A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas

Lü, Jun; Li, X.; Li, H.

doi:10.1016/j.crad.2022.04.005

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…Previous studies have consistently shown the effectiveness of the nomogram in accurately predicting glioma TERT classification (Du et al., 2022 ; Lu et al., 2022 ; Tian et al., 2020 ). These findings highlight the value of this robust tool in clinical practice.…”

Section: Discussionmentioning

confidence: 86%

A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

Li,

Chen,

Huang

et al. 2024

Brain and Behavior

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Background and purposeAs a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients.MethodsThe study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2‐weighted imaging, T1‐weighted imaging (T1WI), contrast‐enhanced T1WI, and fluid‐attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad‐score)with clinical features. Receiver‐operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes.ResultsThe least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850–.963) and.899 (95% CI:.708–.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR = 1.767, p = .019).ConclusionThe radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.

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Section: Discussionmentioning

confidence: 86%

A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

Li,

Chen,

Huang

et al. 2024

Brain and Behavior

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“…Several studies have reported on non-invasive imaging modalities to predict IDH or TERT promoter mutation status to guide treatment strategies for gliomas from the preoperative stage of the initial clinical diagnosis. 27 , 28 To predict the IDH status of gliomas from preoperative MRI, Choi et al 28 developed a model based on deep learning and radiomics using a fully automated hybrid approach. The authors demonstrated that their approach allows for the accurate prediction of IDH status of gliomas.…”

Section: Discussionmentioning

confidence: 99%

“…The authors demonstrated that their approach allows for the accurate prediction of IDH status of gliomas. Lu et al 27 presented a radiomics feature-based nomogram for predicting TERT promoter mutation status from preoperative MRI in patients with lower-grade glioma. The radiomics signature yielded good performance for predicting TERT promoter mutation status, with a high area under the curve (AUC) of 0.900 and 0.873 in the training and validation datasets, respectively.…”

Section: Discussionmentioning

confidence: 99%

Imaging features of localized IDH wild-type histologically diffuse astrocytomas: a single-institution case series

Kibe

Motomura

Ohka

et al. 2023

Sci Rep

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Isocitrate dehydrogenase wild-type (IDHwt) diffuse astrocytomas feature highly infiltrative patterns, such as a gliomatosis cerebri growth pattern with widespread involvement. Among these tumors, localized IDHwt histologically diffuse astrocytomas are rarer than the infiltrative type. The aim of this study was to assess and describe the clinical, radiographic, histopathological, and molecular characteristics of this rare type of IDHwt histologically diffuse astrocytomas and thereby provide more information on how its features affect clinical prognoses and outcomes. We retrospectively analyzed the records of five patients with localized IDHwt histologically diffuse astrocytomas between July 2017 and January 2020. All patients were female, and their mean age at the time of the initial treatment was 55.0 years. All patients had focal disease that did not include gliomatosis cerebri or multifocal disease. All patients received a histopathological diagnosis of diffuse astrocytomas at the time of the initial treatment. For recurrent tumors, second surgeries were performed at a mean of 12.4 months after the initial surgery. A histopathological diagnosis of glioblastoma was made in four patients and one of gliosarcoma in one patient. The initial status of IDH1, IDH2, H3F3A, HIST1H3B, and BRAF was “wild-type” in all patients. TERT promoter mutations (C250T or C228T) were detected in four patients. No tumors harbored a 1p/19q codeletion, EGFR amplification, or chromosome 7 gain/10 loss (+ 7/ − 10). We assessed clinical cases of localized IDHwt histologically diffuse astrocytomas that resulted in malignant recurrence and a poor clinical prognosis similar to that of glioblastomas. Our case series suggests that even in patients with histologically diffuse astrocytomas and those who present with radiographic imaging findings suggestive of a localized tumor mass, physicians should consider the possibility of IDHwt histologically diffuse astrocytomas.

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“…Radiomics analysis, which converts manually delineated regions of interest in medical images into mineable high‐throughput quantitative features, and convolutional neural networks (CNNs), wherein a computer learns to recognize image features on its own, are usually based on a set of labeled training examples 15,16 . Previous studies have established several radiomics models to predict TERT promoter mutation status in patients with gliomas 17,18 . However, the clinical utility of these models is limited by their small sample size and lack of independent validation.…”

mentioning

confidence: 99%

“…15,16 Previous studies have established several radiomics models to predict TERT promoter mutation status in patients with gliomas. 17,18 However, the clinical utility of these models is limited by their small sample size and lack of independent validation. Meanwhile, relevant quantitative features extracted by deep learning (DL) techniques have shown promising results in many medical applications.…”

mentioning

confidence: 99%

Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI

Zhang

et al. 2023

Magnetic Resonance Imaging

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BackgroundStudies have shown that magnetic resonance imaging (MRI)‐based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in patients with glioblastoma (GBM) remains unclear.PurposeTo evaluate the value of deep learning (DL) in multiparametric MRI‐based radiomics in identifying TERT promoter mutations in patients with GBM preoperatively.Study TypeRetrospective.PopulationA total of 274 patients with isocitrate dehydrogenase‐wildtype GBM were included in the study. The training and external validation cohorts included 156 (54.3 ± 12.7 years; 96 males) and 118 (54 .2 ± 13.4 years; 73 males) patients, respectively.Field Strength/SequenceAxial contrast‐enhanced T1‐weighted spin‐echo inversion recovery sequence (T1CE), T1‐weighted spin‐echo inversion recovery sequence (T1WI), and T2‐weighted spin‐echo inversion recovery sequence (T2WI) on 1.5‐T and 3.0‐T scanners were used in this study.AssessmentOverall tumor area regions (the tumor core and edema) were segmented, and the radiomics and DL features were extracted from preprocessed multiparameter preoperative brain MRI images—T1WI, T1CE, and T2WI. A model based on the DLR signature, clinical signature, and clinical DLR (CDLR) nomogram was developed and validated to identify TERT promoter mutation status.Statistical TestsThe Mann–Whitney U test, Pearson test, least absolute shrinkage and selection operator, and logistic regression analysis were applied for feature selection and construction of radiomics and DL signatures. Results were considered statistically significant at P‐value <0.05.ResultsThe DLR signature showed the best discriminative power for predicting TERT promoter mutations, yielding an AUC of 0.990 and 0.890 in the training and external validation cohorts, respectively. Furthermore, the DLR signature outperformed CDLR nomogram (P = 0.670) and significantly outperformed clinical models in the validation cohort.Data ConclusionThe multiparameter MRI‐based DLR signature exhibited a promising performance for the assessment of TERT promoter mutations in patients with GBM, which could provide information for individualized treatment.Level of Evidence3Technical EfficacyStage 2

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A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas

Cited by 13 publications

References 30 publications

A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

Imaging features of localized IDH wild-type histologically diffuse astrocytomas: a single-institution case series

Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI

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