Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection.

PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrence (ER) after curative-intent resection of neuroendocrine liver metastases (NELMs). METHODS: Patients with NELM who underwent resection were identified from a multi-institutional database. ER was defined as recurrence within 12 months of surgery. Different AI-based models were developed to predict ER using 10 clinicopathologic factors. RESULTS: Overall, 473 patients with NELM were included. Among 284 patients with recurrence (60.0%), 118 patients (41.5%) developed an ER. An ensemble AI model demonstrated the highest area under receiver operating characteristic curves of 0.763 and 0.716 in the training and testing cohorts, respectively. Maximum diameter of the primary neuroendocrine tumor, NELM radiologic tumor burden score, and bilateral liver involvement were the factors most strongly associated with risk of NELM ER. Patients predicted to develop ER had worse 5-year recurrence-free survival and overall survival (21.4% vs 37.1% [P = .002] and 61.6% vs 90.3% [P = .03], respectively) than patients not predicted to recur. An easy-to-use tool was made available online: (https://altaf-pawlik-nelm-earlyrecurrence-calculator.streamlit.app/). CONCLUSION: An AI-based model demonstrated excellent discrimination to predict ER of NELM after resection. The model may help identify patients who can benefit the most from curative-intent resection, risk stratify patients according to prognosis, as well as guide tailored surveillance and treatment decisions including consideration of nonsurgical treatment options.

The Association of Socio-Environmental Inequality and Outcomes Among Patients Undergoing Major Surgery.

INTRODUCTION: Environmental hazards may influence health outcomes and be a driver of health inequalities. We sought to characterize the extent to which social-environmental inequalities were associated with surgical outcomes following a complex operation. METHODS: In this cross-sectional study, patients who underwent abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, pneumonectomy, or pancreatectomy between 2016 and 2021 were identified from Medicare claims data. Patient data were linked with social-environmental data sourced from Centers for Disease Control and Agency for Toxic Substances and Disease Registry data based on county of residence. The Environmental Justice Index social-environmental ranking (SER) was used as a measure of environmental injustice. Multivariable regression analysis was performed to assess the relationship between SER and surgical outcomes. RESULTS: Among 1,052,040 Medicare beneficiaries, 346,410 (32.9%) individuals lived in counties with low SER, while 357,564 (33.9%) lived in counties with high SER. Patients experiencing greater social-environmental injustice were less likely to achieve textbook outcome (odds ratio 0.95, 95% confidence interval 0.94-0.96, P < 0.001) and to be discharged to an intermediate care facility or home with a health agency (odds ratio 0.97, 95% confidence interval 0.96-0.98, P < 0.001). CONCLUSIONS: Cumulative social and environmental inequalities, as captured by the Environmental Justice Index SER, were associated with postoperative outcomes among Medicare beneficiaries undergoing a range of surgical procedures. Policy makers should focus on environmental, as well as socioeconomic injustice to address preventable health disparities.

Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.

OBJECTIVE: We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). METHODS: HCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. RESULTS: Overall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719-0.782) and 0.717 (95% CI:0.653-0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835-0.884) and 0.764 (95% CI: 0.704-0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). CONCLUSION: The AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/).