Jinsung Park1, Tomonori Habuchi2, Youichi Arai3, Chikara Ohyama4, Takamitsu Inoue2, Shingo Hatakeyama4, Seong Soo Jeon5, Ghee Young Kwon6, Cheol Kwak7, Kyung Chul Moon8, Choung-Soo Kim9, Hanjong Ahn10. 1. Department of Urology, Eulji University Hospital, Daejeon, Republic of Korea. 2. Department of Urology, Akita University Graduate School of Medicine, Akita, Japan. 3. Department of Urology, Tohoku University School of Medicine, Sendai, Japan. 4. Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan. 5. Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 6. Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 7. Department of Urology, Seoul National University College of Medicine, Seoul, Republic of Korea. 8. Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. 9. Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 10. Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: hjahn@amc.seoul.kr.
Abstract
PURPOSE: We determined whether the 3 pT3 subclassification systems reported by the Asan, Cornell and Nagoya groups provide an accurate estimation of patient prognosis. We also determined which subclassification is most predictive of the heterogeneous oncological outcomes of pT3 renal pelvic urothelial carcinoma. MATERIALS AND METHODS: Using a Korea-Japan multi-institutional, retrospective database 250 pT3 renal pelvic urothelial carcinomas treated with radical nephroureterectomy were assigned to the 3 pT3 subcategories by tumor location and depth of parenchymal invasion after pathological reevaluation. Recurrence-free and cancer specific survival was assessed according to the 3 pT3 subclassifications. Predictive accuracy for survival in 4 models (baseline and each of the 3 pT3 subclassifications) was quantified and predictive accuracy increments for each model were compared. RESULTS: In the baseline multivariate Cox regression model nodal metastasis and high grade were significant for survival. On multivariate analysis including the pT3 subclassifications the 3 subclassifications remained significantly associated with survival rates. Of the 3 pT3 subclassification systems the Cornell subclassification had the highest predictive accuracy for discriminating the heterogeneous prognosis of pT3 renal pelvic urothelial carcinoma, followed by the Nagoya subclassification. Compared with the baseline model adding the Cornell subclassification significantly increased predictive accuracy for recurrence-free survival from 0.687 to 0.742 (p = 0.029) and for cancer specific survival from 0.713 to 0.758 (p = 0.047). CONCLUSIONS: The criteria of microscopic vs macroscopic parenchymal invasion and/or peripelvic fat invasion provide the most accurate differential classification of the prognostic heterogeneity of pT3 renal pelvic urothelial carcinoma. Further studies should be performed to determine the need to modify the current pT3 renal pelvic urothelial carcinoma staging system.
PURPOSE: We determined whether the 3 pT3 subclassification systems reported by the Asan, Cornell and Nagoya groups provide an accurate estimation of patient prognosis. We also determined which subclassification is most predictive of the heterogeneous oncological outcomes of pT3 renal pelvic urothelial carcinoma. MATERIALS AND METHODS: Using a Korea-Japan multi-institutional, retrospective database 250 pT3 renal pelvic urothelial carcinomas treated with radical nephroureterectomy were assigned to the 3 pT3 subcategories by tumor location and depth of parenchymal invasion after pathological reevaluation. Recurrence-free and cancer specific survival was assessed according to the 3 pT3 subclassifications. Predictive accuracy for survival in 4 models (baseline and each of the 3 pT3 subclassifications) was quantified and predictive accuracy increments for each model were compared. RESULTS: In the baseline multivariate Cox regression model nodal metastasis and high grade were significant for survival. On multivariate analysis including the pT3 subclassifications the 3 subclassifications remained significantly associated with survival rates. Of the 3 pT3 subclassification systems the Cornell subclassification had the highest predictive accuracy for discriminating the heterogeneous prognosis of pT3 renal pelvic urothelial carcinoma, followed by the Nagoya subclassification. Compared with the baseline model adding the Cornell subclassification significantly increased predictive accuracy for recurrence-free survival from 0.687 to 0.742 (p = 0.029) and for cancer specific survival from 0.713 to 0.758 (p = 0.047). CONCLUSIONS: The criteria of microscopic vs macroscopic parenchymal invasion and/or peripelvic fat invasion provide the most accurate differential classification of the prognostic heterogeneity of pT3 renal pelvic urothelial carcinoma. Further studies should be performed to determine the need to modify the current pT3 renal pelvic urothelial carcinoma staging system.