A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

Bi, Lei; Liu, Yubo; Xu, Jingxu; Wang, Ximing; Zhang, Tong; Li, Kaiguo; Duan, Mingguang; Huang, Chencui; Meng, Xiangjiao; Huang, Zhaoqin

doi:10.3389/fonc.2021.632176

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…However, the judgement of LN metastasis in CT was mainly based on the enhanced and margin status and other indicators, which had certain subjective dependence. Previous studies have discovered the low accuracy of CT for assessing peripancreatic LNM (63%-81%), which was similarly confirmed in our study (66.7%) [ 24 , 25 ]. Although some radiological features such as parenchyma atrophy and extrapancreatic perineural invasion status have been documented as significant prognostic indicators of PDAC, there were no statistical differences between high and low Ki-67 expression sets and still inability to be used as predictors of Ki-67 expression.…”

Section: Discussionsupporting

confidence: 91%

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

Wen,

Song,

et al. 2024

Insights Imaging

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Objective To construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC). Materials and methods Data from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT–Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve. Results Computed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT–Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT–Radiology nomogram was established based on the DECT–Radiology model, which showed the highest net benefit and satisfactory consistency. Conclusions The DECT–Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients. Critical relevance statement The DECT–Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression. Key points • Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT–Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression. Graphical Abstract

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Section: Discussionsupporting

confidence: 91%

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

Wen,

Song,

et al. 2024

Insights Imaging

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“…In one previous studies, Bi et al established and validated a radiomics nomogram for preoperatively predicting LNM in PCs. The results showed that the radiomics nomogram incorporating radiomics signature and CT-reported LN status obtained favorable discrimination and calibration ability in both training and validation set, with AUC of 0.853 for each set [22]. However, although the radiomics nomogram performed well, it was complicated and timeconsuming, to some extent.…”

Section: Discussionmentioning

confidence: 98%

“…Two radiologists (observer 1 and observer 2, with 8 and 15 years of experience in abdominal MR interpretation, respectively) reviewed all images in consensus. The following image features were evaluated: 1) tumor size, de ned as the lesion's maximum diameter on axial images; 2) vascular involvement, de ned as vessel occlusion, stenosis, or contour deformity due to tumor invasion; and 3) LNM, de ned as LN's short-axis diameter larger than 10 mm, or LNs with central necrosis, or LNs were hyperenhanced than liver parenchyma in portal venous phase [21,22]. The observers knew that all patients were diagnoses of PC but were blind for the clinical and pathological details.…”

Section: Mr Feature Evaluationmentioning

confidence: 99%

Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas

Chen

Zhou

et al. 2023

Preprint

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Background: Accurate preoperative evaluation of lymph node (LN) status is crucial for selecting optimal individualized treatment strategy and predicting patients’ prognosis. This study aimed to evaluate whether whole-volume apparent diffusion coefficient (ADC) histogram parameters of the primary tumor were useful to predict regional lymph node metastasis (LNM) in periampullary carcinomas. Methods: Thirty-eight patients with periampullary carcinoma who underwent pancreaticoduodenectomy between January 2016 to April 2019 were retrospectively enrolled. Whole-volume ADC histogram analysis of the primary tumor was performed by two radiologists independently. Clinical factors, pathological results and histogram parameters were evaluated. Interclass correlation coefficient (ICC) was used to assess agreement between observers. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of parameters in differentiating LNM-positive group and LNM-negative group. Results: Interobserver agreements were good to excellent for histogram analysis between two radiologists, with ICCs ranging from 0.766 to 0.967. Tumor size, MR-reported LN status and most ADC histogram parameters (including mean, minimum ADC value, 10th, 25th, 50th, 75th, and 90th percentile, and kurtosis) were significantly different between LNM-positive group and LNM-negative group (p < 0.050), and revealed significant correlations with LNM (p < 0.050). At ROC analysis, tumor size and minimum ADC value generated highest area under the curve (AUC) (AUC = 0.764, 95% confidence interval [CI]: 0.599, 0.886). When diagnostic predictive values were calculated with the combined model incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained, with AUC of 0.879 (95% CI: 0.771, 0.986), sensitivity of 100.0%, and specificity of 75.0%. Conclusions: Whole-volume ADC histogram parameters of the primary tumor held great potential in differentiating regional LNM in periampullary carcinomas.

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“…Hence, to improve the diagnosis of DPC and its TNM stage, comprehensive diagnosis should include a combination of other clinical characteristics and new technologies. To our knowledge, few studies have constructed models to predict LNM and survival in patients with duodenal malignant tumours via radiomics analysis [23]. Previous studies have shown that T stage and lymph vessel invasion are independent risk factors for LNM [24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma

Tang,

Wu,

Jiang

et al. 2024

Insights Imaging

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Objectives Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival. Methods For radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs). Results For the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869–0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802–0.941). DeLong’s test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival—in line with the findings of the model built with clinical risk factors. Conclusions CT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis. Critical relevance statement The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC). Key Points LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value. Graphical Abstract

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A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

Cited by 3 publications

References 39 publications

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters

Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas

Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma

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