The Radiogenomics and Quantitative Imaging Group led by Prof Evis Sala is a multi-disciplinary team of radiologists, physicists, oncologists and computational scientists. One of the most active areas of research is in ovarian cancer. The group has developed novel computational methods for data integration and prediction of treatment response in the setting of neoadjuvant chemotherapy.

Research Team

Principal Investigator:

• Evis Sala

Research Associates:

• Ramona Woitek
• Leonardo Rundo
• Lorena Escudero Sánchez
• Lucian Beer
• Andrew Gill
• Arnold Benjamin

PhD Students:

• María Delgado Ortet
• Stephan Ursprung
• Marika Reinus
• Paula Martin Gonzalez
• Vencel Somai

Visitors:

• Hilal Sahin
• Margherita Mottola
• Teofanija Trajanovska
• Marta Zerunian

Projects

Real-time habitat-guided multimodal fusion biopsies

In collaboration with Canon® Medical Systems, the group has recently developed and is currently piloting a new ultrasound-guided fusion biopsy technique which allows for selective tissue sampling of tumour areas with distinct or similar radiomic/texture characteristics. The group has achieved to overlay texture-based clustering maps onto diagnostic CT images and PET/MRI habitat maps onto MRI images, respectively and then fuse them with the US during biopsies. This technique will enable biological profiling and longitudinal tracking of tumour habitats in the clinical settings.

Figure 1. CT habitat-guided US fusion biopsies. On the left side of the figure, the CT with the CT texture habitat overlay is shown. The tumour habitats are highlighted in green, red and blue, respectively. On the right side, the fused US image is shown.

Ovarian cancer segmentation and tissue-specific sub-segmentation on CT images

Currently, abdominal CT scans are evaluated visually and response is assessed based on unidimensional tumour measurements at different time points. Segmentation of the whole tumour burden is not routinely performed, as it is labour-intensive and also operator-dependent. Despite the recent advances in machine learning techniques, automated image segmentation for ovarian cancer is not yet available for clinical implementation owing to challenges including low tumour-to-tissue contrast and a lack of large-scale datasets for training and testing. This group has developed a prototype deep learning automated segmentation method for ovarian cancer, and the first approach based on unsupervised techniques is to provide tissue-specific sub-segmentation and quantify solid and cystic components, which are commonly found in ovarian cancer lesions. Exploiting these novel techniques, we aim at providing robust tools for future radiomic biomarker validation.

Integration of Radiomics and Circulating Tumour DNA (ctDNA) for the Development of Predictive and Prognostic Biomarkers on Patients with Ovarian Cancer

Patients with findings suspicious for ovarian cancer or with a definite diagnosis of the disease usually undergo computed tomography (CT) scans as their standard-of-care imaging to assess the extent of the disease. We are collaborating with molecular oncology and computational groups to extract quantitative data from these images (radiomics) and combine them with ctDNA isolated from blood samples of these patients to develop tools that predict response to chemotherapy as well as survival in these patients. These studies also help us understand which features of ovarian cancer affect the extent of ctDNA shedding into circulation and how molecular features of ovarian cancer affect tumour appearance on imaging.

Integration of Proteomics and Radiomics in Ovarian Cancer

In collaboration with colleagues from the Walter Reed National Military Medical Center, the Inova Schar Cancer Institute and Frederick National Laboratory for Cancer Research our group is working on the integration of proteomic- and radiomic data into clinical decision models for patients with ovarian cancer. Over the last years, our group has shown that radiomic metrics correlate with the response to chemotherapy and predict clinical endpoints such as progression-free survival and overall survival. In addition, it is well known that certain protein signatures of the tumour are associated with outcome. We aim to build risk models that integrate both data streams to stratify patients according to risk for progression, thereby allowing for tailored therapy. In an exploratory analysis, we showed for the first time the association between proteomics and CT-based qualitative and texture features in ovarian cancer.

