Nalee Kim1, Jee Suk Chang1, Chan Woo Wee2, In Ah Kim2, Jong Hee Chang3, Hye Sun Lee4, Se Hoon Kim5, Seok-Gu Kang3, Eui Hyun Kim3, Hong In Yoon1, Jun Won Kim6, Chang-Ki Hong7, Jaeho Cho1, Eunji Kim2, Tae Min Kim8, Yu Jung Kim8, Chul-Kee Park9, Jin Wook Kim9, Chae-Yong Kim9, Seung Hong Choi10, Jae Hyoung Kim10, Sung-Hye Park10, Gheeyoung Choe10, Soon-Tae Lee10, Il Han Kim11, Chang-Ok Suh12,13. 1. Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50 Yonsei-ro, 03722, Seoul, Korea (Republic of). 2. Department of Radiation Oncology, Cancer Research Institute, Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, 03080, Seoul, Korea (Republic of). 3. Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Republic of). 4. Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea (Republic of). 5. Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Republic of). 6. Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Republic of). 7. Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Republic of). 8. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (Republic of). 9. Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea (Republic of). 10. Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (Republic of). 11. Department of Radiation Oncology, Cancer Research Institute, Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, 03080, Seoul, Korea (Republic of). ihkim@snu.ac.kr. 12. Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50 Yonsei-ro, 03722, Seoul, Korea (Republic of). COSUH317@yuhs.ac. 13. Department of Radiation Oncology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, 463-712, Seongnam-si, Gyeonggi-Do, Korea (Republic of). COSUH317@yuhs.ac.
Abstract
PURPOSE: To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastoma patients as part of continuous validation. METHODS: We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping. RESULTS: A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5-21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors-IDH1 mutation and tumor contacting subventricular zone-were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated. CONCLUSION: An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy.
PURPOSE: To optimize and validate a current (NRG [a newly constituted National Clinical Trials Network group through National Surgical Adjuvant Breast and Bowel Project [NSABP], the Radiation Therapy Oncology Group [RTOG] and the Gynecologic Oncology Group (GOG)]) nomogram for glioblastomapatients as part of continuous validation. METHODS: We identified patients newly diagnosed with glioblastoma who were treated with temozolomide-based chemoradiotherapy between 2006 and 2016 at three large-volume hospitals. The extent of resection was determined via postoperative MRI. The discrimination and calibration abilities of the prediction algorithm were assessed; if additional factors were identified as independent prognostic factors, updated models were developed using the data from two hospitals and were externally validated using the third hospital. Models were internally validated using cross-validation and bootstrapping. RESULTS: A total of 837 patients met the eligibility criteria. The median overall survival (OS) was 20.0 (95% CI 18.5-21.5) months. The original nomogram was able to estimate the 6‑, 12-, and 24-month OS probabilities, but it slightly underestimated the OS values. In multivariable Cox regression analysis, MRI-defined total resection had a greater impact on OS than that shown by the original nomogram, and two additional factors-IDH1 mutation and tumor contacting subventricular zone-were newly identified as independent prognostic values. An updated nomogram incorporating these new variables outperformed the original nomogram (C-index at 6, 12, 24, and 36 months: 0.728, 0.688, 0.688, and 0.685, respectively) and was well calibrated. External validation using an independent cohort showed C‑indices of 0.787, 0.751, 0.719, and 0.702 at 6, 12, 24, and 36 months, respectively, and was well calibrated. CONCLUSION: An updated and validated nomogram incorporating the contemporary parameters can estimate individual survival outcomes in patients with glioblastoma with better accuracy.
Entities:
Keywords:
Extent of resection; IDH1 mutation; Model; Subventricular zone; Validation
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