Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

Bian, Yun; Guo, Shiwei; Jiang, Hui; Gao, Suizhi; Shao, Chenwei; Cao, Kai; Fang, Xu; Li, Jing; Wang, Li; Hua, Wenda; Zheng, Jianming; Jin, Gang; Lu, Jianping

doi:10.1097/mpa.0000000000001404

Cited by 51 publications

(35 citation statements)

References 33 publications

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“…reported that the greater attenuation differential between lesions and normal pancreas during the arterial phase compared to venous. Bian et al (23). also reported a significant positive association between the arterial-phase-based radiomics Radscore and the risk of LN metastasis in pancreatic ductal adenocarcinoma.…”

Section: Discussionmentioning

confidence: 93%

“…Considering the radiomics feature could be extracted from the single cross section (two dimensional, 2D) or multi-slices (three dimensional, 3D) of the tumor in CT images, the reported radiomics-based pancreatic cancer studies have either applied 2D segmentation (20) or 3D whole-tumor segmentation (14,(21)(22)(23)(24)(25). However, whether to select 2D regions of interest (ROIs) or 3D ROIs still remains unclear for invasive behavior prediction in pSPN.…”

Section: Introductionmentioning

confidence: 99%

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Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Huang

Liu²,

Han

et al. 2021

Front. Oncol.

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PurposeIt is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN.MethodsEighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong’s test were mainly used for model assessment and AUC comparison.ResultsIt retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong’s test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs.ConclusionThe arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Huang

Liu²,

Han

et al. 2021

Front. Oncol.

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“…Although to the best of our knowledge, there are no published studies indicating that pancreatic atrophy is an independent factor for LN status in PDAC, we have reason to believe that it does have a potential association with LNM in such patients. Compared with the previous studies using radiomics to evaluate the LN status for PDAC patients ( 49 , 50 ), we believe that our approach offers advantages for the following reasons: (1) We performed a stratified analysis to further evaluate the prediction efficiency of our model, which can determine the clinical application potential of our model under different conditions. (2) In addition to the CT-reported LN status, we adopted more conventional CT imaging signs in the clinical model.…”

Section: Discussionmentioning

confidence: 99%

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

et al. 2020

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Purpose To construct and verify a CT-based multidimensional nomogram for the evaluation of lymph node (LN) status in pancreatic ductal adenocarcinoma (PDAC). Materials and Methods We retrospectively assessed data from 172 patients with clinicopathologically confirmed PDAC surgically resected between February 2014 and November 2016. Patients were assigned to either a training cohort ( n = 121) or a validation cohort ( n = 51). We acquired radiomics features from the preoperative venous phase (VP) CT images. The maximum relevance–minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) methods were used to select the optimal features. We used multivariable logistic regression to construct a combined radiomics model for visualization in the form of a nomogram. Performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve approach, calibration testing, and analysis of clinical usefulness. Results A Rad score consisting of 10 LN status-related radiomics features was found to be significantly associated with the actual LN status ( P < 0.01). A nomogram that consisted of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status performed well in terms of predictive power in the training cohort (area under the curve, 0.92), and this was confirmed in the validation cohort (area under the curve, 0.95). The nomogram also performed well in the calibration test and decision curve analysis, demonstrating its potential clinical value. Conclusion A multidimensional radiomics nomogram consisting of Rad scores, CT-reported parenchymal atrophy, and CT-reported LN status may contribute to the non-invasive evaluation of LN status in PDAC patients.

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“…Feature selection comprised three steps: variance analysis, spearman correlation analysis, and least absolute shrinkage and selection operator method (LASSO) logistic regression algorithm. This method has been shown to be effective in prior radiomics studies [22]. A retrospective power analysis was performed.…”

Section: Radiomics Workflowmentioning

confidence: 99%

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2

Fang

Bian

et al. 2020

Eur Radiol

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Objectives To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. Methods For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia. Results The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusions The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making. Key Points • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

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Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

Abstract: Supplemental digital content is available in the text.

Cited by 51 publications

References 33 publications

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2

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