Neuro-oncology · 2026
Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial.
Клиническое исследованиеСтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Cepeda Santiago, Hernando-Pérez Elena, Pérez-Riesgo Enrique, Rodríguez-Valle Isabel, Esteban-Sinovas Olga, Arrese Ignacio, Lucero-Salaverry María Miguel, Zamora Tomás, Torres-Nieto María Ángeles, Luppino Luigi Tommaso, Kuttner Samuel, Wodzinski Marek, Escudero Trinidad, Garzón Jesús, Romero-Oraá Roberto, Hornero Roberto, Núñez Lucía, Villalobos Carlos, Sarabia Rosario
Аннотация
BACKGROUND: Glioblastoma recurrence is driven by diffuse microscopic infiltration beyond the contrast-enhancing tumor margin. GlioMap is an open-access AI model predicting voxelwise infiltration and recurrence risk from multiparametric MRI. This prospective study aimed to validate GlioMap's biological accuracy and prognostic relevance through histopathological assessment, transcriptomic profiling, and survival analysis within the SupraGlio trial (NCT05735171).
METHODS: Patients with newly diagnosed glioblastoma underwent neuronavigated biopsies targeting AI-predicted high-risk (HRoR) and low-risk of recurrence (LRoR) regions beyond the contrast-enhancing tumor. Histopathological infiltration served as the ground truth, and transcriptomic profiling characterised each region's molecular phenotype. Model performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Survival analyses assessed the prognostic value of postoperative HRoR volume.
RESULTS: Fifty-eight biopsies from 27 patients were analyzed. GlioMap achieved 0.81 accuracy (95% confidence interval [CI], 0.71-0.91) and 0.84 AUC (95% CI, 0.73-0.93) for histologically confirmed infiltration. Transcriptomic analysis of 48 samples from 16 patients revealed progressive upregulation of invasion- and angiogenesis-related genes (CD44, CHI3L1, STAT3, VEGFA) and downregulation of neuronal markers (MBP, GABRA1) from LRoR to HRoR regions and the tumor core, confirming a neural-to-mesenchymal gradient. Postoperative HRoR volume >1.6 cm³ predicted shorter overall survival (P = .04) and progression-free survival (P = .008).
CONCLUSIONS: To our knowledge, this study provides the first prospective, biopsy-controlled, molecular validation of an AI model for mapping glioblastoma infiltration. By accurately identifying histologically and transcriptionally infiltrated regions, GlioMap offers a biologically grounded imaging biomarker that could guide extended resection and personalized radiotherapy planning, potentially improving tumor control and patient outcomes.
METHODS: Patients with newly diagnosed glioblastoma underwent neuronavigated biopsies targeting AI-predicted high-risk (HRoR) and low-risk of recurrence (LRoR) regions beyond the contrast-enhancing tumor. Histopathological infiltration served as the ground truth, and transcriptomic profiling characterised each region's molecular phenotype. Model performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Survival analyses assessed the prognostic value of postoperative HRoR volume.
RESULTS: Fifty-eight biopsies from 27 patients were analyzed. GlioMap achieved 0.81 accuracy (95% confidence interval [CI], 0.71-0.91) and 0.84 AUC (95% CI, 0.73-0.93) for histologically confirmed infiltration. Transcriptomic analysis of 48 samples from 16 patients revealed progressive upregulation of invasion- and angiogenesis-related genes (CD44, CHI3L1, STAT3, VEGFA) and downregulation of neuronal markers (MBP, GABRA1) from LRoR to HRoR regions and the tumor core, confirming a neural-to-mesenchymal gradient. Postoperative HRoR volume >1.6 cm³ predicted shorter overall survival (P = .04) and progression-free survival (P = .008).
CONCLUSIONS: To our knowledge, this study provides the first prospective, biopsy-controlled, molecular validation of an AI model for mapping glioblastoma infiltration. By accurately identifying histologically and transcriptionally infiltrated regions, GlioMap offers a biologically grounded imaging biomarker that could guide extended resection and personalized radiotherapy planning, potentially improving tumor control and patient outcomes.