Gianluca Nocera, Francesco Sanvito, Jingwen Yao, Sonoko Oshima, Samuel A Bobholz, Ashley Teraishi, Catalina Raymond, Kunal Patel, Richard G Everson, Linda M Liau, Jennifer Connelly, Antonella Castellano, Pietro Mortini, Noriko Salamon, Timothy F Cloughesy, Peter S LaViolette, Benjamin M Ellingson
{"title":"独立的组织学验证磁共振衍生的肿瘤细胞密度放射病理图使用图像引导活检在人脑肿瘤。","authors":"Gianluca Nocera, Francesco Sanvito, Jingwen Yao, Sonoko Oshima, Samuel A Bobholz, Ashley Teraishi, Catalina Raymond, Kunal Patel, Richard G Everson, Linda M Liau, Jennifer Connelly, Antonella Castellano, Pietro Mortini, Noriko Salamon, Timothy F Cloughesy, Peter S LaViolette, Benjamin M Ellingson","doi":"10.1007/s11060-025-05105-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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, R<sup>2</sup> = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm<sup>2</sup>), 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, R<sup>2</sup> = 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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":16425,"journal":{"name":"Journal of Neuro-Oncology","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.\",\"authors\":\"Gianluca Nocera, Francesco Sanvito, Jingwen Yao, Sonoko Oshima, Samuel A Bobholz, Ashley Teraishi, Catalina Raymond, Kunal Patel, Richard G Everson, Linda M Liau, Jennifer Connelly, Antonella Castellano, Pietro Mortini, Noriko Salamon, Timothy F Cloughesy, Peter S LaViolette, Benjamin M Ellingson\",\"doi\":\"10.1007/s11060-025-05105-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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, R<sup>2</sup> = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm<sup>2</sup>), 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, R<sup>2</sup> = 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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":16425,\"journal\":{\"name\":\"Journal of Neuro-Oncology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuro-Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11060-025-05105-x\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuro-Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11060-025-05105-x","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的：在脑胶质瘤中，反映肿瘤细胞结构的非侵入性生物标志物可用于指导边缘上切除和计划立体定向活检。我们的目标是验证先前训练的机器学习算法，该算法从多参数MRI数据生成细胞预测图（CPM），并将CPM和弥散性MRI表观扩散系数（ADC）在预测细胞结构方面的性能进行比较。方法：对treatment-naïve或复发性胶质瘤患者进行前瞻性研究。所有患者均按照标准脑肿瘤成像方案进行术前MRI检查。根据图像引导的活检目标规划手术取样部位，并用苏木精-伊红染色进行细胞密度计数。在假设独立观察和考虑非独立观察的情况下，评估mri衍生CPM值与组织细胞度之间以及ADC与组织细胞度之间的相关性。结果：共采集27例患者66份标本。13例有treatment-naïve肿瘤，14例有复发性病变。CPM值准确预测treatment-naïve患者的组织学细胞数量（b = 1.4, R2 = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cells /mm2），但在复发亚队列中不能预测。同样，ADC值仅在未接受治疗的患者中显示与组织学细胞结构显著相关(b = 1.3, R2 = 0.22, p = 0.007；rho = -0.37, p = 0.03)，与CPM相关无统计学差异。这些发现通过统计检验证实了非独立观察结果。结论：mri衍生的机器学习生成的细胞结构预测图（CPM）能够对treatment-naïve胶质瘤患者的肿瘤细胞结构进行非侵入性评估，尽管在该队列中，CPM并没有明显优于单独的ADC。
Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.
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.
期刊介绍：
The Journal of Neuro-Oncology is a multi-disciplinary journal encompassing basic, applied, and clinical investigations in all research areas as they relate to cancer and the central nervous system. It provides a single forum for communication among neurologists, neurosurgeons, radiotherapists, medical oncologists, neuropathologists, neurodiagnosticians, and laboratory-based oncologists conducting relevant research. The Journal of Neuro-Oncology does not seek to isolate the field, but rather to focus the efforts of many disciplines in one publication through a format which pulls together these diverse interests. More than any other field of oncology, cancer of the central nervous system requires multi-disciplinary approaches. To alleviate having to scan dozens of journals of cell biology, pathology, laboratory and clinical endeavours, JNO is a periodical in which current, high-quality, relevant research in all aspects of neuro-oncology may be found.
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