Change Impact of Body Composition During Neoadjuvant Chemoradiotherapy in Patients with Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma Undergoing Pancreatectomy

Preoperative level of serum transthyretin as a novel biomarker predicting survival in resected pancreatic ductal adenocarcinoma with neoadjuvant therapy

BACKGROUND

Systemic inflammation and altered metabolism are essential hallmarks of cancer. We hypothesized that the rapid turnover protein transthyretin (TTR) (half-life: 2-3 days), compared with the conventional marker albumin (21 days), better reflects the inflammatory/metabolic dynamics of pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant therapy (NAT) and is a useful prognostic marker.

METHODS

Serum TTR and albumin levels were measured in 104 consecutive post-NAT PDAC patients before curative resection. The associations of preoperative TTR and albumin levels with overall survival (OS) after pancreatectomy were retrospectively analyzed.

RESULTS

The mean (SD) TTR and albumin levels were 21.6 (6.4) mg/dL (normal range: ≥22.0 mg/dL) and 3.9 (0.55) g/dL. A low (<22.0 mg/dL) post-NAT TTR level was associated with an advanced tumor stage and higher CEA and CRP levels. Patients with low TTR levels showed significantly worse OS compared with normal levels (3-year OS 39 % vs. 54 %, P = 0.037), although albumin levels did not. We modified prognostic biomarkers of systemic inflammation/metabolism, such as GPS, PNI, and CONUT scores, using the serum TTR instead of albumin level and successfully showed that modified scores were better associated with OS compared with original scores using serum albumin level.

CONCLUSIONS

Our data suggest that the TTR level is a promising prognostic biomarker for PDAC patients after NAT.

Dynamic Anthropometrics in Pancreatic Cancer: Associations Between Body Composition Changes During Neoadjuvant Therapy and Survival Outcomes After Resection

BACKGROUND

Assessment of individual tumor biology and response to systemic therapy in pancreatic ductal adenocarcinoma (PDAC) remains a clinical challenge. The significance of anthropometric (body composition) changes during chemotherapy as a surrogate for tumor biology in the setting of localized PDAC is unknown.

METHODS

A retrospective, single-institution analysis of patients with PDAC who received neoadjuvant therapy (NAT) and pancreatectomy from 2017 to 2021 was performed. Radiologic anthropometric analysis used artificial intelligence-driven software to segment and compute total and sub-compartment muscle area, adipose tissue area, and attenuation values at the level of the L3 vertebra. Kaplan-Meier survival estimates, log-rank tests, and multivariable Cox regression models were used in survival analyses.

RESULTS

The inclusion criteria were met by 138 patients. Although decreases in muscle and adipose tissue areas during NAT were predominant, a subset of patients experienced an increase in these compartments. Increases in muscle greater than 5% (hazard ratio [HR], 0.352; 95% confidence interval [CI] 0.135-0.918; p = 0.033) and increases in adipose tissue greater than 15% (HR, 0.375; 95% CI 0.144-0.978; p = 0.045), were significantly associated with improved survival, whereas loss of visceral fat greater than 15% was detrimental (HR 1.853; CI 1.099-3.124; p = 0.021). No significant associations with single time-point anthropometrics were observed. Gains in total muscle and adipose mass were associated with improved pathologic response to systemic therapy and less advanced pathologic tumor stage.

CONCLUSIONS

Dynamic anthropometric analysis during NAT for PDAC is a stronger prognostic indicator than measurements taken at a single point in time. Repeated anthropometric analysis during preoperative chemotherapy may serve as a biomarker for individual tumor biology and response to therapy.

Digital health and medical technology for perioperative sarcopenia optimisation and prehabilitation - Where to go from here?

Sarcopenia has been shown to cause poorer outcomes in surgical oncology. With the advancement of artificial intelligence technology, body composition analysis can be obtained with ease. Given the lead time between cancer diagnosis and surgery, selected patients at risk of complications secondary to sarcopenia may be amenable to prehabilitation. The COVID-19 pandemic has led to the rapid adaptation of digital health and medical technology. Our perspective piece will review the current available evidence and propose innovative ways to incorporate technology into physical and nutritional prehabilitation.

The survival prediction of advanced colorectal cancer received neoadjuvant therapy-a study of SEER database

PURPOSE

The aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model.

METHODS

All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model.

RESULTS

A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients.

CONCLUSION

A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.

Role of Body Composition in Patients with Resectable Pancreatic Cancer

This study investigates the role of body composition parameters in patients with pancreatic cancer undergoing surgical treatment. The research involved 88 patients diagnosed with pancreatic cancer who underwent surgery at the Modena Cancer Center between June 2015 and October 2023. Body composition parameters were obtained from CT scans performed before and after surgery. The percentage of sarcopenic patients at the time of diagnosis of pancreatic cancer is 56.82%. Of the patients who died between the first and second CT evaluated, 58% were sarcopenic, thus confirming the role of sarcopenia on outcome. The study found that all body composition parameters (TAMA, SMI, VFI, and SFI) demonstrated a trend towards reduction between two examinations, indicating an overall depletion in muscle and adipose tissue. We then evaluated the relationships between fat-related parameters (VFI, SFI and VSR) and survival outcomes: overall survival and progression-free survival. Cox univariate regression model show significant parameter related to outcomes was adipose tissue, specifically VFI. The study found that higher VFI levels were associated with greater survival rates. This research holds promise for advancing our understanding of the link between body composition and the prognosis of pancreatic cancer patients.