Predictive and Prognostic Factors Related to Chemoradioresistance in Neoadjuvant Treatment of Locally Advanced Rectal Cancer at Panama

Abstract

<i>Background:</i> In Panama, and the rest of Latin America, there are few publications on chemoradioresistance, it leads to poor prognosis and represents the main reason for failure of therapy, ultimately it can lead to tumor recurrence and metastasis. <i>Objective:</i> Identify factors associated with chemoradioresistance in the neoadjuvant treatment of locally advanced rectal cancer at the Instituto Oncológico Nacional de Panamá, period 2016-2020. Methodology: This retrospective study included 71 patients with LARC who received neoadjuvant chemoradiotherapy and surgery. Chemoradioresistant patients were those who did not reach a higher pathological stage of ypT2NO and the results were compared. <i>Results:</i> Of the 71 patients, pathological complete response (pCR) was achieved in 34 patients (48%). 49% of patients met chemoradioresistance criteria. In this group, the median CEA was 136 ng/ml, the median hemoglobin was 12 g/dl, and the median BMI was 26 kg/m2. The median radiation dose was 5000Gy. The time between completion of chemoradiotherapy and surgery was 110 days (16 weeks). Depending on the type of surgery, 65% who underwent low anterior resection were chemoradioresistant. The variables with statistical significance were the CEA value >5 ng/mL (OR=1.81, p=0.026) prior to the start of neoadjuvant treatment, with a lower pCR rate. Likewise, the ECOG scale (OR=2.51, p=0.015) was a risk factor related to chemoradioresistance, the lower the ECOG, the lower the risk of chemoradioresistance. The median overall survival and median recurrence-free survival was not reached in both groups and there was no statistically significant difference. <i>Conclusions:</i> Significant interactions were identified between CEA levels prior to the start of neoadjuvant treatment with the pathological complete response rate and the ECOG score with chemoradioresistance. Therefore, these factors can be used to predict patient outcomes, will help optimize personalized treatment strategies and improve patient outcomes.