Comparison and Prognostic Analysis of Elective Nodal Irradiation Using Definitive Radiotherapy versus Chemoradiotherapy for Treatment of Esophageal Cancer
Objective: To investigate the prognostic factors for esophageal cancer (EC) patients treated by elective nodal irradiation (ENI) using chemo radiotherapy (CRT) and radiotherapy (RT)-alone. Methods: Data from 340 patients with EC were randomized to receive RTalone or CRT between January 2008 and December 2012. All patients received ENI either with late course RT or simultaneous integrated boost (SIB) - Intensity Modulated Radiotherapy (IMRT). The impact of clinic pathological factors and treatment modality on the overall survival (OS), and progression-free survival (PFS) were analyzed using Logrank test, Cox proportional regression model, and propensity score matching (PSM). P<0.05 was considered statistically significant. Results: A total of 340 patients were included, 174 patients (51.2%) underwent RT- alone and 166 patients (48.8%) received CRT. After the PSM, the median OS and median PFS times were 37.3 and 13.0 months for the RT–group, while those of the CRTgroup were 39.0 and 16.2 months, respectively. The 5-year OS rates was 32.9% for the RT-group, while those of the CRT-group was 31.3%, respectively (χ2=0.002, p=0.961). The 5-year PFS rate was 7.8% for the RT - group whereas, those of the CRT- group was 22.9%, respectively (χ2=3.911, p=0.048). Subgroup analysis showed that, late-course RT was signiï¬cantly associated with improve PFS in CRT – group for patients within ≤ 60 years, female gender with cT3-4, N0- status, cTNM- stage III-IV, T- length> 5 cm, SCC subtype, GTV volume >30 cm3, (p<0.05 for all analysis). Conclusion: Compared with RT- alone, ENI using CRT and late- course RT provides a PFS benefit to EC patients, especially in those within ≤ 60 years old, cT3-4, N0- status, cTNM- stage III-IV, SCC subtype, T- length >5 cm, and GTV- volume >30 cm3 but it did not improve OS. Therefore, this finding could be of a particularly important pathway to the stratification parameters for a personalized treatment.
Keita M, Zhang Xueyuan, Deng Wenzhao, Li Juan, Su Jingwei, Shen Wenbin, Traoré B and Zhu Shuchai