A Pilot Study of Prognostic Value of Metastatic Lymph Node Count and Size in Patients with Different Stages of Gastric Carcinoma

Nomogram for predicting overall survival after curative gastrectomy using inflammatory, nutritional and pathological factors

PURPOSE

To establish a nomogram for predicting the overall survival (OS) in patients with gastric cancer (GC) based on inflammatory, nutritional and pathological factors.

METHODS

GC patients underwent curative gastrectomy from January 2012 to June 2017 in our hospital were included, and were classified into training set and validation set with a ratio of 7:3. Then variables associated with OS were analyzed using univariate and multivariate Cox regression analysis. Nomograms predicting OS were built using variables from multivariable Cox models. Finally, Kaplan-Meier curve and Log-rank test were also conducted to analyze the 1-yr, 3-yr and 5-yr OS to validate the efficiency of risk stratification of the nomogram.

RESULTS

A total of 366 GC patients were included. After univariate and multivariate Cox regression analysis, age (HR = 1.52, 95% CI = 1.01-2.30, P = 0.044), CA50 (HR = 1.90, 95% CI = 1.12-3.21, P = 0.017), PNI (HR = 1.65, 95% CI = 1.13-2.39, P = 0.009), SII (HR = 1.46, 95% CI = 1.03-2.08, P = 0.036), T stage (HR = 2.26, 95% CI = 1.01-5.05, P = 0.048; HR = 7.24, 95% CI = 3.64-14.40, P < 0.001) were independent influencing factors on the survival time of GC patients. Five factors including CEA, prognostic nutritional index (PNI), systemic immune-inflammation index (SII), ln (tumor size), T stage, and N stage were identified and entered the nomogram, which showed good discrimination and calibration in both sets. On internal validation, 1-yr, 3-yr and 5-yr nomogram demonstrated a good discrimination with an area under the ROC curve (AUC) of 0.77, 0.84 and 0.86, respectively. The AUC for 1-yr, 3-yr and 5-yr nomogram in validation set was 0.77, 0.79 and 0.81, respectively. The OS in low risk group of training cohort and validation cohort was significantly higher than that of intermediate risk group and high risk group, respectively.

CONCLUSIONS

We established a nomogram based on PNI, SII and pathological factors for predicting OS in GC patients. In addition, its efficiency was validated by validation set and stratified analysis.

Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI

Objective

To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

Methods

Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week of therapy. The image data were collected by DCE-MRI, and the semiquantitative parameters including positive enhancement integral (PEI), signal enhancement ratio (SER), maximum slope of increase (MSI), and time to peak (TTP) were calculated. After chemotherapy, the parameters and relevant variations between the responders and nonresponders were compared with Mann–Whitney U tests. Student's t-test for paired samples was used to evaluate the temporal changes between pre- and posttreatment images.

Results

The patients were categorized as 13 responders and 9 nonresponders based on the tumor response evaluation. After chemotherapy, the PEI, SER, and MSI were significantly increased in responders compared with the pretreatment values (P < 0.05), while no obvious decrease in TTP was observed (P > 0.05). However, 9 nonresponders showed no significant changes in PEI, SER, MSI, and TTP values, as compared with those of pretreatment (P > 0.05). Moreover, the increase of PEI was more dramatically in responders than in nonresponders (P < 0.05), but no significantly differences were observed in SER, MSI, and TTP (P > 0.05).

Conclusion

Semiquantitative analysis of DCE-MRI could provide a reliable noninvasive method for assessing early chemotherapy response in lung cancer patients.