An Independently Validated Nomogram for Individualized Estimation of Survival Among Patients With Newly Diagnosed Glioblastoma: NRG Oncology/RTOG 0525 and 0825

Gittleman, Haley; Lim, D.; Kattan, Michael W.; Chakravarti, Arnab; Gilbert, Mark R.; Lassman, Andrew B.; Lo, Simon S.; Machtay, Mitchell; Sloan, Anthony; Sulman, E.; Tian, Devin; Vogelbaum, Michael A.; Wang, T.J.C.; Penas‐Prado, Marta; Youssef, Emad; Blumenthal, Deborah T.; Zhang, P.; Mehta, Minesh P.; Barnholtz‐Sloan, Jill S.

doi:10.1016/j.ijrobp.2016.06.231

Cited by 16 publications

(27 citation statements)

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“…Finally, adding therapeutic information led to a further increase in prognostic performance for OS and PFS with a higher predictive performance compared to the formerly proposed nomogram by Gittleman et al 26 Kaplan-Meier survival analysis showed a significant separation of high-risk from low-risk patients for the OS model.…”

Section: Discussionmentioning

confidence: 91%

“…The recently proposed nomogram from the work of Gittleman et al 26 was tested on the independent test set using the 12-month survival probabilities. It achieved a predictive performance with an AUC of 0.64 in comparison with AUCs of 0.75 and 0.80 for models M6 and M7.…”

Section: Prediction Of Patients' Osmentioning

confidence: 99%

“…24,25 Originally, prognostic models aiming at predicting patients' survival or progression were often based on statistical models. For example, a recently published model by Gittleman et al 26 predicted survival on the basis of age, gender, MGMT-promoter methylation status, and KPS. In recent years, machine learning (ML) approaches have gained significant importance as alternative way for model generation.…”

Section: Introductionmentioning

confidence: 99%

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Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken

Goldberg²,

Pyka

et al. 2018

Cancer Medicine

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Background For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐derived information. Finally, the value of adding treatment features was evaluated. Methods One hundred and eighty‐nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET‐PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI‐based," and "FET‐PET/CT‐based" models, as well as combinations. Treatment features were combined with all other features. Results Of all single feature class models, the MRI‐based model had the highest prediction performance on the validation set for OS (C‐index: 0.61 [95% confidence interval: 0.51‐0.72]) and PFS (C‐index: 0.61 [0.50‐0.72]). The combination of all features did increase performance above all single feature class models up to C‐indices of 0.70 (0.59‐0.84) and 0.68 (0.57‐0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C‐indices of 0.73 (0.62‐0.84) and 0.71 (0.60‐0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. Conclusions MRI‐based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

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Section: Discussionmentioning

confidence: 91%

Section: Prediction Of Patients' Osmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken

Goldberg²,

Pyka

et al. 2018

Cancer Medicine

View full text Add to dashboard Cite

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“…In addition, Haley et al. used gender for analysis and found that age at diagnosis, KPS, EOR and MGMT promoter methylation status were all prognostic factors ( 16 ). Then, they added concurrent chemoradiotherapy and confirmed its predictive value in a subsequent nomogram of a wild type IDH nGBM cohort ( 17 ).…”

Section: Discussionmentioning

confidence: 99%

“…The nomogram was first developed to identify new prognostic factors for survival in 2008 by Thierry Gorlia ( 15 ). Other established nomograms have since been widely used to predict prognosis in GBM ( 16 , 17 ). Although these nomograms presented good prediction capacity, they did not include EOR-NCE for analysis.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram Predicts Individual Prognosis in Patients With Newly Diagnosed Glioblastoma by Integrating the Extent of Resection of Non-Enhancing Tumors