Figure 2. Schematic workflow for the integration of proteomics with CT imaging traits and radiomics in patients with ovarian cancer (adapted from Beer et al. 2020 European Radiology)

Hyperpolarised Carbon-13 MRI

Our group is assessing the feasibility of multiparametric multinuclear MRI (including hyperpolarised 13C MRI) for assessing early and heterogeneous response to treatment in NACT setting. The metabolic imaging might be able to assess treatment response earlier compared to clinically used techniques. It may also help detect heterogeneous treatment response, often a cause of treatment failure.

Targeted Molecular Imaging of CA125 in High Grade Serous Ovarian Cancer

High grade serous ovarian cancer cells over express the Cancer Antigen 125 (CA125), that is used clinically as a tumour marker measured in the blood serum. Negative serum CA-125 assays do not always indicate absence of recurrent ovarian malignancy, while elevation of serum CA125 levels can also be observed in non-gynaecologic malignancies or benign conditions. Our team is performing a physiological study to assess CA125 expression and heterogeneity in patients with ovarian cancer. This study will establish the first application of immuno-PET imaging in Cambridge, creating a platform for the wider application of this powerful technique for tracking and quantification of mAbs in vivo.

Renal Cancer

Imaging in renal cancer is another area of active research which is pursued both in independent radiological research and as part of clinical trials. Being one of the medical services with the highest volume of patients with kidney cancer undergoing surgical or oncological treatment, Addenbrooke’s Hospital and the University of Cambridge are ideally placed for renal cancer research.

Far reaching alterations in cellular energy metabolism are a hallmark of renal cancer. Hyperpolarised [1-13C]-pyruvate MRI is a non-ionising, quantitative imaging technique which allows our group to investigate the core of these metabolic changes. Our interest covers both clinical and translational aspects of this technique.

While hyperpolarised [1-13C]-pyruvate MRI enables unknown insights into patient tumour metabolism, it is currently reserved for a small number of centres worldwide which have the relevant infrastructure and expertise. We also investigate the ability of radiomics of standard of care imaging in RCC to support clinical decision making. Radiomics models may help address clinical questions which are difficult to assess with subjective image interpretation. Questions, which are of particular interest to our group, are the imaging-based prediction of tumour subtype, aggressiveness and early response to therapy.

Imaging plays an important role in oncological treatment response assessment for many cancer types. With the availability of an increasing number of targeted and non-targeted anti-cancer drugs for renal cancer, the rapid detection of treatment response and relapse become of increasing clinical relevance. In collaboration with colleagues from the departments of urology and oncology, we investigate how multiparametric MRI can be utilised for treatment stratification in current and future clinical trials.

Pancreatic cancer

Pancreatic cancer (PCa) is the 4th leading cause of cancer-related deaths, with approximately equal rates of annual incidence and mortality. It is estimated that it will become the second leading cause of cancer-related deaths by 2030. There is still a critical unmet need for better ways to non-invasively assess PCa at the different stages of diagnosis. Finding new imaging approaches to better clinically evaluate pancreatic cancer is a field of active research both by radiology led research and also as part of ongoing Oncology clinical trials.

Liver cancer

Liver cancer is the 6th most common cancer in the world, with hepatocellular carcinoma (HCC) being the most common type. Our research is focused on HCC in patients to whom liver transplant was offered as a curative treatment. As one of the main transplant units in the country, the Addenbrooke’s Hospital and the University of Cambridge provide unique research opportunities in this field. We work in close collaboration with the clinical Hepatology team and the Transplant Unit, with most projects using a quantitative imaging approach.

Metastatic Breast Cancer

Advanced breast cancer frequently metastasises to the bones. Computed tomography (CT) is the most commonly used imaging technique to assess treatment response in these patients. With metastases frequently presenting as a mixture of lytic and sclerotic metastases at diagnosis, it can be very challenging to distinguish between disease progression and response to treatment if only sclerotic lesions increase over time. We are currently working on the development of quantitative methods for the monitoring of metastatic disease and are collaborating with other groups in Cambridge working on molecular oncology to correlate our imaging findings in metastatic breast cancer with liquid biopsies.

From a clinical perspective, deformable image registration of longitudinal datasets is a necessary step towards automated quantitative and objective response assessment. Together with the Centrum Wiskunde & Informatica, Amsterdam, The Netherlands, and the Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK, we devised a novel deep learning based deformable approach for abdominal CT scans. The developed method achieves accurate registration results and is two orders of magnitude faster than classic iterative registration methods, thus allowing for real-time registration.

