TexRAD - Lung Cancer

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TexRAD is a software application that analyses the textures in existing radiological scans to assist the clinician in assessing the prognosis of patients with cancer. Currently applicable to colorectal, breast and lung cancers and we are offering licensing and collaboration opportunities for commercialisation and development of the technology.

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TexRAD - Lung Cancer

  1. 1. Lung Homepage Lung Cancer Case Study | Demo | Evidence PACS workstation TexRAD screen shot of Lung lesion
  2. 2. Lung Case Study Lung Case Study Typical example: A patient diagnosed with lung cancer requires an accurate staging of the tumour and lymph node disease. Radiologists are unable to obtain a confident risk stratification from CT alone, which is critical for early prognosis and favourable patient outcome. The patient undergoes FDG PET-CT imaging, which overcomes the limitations of CT alone. However, this is an additional and expensive procedure. TexRAD can improve the performance of CT and potentially be employed for selection of patients for PET. 1 2 3 4 5 6 6 7 8 HOW? Case Study | Demo | Evidence
  3. 3. Lung CS 2 Lung Case Study How TexRAD supports clinician & patient in case study From routine CT scans taken in the clinic TexRAD software uniquely extracts and measures fine , medium and coarse textures - in this example, from the Lung lesion on CT - and classifies degree of adverse tumour biology These texture gradation can be used to predict tumour stage and the risk of metastatic and lymph node disease Based on this additional information, radiologist may optimally select patients who will benefit from FDG PET 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
  4. 4. Lung Demo 1 Lung TexRAD Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence PACS workstation TexRAD screen shot of Lung lesion 8
  5. 5. Lung Demo 2 Lung TexRAD analyses a focal cancerous lesion as seen on the conventional CT image of a patient with NSCLC could predict tumour stage, metabolism and lymph node disease involvement Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
  6. 6. Lung Demo 3 Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Clinician’s Workflow
  7. 7. Lung Demo 4 STAGE 1 - Display the target clinical image of interest A TexRAD analysis is applied to the appropriate 2D CT image highlighting the lung lesion (tissue of interest - TOI). The specialist clinical consultant (e.g. Radiologist) will select the image containing this TOI. Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Conventional Lung CT Image
  8. 8. Lung Demo 5 STAGE 2 – Draw region of interest (ROI) to be analysed Using TexRAD’s graphical user interface tools, image window level/width can be altered to clearly delineate this TOI, interactive magnification/panning/centring can be used for better visualisation of this TOI. Clinician can choose an appropriate ROI tool (e.g. Elliptical ROI, which encloses the TOI in an automated fashion) from a list of options based on the application. This ROI is super-imposed on the TOI within the original image. Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Magnified CT Image For Drawing ROI
  9. 9. Lung Demo 6 Lung - Demo STAGE 3 – Texture Analysis TexRAD employs a novel algorithm (patent applied for) primarily to extracts subtle but prognostic metrics currently not available in clinic. The software also graphically displays clinically relevant fine, medium and coarse lung lesion textures separately (below) in addition to their fusion with the original CT image. 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Fine Lung Lesion Texture Medium Lung Lesion Texture Coarse Lung Lesion Texture
  10. 10. Lung Demo 7 Lung - Demo STAGE 4 – TexRAD Spectroscopy Summarises the entire texture results graphically for the lung cancer patient. Clinician can easily interpret the results for a quick assessment. 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
  11. 11. Lung Demo 8 Lung - Demo STAGE 5 – Risk Stratification Report A risk stratification report specific to the lung cancer is generated, which should be used only to assist the clinician to make an accurate decision. The report contains patient ID and scan details, TexRAD analysis result, explanation and contact information. 1 2 3 4 5 6 6 Case Study | Demo | Evidence 8 7 SAMPLE REPORT ONLY FOR ILLUSTRATION
  12. 12. Lung Background Lung - Background Facts about lung Cancer Lung cancer is the most common form of death related to cancer in men and second most common in woman [1, 2], accounting for 1.3 million deaths worldwide annually [3]. Non-small cell lung carcinoma (NSCLC) is the most common form of lung cancer prevalent in 80% of all cases [4]. Following initial diagnosis, patients with NSCLC undergo staging. The most common imaging staging procedure has been CT. However, due to low accuracy for CT staging, clinical guidelines now recommend Fluoro-deoxy-glucose (FDG) PET-CT unless the initial CT imaging shows evidence of inoperable disease. An improvement in the accuracy of CT could improve the selection of patients for FDG-PET. Case Study | Demo | Evidence 5 6 6 7 8 1 2 3 4
  13. 13. Lung Evidence 1 Lung Evidence Clinical Evidence – ICIS 2008 [5] 75 patients, PET identified tumour and nodal stages I, II, III & IV PET glucose uptake (SUV) was also measured Non-contrast CT images were employed for texture analysis Case Study | Demo | Evidence 5 6 6 7 8 1 2 3 4
  14. 14. Lung Evidence 2 Lung Evidence Case Study | Demo | Evidence Texture-Tumour Stage Association MGI (fine) vs Stage: rs =0.77, p=0.0002 Entropy (fine) vs Stage: rs =0.51, p=0.03 SUV vs Stage: rs =0.50, p=0.04 Texture-Nodal Stage Prediction Uniformity (fine) predicted node positive disease - area under the ROC curve = 0.7, p < 0.005, sensitivity=65%, specificity=76% In comparison with CT alone, sensitivity=43%, specificity=85% Combining CT and Texture, sensitivity=87%. Specificity=67% Texture-Metabolic Association Entropy (coarse) vs SUV: r=0.51, p=0.03 Uniformity (coarse) vs SUV: r=-0.52, p=0.03 5 6 6 7 8 1 2 3 4
  15. 15. Lung Evidence 3 Lung Evidence Clinical Evidence - Recent Findings accepted in ECR’10 [6] Case Study | Demo | Evidence Kaplan-Meier survival curves for NSCLC patients with lung lesions separated by (A) Texture analysis on CT and (B) PET glucose uptake (SUV). Survival curves were significantly different for Texture (p<0.001)]. 5 6 6 7 8 1 2 3 4
  16. 16. References - Lung <ul><li>LUNG </li></ul><ul><li>1. Deaths by cause, sex and mortality stratum (PDF).  World Health Organization (WHO) 2004 . </li></ul><ul><li>2. Lung Cancer Facts (Women). National Lung Cancer Partnership 2006 . </li></ul><ul><li>3. Cancer.  World Health Organization (WHO) 2006 . </li></ul><ul><li>4. Travis WD, Travis LB, Devesa SS (January 1995). Lung cancer. Cancer 1995 ; 75(1): 191-202. </li></ul><ul><li>5. Ganeshan B, Panayiotou E, Abaleke SC, Young RCD, Chatwin CR, Miles KA. ‘TEX-CT Vs PET-CT’ as a prognostic tool in Non-Small Cell Lung Carcinoma. In Cancer Imaging, ICIS 2008, Bath, UK. </li></ul><ul><li>6. Ganeshan B, Burnand K, Panayiotou E, Young RCD, Chatwin CR, Miles KA. Prognostic value of CT texture analysis in patients with non-small cell lung cancer: Comparison with FDG-PET. Accepted as a poster presentation in European Society of Radiology 2010, Vienna, Austria. </li></ul>References - Lung Cancer Colorectal | Lung | Breast | Prostate | Renal | Lung | Brain | DTA

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