Dataset Information

Effect of body fat mass loss on prognosis of radical resection for pancreatic ductal adenocarcinoma based on bioelectrical impedance analysis.

ABSTRACT:

Background

Few reports have performed a prognostic analysis based on bioelectrical impedance analysis in patients with radical resection of pancreatic ductal adenocarcinoma (PDAC), and its usefulness in prognostic analysis remains unclear. This study aimed to evaluate body composition changes in patients undergoing radical resection for PDAC and analyze its impact on prognosis.

Methods

The medical records of radical resection for patients with PDAC were retrospectively reviewed, and the parameters of body composition, including body weight, skeletal muscle mass, body fat mass (BFM), and extracellular water-total body water ratio, from preoperatively to 12 months postoperatively, for each surgical procedure were measured based on direct segmental multifrequency bioelectrical impedance analysis with an InBody 770 (InBody Inc., Tokyo, Japan) device. The clinicopathological and prognostic factors were analyzed.

Results

Among 79 patients who underwent radical resection for PDAC, 36 (46%), 7 (8%), and 36 (46%) underwent pancreatoduodenectomy, total pancreatectomy, and distal pancreatectomy, respectively. The multivariate overall survival analysis demonstrated that BFM loss percentage at 1 month postoperatively ≧14% (p = 0.021), lymph node metastasis (p = 0.014), and non-adjuvant chemotherapy (p < 0.001) were independent poor prognostic factors. Multivariate analysis revealed that preoperative BFM < 12 kg and preoperative albumin < 3.5 g/dL were independently associated with BFM loss percentage at 1 month postoperatively ≧14% (p = 0.021 and p = 0.047, respectively).

Conclusions

Loss of BFM in the early postoperative period may have a poor prognosis in radical resection of PDAC.

SUBMITTER: Shibata Y

PROVIDER: S-EPMC10785500 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Effect of body fat mass loss on prognosis of radical resection for pancreatic ductal adenocarcinoma based on bioelectrical impedance analysis.

Shibata Yoshiyuki Y Sudo Takeshi T Tazuma Sho S Sada Haruki H Tanimine Naoki N Shimada Norimitsu N Tazawa Hirofumi H Suzuki Takahisa T Onoe Takashi T Shimizu Yosuke Y Tashiro Hirotaka H Yamaguchi Atsushi A Takahashi Shinya S

BMC surgery 20240111 1

<h4>Background</h4>Few reports have performed a prognostic analysis based on bioelectrical impedance analysis in patients with radical resection of pancreatic ductal adenocarcinoma (PDAC), and its usefulness in prognostic analysis remains unclear. This study aimed to evaluate body composition changes in patients undergoing radical resection for PDAC and analyze its impact on prognosis.<h4>Methods</h4>The medical records of radical resection for patients with PDAC were retrospectively reviewed, a ...[more]

PMID: 38212728

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Project description:BackgroundThe high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.MethodsDatasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.FindingsThe developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.InterpretationThis study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.FundingThis work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Project description:Objectives This study aimed to develop and externally validate a nomogram for predicting liver metastasis after radical resection in patients with pancreatic ductal adenocarcinoma (PDAC). Methods A total of 247 PDAC patients who underwent radical resection were retrospectively reviewed from January 2015 to March 2022 at Ningbo Medical Centre Lihuili Hospital Eastern Section, and used as a training cohort to develop the nomogram. 83 PDAC patients from the Ningbo Medical Centre Lihuili Hospital Xingning Section were enrolled as the validation cohort. The postoperative liver metastasis was recorded during the follow-up, and the liver metastasis-free survival was defined as the time from operation to the date of liver metastasis diagnosis or death. The nomogram was established based on independent prognostic factors selected by LASSO and multivariate Cox regression model. The performance was assessed using the concordance index (C-index) and calibration curves. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to determine the clinical utility of the nomogram model. Results From the training cohort of 247 patients, a total of 132 patients developed liver metastasis during the follow-up, the 1-, 2- and 3- year liver metastasis-free survival were 52.4%, 43.5% and 40% respectively. The LASSO and multivariate Cox regression analysis indicated that postoperative CA125 (hazard ratio [HR] = 1.007, p <0.001), tumor differentiation (HR = 1.640, p = 0.010), tumor size (HR = 1.520, p = 0.029), lymph node ratio (HR = 1.897, p = 0.002) and portal/superior mesenteric/splenic vein invasion degree (PV/SMV/SV) (HR = 2.829, p <0.001) were the independent factors of liver metastasis. A nomogram with independent factors was developed and the C-index was 0.760 (95% confidence interval [CI], 0.720-0.799) and 0.739 (95% CI, 0.669-0.810) in the training and validation cohorts, respectively. The areas under curve (AUC) of the nomogram at 1-, 2- and 3-year were 0.815, 0.803 and 0.773 in the training cohort, and 0.765, 0.879 and 0.908 in the validation cohort, respectively, higher than those in TNM stage. Decision curve analysis (DCA) analysis revealed that the nomogram model provided superior net benefit in clinical utility. Liver metastasis-free survival curves showed a significant discriminatory ability for liver metastasis risk based on the nomogram (p <0.001). Conclusions The nomogram showed high accuracy in predicting liver metastasis for PDAC after radical resection, and may serve as a clinical support tool to guide personalized and prescient intervention.

Dataset Information

Effect of body fat mass loss on prognosis of radical resection for pancreatic ductal adenocarcinoma based on bioelectrical impedance analysis.

Background

Methods

Results

Conclusions

Publications

Effect of body fat mass loss on prognosis of radical resection for pancreatic ductal adenocarcinoma based on bioelectrical impedance analysis.

Similar Datasets

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