Hyoung Uk Je1, Seungbong Han1, Young Seok Kim2, Joo-Hyun Nam1, Hak Jae Kim3, Jae Weon Kim3, Won Park4, Duk-Soo Bae4, Jin Hee Kim5, So Jin Shin5, Juree Kim6, Ki-Heon Lee6, Mee Sun Yoon7, Seok Mo Kim7, Ji-Yoon Kim8, Won Sup Yoon9, Nak Woo Lee9, Jin Hwa Choi10, Sang-Yoon Park11, Joo-Young Kim11. 1. Asan Medical Center, University of Ulsan, College of Medicine, Seoul, Republic of Korea. 2. Asan Medical Center, University of Ulsan, College of Medicine, Seoul, Republic of Korea. Electronic address: ysk@amc.seoul.kr. 3. Seoul National University Hospital, Republic of Korea. 4. Samsung Medical Center, Sungkyunkwan University Scholl of Medicine, Seoul, Republic of Korea. 5. Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Republic of Korea. 6. Cheil General Hospital and Women's Healthcare Center, Kwandong University, College of Medicine, Seoul, Republic of Korea. 7. Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Republic of Korea. 8. The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea. 9. Korea University Ansan Hospital, Republic of Korea. 10. Chung-Ang University Hospital, Seoul, Republic of Korea. 11. Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea.
Abstract
PURPOSE: To develop a nomogram predicting the risks of distant metastasis following postoperative adjuvant radiation therapy for early stage cervical cancer. MATERIALS AND METHODS: We reviewed the medical records of 1069 patients from ten participating institutions. Patients were divided into two cohorts: a training set (n=748) and a validation set (n=321). The demographic, clinical, and pathological variables were included in the univariate Cox proportional hazards analysis. Clinically established and statistically significant prognostic variables were utilized to develop a nomogram. RESULTS: The model was constructed using four variables: histologic type, pelvic lymph node involvement, depth of stromal invasion, and parametrial invasion. This model demonstrated good calibration and discrimination, with an internally validated concordance index of 0.71 and an externally validated c-index of 0.65. Compared to FIGO staging, which showed a broad range in terms of distant metastasis, the developed nomogram can accurately predict individualized risks based on individual risk factors. CONCLUSIONS: The devised model offers a significantly accurate level of prediction and discrimination. In clinical practice it could be useful for counseling patients and selecting the patient group who could benefit from more intensive/further chemotherapy, once validated in a prospective patient cohort.
PURPOSE: To develop a nomogram predicting the risks of distant metastasis following postoperative adjuvant radiation therapy for early stage cervical cancer. MATERIALS AND METHODS: We reviewed the medical records of 1069 patients from ten participating institutions. Patients were divided into two cohorts: a training set (n=748) and a validation set (n=321). The demographic, clinical, and pathological variables were included in the univariate Cox proportional hazards analysis. Clinically established and statistically significant prognostic variables were utilized to develop a nomogram. RESULTS: The model was constructed using four variables: histologic type, pelvic lymph node involvement, depth of stromal invasion, and parametrial invasion. This model demonstrated good calibration and discrimination, with an internally validated concordance index of 0.71 and an externally validated c-index of 0.65. Compared to FIGO staging, which showed a broad range in terms of distant metastasis, the developed nomogram can accurately predict individualized risks based on individual risk factors. CONCLUSIONS: The devised model offers a significantly accurate level of prediction and discrimination. In clinical practice it could be useful for counseling patients and selecting the patient group who could benefit from more intensive/further chemotherapy, once validated in a prospective patient cohort.
Authors: Won Sup Yoon; Dae Sik Yang; Jung Ae Lee; Nam Kwon Lee; Young Je Park; Chul Yong Kim; Nak Woo Lee; Jin Hwa Hong; Jae Kwan Lee; Jae Yun Song Journal: Biomed Res Int Date: 2017-04-27 Impact factor: 3.411
Authors: Claudia Marchetti; Francesca De Felice; Anna Di Pinto; Alessia Romito; Angela Musella; Innocenza Palaia; Marco Monti; Vincenzo Tombolin; Ludovico Muzii; PierLuigi Benedetti Panici Journal: Cancer Res Treat Date: 2017-07-19 Impact factor: 4.679