Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes.
Purpose: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC). Methods: A total of 196 resectable...
| Published in: | Abdominal Radiology Vol. 50; no. 3; pp. 1123 - 1133 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
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Springer Nature
Mar2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182974817&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182974817 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Mar2025 vid: 50 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182974817 179733023 10.1007/s00261-024-04585-1 182974817 ppf: 1123 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes. aug: au: Wu, Yu-ping Wu, Lan Ou, Jing Tang, Sun Cao, Jin-ming Fu, Mao-Yong Chen, Tian-wu affil: https://ror.org/00r67fz39 Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China sug: ab: Purpose: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC). Methods: A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC). Results: The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts. Conclusion: The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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