Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.