Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: A preliminary study

Li, Qingyang; Bao, Jiaqi; Zhang, Yuqi; Dou, Yana; Liu, Aishi; Liu, Miaomiao; Wu, Hui; Wu, Jing; Zhao, Lei; Yang, Zhenxing; Zhu, Lu; Gao, Wei; Zhao, Wei; Wei, Mingjie; Hao, Fene

doi:10.1016/j.ejrad.2023.110811

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…This observation aligns with Takumi et al's discovery 20 that thymic epithelial tumors exhibit lower ECV on CT images. The parallel conclusion is consistent with Li et al's investigation, 33 where statistically significant differences in ECV, based on CT images, were noted between highgrade and low-grade rectal adenocarcinoma. Higher ECV values correlated with lower degrees of differentiation and higher pathological grades.…”

Section: Discussionsupporting

confidence: 91%

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Xu,

Huang,

Yang

et al. 2024

Cancer Medicine

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ObjectivesTo investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre‐operation.MethodsFrom January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT‐derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.ResultsAge and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).ConclusionsECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

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Section: Discussionsupporting

confidence: 91%

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Xu,

Huang,

Yang

et al. 2024

Cancer Medicine

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“…ECV fraction relates to the factors that effects the extracellular space such as extracellular matrix [29], the status of tumor vascularity [30]. Previous studies showed equilibrium contrast-enhanced based ECV fraction have value in different tumor diagnosis, evaluation of pathologic characteristics and prediction of prognosis [31][32][33]. Whereas, CECTbased ECV fraction requires unenhanced images before administrating contrast agent, which may lead to the misregistration.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma

Song,

Li,

et al. 2024

Preprint

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Objectives: To investigate the feasibility of using the iodine concentration (IC) parameter and extracellular volume (ECV) fraction derived from dual-energy CT for distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma (EOC). Methods: This study retrospectively included 140 patients with EOC preoperatively underwent dual-energy CT scans. Patients were grouped as type Ⅰ and type Ⅱ EOC according to postoperatively pathologic results. Normalized IC (NIC, %) values from arterial-phase (AP), venous-phase (VP) and delay-phase (DP) were measured by two observers. ECV fraction (%) was calculated by DP-NIC and hematocrit. Intra-observer correlation coefficient (ICC) was used to assess the agreement between measurements made by two observers. The differences of imaging parameters between the two groups were compared. Logistic regression was used to select independent predictive factors and establish combined parameter. Receiver operating characteristic curve was used to analyze performance of all parameters. Results: The ICCs for all parameters exceeded 0.75. All parameters in type Ⅱ EOC were all significantly higher than those in type Ⅰ EOC (all P < 0.05). DP-NIC exhibited the highest Area under the curve (AUC) of 0.828, along with 88.51% sensitivity and 62.26% specificity. DP-NIC was identified as the independent factor. The sensitivity and specificity of ECV fraction were 83.91% and 67.92%, respectively. The combined parameter consisting of AP-NIC, VP-NIC, DP-NIC, and ECV fraction yielded an AUC of 0.848, with sensitivity of 82.76% and specificity of 75.47%. The AUC of the combined parameter was significantly higher than that of VP-NIC (P = 0.042). Conclusion: It is valuable for dual-energy CT IC-based parameters and ECV fraction in preoperatively identifying type Ⅰ and type Ⅱ EOC. Critical relevance statement Dual-energy CT-normalized iodine concentration and extracellular volume fraction achieved satisfactory discriminative efficacy, distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma.

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Enhanced prediction of preoperative mesenteric lymph node metastasis in colorectal cancer using machine learning with CT‐based data

Zhou,

Ouyang,

Guo

et al. 2024

MedComm – Future Medicine

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The detection of lymph node (LN) involvement is fundamental for staging colorectal cancer (CRC) and aids in clinical decision‐making. Traditionally, determining LN status predominantly relies heavily on histological examination of LN specimens, which can occasionally lead to overtreatment. This study aims to develop a clinical prediction model using machine learning algorithms to assess the risk of mesenteric LN metastasis preoperatively, based on computed tomography images and clinicopathological data from CRC patients. Our findings demonstrate that the predictive model based on XGBoost algorithms exhibited the optimal performance, with area under the curve values consistently stable across training (0.836, 95% confidence interval [CI]: 0.750–0.902) and validation (0.831, 95% CI: 0.688–0.927) cohorts. The model was further elucidated using SHapley Additive Explanation values, which ranked predictors in the XGBoost model by their importance, providing insights into the model's decision‐making process. Additionally, the force plot visualizes the contribution of each variable to the prediction for individual samples. The as‐obtained model may have the potential to aid in clinical treatment planning, optimize the selection of surgical methods, and guide the decision‐making process for adjuvant therapy before surgery.

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Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: A preliminary study

Cited by 8 publications

References 21 publications

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast‐enhanced CT imaging: A multicenter study

Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma

Enhanced prediction of preoperative mesenteric lymph node metastasis in colorectal cancer using machine learning with CT‐based data

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