A PD-L1 Negative Advanced Gastric Cancer Patient With a Long Response to PD-1 Blockade After Failure of Systematic Treatment: A Case Report

BackgroundIt was widely accepted that programmed death-ligand 1 (PD-L1) positive, tumor mutational burden-high (TMB-H) or microsatellite instability-high (MSI-H) tumor are prone to have better treatment response to immune checkpoint blockade. The value of immune checkpoint blockade in PD-L1 negative gastric cancer patients has been questioned due to lower objective response rate (ORR).Case PresentationWe report an unusual case of a PD-L1 negative, proficient mismatch repair (pMMR)/microsatellite stability (MSS), tumor mutational burden-low (TMB-L) gastric cancer patient who achieved good response to immune checkpoint blockade after failure of systematic treatment. Multiple lymph nodes and bone metastases are the main characteristics of this patient. The patient survived for more than 30 months after diagnosis.ConclusionsThis case suggested that PD-L1 negative gastric cancer patient may also benefit from immune checkpoint blockade. In gastric cancer, patients with lymph node metastasis may be potential beneficiaries.

Postrenal Azotemia in a Gastric Cancer Patient Revealed the Coincidence of Ureteral Metastasis and Contralateral Ureteral Stone: A Case Report

Metastatic ureteral masses are not rare, but isolated ureteral metastasis from the origin of gastric cancer is rare. Ureteral metastasis is usually unilateral and does not lead to postrenal azotemia unless in single kidney patients. Herein, we describe an 80-year-old man with a history of nonmetastatic gastric cancer who presented with postrenal azotemia due to the coincidence of right distal ureteral metastasis and left distal ureteral stone.

Gastric cancer revealed by a pathologic fracture due to bone metastasis

Gastric cancer rarely disseminates to the bone. Initial clinical presentation of bone metastasis without any preceding gastrointestinal symptoms in gastric cancer patient is also extremely infrequent. Herein, we report an original case of gastric cancer revealed by a pathologic fracture due to bone metastasis.

Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model

IntroductionIt’s very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification. We propose a new Random Forest weighting method and apply it to the gastric cancer patient data from the Surveillance, Epidemiology, and End Results (SEER) program, and then evaluated the generalization ability of this weighted Random Forest algorithm on 10 public medical datasets. Furthermore, for the same weighting mode, the difference between using out-of-bag (OOB) data and all training sets as the weighting basis is explored.Material and methods110697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were contained in the experiment. In addition, 10 public medical datasets are used for the generalization ability evaluation of this weighted Random Forest algorithm.ResultsThrough experimental verification, on the SEER gastric cancer patient data, the weighted Random Forest algorithm improves the accuracy by 0.79% compared with the original Random Forest. In AUC, Macro-averaging increased by 2.32% and Micro-averaging increased by 0.51% on average. Among the 10 public datasets, the Random Forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1.44% in accuracy and an average increase of 1.2% in AUC.ConclusionsCompared with the original Random Forest, the weighted Random Forest model has a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data.