Hero Imaging and Cardiff University introduce SPAARC Radiomics

We are proud to announce that Hero Imaging and Cardiff University have an official collaboration to bring our users an extensive Radiomics package developed at Cardiff University, which integrates seamlessly into any pipeline.

Simon Lindgren, CEO of Hero Imaging says “Radiomics is a rapidly growing research field as the world looks for image-based biomarkers and it is a tremendous opportunity for us to collaborate with one of the strongest research groups in Europe. This collaboration blueprints how we hope to bring our users novelties and rapidly expand our value offer to cover more research fields.”

‍The SPAARC-radiomics plugin is a tool for multimodal quantitative image analysis based on Cardiff University’s research, incorporating 164 features all compliant and validated in accordance with the Image Biomarker Standardization Initiatives recommendations  (https://theibsi.github.io). Features include morphology, intensity-based statistics, intensity and intensity volume histograms and grey level matrixes making SPAARC one of the most comprehensive tools for radiomics available.

Emiliano Spezi, Professor of Healthcare Engineering at Cardiff University says, “We are thrilled to have our SPAARC-radiomics code operating seamlessly within one of the most intuitive graphical programming platforms on the market. We are making available to the research community our high-quality radiomics algorithms integrated within a very powerful environment for advanced image analytics, and we can’t wait for researchers to use it.”

SPAARC-radiomics will be distributed under a commercial license as an add-on plugin that integrates seamlessly in the HERO infrastructure. As always – intuitive, flexible, easy to use and no manual coding.

The SPAARC plugin will be available for demonstration at ESTRO 2022 and beyond, book your demo below.

Online demonstrations

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Selected SPAARC-radiomics references:

·       Palumbo, D. et al. 2021. Prediction of early distant recurrence in upfront resectable pancreatic adenocarcinoma: A multidisciplinary, machine learning-based approach. Cancers 13(19), article number: 4938. (10.3390/cancers13194938)
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·       Mori, M. et al. 2020. Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer. Radiotherapy and Oncology 153, pp. 258-264. (10.1016/j.radonc.2020.07.003)
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·       Zwanenburg, A. et al. 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology 295(2), pp. 328-338. (10.1148/radiol.2020191145)

·       Piazzese, C. et al. 2019. Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer. PLoS ONE 14(11), article number: e0225550. (10.1371/journal.pone.0225550)

·       Whybra, P. et al. 2019. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Scientific Reports 9(1), article number: 9649. (10.1038/s41598-019-46030-0)