NCITA Imaging Repository

Prof Sala is the Cambridge Principal Investigator of the CRUK funded National Cancer Imaging Translational Accelerator (NCITA) award. She is also the chair of its Repository Unit, whose aim is to build a common image data repository and establish a fast and robust pipeline across seven different UK institutions (University of Cambridge, ICR, University College London, Oxford, Manchester, King’s College London and Imperial College London). This collaboration aims to expand our capabilities in cancer research by building a unified infrastructure for discovery, validation and adoption of cancer imaging biomarkers into clinical trials and the NHS.

Our group is working on building one of the NCITA repository nodes that will contain anonymised images and related metadata, and will be connected with other institutions via an NCITA XNAT federation. Our repository is a web-based database using the XNAT software (developed by the Neuroinformatics Research Group at the Washington University), and it is deployed using the cloud server of the High Performance Computing services of the University of Cambridge.

Further work from our team will involve development and integration in XNAT of advanced segmentation tools, such as NVIDIA Clara tools.

Technologies / Methods

• Advanced MRI (DKI-DWI, IVIM-DWI, DCE-analysis, MT)
• Radiomics
• Metabolic imaging (Hyperpolarised Carbon-13 MRI, FDG-PET/MRI, FDG-PET/CT)
• Ultrasound guided fusion biopsies
• Data Integration (Radiomics, Proteomics, Genomics, ctDNA, clinical data)

Recent Awards

• Evis Sala
  • Fellow, European Society for Urogenital Radiology 09/2018
• Lucian Beer
  • Theodor Billroth Prize (Austria) 10/2019
  • Scientific Award Lower Austria 08/2019 

Funding

• Cambridge Cancer Center (CRUK)
• Mark Foundation for Cancer Research
• Wellcome Trust
• National Cancer Imaging Translational Accelerator (NCITA)
• Armstrong Foundation

Collaborators

• Apollo
• Canon Medical Systems
• Centrum Wiskunde & Informatica, Amsterdam, The Netherlands – Computational Imaging Group
• CRUK-CI groups:
  • James Brenton
  • Kevin Brindle
  • Florian Markowetz
  • Martin Miller
  • Carlos Caldas
• DAMTP – Cambridge Image Analysis group
• GE Healthcare
• MSKCC – The Jason Lewis Lab
• NCITA (Twitter)
• TCGA
• TCIA
• VUMC

Outreach

• Cambridge Science Festival 2020  (postponed until 2021)

Initiatives

• Cambridge Imaging Festival 2019
• Workshop: Collaborative Approaches to Cancer Imaging. Applying known methods in new ways
• Cambridge Imaging Festival 2020 (postponed until May 2021)

Latest Publications (01/01/2018 – Present)

