Dataset Information

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

ABSTRACT:

Background

Many imaging scoring models have been developed for tumor surgery to provide critical guidance for the selection of surgical methods. However, little research has been aimed at developing scoring models for adrenal tumors and retroperitoneal laparoscopic adrenal surgery (RLAS), which has become the primary technique for treating adrenal tumors. The study set out to establish a computed tomography (CT)-based adrenal tumor scoring model for predicting perioperative outcomes in patients with adrenal tumors who have undergone RLAS.

Methods

The retrospective analysis included 306 patients with adrenal tumors diagnosed by preoperative unenhanced or enhanced CT from January 2014 to August 2018 in the First Affiliated Hospital of Fujian Medical University. CT images were used to quantify the tumor location and size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the adhesion of periadrenal fat (PF); and the tumor CT enhancement value. We conducted multivariate ordinal logistic regression analysis to screen variables and performed principal component analysis to construct a novel scoring model for RLAS. The perioperative outcomes of RLAS were evaluated according to postoperative length of stay, operative time (OT), intraoperative blood loss (IBL), and postoperative complications.

Results

The final scoring model included tumor size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the tumor CT enhancement value; the adhesion of the PF; and the functional status of adrenal tumors. The total score had positive correlations with the OT (r_s=0.431), IBL (r_s=0.446), and postoperative length (r_s=0.180) (all P values <0.001). Compared to any single metric, the total score provided better prediction of OT and IBL. The grading system for RLAS based on the scoring model also performed well in predicting the complexity and difficulty of RLAS. The coincidence rate for these factors was good (all P values <0.001).

Conclusions

The developed model is feasible and repeatable in the prediction of the perioperative outcomes, complexity, and difficulty of RLAS.

SUBMITTER: Xue YT

PROVIDER: S-EPMC10784056 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

Xue Yu-Ting YT Chen Jia-Yin JY Yan Xiao-Li XL Lin Fei F Chen Dong-Ning DN Zheng Jian-Jian JJ Chen Ye-Hui YH Xue Xue-Yi XY Wei Yong Y Zheng Qing-Shui QS Li Xiao-Dong XD Xu Ning N

Quantitative imaging in medicine and surgery 20240102 1

<h4>Background</h4>Many imaging scoring models have been developed for tumor surgery to provide critical guidance for the selection of surgical methods. However, little research has been aimed at developing scoring models for adrenal tumors and retroperitoneal laparoscopic adrenal surgery (RLAS), which has become the primary technique for treating adrenal tumors. The study set out to establish a computed tomography (CT)-based adrenal tumor scoring model for predicting perioperative outcomes in p ...[more]

PMID: 38223067

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Project description:BackgroundWhile laparoscopic adrenalectomy (LA) represents a gold standard for treating most adrenal lesions, no effective visual model for the prediction of perioperative complications of retroperitoneal laparoscopic adrenalectomy (RLA) exists.MethodsA retrospective study was conducted involving all consecutive patients underwent unilateral RLA for adrenal disease from January 2012 to December 2021. The entire cohort was randomly divided into 2 subsets (70% of the data for training, 30% for validation). Subsequently, a Least Absolute Shrinkage Selection Operator (LASSO) regression was performed to select the predictor variables, which were further consolidated via random forest (RF) and Boruta algorithm. Then the nomogram was established using the bivariate logistic regression analysis. Eventually, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were employed to evaluate discrimination, calibration and clinical usefulness of the model, respectively.ResultsA total of 610 patients underwent unilateral RLA for adrenal diseases were enrolled. After machine learning analyses, a weighted nomogram was established with 7 factors associated with complications including operative time, lesion laterality, intraoperative blood loss, pheochromocytoma, body mass index (BMI) and 2 preoperative comorbidities [respiratory diseases, cardiovascular diseases (CVD)]. The model displayed a fine calibration curve for perioperative complications evaluation in both the training dataset (P=0.847) and validation dataset (P=0.248). ROC with area under the curve (AUC) revealed excellent discrimination in the training dataset (0.817, 95% CI: 0.758-0.875) and validation dataset (0.794, 95% CI: 0.686-0.901). DCA curves showed that using this nomogram provided a more net benefit where threshold probabilities lay in the range of 0.1 to 0.9.ConclusionsAn effective nomogram that incorporating 7 predictors was established in this study to identify patients at high risk of perioperative complications for RLA. It would contribute to the improvement of perioperative strategy due to its accuracy and convenience.

Project description:PurposeTumorous texture is a marker for tumor tissue inhomogeneity. Based on this assumption, this study aims to evaluate the value of computed tomography texture analysis for imaging-based prediction of perioperative complications during laparoscopic partial tumor nephrectomy.MethodsA total of 106 patients with histologically confirmed renal cell carcinoma and pre-operative CT were included and volumetric texture analysis of the tumors was performed by two readers. Texture analysis parameter ratios and differences were calculated using the kidney parenchyma as reference ("reference-corrected"). Regression analysis was performed, regarding the value of the texture analysis parameters, for assessment of the tumor nuclear grade and the prediction of peri- and postoperative complications and approximated blood loss. Moreover, the inter-rater agreement in terms of the intra-class correlation coefficient (ICC) was calculated.ResultsRegarding the reference-corrected values, the predictive value of texture analysis parameters for severe perioperative complications was highest for the standard deviation of the mean attenuation (Area under curve/AUC, .615; sensitivity, 93.8%, specificity, 30.0%), followed by the uniformity (AUC, .599; sensitivity, 62.5%, specificity, 60.0%), and the uniformity of distribution of positive pixels (AUC, .597; sensitivity, 62.5%; specificity, 61.1%). Regarding the blood loss, the uniformity of positive pixel values (UPP; AUC, 0.638), uniformity (AUC, 0.635), and entropy (AUC, 0.633) yielded the best predictive values, whilst the tumor grade was a weaker predictor (AUC, 0.574). The applied texture analysis parameters did not correlate with the time of surgery or the warm ischemic time. All measured parameters were better predictors for complications than the tumor diameter alone. The inter-rater agreement was almost perfect (ICC, .982).ConclusionCT and CT texture analysis parameters are valuable for prediction of perioperative outcome before laparoscopic partial nephrectomy in patients with renal cell carcinoma.

Dataset Information

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

Background

Methods

Results

Conclusions

Publications

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

Similar Datasets

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