Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Huang, Weicai; Zhou, Kangneng; Jiang, Yuming; Chen, Chuanli; Yuan, Qingyu; Han, Zhen; Xie, Jingjing; Yu, Shitong; Sun, Zhiwei; Hu, Yanfeng; Yu, Jiang; Liu, Hao; Xiao, Ruoxiu; Xu, Yu; Zhou, Zhiwei; Li, Guoxin

doi:10.3389/fonc.2020.01416

Cited by 23 publications

(20 citation statements)

References 47 publications

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“…Previous studies have revealed the diagnostic value of radiomics for PM in GC based on conventional CT derived radiomics, with AUCs in the range of 0.724 to 0.873 in the validation cohort (32)(33)(34). In our study, similar performance was also observed in the R_MIX model built by 120kV-equivalent…”

Section: Discussionsupporting

confidence: 84%

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Chen

Yao

et al. 2021

Front. Oncol.

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ObjectiveTo develop and validate a dual-energy computed tomography (DECT) derived radiomics model to predict peritoneal metastasis (PM) in patients with gastric cancer (GC).MethodsThis retrospective study recruited 239 GC (non-PM = 174, PM = 65) patients with histopathological confirmation for peritoneal status from January 2015 to December 2019. All patients were randomly divided into a training cohort (n = 160) and a testing cohort (n = 79). Standardized iodine-uptake (IU) images and 120-kV-equivalent mixed images (simulating conventional CT images) from portal-venous and delayed phases were used for analysis. Two regions of interest (ROIs) including the peritoneal area and the primary tumor were independently delineated. Subsequently, 1691 and 1226 radiomics features were extracted from the peritoneal area and the primary tumor from IU and mixed images on each phase. Boruta and Spearman correlation analysis were used for feature selection. Three radiomics models were established, including the R_IU model for IU images, the R_MIX model for mixed images and the combined radiomics model (the R_comb model). Random forest was used to tune the optimal radiomics model. The performance of the clinical model and human experts to assess PM was also recorded.ResultsFourteen and three radiomics features with low redundancy and high importance were extracted from the IU and mixed images, respectively. The R_IU model showed significantly better performance to predict PM than the R_MIX model in the training cohort (AUC, 0.981 vs. 0.917, p = 0.034). No improvement was observed in the R_comb model (AUC = 0.967). The R_IU model was the optimal radiomics model which showed no overfitting in the testing cohort (AUC = 0.967, p = 0.528). The R_IU model demonstrated significantly higher predictive value on peritoneal status than the clinical model and human experts in the testing cohort (AUC, 0.785, p = 0.005; AUC, 0.732, p <0.001, respectively).ConclusionDECT derived radiomics could serve as a non-invasive and easy-to-use biomarker to preoperatively predict PM for GC, providing opportunity for those patients to tailor appropriate treatment.

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Section: Discussionsupporting

confidence: 84%

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Chen

Yao

et al. 2021

Front. Oncol.

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“…Our results gained outbreaking progress compared with a previous study. According to the nomogram (AUC 0.90) in the training set and (AUC 0.88) in the validation set, the accuracy of the prediction was obviously higher than the investigation that was based on CT with an AUC of 0.75 (43). In contrast to CT, PET has better performance, which means metabolic parameters play an important role in the model.…”

Section: Discussionmentioning

confidence: 89%

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

et al. 2021

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ObjectivesThe aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC).MethodsIn this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets.ResultsAfter dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram.ConclusionsThe comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.

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“…Compared with the abovementioned studies, our nomograms have several improvements. First, most of the studies were focused on the survival status of whole patients with GC or AGC (10)(11)(12)(13)(14)(15)(16)18). Instead, we went a step further and focused on T4a GC in the subdivided perspective.…”

Section: Discussionmentioning

confidence: 99%

“…The nomogram is a practical tool in medical practice, which could pictorially represent a multivariable model. Recently, nomograms mostly concentrate on the patients of early-stage or under single treatment, and they mostly adequate for prognosis predicting intraoperatively or postoperatively (10)(11)(12)(13)(14)(15)(16). However, there still few nomograms integrating all the relevant clinicopathologic characters for patients with T4a GC.…”

Section: Introductionmentioning

confidence: 99%

Incidence, Risk Factors and Prognosis of T4a Gastric Cancer: A Population-Based Study

Zuo

Han

et al. 2022

Front. Med.

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Background: T4a gastric cancer (GC) is a subtype of advanced GC (AGC), which urgently needs a comprehensive grade method for better treatment strategy choosing. The purpose of this study was to develop two nomograms for predicting the prognosis of patients with T4a GC.Methods: A total of 1,129 patients diagnosed as T4a GC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Result (SEER) program database. Univariate and multivariate Cox analyses were performed to explore the independent predictors and to establish nomogram for overall survival (OS) of the patients, whereas competing risk analyses were performed to find the independent predictors and to establish nomogram for cancer-specific survival (CSS) of the patients. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier analysis were performed to evaluate the nomograms.Results: Older age, larger tumor size, black race, signet ring cell carcinoma (SRCC), more lymph node involvement, the absence of surgery, the absence of radiotherapy, and the absence of chemotherapy were identified as independent prognostic factors for both OS and CSS. In the training cohort, the AUCs of the OS nomogram were 0.760, 0.743, and 0.723 for 1-, 3-, and 5-year OS, whereas the AUCs of the CSS nomogram were 0.724, 0.703, and 0.713 for 1-, 3-, and 5-year CSS, respectively. The calibration curve and DCA indicated that both nomograms can effectively predict OS and CSS, respectively. The abovementioned results were also confirmed in the validation cohort. Stratification of the patients into high- and low-risk groups highlighted the differences in prognosis between the two groups both in training and in validation cohorts.Conclusions: Age, tumor size, race, histologic type, N stage, surgery status, radiotherapy, and chemotherapy were confirmed as independent prognostic factors for both OS and CSS in patients with T4a GC. Two nomograms based on the abovementioned variables were constructed to provide more accurate individual survival predictions for them.

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Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Cited by 23 publications

References 47 publications

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

Incidence, Risk Factors and Prognosis of T4a Gastric Cancer: A Population-Based Study

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