Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.
Purpose: This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. Methods: We included a total of 68 PNI patients and 80 LV...
| Published in: | Abdominal Radiology Vol. 50; no. 3; pp. 1069 - 1079 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
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=182974797&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182974797 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: 182974797 179568915 10.1007/s00261-024-04556-6 182974797 ppf: 1069 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. aug: au: Zhang, Yumeng Tan, Huaqing Huang, Bin Guo, Xinjian Cao, Yuntai affil: https://ror.org/000j1tr86 Qinghai University Affiliated Hospital, Xining, China sug: ab: Purpose: This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. Methods: We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. Results: Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591–0.878) for PNI in the training set and 0.731 (95% CI: 0.509–0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561–0.841), and the validation set AUC is 0.685 (95% CI: 0.439–0.932). External validation showed an AUC of 0.722 (95% CI: 0.565–0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481–0.931) for LVI. Conclusions: This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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