univariate analysis ECOG >2, FIGO stage III-IVA, PLN(+)/PRT and. PAN(+) predicted worse outcomes for DFS and OS. When compared to. PAN(+)/EFRT; N0 ...
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Volume 84 Number 3S Supplement 2012 Results: After a median follow up of 40 months (1-148), 72 (33.02%) patients failed and 65 (29.81%) died. Actuarial 5 years disease free (DFS) and overall survival (OS) was 65.66% and 69.63%. We grouped patients according to lymph node stage and EFRT or pelvic only RT (PRT). On univariate analysis ECOG >2, FIGO stage III-IVA, PLN(+)/PRT and PAN(+) predicted worse outcomes for DFS and OS. When compared to PAN(+)/EFRT; N0 and PLN(+)/EFRT groups had HR 0.50 and HR 0.27 respectively for OS; no differences were found with N0/PRT group. HR for PLN(+)/PRT patients was 3.70 (95%CI 1.43-9.56; p < 0.01). On multivariate analysis, when compared with PAN(+)/EFRT patients, PLN(+)/ EFRT presented 0.26 HR (95%CI 0.09-0.73; p: 0.01) while PLN(+)/PRT 4.15 HR (95%CI 1.60-10.75; p: 0.003). No statistically significant differences were found in N0 patients, regardless of the treatment modality when compared with PAN(+)/EFRT (HR 95%CI 0.32-1.24 for EFRT and 0.40-1.65 for PRT). ECOG and FIGO stage remained statistically significant on multivariate analysis [HR 2.44 (1.37-4.36) for FIGO III-IVA and HR 0.48 (0.26-0.89) for ECOG 2]. Lymph node status, since it is closely related to FIGO stage, did not, independently, predict outcome on multivariate analysis. Conclusions: The results from our study have shown that lymph node status on image studies, when grouped by RT modality, impacts survival in cervical cancer patients. According to our results, patients with clinically positive lymph nodes (Both PLN and PAN) benefit from EFRT, and prophylactic treatment should be offered to PLN+ patients. Author Disclosure: R. D’Ambrosi: None. J. Perez-Regadera: None. S. Gomez Ordon˜ez: None. O. Hernandez Arteaga: None. A. Bartolome: None. M. Cabeza: None. A. Ruiz Alonso: None. M. Perez Escutia: None. E. Lanzos: None.
2570 A Clinically Useful Watershed-based Method of Auto-segmenting Apparent Diffusion Coefficient (ADC) Maps of Cervical Cancer Y. Rao,1 D. Ma,2 H. Li,1 J. Esthappan,1 A. Chang,1 and P. Grigsby1; 1 Washington University School of Medicine, Saint Louis, MO, 2Mayo Clinic School of Medicine, Rochester, MN Purpose/Objectives: Defining the boundaries of tumors (segmentation) on clinical imaging is required for the accurate delivery of radiation therapy. Previous work by our group has highlighted the utility of ADC maps from diffusion weighted MRI in the manual segmentation of cervical cancer. An auto-segmentation method would shorten the time required and provide a standardized means for generating contours. We now present a novel hybrid algorithm based on the watershed transform as a solution to this problem. Materials/Methods: An extension of the watershed algorithm including two pre-processing steps, the normalized discrete derivative calculation of the ADC map with the Sobel operator, and over-segmentation minimization with an adjustable H-minima transform, was used to auto-segment cervical tumors. This method was compared against manual contouring on ADC maps of 11 cervical cancer patients by comparing volumes and overlaps of these methods to metabolic tumor volumes delineated in registered FDG-PET scans, an imaging gold standard. Our method was also tested against a previously validated threshold method of 40% SUVmax on microPET images of 9 mice with implanted tumors based on how accurately the watershed or threshold delineated volumes matched volumes measured from calibrated images of tomographically sliced sections of these tumors after sacrifice, a histological gold standard. Conformity index, defined as the volume measured by the test method divided by the volume measured by the gold standard method (CFI Z Vt/Vo), and Dice coefficient, defined as twice the overlap of the volumes of the test method and gold standard method divided by the sum (DC Z 2* [VtXVo]/[Vt+Vo]), were compared using paired t-tests. Results: We compared the CFI of watershed contoured ADC maps (1.26 0.20) and manual contoured ADC maps (1.24 0.19) in reference to FDG-PET (p Z .14) as well as the DC of watershed contoured ADC maps (0.60 0.04) and manual contoured ADC maps (0.64 0.04) in