et al. 2020

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BackgroundThe extent of resection of non-contrast enhancing tumors (EOR-NCEs) has been shown to be associated with prognosis in patients with newly diagnosed glioblastoma (nGBM). This study aimed to develop and independently validate a nomogram integrated with EOR-NCE to assess individual prognosis.MethodsData for this nomogram were based on 301 patients hospitalized for nGBM from October 2011 to April 2019 at the Beijing Tiantan Hospital, Capital Medical University. These patients were randomly divided into derivation (n=181) and validation (n=120) cohorts at a ratio of 6:4. To evaluate predictive accuracy, discriminative ability, and clinical net benefit, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were calculated for the extent of resection of contrast enhancing tumor (EOR-CE) and EOR-NCE nomograms. Comparison between these two models was performed as well.ResultsThe Cox proportional hazards model was used to establish nomograms for this study. Older age at diagnosis, Karnofsky performance status (KPS)<70, unmethylated O6-methylguanine-DNA methyltransferase (MGMT) status, wild-type isocitrate dehydrogenase enzyme (IDH), and lower EOR-CE and EOR-NCE were independent factors associated with shorter survival. The EOR-NCE nomogram had a higher C-index than the EOR-CE nomogram. Its calibration curve for the probability of survival exhibited good agreement between the identical and actual probabilities. The EOR-NCE nomogram showed superior net benefits and improved performance over the EOR-CE nomogram with respect to DCA and ROC for survival probability. These results were also confirmed in the validation cohort.ConclusionsAn EOR-NCE nomogram assessing individualized survival probabilities (12-, 18-, and 24-month) for patients with nGBM could be useful to provide patients and their relatives with health care consultations on optimizing therapeutic approaches and prognosis.

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Interval brain imaging for adults with cerebral glioma

Thompson

Lawrie²,

Kernohan

et al. 2019

Cochrane Database of Systematic Reviews

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BackgroundClinical practice guidelines suggest that magnetic resonance imaging (MRI) of the brain should be performed at certain time points or intervals distant from diagnosis (interval or surveillance imaging) of cerebral glioma, to monitor or follow up the disease; it is not known, however, whether these imaging strategies lead to better outcomes among patients than triggered imaging in response to new or worsening symptoms. ObjectivesTo determine the e ect of di erent imaging strategies (in particular, pre-specified interval or surveillance imaging, and symptomatic or triggered imaging) on health and economic outcomes for adults with glioma (grades 2 to 4) in the brain. Search methodsThe Cochrane Gynaecological, Neuro-oncology and Orphan Cancers (CGNOC) Group Information Specialist searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE and Embase up to 18 June 2019 and the NHS Economic Evaluation Database (EED) up to December 2014 (database closure). Selection criteriaWe included randomised controlled trials, non-randomised controlled trials, and controlled before-a er studies with concurrent comparison groups comparing the e ect of di erent imaging strategies on survival and other health outcomes in adults with cerebral glioma; and full economic evaluations (cost-e ectiveness analyses, cost-utility analyses and cost-benefit analyses) conducted alongside any study design, and any model-based economic evaluations on pre-and post-treatment imaging in adults with cerebral glioma. Data collection and analysisWe used standard Cochrane review methodology with two authors independently performing study selection and data collection, and resolving disagreements through discussion. We assessed the certainty of the evidence using the GRADE approach. Main resultsWe included one retrospective, single-institution study that compared post-operative imaging within 48 hours (early post-operative imaging) with no early post-operative imaging among 125 people who had surgery for glioblastoma (GBM: World Health Organization (WHO) grade 4 glioma). Most patients in the study underwent maximal surgical resection followed by combined radiotherapy and temozolomide treatment. Although patient characteristics in the study arms were comparable, the study was at high risk of bias overall.Interval brain imaging for adults with cerebral glioma (Review)

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An Independently Validated Nomogram for Individualized Estimation of Survival Among Patients With Newly Diagnosed Glioblastoma: NRG Oncology/RTOG 0525 and 0825

Cited by 16 publications

References 0 publications

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

A Nomogram Predicts Individual Prognosis in Patients With Newly Diagnosed Glioblastoma by Integrating the Extent of Resection of Non-Enhancing Tumors

Interval brain imaging for adults with cerebral glioma

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