News
Article
Author(s):
A nomogram model that included log odd of positive lymph node was effective in predicting prognosis of early-onset colorectal cancer (EOCRC).
Early-onset colorectal cancer (EOCRC) was effectively predicted using a nomogram model that included log odd of positive lymph node (LODDS), according to a study published in World Journal of Surgical Oncology.1 The prognosis of advanced CRC that was treated with neoadjuvant therapy was also a possible calculation achieved with the nomogram model.
CRC is the third most common cancer in the world and imposes a significant burden on Chinese patients, specifically in cost of treatment. Metastasis and recurrence are at high risk in patients with advanced CRC, with 70% of patients with metastasis eventually developing relapse.2 The number of positive lymph nodes and lymph node dissection is an important component when it comes to prognosis and LODDS have been used as a predictor of survival status. This study aimed to focus on predicting survival in patients with advanced CRC with lymph node metastasis who were treated with neoadjuvant therapy.
A nomogram model could help predict overall survival in early-onset colorectal cancer | Image credit: Dr_Microbe - stock.adobe.com
The SEER database was used to obtain information about all the patient included in the study. The SEER database includes 17 registries as well as the Department of Gastrointestinal Surgery data from the Tang Du Hospital. Patients were included if they were diagnosed with CRC between 2010 and 2015. Patients were excluded if they did not have surgery, had a survival time of less than a month, were aged 19 years and younger, had no confirmed diagnosis, did not have metastasis or had unknown metastasis, no neoadjuvant therapy, the primary site was in the appendix, or were an unknown race or ethnicity.
The patients were separated in a 3:2 ratio into training and testing groups. Year of diagnosis, age, race, marital status, and diagnostic confirmation were all extracted from the data.
There were 1833 total participants in this study, of which 1759 were from the SEER database and separated into training and testing groups, with the remaining participants acting as the validation group. Late-onset colorectal cancer (LOCRC) was found in 80% of the participants with 75% having a tumor location in the rectum. Low levels of LODDS and lymph nodes ratio (LNR) were the main characteristics of the patients who received radiotherapy and chemotherapy.
The nomogram survival prediction model used variables screened by univariate cox regression, including age, chemotherapy, perineural invasion, tumor size, LODDS, liver metastasis, and radiation. The 1-, 3-, and 5-year survival rates along with the clinical characteristic score for the patient were used to calculate a score for patients with advanced CRC who were treated with neoadjuvant therapy.
The area under curve for predicting 1-year OS in the training, testing, and validation groups were found to be 0.765, 0.772, and 0.742. For 3-year OS, the AUC for these respective groups was 0.761, 0.742, 0.733. For 5-year OS, the AUCs were 0.742, 0.746, and 0.838 for the respective groups. The predictive value was considered to be good with all AUCs equaling 0.70.
The nomogram model found that the AUCs for EOCRC in patients receiving neoadjuvant chemotherapy for 1, 3, and 5-year OS were 0.853, 0.784, and 0.767, respectively. For LOCRC, the AUCs were 0.768, 0.743, and 0.735 for 1, 3, and 5-year OS, respectively.
There were some limitations to this study. Only some clinical characteristics were accounted for in the analysis, with factors like genetics, social factors, and response after neoadjuvant therapy not included. The external validation had a small sample size due to fewer patients receiving neoadjuvant therapy. Information bias is possible due to the retrospective design of the study.
The researchers concluded that the survival prediction model performed well when evaluating patients with advanced CRC who had received neoadjuvant therapy. The model was more effective in predicting prognosis in EOCRC compared with LOCRC.
References