NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

Abstract BACKGROUND Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a machine learning model constructed from a diverse, inter-institutional dataset can improve accuracy of generated tumor infiltration maps, thus guiding precision targeted therapies. METHODS 731 MRI scans of treatment-naïve glioblastoma patients from 10 institutions were included. All patients had pre-operative multiparametric-MRI (T1, T1Gd, T2, T2-FLAIR, ADC), and underwent complete resection of the enhancing tumor followed by standard-of-care chemoradiotherapy. 42 patients were used as an independent validation set, and 689 were used for training. Of these 239 patients had histopathologically confirmed recurrence with corresponding MRI scans, which were used as ground-truth for evaluating the location of recurrence using a leave-one-site-out (LSO) method. An AI model combining deep learning and SVM was used to develop a predictive model for infiltration. We validated the generalizability of our results in an unseen, multi-institutional data set. RESULTS Our model predicted locations of recurrence with odds ratio (99% CI) 37.6 (37.1-38.1) on the LSO testing set and 24.3 (23.4-25.2) on the validation set, indicating that areas labeled highly infiltrated were over 37 and 24 times more likely to coincide with future recurrence respectively. CONCLUSIONS We demonstrate that AI-based pattern analysis from multiparametric-MRI can predict tumor infiltration in peritumoral regions with high likelihood of recurrence by decrypting the visually imperceptible heterogeneity of peritumoral tissue. Model performance improved from training on a larger/diverse dataset and combining results of multiple AI methods. Independent validation confirmed the model’s ability to generalize to unseen data. We believe this will serve to advance AI-based biomarkers for predicting future recurrence and facilitate development of multi-modal targeted therapies in this era of precision neuro-oncology.

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Publication Details

Journal
Neuro-Oncology
Published
2024-11-01
DOI
https://doi.org/10.1093/neuonc/noae165.0811
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

Charit Tippareddy, Aristeidis Sotiras, Rajan Jain, Yuji Matsumoto et al.
Neuro-Oncology
Radiomics and Machine Learning in Medical Imaging
article

NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

Charit Tippareddy, Aristeidis Sotiras, Rajan Jain, Yuji Matsumoto, Jaume Capellades, Santiago Cepeda, Adam E. Flanders, Daniel S. Marcus, Mahmoud Shalaby, Christos A. Davatzikos, Joshua D. Palmer, Benedikt Wiestler, Spyridon Bakas, Peter Kamel, Adam P. Dicker, Josep Puig, Jill S. Barnholtz‐Sloan, MacLean P. Nasrallah, Carmen Balañá, Wenyin Shi, Pamela LaMontagne, Chaitra Badve, Donald M. O’Rourke, Suyash Mohan, Anna Luisa Di Stefano, Hamed Akbari, Graeme F. Woodworth, Andrew Edward Sloan, Matthew Lee, José García, Arnab Chakravarti, Sunwoo Kwak, K. H. Baik
article en

Abstract

Abstract BACKGROUND Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a machine learning model constructed from a diverse, inter-institutional dataset can improve accuracy of generated tumor infiltration maps, thus guiding precision targeted therapies. METHODS 731 MRI scans of treatment-naïve glioblastoma patients from 10 institutions were included. All patients had pre-operative multiparametric-MRI (T1, T1Gd, T2, T2-FLAIR, ADC), and underwent complete resection of the enhancing tumor followed by standard-of-care chemoradiotherapy. 42 patients were used as an independent validation set, and 689 were used for training. Of these 239 patients had histopathologically confirmed recurrence with corresponding MRI scans, which were used as ground-truth for evaluating the location of recurrence using a leave-one-site-out (LSO) method. An AI model combining deep learning and SVM was used to develop a predictive model for infiltration. We validated the generalizability of our results in an unseen, multi-institutional data set. RESULTS Our model predicted locations of recurrence with odds ratio (99% CI) 37.6 (37.1-38.1) on the LSO testing set and 24.3 (23.4-25.2) on the validation set, indicating that areas labeled highly infiltrated were over 37 and 24 times more likely to coincide with future recurrence respectively. CONCLUSIONS We demonstrate that AI-based pattern analysis from multiparametric-MRI can predict tumor infiltration in peritumoral regions with high likelihood of recurrence by decrypting the visually imperceptible heterogeneity of peritumoral tissue. Model performance improved from training on a larger/diverse dataset and combining results of multiple AI methods. Independent validation confirmed the model’s ability to generalize to unseen data. We believe this will serve to advance AI-based biomarkers for predicting future recurrence and facilitate development of multi-modal targeted therapies in this era of precision neuro-oncology.

Neuro-OncologyVol. 26(Supplement_8)
University of Maryland, Baltimore (US), Catholic Medical Center (US), University of Maryland Medical Center (US), Thomas Jefferson University (US), Washington University in St. Louis (US), Piedmont Cancer Institute (US), Mercy Catholic Medical Center (US), Parc de Salut (ES), National Cancer Institute (MY), Badalona Serveis Assistencials (ES), Ospedale di Livorno (IT), Hospital Universitario Río Hortega (ES), Indiana University School of Medicine, The Ohio State University (US), Indiana University – Purdue University Indianapolis (US), New York University (US), Case Western Reserve University (US), University of Pennsylvania (US)
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