Measuring cell density in prostate cancer imaging as an input for radiotherapy treatment planning

Transcription

1 Measuring cell density in prostate cancer imaging as an input for radiotherapy treatment planning Poster No.: R-0262 Congress: 2014 CSM Type: Scientific Exhibit Authors: H. Reynolds, S. Williams, A. Zhang, C. S. Ong, D. Rawlinson, R. Chakravorty, C. Mitchell, A. Haworth; MELBOURNE/AU Keywords: Computer Applications-General, CAD, Oncology, Pathology DOI: /ranzcr2014/R-0262 Any information contained in this pdf file is automatically generated from digital material submitted to EPOS by third parties in the form of scientific presentations. References to any names, marks, products, or services of third parties or hypertext links to thirdparty sites or information are provided solely as a convenience to you and do not in any way constitute or imply RANZCR/AIR/ACPSEM's endorsement, sponsorship or recommendation of the third party, information, product or service. RANZCR/AIR/ ACPSEM is not responsible for the content of these pages and does not make any representations regarding the content or accuracy of material in this file. As per copyright regulations, any unauthorised use of the material or parts thereof as well as commercial reproduction or multiple distribution by any traditional or electronically based reproduction/publication method ist strictly prohibited. You agree to defend, indemnify, and hold RANZCR/AIR/ACPSEM harmless from and against any and all claims, damages, costs, and expenses, including attorneys' fees, arising from or related to your use of these pages. Please note: Links to movies,.ppt slideshows,.doc documents and any other multimedia files are not available in the pdf version of presentations. Page 1 of 16

2 Aim Prostate cancer is characterized by degradation of the healthy glandular structure of prostate tissue, resulting in an irregular cell distribution as well as a higher density of cells within tumours. Gleason Score (GS) is a measure used to grade prostate tumours, where a higher grade indicates a more aggressive tumour with more irregularly arranged cells. Such changes in prostate tissue are visible when assessing histology slides under a microscope, particularly in Haeomotoxylin and Eosin (H&E) stained slides, in which cell nuclei appear blue (see Figures 2 & 3). In light of this knowledge, the aims of this study were to: Develop an automatic technique to measure cell density in high resolution prostate histopathology images. Determine whether cell density can be used as a feature to assist tumour segmentation and classification. Methods were developed as part of a larger study correlating 'ground truth' histopathology with multiparametric MRI (mpmri). Validated MR imaging data will then provide input to a radiobiological model we have developed and validated [1]. With this model, focal radiotherapy treatment plans will be designed for future patients to reduce treatment related side effects, as dose distributions will be tailored to tumour location and tumour characteristics shown on in-vivo mpmri. Images for this section: Page 2 of 16

3 Fig. 1: Anatomy of the prostate and surrounding organs. Page 3 of 16

4 Fig. 2: H&E stained prostate histology slide, containing Gleason Score 9 tumour (blue outlines) as well as Gleason Score 6 tumour (yellow outlines). Fig. 3: Prostate histology tissue at 20x magnification, showing Gleason Score 6 tumour (a) and Gleason Score 9 tumour (b). Page 4 of 16

5 Methods and materials Patient Data Histopathology data was obtained from five patients treated by radical prostatectomy at the Peter MacCallum Cancer Centre, Melbourne. Prostate specimens were processed using the standard clinical protocol, where the middle section was cut into axial slices of 5mm thickness and the apex and base sections were cut parasagitally. In this study, all axial mid-section slides were analysed where each axial slice was wholemounted, stained with haemotoxylin-eosin (H&E) and scanned at 20x magnification to give per pixel resolution of 0.502µm. Tumour position and grade was annotated on each slide by an expert pathologist (CM). Patient Gleason Score (dominant #Axial Slides and other nodules) 1 7& & Histology data used in the analysis, including Gleason Score as assessed by an expert pathologist Cell density measurements Cell density was computed for each high resolution histopathology image using the following steps [2]: Histology images were divided into tiles measuring 320 x 320 pixels and processed as a series of independent images. Colour deconvolution was applied to each tile to separate the H&E stains (Figure 4) [3]. A radial symmetry transform was applied to the haemotoxylin images (i.e. Figure 4b) to detect cell nuclei [4]. An example result is shown in Figure 5. Tiles were then combined to produce a global cell density map for each slide, where each tile provided one cell density value per pixel. Page 5 of 16

6 During this process, parameters were chosen empirically using a subset of representative benign and tumour tiles. Images for this section: Fig. 4: A typical image tile from an H&E stained histology slide (a) separated into haemotoxylin (b) and eosin (c) stains using colour deconvolution. Fig. 5: Output of the radial symmetry transform (a) and the final cell nuclei detection result (b). Page 6 of 16

