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.

Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors / Nocera, G.; Sanvito, F.; Yao, J.; Oshima, S.; Bobholz, S. A.; Teraishi, A.; Raymond, C.; Patel, K.; Everson, R. G.; Liau, L. M.; Connelly, J.; Castellano, A.; Mortini, P.; Salamon, N.; Cloughesy, T. F.; Laviolette, P. S.; Ellingson, B. M.. - In: JOURNAL OF NEURO-ONCOLOGY. - ISSN 0167-594X. - (2025). [Epub ahead of print] [10.1007/s11060-025-05105-x]

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

Nocera G.;Castellano A.;Mortini P.;
2025-01-01

Abstract

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.
2025
Artificial intelligence
Diffusion imaging
Glioma
Histological validation
Imaging biomarker
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/186896
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