1. Meyer A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa, R, Soslow RA, Sutton EJ, Hricak H, Sala E, Vargas HA. Association Between CT-Texture-Derived Tumor Heterogeneity, Outcomes, And BRCA Mutation Status in Patients with High-Grade Serous Ovarian Cancer. Abdominal Radiology. 2019 Jun;44(6):2040-2047. https://doi.org/10.1007/s00261-018-1840-5
2. Bodo S*, Campagne S*, Htwe Thin T*, Higginson1 DS*, Vargas HA*, Hua G, Fuller JD, Ackerstaff E, Russell J, Klingler S, Cho H, Kaag M, Mazaheri Y, Rimner1 A, Manova-Todorova K, Epel B, Zatcky J, Cleary CR, Rao SS, Yamada Y, Zelefsky MJ, Halpern7 HJ, Koutcher JA, Cordon-Cardo C, Greco C, Haimovitz-Friedman A, Sala E*, Powell SN*, Kolesnick R*, Fuks Z*. Single Dose Radiotherapy Disables Tumor Cell Homologous Recombination Via Ischemia/Reperfusion Injury. J Clin Invest. 2019 Feb 1;129(2):786-801. https://doi.org/10.1172/JCI97631
3. Barrett T, Lawrence EM, Priest A, Warren AY, Gnanapragasam VJ, Sala E. Repeatability of Diffusion-Weighted MRI of The Prostate Using Whole Lesion ADC Values, Skew And Histogram Analysis. Eur J Radiol, 2019; 110:22–29.https://doi.org/10.1016/j.ejrad.2018.11.014
4. Kaggie J, Deen S, Kessler DA, McLean MA, Buonincontri G, Schulte RF, Addley H, Sala E, Brenton JD, Graves MJ, Gallagher F. Feasibility of Quantitative Magnetic Resonance Fingerprinting in Ovarian Tumours for T1 and T2 Mapping. IEEE Transactions on Radiation and Plasma Medical Sciences, 2019;3:509-515. https://doi.org/10.1109/TRPMS.2019.2905366
5. Weigelt B, Vargas HA, Selenica P, Geyera PC Mazaheri Y, Blecua P, Conlon N, Hoang LN, Jungbluth AA, Snyder A, Ng CKY, Papanastasiou AD, Sosa RA, Soslow RA, Chi DS, Gardner GJ, Shen R, Reis-Filho JS, Sala E. Radiogenomics Analysis of Intra-Tumor Heterogeneity in a Patient With High-Grade Serous Ovarian Cancer. JCO PO 2019;3:1-9. doi: https://doi.org/10.1200/PO.18.00410
6. Deen S, Riemer F, McLean MA, Gill AB, Kaggie J, Grist JT, Crawford R, Latimer J, Baldwin P. Earl HM, Parkinson CA, Smith SA, Hodgkin C, Moore E, Jimenez-Linan M, Brodie CR, Addley HC, Freeman SJ, Moyle PL, Sala E, Graves MJ, Brenton JD, Gallagher F. Sodium MRI With 3D-Cones As a Measure of Tumour Cellularity in High Grade Serous Ovarian Cancer. Eur J Radiol Open 2019 Apr 19;6:156-162. https://doi.org/10.1016/j.ejro.2019.04.001
7. Barrett T, Slough R. Sushentsev N, Shaida N, Koo BC, Caglic I, Kozlov V, Warren AY, Thankappannair V, Pinnock C, Shah N, Saeb-Parsy K, Gnanapragasam VJ, Sala E, Kastner C. Three-Year Experience of a Dedicated Prostate Mpmri Pre-Biopsy Programme and Effect on Timed Cancer Diagnostic Pathways. Clin Rad 2019;894,e1-894,e9. https://doi.org/10.1016/j.crad.2019.06.004
8. Himoto Y, Veeraraghavan H, Zheng J, Zamarin D, Snyder A, Capanu M, Nougaret S, Vargas HA, Shitano F, Callahan M, Wang W, Sala E*, Lakhman Y*. Computed Tomography-Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer. JCO PO 2019;3:1-13. https://doi.org/10.1200/PO.19.00038
9. Rundo L, Tangherloni A, Galimberti S, Cazzaniga P, Woitek R, Sala E, Nobile MS, Mauri G. HaraliCU: GPU-powered Haralick feature extraction on medical images exploiting the full dynamics of gray-scale levels. In International Conference on Parallel Computing Technologies 2019 Aug 19 (pp. 304-318). Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_24
10. Deen S, Priest A, McLean MA, Gill AB, Brodie CR, Crawford R, Latimer J, Baldwin P. Earl HM, Parkinson CA, Smith SA, Hodgkin C, Patterson I, Addley HC, Freeman SJ, Moyle PL, Jimenez-Linan M, Graves MJ, Sala E, Brenton JD, Gallagher F. Diffusion Kurtosis MRI as A Predictive Biomarker of Response to Neoadjuvant Chemotherapy in High Grade Serous Ovarian Cancer. Sci Reports 2019; 9:10742. https://doi.org/10.1038/s41598-019-47195-4