reference to FDG-PET (p Z .71). Similarly in the mice models, we compared the CFI of watershed contoured microPET (1.04 0.08) and threshold contoured microPET (1.09 0.09) with respect to histology (p Z 0.86). Thus, contours generated using the watershed method were not significantly different at determining tumor volumes or overlap compared to manual contouring for ADC images or threshold autocontouring for PET images. Conclusions: We propose a novel watershed based hybrid algorithm for segmentation of ADC maps of cervical tumors in comparison with a gold standard imaging method, FDG-PET, in humans. This method was also cross-validated against a threshold method in microPET in mice to confirm accuracy in a second imaging modality. Overall, the proposed algorithm achieved similar accuracy to manual segmentation. Author Disclosure: Y. Rao: None. D. Ma: None. H. Li: None. J. Esthappan: None. A. Chang: None. P. Grigsby: None.
2571 The Use of Cervical Cancer MRI Contouring Guidelines in Clinical Practice M. Dimigen,1,2 S. Vinod,3,4 N. Borok,1 L. Holloway,5,6 J. Dowling,7 and K. Lim3,4; 1Radiology Department, Liverpool Hospital, Liverpool, Australia, 2University of Western Sydney, Campbelltown, Australia, 3 Cancer Therapy Centre, Liverpool Hospital, Liverpool, Australia, 4 University of New South Wales, Sydney, Australia, 5Cancer Therapy Centre, Liverpool Hospital, Liverpool, Australia, 6Institute of Medical Physics, Sydney University, Sydney, Australia, 7CSIRO ICT Centre, The Australian e-Health Research Centre, Brisbane, Australia Purposes/Objectives: The study aimed to evaluate how effectively the ‘Consensus guidelines for the delineation of clinical target volume (CTV) for intensity modulated pelvic radiation therapy (IMRT) for the definitive treatment of cervical cancer’ could be applied in clinical practice, without specific training. Materials/Methods: The CTV for four cervical cancer patients undergoing radiation therapy, were independently contoured by two radiation oncologists and two radiologists. The consensus guidelines, patient’s clinical summaries and MRI reports were used as a guide to aid the observers in contouring the GTV (gross tumor volume), parametrium, uterus and vagina on planning MRI images. Subsequently the observers agreed upon consensus contours, creating a gold standard CTV for each patient. The mean absolute surface distance (MASD), the Dice similarity co-efficient (DSC) and tissue volume were calculated and used to compare each observers’ contour with the consensus gold standard by evaluating the distance between the contoured surfaces and the difference in over/ underlap of the contoured volumes. The inter-observer variation and the consistency between radiologists’ and radiation oncologists’ contours were also compared. Results: Although a wide ranging result for the gross tumor volume was demonstrated (MASD 2.0 to 9.2mm, mean DSC Z 0.39 to 0.66) for all observers when compared to the consensus, all the contours were still within the recommended planning target volume (PTV) margin of 20mm. There was much less inter-observer variation between the contours for the vagina, parametrium and uterus. For the inter-group variation, the radiation oncologists had a significantly less inter-group variation for DSC (p < 0.01) and MASD (p < 0.05), compared with the radiologists. Their contours also varied significantly less from the gold standard consensus contours, for all of the metrics DSC, MASD and Volume (p < 0.01 for all). Conclusion: Whilst all contoured GTVs were within the associated PTV, large variations in contours were seen between observers. Steep learning curves affecting the radiation oncologists with interpretation of MRI images and for the radiologists in the use of contouring software, should be considered in future studies and additional specific training may be necessary in the application of the of consensus guidelines. Author Disclosure: M. Dimigen: None. S. Vinod: None. N. Borok: None. L. Holloway: None. J. Dowling: None. K. Lim: None.
