Surgical Outcomes in Gastric Cancer Management

Gastric cancer remains a leading cause of cancer-related mortality worldwide. Surgical resection, often combined with perioperative therapies, represents the cornerstone of curative treatment. Advances in operative techniques, patient selection and perioperative care have collectively reduced mortality and morbidity following gastrectomy. Current practice emphasises complete tumour removal with adequate lymphadenectomy while minimising postoperative complications and preserving quality of life. The adoption of minimally invasive approaches, rigorous complication classification systems and centralisation in high-volume centres has standardised care and enabled more reliable benchmarking of outcomes. Composite metrics such as textbook outcome have emerged to capture multiple facets of surgical success, integrating technical achievement, complication avoidance and efficient recovery. Concurrently, machine-learning tools have shown promise in refining risk prediction for short-term mortality, facilitating personalised risk stratification and informed consent. Despite these gains, postoperative morbidity—particularly anastomotic leakage, pulmonary infection and nutritional derangements—continues to challenge recovery and long-term survival. Ongoing efforts are directed towards enhancing prehabilitation, optimising perioperative pathways and integrating multimodal therapies to improve both short- and long-term outcomes. This overview synthesises recent developments in surgical outcomes for gastric cancer, highlighting global initiatives to standardise reporting, improve prognostication and inform clinical decision-making.

Research from Nature Portfolio

Automated machine learning has been applied to predict 90-day mortality after gastrectomy in a large national cohort. Using an ensemble of preoperative and postoperative variables, models achieved good discrimination (area under the curve approximately 0.77) and identified age, nodal ratio and length of stay as key predictors. Secondary modelling demonstrated the feasibility of preoperative risk stratification by predicting intermediate clinical features. This work illustrates the potential of data-driven algorithms to support surgical planning and patient counselling in gastric cancer care.

Surgical Outcomes in Gastric Cancer Management publication trend

The graph below shows the total number of articles in surgical outcomes in gastric cancer management across all publications each year (not limited to Nature Index journals).

Technical terms

Gastrectomy: Surgical removal of part or all of the stomach for cancer treatment.

Anastomotic leakage: Breakdown of the surgical connection between gastric and intestinal tissues, leading to leakage of luminal contents into the abdominal cavity.

Lymphadenectomy: Removal of regional lymph nodes to assess and control tumour spread.

Minimally invasive surgery: Laparoscopic or robotic approach using small incisions to reduce tissue trauma and enhance recovery.

Textbook outcome: Composite measure of optimal perioperative results, including complete resection, absence of major complications and timely discharge.

Automated machine learning (AutoML): Computational methods that automate the development and optimisation of predictive models.

References

1. Outcomes after gastrectomy according to the Gastrectomy Complications Consensus Group (GCCG) in the Dutch Upper GI Cancer Audit (DUCA). Gastric Cancer (2024).
2. Defining benchmarks for total and distal gastrectomy: global multicentre analysis. British Journal of Surgery (2024).
3. Textbook Neoadjuvant Outcome—Novel Composite Measure of Oncological Outcomes among Gastric Cancer Patients Undergoing Multimodal Treatment. Cancers (2024).
4. Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer. Scientific Reports (2023).
5. Population-based nationwide incidence of complications after gastrectomy for gastric adenocarcinoma in Finland. BJS Open (2023).