11. Himoto Y, Cybulska, P, Shitano F, Sala E, Zheng J, Capanu M, Nougaret S, Nikolovski I, Vargas HA, Wang W, Mueller JJ, Chi DS, Lakhman Y. Does the method of primary treatment affect the pattern of first recurrence in high-grade serous ovarian cancer? Gynec Onc 2019 Sep 12. pii: S0090-8258(19)31462-3. https://doi.org/10.1016/j.ygyno.2019.08.011
12. Lapa P, Castelli M, Gonçalves I, Sala E, Rundo L. A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI. Appl Sci 2020 Jan;10(1):338. https://doi.org/10.3390/app10010338
13. Rundo L, Militello C, Vitabile S, Russo G, Sala E, Gilardi MC. A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration. Fundamenta Informaticae. 2020 Jan 1;171(1-4):345-65. https://doi.org/10.3233/FI-2020-1887
14. Gallagher FA, Woitek R, McLean MA, Gill A, Manzano Garcia R, Provenzano E, Riemer F, Kaggie J, Chhabra A, Ursprung S, Grist JT, Daniels CJ, Zaccagna F, Laurent MC, Locke M, Hilborne S, Frary A, Torheim T, Boursnell C, Schiller A, Patterson I, Slough R, Carmo B, Kane J, Biggs H, Deen SS, Patterson A, Lanz T, Kingsbury Z, Ross M, Basu B, Baird R, Lomas DJ, Sala E, Wason J, Rueda OM, Chin SF, Wilkinson IB, Graves MJ, Abraham J, Gilbert FJ, Caldas C, Brindle KM. Imaging Breast Cancer Using Hyperpolarized Carbon-13 MRI. PNAS 2020, https://doi.org/10.1073/pnas.1913841117
15. Ursprung S, Beer L, Bruining A, Woitek R, Stewart GD, Gallagher FA, Sala E. Radiomics of Computed Tomography and Magnetic Resonance Imaging in Renal Cell Carcinoma – A Systematic Review And Meta-Analysis. Eur Radiol, 2020 Feb 14. https://doi.org/10.1007/s00330-020-06666-3
16. Sushentsev N, Caglic I, Sala E, Shaida N, Slough RA, Carmo B, Kozlov V, Gnanapragasam V, Barrett T. The Effect of Capped Biparametric Magnetic Resonance Imaging Slots on Weekly Prostate Cancer Imaging Workload. Br J Radiol 2020 Feb 3:20190929. https://doi.org/10.1259/bjr.20190929
17. Smith CG, Moser T, Mouliere F, Field-Rayner J, Eldridge I, Riediger AL, Chandrananda D, Heider K, Wan JCM, Warren AY, Morris J, Hudecova I, Cooper WN, Mitchell TJ, Gale D, Ruiz-Valdepenas A, Klatte T, Ursprung S, Sala E, Riddick ACP, Aho TF, Armitage JN, Perakis S, Pichler M, Seles M, Wcislo G, Welsh SJ, Matakidou A, Eisen T, Massie CE, Rosenfeld N, Heitzer E, Stewart GD. Comprehensive Characterisation of Cell-Free Tumour DNA in Plasma and Urine of Patients with Renal Tumours. Genome Medicine, 2020. https://doi.org/10.1186/s13073-020-00723-8
18. Beer L, Sahin H, Bateman NW, Blazic I, Vargas HA, Veeraraghavan H, Kirby J, Fevrier-Sullivan B, Freymann JB, Jaffe C, Brenton JD, Miccó M, Nougaret S, Darcy KM, Maxwell LG, Conrads TP, Huang E, Sala E. Integration of Proteomics With CT-Based Qualitative and Texture Features in High-Grade Serous Ovarian Cancer Patients: An Exploratory Analysis. Eur Radiol, 2020
19. Zawaideh JP, Sala E, Shaida N, Koo B, Warren AY, Carmisciano L, Saeb-Parsy K, Gnanapragasam VJ, Kastner C, Barrett T. Diagnostic Accuracy of Biparametric Versus Multiparametric MRI: Assessment of Contrast Benefit in Clinical Practice. Eur Radiol, 2020 (in press)
20. Rundo L, Beer L, Ursprung S. Martin-Gonzalez P, Markowetz F, Brenton JD, Crispin-Ortuzar M, Sala E, Woitek R. Tissue-specific and Interpretable Sub-segmentation of Whole Tumour Burden on C T Images by Unsupervised Fuzzy Clustering. Computers in Biology and Medicine, 2020 (in press)