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DOI 10.1007/s11060-025-05105-x

Медицинская библиотека · Journal of neuro-oncology · 2025

Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.

Nocera Gianluca, Sanvito Francesco, Yao Jingwen, Oshima Sonoko, Bobholz Samuel A, Teraishi Ashley, Raymond Catalina, Patel Kunal, Everson Richard G, Liau Linda M, Connelly Jennifer, Castellano Antonella, Mortini Pietro, Salamon Noriko, Cloughesy Timothy F, LaViolette Peter S, Ellingson Benjamin M

СтатьяВалидационное исследованиеПеревод в очереди

Аннотация

PURPOSE: In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity.

METHODS

A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations.

RESULTS

Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R2 = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm2), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R2 = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations.

CONCLUSION

MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

Для цитирования: Nocera Gianluca, Sanvito Francesco, Yao Jingwen, Oshima Sonoko, Bobholz Samuel A, Teraishi Ashley, Raymond Catalina, Patel Kunal, Everson Richard G, Liau Linda M, Connelly Jennifer, Castellano Antonella, Mortini Pietro, Salamon Noriko, Cloughesy Timothy F, LaViolette Peter S, Ellingson Benjamin M. Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.. Journal of neuro-oncology. 2025. DOI: 10.1007/s11060-025-05105-x.

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