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Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT
Published on: January 22, 2018
Microscopic peritoneal carcinomatosis in gastric cancer: Prevalence, prognosis and predictive factors
Enrico Maria Pasqual1, Serena Bertozzi1, Ambrogio P Londero2
1Department of Medical Area, University of Udine, Santa Maria della Misericordia University Hospital Udine, I-33100 Udine, Italy.
Abstract:
Peritoneal carcinomatosis (PC) is typically identified in advanced stage gastric cancer and is frequently considered to be an incurable disease. Along with macroscopic PC, microscopic PC may be diagnosed through pathological examination of tissue specimens and is not detectable during surgical intervention. The present study aimed to analyse the prevalence, prognostic value and predictive factors for microscopic PC. In the present retrospective study, data from patients with epithelial gastric cancer that were treated with curative intent surgery were examined. Patients with macroscopic PC were excluded. Additionally, the study population was divided into two groups based on the presence or absence of microscopic PC. The prevalence of microscopic PC was 5.5%. Microscopic PC exhibited a significant negative effect on overall survival. In addition, multivariate analyses revealed that the significant predictive factors for the presence of microscopic PC were adenocarcinoma of a diffuse type, lymphatic and vascular invasion, cancer location at the site of previous gastric surgery and a tumour extent >T2. In particular, the presence of lymphatic and vascular invasion was the most significant predictive factor. These results indicate that ≥5.5% of patients with gastric cancer who undergo surgery with a curative intent may benefit from more aggressive loco-regional treatment against microscopic PC at the time of surgery.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

