The novel developed and validated multiparametric MRI-based fusion radiomic and clinicoradiomic models predict the postoperative progression of primary skull base chordoma.

Autor: Li Z; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Fan Y; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Ma J; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Wang K; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Li D; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Zhang J; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Wu Z; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China. wuzhen1966@aliyun.com., Wang L; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China. saintage7@126.com., Tian K; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China. tiankaibing@126.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 20; Vol. 14 (1), pp. 28752. Date of Electronic Publication: 2024 Nov 20.
DOI: 10.1038/s41598-024-80410-5
Abstrakt: Local progression of primary skull base chordoma (PSBC) is a sign of treatment failure. Predicting the postoperative progression of PSBC can aid in the development of individualized treatment plans to improve patients' progression-free survival (PFS) after surgery. This study aimed to develop a multiparametric MRI-based fusion radiomic model (FRM) and clinicoradiomic model (CRM) via radiomic and clinical analysis and to explore their validity in predicting postoperative progression in PSBC patients before surgery. In this retrospective study, a total of 129 patients with PSBC from our institution, including 57 patients with progression, were enrolled and randomized to the training set (TS) or the validation set (VS) at a 2:1 ratio. Radiomic features were extracted and dimensionally reduced from 3.0 T/axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences for each patient, and the features were used for radiomic analysis. Univariate and multivariate Cox regression analyses were used to screen for key clinical factors. We constructed models on the basis of multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the clinical model (CM), FRM and CRM. Through analysis, we found that blood supply was the only significantly different clinical factor in the CM. For the FRM, the area under the receiver operating characteristic curve (AUC) of the TS was 0.925, and the calibration curves were consistent across the TS. In the CRM, the AUC of the TS was 0.929, the calibration curve analysis was consistent for both the TS and the VS, and the DCA showed that the net benefit was greater at a threshold probability of > 0% for both the TS and the VS. Our proposed FRM can help clinicians better predict PSBC progression preoperatively, and the use of the CRM can lead to the development of more appropriate protocols to improve patients' PFS after surgery.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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