Dong Myung Yeo1, Soon Nam Oh2, Myung Ah Lee3, In Kyu Lee4, Yoon Suk Lee4, Seong Taek Oh5, Sung Hak Lee6, Mi Sun Park7, Hyeon Woo Yim8. 1. Department of Radiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 2. Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 06591 #222 Banpo-daero, Seocho-gu, Seoul, Republic of Korea. hiohsn@gmail.com. 3. Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 4. Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 5. Department of Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 6. Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. 7. Department of Biostatistics, Clinical Research Coordinating Center, The Catholic University of Korea, Seoul, Republic of Korea. 8. Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Abstract
OBJECTIVE: To develop a prediction model for recurrence by incorporating radiological and clinicopathological prognostic factors in rectal cancer patients. METHODS: All radiologic and clinicopathologic data of 489 patients with rectal cancer, retrospectively collected from a single institution between 2009 and 2013, were used to develop a predictive model for recurrence using the Cox regression. The model performance was validated on an independent cohort between 2015 and 2017 (N = 168). RESULTS: Out of 489 derivative patients, 103 showed recurrence after surgery. The prediction model was constructed with the following four significant predictors: distance from anal verge, MR-based extramural venous invasion, pathologic nodal stage, and perineural invasion (HR: 1.69, 2.09, 2.59, 2.29, respectively). Each factor was assigned a risk score corresponding to HR. The derivation and validation cohort were classified by sum of risk scores into 3 groups: low, intermediate, and high risk. Each of these groups showed significantly different recurrence rates (derivation cohort: 13.4%, 35.3%, 61.5 %; validation cohort: 6.2%, 23.7%, 64.7%). Our new model showed better performance in risk stratification, compared to recurrence rates of tumor node metastasis (TNM) staging in the validation cohort (stage I: 3.6%, II: 12%, III: 30.2%). The area under the receiver operating characteristic curve of the new prediction model was higher than TNM staging at 3-year recurrence in the validation cohort (0.853 vs. 0.731; p = .009). CONCLUSIONS: The new risk prediction model was strongly correlated with a recurrence rate after rectal cancer surgery and excellent for selection of high-risk group, who needs more active surveillance. KEY POINTS: • Multivariate analysis revealed four significant risk factors to be MR-based extramural venous invasion, perineural invasion, nodal metastasis, and the short distance from anal verge among the radiologic and clinicopathologic data. • Our new recurrence prediction model including radiologic data as well as clinicopathologic data showed high predictive performance of disease recurrence. • This model can be used as a comprehensive approach to evaluate individual prognosis and helpful for the selection of highly recurrent group who needs more active surveillance.
OBJECTIVE: To develop a prediction model for recurrence by incorporating radiological and clinicopathological prognostic factors in rectal cancerpatients. METHODS: All radiologic and clinicopathologic data of 489 patients with rectal cancer, retrospectively collected from a single institution between 2009 and 2013, were used to develop a predictive model for recurrence using the Cox regression. The model performance was validated on an independent cohort between 2015 and 2017 (N = 168). RESULTS: Out of 489 derivative patients, 103 showed recurrence after surgery. The prediction model was constructed with the following four significant predictors: distance from anal verge, MR-based extramural venous invasion, pathologic nodal stage, and perineural invasion (HR: 1.69, 2.09, 2.59, 2.29, respectively). Each factor was assigned a risk score corresponding to HR. The derivation and validation cohort were classified by sum of risk scores into 3 groups: low, intermediate, and high risk. Each of these groups showed significantly different recurrence rates (derivation cohort: 13.4%, 35.3%, 61.5 %; validation cohort: 6.2%, 23.7%, 64.7%). Our new model showed better performance in risk stratification, compared to recurrence rates of tumor node metastasis (TNM) staging in the validation cohort (stage I: 3.6%, II: 12%, III: 30.2%). The area under the receiver operating characteristic curve of the new prediction model was higher than TNM staging at 3-year recurrence in the validation cohort (0.853 vs. 0.731; p = .009). CONCLUSIONS: The new risk prediction model was strongly correlated with a recurrence rate after rectal cancer surgery and excellent for selection of high-risk group, who needs more active surveillance. KEY POINTS: • Multivariate analysis revealed four significant risk factors to be MR-based extramural venous invasion, perineural invasion, nodal metastasis, and the short distance from anal verge among the radiologic and clinicopathologic data. • Our new recurrence prediction model including radiologic data as well as clinicopathologic data showed high predictive performance of disease recurrence. • This model can be used as a comprehensive approach to evaluate individual prognosis and helpful for the selection of highly recurrent group who needs more active surveillance.
Entities:
Keywords:
Magnetic resonance imaging; Nomograms; Prognosis; Rectal neoplasms; Recurrence