MRI Signal Intensity and Electron Ultrastructure Classification Predict the Long-Term Outcome of Skull Base Chordomas

Abstract

BACKGROUND AND PURPOSE: MR imaging is a useful and widely used evaluation for chordomas. Prior studies have classified chordomas into cell-dense type and matrix-rich type according to the ultrastructural features. However, the relationship between the MR imaging signal intensity and ultrastructural classification is unknown. We hypothesized that MR imaging signal intensity may predict both tumor ultrastructural classification and prognosis.MATERIALS AND METHODS: Seventy-nine patients with skull base chordomas who underwent 95 operations were included in this retrospective single-center series. Preoperative tumor-to-pons MR imaging signal intensity ratios were calculated and designated as ratio on T1 FLAIR sequence (R T1 ), ratio on T2 sequence (R T2 ), and ratio on enhanced T1 FLAIR sequence (R EN ), respectively. We assessed the relationships among signal intensity ratios, ultrastructural classification, and survival. RESULTS:Compared with the matrix-rich type group, the cell-dense type chordomas showed lower R T2 (cell-dense type: 1.90 6 0.38; matrix-rich type: 2.61 6 0.60 P , .001). The model of predicting cell-dense type based on R T2 had an area under the curve of 0.83 (95% CI, 0.75-0.92). In patients without radiation therapy, both progression-free survival (P ¼ .003) and overall survival (P ¼ .002) were longer in the matrix-rich type group than in the cell-dense type group. R EN was a risk factor for progression-free survival (hazard ratio ¼ 10.24; 95% CI, 1.73-60.79); R T2 was a protective factor for overall survival (hazard ratio ¼ 0.33; 95% CI, 0.12-0.87); and R EN was a risk factor for overall survival (hazard ratio ¼ 4.76; 95% CI, 1.51-15.01). CONCLUSIONS:The difference in MR imaging signal intensity in chordomas can be explained by electron microscopic features. Both signal intensity ratios and electron microscopic features may be prognostic factors.

Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study

“…Therefore, in this study, the SIR was used as a quantitative parameter to eliminate the influence of different MRI scanners and imaging parameters on the results. Similar to our study design, the SIR also showed potential for differential diagnosis in other scenarios (12,(24)(25)(26). However, different from previous studies, we used an external test cohort to further clarify the actual diagnostic performance of the SIR.…”

Predicting overall survival in chordoma patients using machine learning models: a web-app application

“…Some of the factors that influence survival time in patients with chordoma include age, tumor size, histological type, tumor grade, and metastasis have been widely reported [ 7 , 24 , 25 ]. As research into chordoma continues, more and more prognostic factors such as imaging [ 26 , 27 ], genetics [ 28 , 29 ], and biomarkers [ 30 , 31 ] have been explored for use in the survival analysis of chordoma patients. The limitations of linear relational models based on the traditional CoxPH model have become increasingly apparent in the face of the massive amount of multidimensional data [ 32 ].…”

Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma

“…Additionally, the limited phenotypic characterization of SBC prevents an optimal patient stratification to improve treatment outcomes. In this context, the growing availability of imaging data can be favourably exploited as a source of prognostic factors [ 8 , 9 ], with studies in the literature supporting the predictive power of the appearance of chordomas on diagnostic imaging, such as CT and MRI [ 10 ]. More recently, qualitative imaging factors are being complemented by quantitative ones, such as radiomic features [ 11 ].…”