7 Results Cell density maps were computed successfully for all 22 slides from five patients, containing tumours with Gleason Scores ranging from 6 to 9. Figures 6-8 show results for three slides: patient 2 slide 6 (Figure 6), patient 3 slide 9 (Figure 7) and patient 5 slide 9 (Figure 8). In each Figure, the original annotatd histology image is shown along with the computed cell density map and a stepped histogram characterising the different cell density values in the tumour regions versus benign regions of tissue. Note that the colour scale bar for the cell density maps and the 2 horizontal axis for the histograms ranges from 0 to 8000 cells/mm. Histograms shown in Figures 6c and 7c from patients 2 and 3 slides respectively, demonstrate a clear distinction between the cell density in tumour and benign regions of tissue. In contrast, Figure 8c from a patient 5 slide shows almost no different in the histogram profiles between tumour and benign tissue. Despite differences between patient slides, Kolmogorov Smirnov tests confirmed that cell density in tumour was significantly different than in benign regions of tissue for all 22 histopathology images, using ground truth annotations from an expert pathologist (p < 0.05). To assess the sensitivity and specificity of cell density as a tumour classifier, ROC curves were calculated. Figures 9, 10 and 11 show ROC curves for patients 2, 3 and 5. It can be observed that cell density performed as a consistently accurate classifier for patient 2, while slides for patient 3 demonstrated a reduction in accuracy as slides moved from the apex to the base of the prostate. Cell density performed as the weakest classifier for patient 5. Overall, a higher cell density was indicated in higher GS tumours as evident in patient 3 who had a GS tumour of 9 (Figure 7) in comparison with patient 5 who had a lower grade tumour of GS 6 (Figure 8). Tumour appearance also appeared to influence classification accuracy as shown in patient 3 where the tumour size reduced towards the base of the prostate and in patient 5 where the tumour was very small for all slides. Results also showed that benign regions of tissue which contained inflammation, acute prostatitis and prostatic intraepithelian neoplasia (PIN) may show very high cell density even though they are not tumour tissue. Images for this section: Page 7 of 16

8 Fig. 6: H&E stained slide (a), computed cell density map (b) and histogram showing the cell density in tumour versus benign regions of tissue (c) for patient 2 slide 6. Page 8 of 16

9 Fig. 7: H&E stained slide (a), computed cell density map (b) and histogram showing the cell density in tumour versus benign regions of tissue (c) for patient 3 slide 9. Page 9 of 16

10 Fig. 8: H&E stained slide (a), computed cell density map (b) and histogram showing the cell density in tumour versus benign regions of tissue (c) for patient 5 slide 9. Page 10 of 16

11 Fig. 9: ROC curve showing the sensitivity and specificity of the cell density classifier for patient 2 histology slides. Page 11 of 16

12 Fig. 10: ROC curve showing the sensitivity and specificity of the cell density classifier for patient 3 histology slides. Page 12 of 16

13 Fig. 11: ROC curve showing the sensitivity and specificity of the cell density classifier for patient 5 histology slides. Page 13 of 16

14 Conclusion We have successfully developed an automatic method to measure cell density in high resolution histopathology images of the prostate. Results have shown that cell density measurements are highly correlated with expert pathology markup, indicating that cell density is useful for tumour segmentation and classification in un-annotated histopathology images. We propose, however, that the sensitivity and specificity needs to be improved to create a more accurate classifier. To work towards this goal, we are currently incorporating this cell density algorithm into CAD software developed by DiFranco [5] which will take into account other tissue types such as PIN and inflammation. In addition, preliminary analysis has indicated that cell density correlates with decreased signal intensity on T2-weighted, ADC maps from diffusion weighted MRI and parameter maps from dynamic contrast-enhanced MRI (see Figure 12). Current work includes quantifying the correlation of cell density with co-registered mpmri to provide parameters for our radiobiological model. This model will then be utilised to inform focal radiotherapy planning for future prostate cancer patients using in-vivo mpmri. Images for this section: Page 14 of 16

15 Fig. 12: Computed cell density maps will be registered and correlated with T2-weighted MRI, ADC maps from DWI and DCE-MRI parameter maps. Page 15 of 16

16 Personal information H. M. Reynolds PhD. Dept. Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia. Sir Peter MacCallum Dept. of Oncology, University of Melbourne, Melbourne, VIC, Australia References 1. Haworth A, Williams S, Reynolds H, Waterhouse D, Duchesne GM, Bucci J, et al. Validation of a radiobiological model for low-dose-rate prostate boost focal therapy treatment planning. Brachytherapy. Elsevier Inc.; 2013 Jul 18;12(6): Reynolds HM, Williams S, Zhang AM, Soon C, Rawlinson D, Chakravorty R, et al. Cell density in prostate histopathology images as a measure of tumour distribution. SPIE Med. Imaging conference. San Diego; Ruifrok AC and Johnston DA. Quantification of histochemical staining by color deconvolution. Analytical and quantitative cytology and histology; 2001; 23(4), Loy G and Zelinsky A. A Fast Radial Symmetry Transform for Detecting Points of Interest. In Proc. 7th Eur. Conf. Comput. Vision-Part I, 2002; DiFranco MD. Automated Prostate Cancer Detection in Whole-Mount Prostate Histopathology Images. PhD Thesis, Page 16 of 16