhance your understanding of the disease on a lesion, organ or patient level.
With radiomics.bio you can characterise disease using volumetric analysis and radiomic imaging features to enhance your understanding of disease on a patient, organ, or lesion level, leading to insightful and cost-effective clinical trial designs. radiomics.bio also employs longitudinal tumour burden analysis to track progression or regression of all lesions (all organs) over time.
Screen and diagnose biomarkers such as MET amplification, EGFR, and HPV status
Determine prognosis based on baseline tumour burden assessment
Assess prognosis based on non-tumour specific imaging markers such as lesion location, vascularity variations, diffuse disease status and lesion malignancy
Case Study
Development of predictive models for lesion and patient response
Response Evaluation
Facilitate objective decision making for each phase of clinical development.
Radiomics provides vital information related to the efficacy of treatment, with radiomics.bio you can measure biological response and validate the mechanisms of actions of therapeutics through the evaluation of phenotypic and anatomical changes of lesions in response to treatment.
The quantification of treatment effect through comprehensive tumour burden assessment provides vital information related to the efficacy of treatment including phenotypic and anatomical changes such as volume, shape and texture to distinguish responding lesions from non-responding lesions.
Measure response using Standard Response Evaluation Criteria: RECIST v1.1, iRECIST, mRECIST, irRECIST suite, PERCIST, WHO, RANO, Choi, etc.
Quantify 3D volumetric response of each measurable lesion to identify the most effective therapeutic doses, compare response between treatment arms, indications, etc. through longitudinal total tumour burden assessment
Measure dynamic therapeutic effects (daily shrink/growth rate, decay constants, time to progression, etc.) through tumor growth kinetics analysis of each quantifiable lesions.
Case Study
Dose escalation and selection
Response Characterisation
Explore and validate drug mechanisms of action
Radiomics allows you to characterise biological response to explore and validate mechanism of actions of your drug. With radiomics.bio you can go beyond the qualitative and non-comprehensive nature of RECIST by fully quantifying the effect of your drug in selected organs or through total tumour burden analysis. The radiomics approach has the potential to identify quantitative markers of treatment response earlier in the course of treatment. This can enable treatment to be adapted, intensified or altered earlier in the course of disease in order to improve patient outcomes.
Detect tumor phenotypic changes (shape, texture, density) in response and resistance
Unveil lesion characteristic changes (pseudo progression, necrosis, edema, etc.) with treatment response
Explore microenvironment, vascular, and organ modifications as result of therapy
Case Study
Response characterisation through Total Tumour Burden analysis
Outcome Prediction
Receive more personalised & accurate predictions of disease outcomes and treatment response
You can predict outcome using imaging and clinical data to obtain early insights during clinical trials by leveraging radiomics. With radiomics.bio you can predict disease progression, treatment response, and clinical outcome, supporting clinical and research decision making. Radiomic analysis can also predict which patients are more likely to respond to treatment.
Adverse events and unsuitable patients have the potential to derail the development of therapeutics, which not only subjects patients to incorrect and unnecessary treatments but also leads to significant costs and time loss. Radiomic insights are vital to optimisation of patient selection a personalised treatment leading to the successful development of therapeutics.
Detect and predict therapy-specific adverse events e.g. ILD/Pneumonitis
Predict final response of individual lesions from pre-treatment, baseline, and or follow-up visits
Predict patient level outcomes (progression free survival, survival likelihood, overall survival etc.)
Case Study
Response prediction
Central Imaging Services
Expert Central Imaging Services for High-Quality Clinical Trial Support
radiomics.bio optimises clinical trials not only through advanced image analysis but also through central imaging lab services, including the collection, curation, control and storage of medical images in a clinical trial. We employ our ISO 27001 validated QMS and radiological expertise to provide high quality treatment efficacy assessment services using well stablished methodologies such as the RECIST suite, PERSIST, RANO, etc.
Image protocol optimisation
Leveraging our clinical imaging expertise, radiomics.bio can provide support in adjusting, detailing or fully developing the optimal imaging protocol for your clinical study. Our experts can liaise with clinical sites to train key stakeholders and support the implementation of a reliable protocol.
Centralised storage
radiomics.bio can collect and hold images from your clinical trials using secured servers located in Europe in the US. radiomics.bio will always ensure the pseudo-anonymisation of data by checking and removing personal identifiable information (PII) prior to any data ingestion. We can also perform image quality assessment from technical assessments (right modality, right organ, etc.) to thorough medical checks (right contract phase, selection of timepoints, etc.) depending on the expected use of the data.
Centralised Imaging review
radiomics.bio provides standard centralised assessment of medical images to assess the effectiveness of drugs in oncology. RECIST based measurements are known to be prone to intra and inter-reader variability therefore radiomics.bio employs a robust and reliable measurement of the treatment regime and mobilises its internal resources and external network to ensure maximum quality of your trial readings.
Development of predictive models for lesion and patient response
Disease
Hepatocellular Carcinoma (HCC)
Radiomics Insight
We retrospectively reviewed approximately 120 HCC patients who either received immunotherapy in mono, or in combo (multiple-IO agents) to analyse the magnitude and rate of response to treatment of all segmented lesions. Total tumour burden and radiomic analysis were performed on each lesion (and entire liver organ) on baseline and end of treatment evaluations. In this project we were able to compare and identify patient response in accordance with the therapeutic received.
Furthermore, we were able to identify features correlated to response, for example patients with lower intensity at baseline in lesions and peri-lesion areas correlated with shrinking and growing lesions. Among other predictive models developed, our multivariate model with an AUC of 0.86 could accurately classify lesions into growing or shrinking categories.
Dose escalation and selection
Disease
Metastatic NSCLC & SCLC (Stage IV)
Radiomics Insight
In a Phase 1 Dose Escalation trial, we quantified the total tumour burden change between baseline & a follow up timepoint (BOR) as a function of the dose received. The observed optimal doses based on maximum therapeutic effect were doses D-E.
In this case, the different lesions were mapped between different timepoints to ensure the ability to track lesions through time. Radiomic features were extracted to characterise lesion evolution, the changes were then correlated with dose level. The lesion evolution per organ enabled a better understanding of the evolution of the tumor burden and to study the tumor characteristics in more detail
Response characterisation through Total Tumour Burden analysis
Disease
Metastatic Uveal Melanoma
Radiomics Insight
In this clinical study we were able to provide a comprehensive characterisation of response through total tumour burden analysis, organ-specific monitoring, descriptive analysis volumetric evolution of lesions and quantitative imaging biomarkers to capture the complexity of treatment response in highly metastatic disease. We extracted key information from the longitudinal analysis of radiomic features which revealed distinct temporal evolution patterns between progressive and responsive lesions, with significant differences observed in shape, intensity, and texture characteristics and more. These insights allowed for informed decision-making in drug development which would ultimately improve patient care through more precise treatment response evaluation.
Response prediction
Disease
Advanced NSCLC
Radiomics Insight
In this example we were able to develop a model that could identify patients with advanced NSCLC who were more likely to benefit from immunotherapy to improve the success rates due to better selection of trial patients. A delta-radiomic signature was developed to analyse NSCLC patients treated with PD-1/PD-L1 inhibitors. Radiomic analysis was performed on pre-treatment and follow-up contrast-enhanced CT after 2 to 4 treatment cycles, in addition to these scans, clinical data and demographic data were collected. The model could successfully determine response at just 6 months.
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