Obesity, obesities and gastrointestinal cancers

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer

BACKGROUND

The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure.

METHODS

This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively.

RESULTS

In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension.

CONCLUSION

In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

Gastrointestinal cancers in lean individuals with non-alcoholic fatty liver disease: A systematic review and meta-analysis

BACKGROUND & AIMS

Obesity and non-alcoholic fatty liver disease (NAFLD) are known risk factors for gastrointestinal (GI) cancers. However, GI carcinogenesis in lean NAFLD patients remains unclear. This systematic review and meta-analysis aims to investigate the association between lean NAFLD and GI cancer risk.

METHODS

PubMed, Embase and Cochrane Library databases were systematically searched (from inception date to April 2023) for cohort studies assessing GI cancers in lean (body mass index [BMI] < 25 kg/m2 or < 23 kg/m2 in Asians) and non-lean (BMI ≥25 kg/m2 or ≥ 23 kg/m2 in Asians) NAFLD individuals. Data from eligible studies were extracted, and meta-analysis was carried out using a random effects model to obtain risk ratios (RRs) with 95% confidence intervals (CIs). Subgroup analyses, meta-regressions and sensitivity analyses were also performed. This study was registered in PROSPERO (CRD42023420902).

RESULTS

Eight studies with 56,745 NAFLD individuals (11% were lean) and 704 cases of incident GI cancers were included. Lean NAFLD was associated with higher risk of hepatic (RR 1.77, 95% CI 1.15-2.73), pancreatic (RR 1.97, 95% CI 1.01-3.86) and colorectal cancers (RR 1.53, 95% CI 1.12-2.09), compared to non-lean NAFLD. No significant differences were observed for oesophagus, gastric, biliary and small intestine cancers.

CONCLUSIONS

This study shows that lean NAFLD patients have an increased risk of liver, pancreatic and colorectal cancers compared to non-lean NAFLD patients, emphasizing the need to explore tailored cancer prevention strategies for this specific patient group. Further research is required to explore the mechanisms underlying the association between lean NAFLD and specific GI cancers.

Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care.