AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium

Davatzikos, Christos; Barnholtz‐Sloan, Jill S.; Bakas, Spyridon; Colen, Rivka R.; Mahajan, Abhishek; Balaña, Carmen; Font, J; Puig, Josep; Jain, Rajan; Sloan, Andrew E.; Badve, Chaitra; Marcus, Daniel S.; Choi, Yoon Seong; Lee, Seung Koo; Chang, Jong Hee; Poisson, Laila; Griffith, Brent; Dicker, Adam P.; Flanders, Adam E.; Booth, Thomas C.; Rathore, Saima; Akbari, Hamed; Sako, Chiharu; Bilello, Michel; Shukla, Gaurav; Kazerooni, Anahita Fathi; Brem, Steven; Lustig, Robert A.; Mohan, Suyash; Bagley, Stephen; Nasrallah, MacLean P.; O’Rourke, Donald M.

doi:10.1093/neuonc/noaa045

Cited by 44 publications

(39 citation statements)

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“…However, this article represents an early age of a promising future in which the ultimate link between image, diagnosis and prognosis could nally be decoded to provide instant, useful and precise information to individual patients based on their speci c features. Multiinstitutional studies 49 would allow the generalization of predictive models or even adapt the mechanisms of data preprocessing, extraction, and analysis to the MRI from each center since the standardization of acquisition protocols is not feasible. Finally, we believe that in this catastrophic disease, the quality of life of our patients should be our rst consideration, and maximum exploitation of available neuroimaging techniques should be pursued to optimize management strategies avoiding unnecessarily aggressive therapies in those patients who will not bene t from them.…”

Section: Discussionmentioning

confidence: 99%

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Cepeda

Pérez-Núñez

García-García

et al. 2021

Preprint

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Section: Discussionmentioning

confidence: 99%

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Cepeda

Pérez-Núñez

García-García

et al. 2021

Preprint

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“…However, this article represents an early age of a promising future in which the ultimate link between image, diagnosis and prognosis could nally be decoded in order to provide instant, useful and precise information to individual patients based on their speci c features. Multi-institutional studies 49 would allow the generalization of predictive models or even adapt the mechanisms of data pre-processing, extraction, and analysis to the MRI from each center since the standardization of acquisition protocols is not feasible. Finally, we believe that in this catastrophic disease, the quality of life of our patients should be our rst consideration, and maximum exploitation of available neuroimaging techniques should be pursued to optimize management strategies avoiding unnecessarily aggressive therapies in those patients who won't bene ce from them.…”

Section: Discussionmentioning

confidence: 99%

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Cepeda

Pérez-Núñez

García-García

et al. 2021

Preprint

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Background Radiomics, in combination with artificial intelligence, emerges as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Also, a survival analysis was performed using the Random Survival Forest (RSF) algorithm. Results A total of 203 patients were enrolled in this study. In the classification task, the Naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. RSF model allowed the stratification of patients into low and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we have developed a reliable predictive model of short-term survival in GBM applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering the individual patient characteristics.

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“…In this particular study, we utilized retrospective data with available preoperative MRI (T1, T2, T1-Gd, T2-FLAIR) from patients diagnosed with gliomas from 3 collections, all being part of the ReSPOND consortium 17 (collection 1 [ n = 248]: Hospital of the University of Pennsylvania [HUP]; collection 2 [ n = 192]: The Cancer Imaging Archive [TCIA]; collection 3 [ n = 33]: Ohio Brain Tumor Study 18 , 19 ; Table 1 ). Data from collection 1 also had advanced MRI, including DSC-MRI and diffusion-weighted imaging (DWI), available.…”

Section: Methodsmentioning

confidence: 99%

Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)

Rathore

Mohan

Bakas

et al. 2020

Neuro-Oncology Advances

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Background Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm. Methods We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70–30% training–testing splits or 10-fold cross-validation within individual collections, (2) 70–30% training–testing splits within merged collections, and (3) training on one collection and testing on another. Results These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset. Conclusions Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.

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AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium

Cited by 44 publications

References 4 publications

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)

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