Published online May 21, 2020. doi: 10.3748/wjg.v26.i19.2427
Peer-review started: January 21, 2020
First decision: March 6, 2020
Revised: April 23, 2020
Accepted: April 28, 2020
Article in press: April 28, 2020
Published online: May 21, 2020
Processing time: 121 Days and 9 Hours
Survival benefit of neoadjuvant chemotherapy (NAC) for advanced gastric cancer (AGC) is a debatable issue. Studies had shown that the survival benefit of NAC is dependent on the pathological response to chemotherapy drugs. For those who achieve pathological complete response (pCR), NAC significantly prolonged the prolapsed-free survival and overall survival. For those with poor response, NAC yielded no survival benefit, only toxicity and increased risk of tumor progression during chemotherapy, which may hinder surgical resection. Thus, predicting pCR to NAC is of great clinical significance and can help achieve individualized treatment in AGC patients.
Our goal was to establish a nomogram to assist with individualized therapy: To identify those who will have a positive response to NAC and advise them to adopted NAC strategy; to identify those who will have a poor response to NAC and avoid the NAC-related harm.
Our main goal was to establish a nomogram for the prediction of pCR using only easily available pre-treatment clinical parameters.
A total of 208 patients were identified from the gastric cancer database of The Sixth Affiliated Hospital, Sun Yat-sen University from March 2012 to July 2019. Included patients were diagnosed with AGC with a clinical stage of T3N+ or T4N0/+. All patients received NAC and subsequent gastrectomy with D2 lymphadenectomy. NAC regimen mainly consisted of mFLOT, Folfox6, SOX and XELOX. pCR was defined as absence of residual cancer cell in the primary tumor and the dissected regional lymph node.
A total of 208 patients diagnosed with adenocarcinoma of the stomach or esophagogastric junction were enrolled in the study. Patients characteristic and the treatments received are depicted in Table 1. Patients received a median of four cycles of NAC before surgery; 58.7% of patients received mFLOT regimen (122/208) while 26.1% of patients received FOLFOX or its analogue (75/208). The most common grade 3/4 hematological toxicities were anemia (92/208, 44.2 %) and neutropenia (86, 41.3 %). All treatments were followed by a radical resection surgery. Peritoneal washing cytology test was conducted in only three patients, and the results were all negative. Postoperative complications were observed in 44 patients (21.2%). The incidence rate does not differ between pCR (41/181, 22.7%) and Non-pCR group (3/27, 11.1%) statistically. Abdominal abscess was the most frequent complication in both groups, and all were resolved by non-surgical management, such as percutaneous centesis drainage, enteral nutrition support and antibiotic therapy. Based on the treatments above, only 13% (27/208) can be classified as achieving pCR. Univariable associations between the clinical parameters and pCR are shown in Table 1. Statistically significant factors (P < 0.05) include tumor differentiation, carcinoembryonic antigen level (CEA), lymphocyte ratio (LYMR), monocyte count (MONO), blood type and uric acid level. The multivariable analysis showed that higher CEA level and LYMR and lower MONO and tumor differentiation grade are independent predictors of pCR with their respective odd ratios and corresponding 95% confidence intervals, as shown in Table 5. The established logistic linear regression model was used to build a nomogram as shown in Figure 1, while the receiver operating characteristic curve of the nomogram is shown in Figure 2. Area under the curve was 0.823. The apparent concordance statistic was 0.767, indicating a strong discriminative ability in prediction. Calibration curves between predicted and actual observations were plotted for internal validation. The outcome demonstrated that this nomogram showed good statistical performance for predicting the probability of pCR, as shown in Figure 3.
A nomogram predicting pCR is built based on clinical parameters prior to the start of NAC. In this model, higher CEA level and LYMR and lower MONO and tumor differentiation grade are correlated with higher probability of pCR. Interestingly, the correlation between CEA level and pCR is opposite to the previous report, in which higher CEA level is often the predictor of poor response to NAC. The model was internally validated using bootstrap method and showed satisfactory predictive power. However, it should also be acknowledged that there is a lack of external validation in an independent cohort, and the survival impact of pCR was not elucidated owning to insufficient data.
In the future, we plan to add an external cohort for validation to strengthen the reliability of the nomogram. In addition, we plan to analyze the impact of pCR on survival.