Surgery for locally advanced gastric cancer in the era of neoadjuvant therapies: something new?

BACKGROUND

Locally advanced gastric cancer (LAGC) represents a therapeutic challenge, particularly as it often involves adjacent organs. The necessity of neoadjuvant treatments for LAGC patients is still controversial. The aim of this study was to analyze the factors affecting prognosis and survival in patients with LAGC with particular regard to the effect of neoadjuvant therapies.

METHODS

Between January 2005 and December 2018, the medical records of 113 patients with LAGC who underwent curative resection were retrospectively reviewed. Patient characteristics, related complications, long-term survival, and prognostic factors were analyzed at uni- and multivariate analyses.

RESULTS

Postoperative mortality and morbidity rates of patients undergoing neo-adjuvant therapies were 2.3% and 43.2%, respectively. Whereas in patients undergoing upfront surgery were 4.6% and 26.1%, respectively. R0 resection was achieved 79.5% and in 73.9% of patients undergoing neoadjuvant therapy and upfront surgery, respectively (P<0.001). Multivariate analysis revealed that neoadjuvant therapy, completeness of resection (R0), number of lymph nodes retrieved, N status and the adoption of hyperthermic intraperitoneal chemotherapy were independent prognostic factors associated with longer survival. Five-year overall survival for NAC group and upfront surgery group was 46% and 32%, respectively (P=0.04). Five-year disease-free survival for NAC group and upfront surgery group was 38% and 25%, respectively (P=0.02).

CONCLUSIONS

Patients with LAGC undergoing surgery plus neoadjuvant therapy had a better OS and DFS with respect to patients treated with surgery alone.

Acute pancreatitis after pancreatoduodenectomy: clinical outcomes and predictive factors analysis according to the International Study Group of Pancreatic Surgery definition

BACKGROUND

Post-pancreatectomy acute pancreatitis (PPAP) is an increasingly described complication after pancreatic resection. No uniform definition criteria were present in the literature until the recent proposal of the International Study Group of Pancreatic Surgery (ISGPS). Aim of this study is to evaluate the clinical significance of the novel ISGPS definition of PPAP.

METHODS

Patients who underwent pancreatoduodenectomy (PD) between 2006 and 2022 were enrolled. PPAP was defined and graded according to the ISGPS criteria.

RESULTS

Among 520 PDs, 120 (23%)patients developed post-operative hyperamylasemia (POH), while PPAP occurred in 63(12.1%) cases. PPAP occurrence related to a higher rate of more severe complications (48-76.1%vs118-25.8%; p < 0.0001), delayed gastric emptying (DGE) (27-42.9%vd114-24.9%; p = 0.003) and post-operative pancreatic fistula (POPF) (57-90.5%vs186-40.8%; p < 0.0001). When stratified for PPAP severity, grade B and C patients more frequently developed major complications (p < 0.0001), POPF (p < 0.0001), DGE (p = 0.02) and post-operative hemorrhage (p < 0.0001) as compared to POH. At the multivariable analysis, soft pancreatic texture (p = 0.01)and a Wirsung diameter ≤3 mm (p = 0.01) were recognized as prognostic factors for PPAP onset, while a pancreatic duct ≤3 mm was the only feature significantly influencing a more severe course of PPAP (p = 0.01).

CONCLUSION

The ISGPS classification is confirmed as a valuable method for a uniform definition and clinical course evaluation. Further studies in a prospective manner are still needed for a further confirmation.

New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach

OBJECTIVE

Patients with acute severe and medical refractory ulcerative colitis have a high risk of postoperative complications after total abdominal colectomy (TAC). The objective of this retrospective study is to use machine learning to analyze and predict short-term outcomes.

PATIENTS AND METHODS

32 patients with ulcerative colitis were treated with total abdominal colectomy between 2011 and 2017. Biographical data, preoperative therapy, blood chemistry, nutritional status, surgical technique, blood transfusion and preoperative length of stay were the features selected for the statistical analyses and were used as input for the machine learning algorithms to predict the rate of complications.

RESULTS

Traditional statistical analysis showed an overall postoperative morbidity rate of 34% and a mortality rate of 3%. Preoperative low serum albumin levels (<2.5 g/dL) were related to a higher risk of minor infectious complications with statistical significance (p<0.05). Preoperative length of stay (>4 days), blood transfusions (≥1 unit) and body temperature (≥37.5°C) demonstrated a major impact on infectious morbidity with statistical significance (p<0.05). Patients treated with steroids and rescue therapy presented a higher risk of minor infectious complications (p<0.05). Evaluating only preoperative features, machine learning algorithms were able to predict minor postoperative complications with a high strike rate (84.3%), high sensitivity (87.5%) and high specificity (83.3%) during the testing phase.

CONCLUSIONS

Machine learning is demonstrated to be useful in predicting the rate of minor postoperative complications in high-risk ulcerative colitis patients, despite the small sample size. It represents a major step forward in data analysis by implementing a retrospective study from a prospective point of view.