The value of enhanced multiparameteric MRI diagnostic model for preoperatively predicting surgical methods of inferior vena cava in patients with renal tumors and inferior vena cava tumor thrombus.


Journal

BMC medical imaging
ISSN: 1471-2342
Titre abrégé: BMC Med Imaging
Pays: England
ID NLM: 100968553

Informations de publication

Date de publication:
24 06 2023
Historique:
received: 02 11 2022
accepted: 05 06 2023
medline: 26 6 2023
pubmed: 25 6 2023
entrez: 24 6 2023
Statut: epublish

Résumé

Inferior vena cava tumor thrombus (IVCTT) invading the IVC wall majorly affects the surgical method choice and prognosis in renal tumors. Enhanced multiparameteric MRI plays an important role in preoperative evaluation. In this work, an MRI-based diagnostic model for IVCTT was established so as to guide the preoperative decisions. Preoperative MR images of 165 cases of renal tumors with IVCTT were retrospectively analyzed, and imaging indicators were analyzed, including IVCTT morphology and Mayo grade, IVCTT diameter measurements, bland thrombosis, primary MRI-based diagnosis of renal tumor, and involvement of contralateral renal vein. The indicators were analyzed based on intraoperative performance and resection scope of the IVC wall. Multivariate logistic regression analysis was used to establish the diagnostic model. The morphological classification of the IVCTT, primary MRI-based diagnosis of renal tumors, maximum transverse diameter of IVCTT, and length of the bland thrombus were the main indexes predicting IVC wall invasion. The MRI-based diagnostic model established according to these indexes had good diagnostic efficiency. The prediction probability of 0.61 was set as the cutoff value. The area under the curve of the test set was 0.88, sensitivity was 0.79, specificity was 0.85, and prediction accuracy was 0.79 under the optimal cutoff value. The preoperative MRI-based diagnostic model could reliably predict IVC wall invasion, which is helpful for better prediction of IVC-associated surgical operations.

Sections du résumé

BACKGROUND
Inferior vena cava tumor thrombus (IVCTT) invading the IVC wall majorly affects the surgical method choice and prognosis in renal tumors. Enhanced multiparameteric MRI plays an important role in preoperative evaluation. In this work, an MRI-based diagnostic model for IVCTT was established so as to guide the preoperative decisions.
METHODS
Preoperative MR images of 165 cases of renal tumors with IVCTT were retrospectively analyzed, and imaging indicators were analyzed, including IVCTT morphology and Mayo grade, IVCTT diameter measurements, bland thrombosis, primary MRI-based diagnosis of renal tumor, and involvement of contralateral renal vein. The indicators were analyzed based on intraoperative performance and resection scope of the IVC wall. Multivariate logistic regression analysis was used to establish the diagnostic model.
RESULTS
The morphological classification of the IVCTT, primary MRI-based diagnosis of renal tumors, maximum transverse diameter of IVCTT, and length of the bland thrombus were the main indexes predicting IVC wall invasion. The MRI-based diagnostic model established according to these indexes had good diagnostic efficiency. The prediction probability of 0.61 was set as the cutoff value. The area under the curve of the test set was 0.88, sensitivity was 0.79, specificity was 0.85, and prediction accuracy was 0.79 under the optimal cutoff value.
CONCLUSION
The preoperative MRI-based diagnostic model could reliably predict IVC wall invasion, which is helpful for better prediction of IVC-associated surgical operations.

Identifiants

pubmed: 37355601
doi: 10.1186/s12880-023-01043-0
pii: 10.1186/s12880-023-01043-0
pmc: PMC10290788
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

86

Informations de copyright

© 2023. The Author(s).

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Auteurs

Xinlong Pei (X)

Department of Radiology, Peking University Third Hospital, Beijing, China.

Min Lu (M)

Department of Pathology, Peking University Third Hospital, Beijing, China.

Zhuo Liu (Z)

Department of Urology, Peking University Third Hospital, Beijing, China.

Baohua Liu (B)

School of Public Health, Peking University, Beijing, China.

Yuhan Deng (Y)

School of Public Health, Peking University, Beijing, China.

Huishu Yuan (H)

Department of Radiology, Peking University Third Hospital, Beijing, China. huishuy@bjmu.edu.cn.

Lulin Ma (L)

Department of Urology, Peking University Third Hospital, Beijing, China. malulinpku@163.com.

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