PN3b as an independent risk factor for poor prognosis and peritoneal recurrence in Borrmann type IV gastric cancer: A retrospective cohort study

Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer

OBJECTIVE

The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features.

METHODS

This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator.

RESULTS

Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696).

CONCLUSIONS

The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.

Machine learning methods predict recurrence of pN3b gastric cancer after radical resection

Background

The incidence of stage pN3b gastric cancer (GC) is low, and the clinical prognosis is poor, with a high rate of postoperative recurrence. Machine learning (ML) methods can predict the recurrence of GC after surgery. However, the prognostic significance for pN3b remains unclear. Therefore, we aimed to predict the recurrence of pN3b through ML models.

Methods

This retrospective study included 336 patients with pN3b GC who underwent radical surgery. A 3-fold cross-validation was used to partition the participants into training and test cohorts. Linear combinations of new variable features were constructed using principal component analysis (PCA). Various ML algorithms, including random forest, support vector machine (SVM), logistic regression, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and Gaussian naive Bayes (GNB), were utilized to establish a recurrence prediction model. Model performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Python was used for the analysis of ML algorithms.

Results

Nine principal components with a cumulative variance interpretation rate of 90.71% were identified. The output results of the test set showed that random forests had the highest AUC (0.927) for predicting overall recurrence with an accuracy rate of 80.5%. Random forests had the highest AUC (0.940) for predicting regional recurrence with an accuracy of 89.7%. For predicting distant recurrence, random forests had the highest AUC (0.896) with an accuracy of 84.3%. For peritoneal recurrence, random forests had the highest AUC (0.923) with an accuracy of 83.3%.

Conclusions

ML can personalize the prediction of postoperative recurrence in patients with GC with stage pN3b.

The efficacy of neoadjuvant chemotherapy is different for type 4 and large type 3 gastric cancer

BACKGROUND

In the JCOG0501 study, neoadjuvant chemotherapy (NAC) failed to demonstrate survival benefits for type 4 and large type 3 gastric cancer (GC). The prognosis of these patients is still poor. We conducted this study to explore the value of NAC with non-SP regimens for type 4 and large type 3 ​GC in the Chinese population.

METHODS

We retrospectively collected data from our electronic medical record system. Patients with large type 3 or type 4 ​GC who underwent D2 gastrectomy and AC were included. Patients were divided into two groups based on whether they received NAC: the CSC (NAC ​+ ​surgery ​+ ​AC) and SC (surgery ​+ ​AC) groups. The survival and perioperative outcomes for large type 3 or type 4 ​GC were analyzed between the CSC and SC groups, separately.

RESULTS

Between May 2009 and December 2018, 189 patients were reviewed. Among large type 3 ​GC, the 5-year overall survival (OS) rates for patients in the CSC and SC groups were 54.4 ​% and 28.0 ​%, respectively (P ​= ​0.0008). Among type 4 ​GC, the 5-year OS rates for patients in the CSC and SC groups were 15.8 ​% and 24.8 ​%, respectively (P ​> ​0.05).

CONCLUSIONS

This study showed NAC can improve the prognosis of large type 3 ​GC. However, NAC did not demonstrate significant survival advantages for type 4 ​GC.