T2-Weighted Image Radiomics Nomogram to Predict Pancreatic Serous and Mucinous Cystic Neoplasms

Fang, Xu; Zhang, Qianru; Liu, Fang; Li, Jing; Wang, Tiegong; Cao, Kai; Zhang, Hao; Li, Qi; Yu, Jieyu; Zhou, Jian; Zhu, Mengmeng; Li, Na; Jiang, Hui; Shao, Chengwei; Lu, Jianping; Wang, Li; Bian, Yun

doi:10.1016/j.acra.2022.10.008

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Several studies have reported using radiomic methods to differentiate between cystic lesions in other organs. For example, Fang et al found that MRI T2-weighted imaging-based radiomics showed good performance in discriminating between pancreatic serous and mucinous cystic neoplasms (training cohort: AUC, 0.93; validation cohort: AUC, 0.86) (Fang et al 2022 ). Another radiomics model based on CT developed by Pan et al also exhibited great potential for discriminating between ovarian serous and mucinous cystadenoma (Pan et al 2020 ).…”

Section: Discussionmentioning

confidence: 99%

A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid

Wang,

Feng,

Qiu

et al. 2024

J Cancer Res Clin Oncol

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Objective To develop and validate a radiomics nomogram based on computed tomography (CT) to distinguish appendiceal mucinous neoplasms (AMNs) from appendicitis with intraluminal fluid (AWIF). Method A total of 211 patients from two medical institutions were retrospectively analysed, of which 109 were pathologically confirmed as having appendicitis with concomitant CT signs of intraluminal fluid and 102 as having AMN. All patients were randomly assigned to a training (147 patients) or validation cohort (64 patients) at a 7:3 ratio. Radiomics features of the cystic fluid area of the appendiceal lesions were extracted from nonenhanced CT images using 3D Slicer software. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods were employed to screen the radiomics features and develop a radiomics model. Combined radiomics nomogram and clinical-CT models were further developed based on the corresponding features selected after multivariate analysis. Lastly, receiver operating characteristic curves, and decision curve analysis (DCA) were used to assess the models’ performances in the training and validation cohorts. Results A total of 851 radiomics features were acquired from the nonenhanced CT images. Subsequently, a radiomics model consisting of eight selected features was developed. The combined radiomics nomogram model comprised rad-score, age, and mural calcification, while the clinical-CT model contained age and mural calcification. The combined model achieved area under the curves (AUCs) of 0.945 (95% confidence interval [CI]: 0.895, 0.976) and 0.933 (95% CI: 0.841, 0.980) in the training and validation cohorts, respectively, which were larger than those obtained by the radiomics (training cohort: AUC, 0.915 [95% CI: 0.865, 0.964]; validation cohort: AUC, 0.912 [95% CI: 0.843, 0.981]) and clinical-CT models (training cohort: AUC, 0.884 [95% CI: 0.820, 0.931]; validation cohort: AUC, 0.767 [95% CI: 0.644, 0.863]). Finally, DCA showed that the clinical utility of the combined model was superior to that of the clinical CT and radiomics models. Conclusion Our combined radiomics nomogram model constituting radiomics, clinical, and CT features exhibited good performance for differentiating AMN from AWIF, indicating its potential application in clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid

Wang,

Feng,

Qiu

et al. 2024

J Cancer Res Clin Oncol

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show abstract

“…Combining imaging features and clinical indicators could provide more accurate results for the diagnosis of PCNs. In addition, Fang et al [ 43 ] combined four T2-weighted imaging (T2WI) features and five clinical features (gender, pancreatitis, location, tumour size, and tumour shape) to differentiate MCN and SCN by building a nomogram, which achieved AUCs of 0.93 and 0.86 in the training and validation groups, respectively.…”

Section: Advances Of Radiomics In Pcnmentioning

confidence: 99%