Residual Cancer Volume Predicts Clinical Outcome in Patients With Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemotherapy.
Ryuta NakaoEiichi KonishiHitoshi FujiwaraEigo OtsujiIsao YokotaYoji UrataAkio YanagisawaPublished in: International journal of surgical pathology (2019)
Background. The aim of this study was to assess the prognostic significance of residual cancer volume (RCV) in patients with esophageal squamous cell carcinoma (ESCC) who received esophagectomy after neoadjuvant chemotherapy. Methods. We measured RCV by using complete stepwise sections at 6- to 8-mm intervals obtained from 81 ESCC patients with clinical stages IB to III. RCV was defined as the summation of all products of residual cancer area and thickness, and its cutoff value was set by receiver operator characteristic curve analysis on 3-year disease-specific survival (DSS). The multivariate analyses were performed in comparison with histopathological factors including tumor regression grades according to the Japanese Classification of Esophageal Cancer (TRG-JPN) or reported by Becker et al (TRG-Becker). Results. The range of RCV was 0 to 49.3 cm3 (median = 1.4 cm3), and the cutoff value was set at 1.0 cm3 (sensitivity = 78%; specificity = 68%). In the Kaplan-Meier curve analysis with the log-rank test, RCV > 1.0 cm3 predicted poorer prognosis for relapse-free survival (RFS; 5-year RFS rate, 12% vs 47%; P < .001) and DSS (5-year DSS rate, 27% vs 61%; P < .001). The multivariate analyses by the Cox hazards model revealed that RCV > 1.0 cm3 was a factor predicting poor prognosis for RFS (P = .013; hazard ratios [HR] = 2.62) and DSS (P = .028; HR = 2.56) compared with histopathological factors including TRG-JPN; RFS (P = .014; HR = 3.03) and DSS (P = .045; HR = 2.71) compared with histopathological factors including TRG-Becker. Conclusions. The study suggested that determining RCV is a new method of predicting prognosis in ESCC patients after neoadjuvant chemotherapy.
Keyphrases
- neoadjuvant chemotherapy
- locally advanced
- free survival
- poor prognosis
- papillary thyroid
- lymph node
- sentinel lymph node
- muscular dystrophy
- squamous cell
- end stage renal disease
- duchenne muscular dystrophy
- squamous cell carcinoma
- machine learning
- chronic kidney disease
- rectal cancer
- early stage
- newly diagnosed
- ejection fraction
- lymph node metastasis
- optical coherence tomography
- deep learning
- childhood cancer
- young adults