Literature DB >> 35284257

Development and validation of an MRI-based nomogram for the preoperative prediction of tumor mutational burden in lower-grade gliomas.

En-Tao Liu1, Shuqin Zhou2, Yingwen Li2, Siwei Zhang2, Zelan Ma2, Junbiao Guo2, Lei Guo2, Yue Zhang2, Quanlai Guo2, Li Xu2.   

Abstract

Background: High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors. In this study, we aimed to determine the value of magnetic resonance (MR)-based preoperative nomogram in predicting TMB status in lower-grade glioma (LGG) patients.
Methods: Overall survival (OS) data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by using the Kaplan-Meier method and time-dependent receiver operating characteristic (tdROC) analysis. The magnetic resonance imaging (MRI) data of 168 subjects obtained from The Cancer Imaging Archive (TCIA) were retrospectively analyzed. The correlation was explored by univariate and multivariate regression analyses. Finally, we performed tenfold cross validation. TMB values were retrieved from the supplementary information of a previously published article.
Results: The high TMB subtype was associated with the shortest median OS (high vs. low: 50.9 vs. 95.6 months, P<0.05). The tdROC for the high-TMB tumors was 74% (95% CI: 61-86%) for survival at 12 months, and 71% (95% CI: 60-82%) for survival at 24 months. Multivariate logistic regression analysis confirmed that three risk factors [extranodular growth: odds ratio (OR): 8.367, 95% CI: 3.153-22.199, P<0.01; length-width ratio ≥ median: OR: 1.947, 95% CI: 1.025-3.697, P<0.05; frontal lobe: OR: 0.455, 95% CI: 0.229-0.903, P<0.05] were significant independent predictors of high-TMB tumors. The nomogram showed good calibration and discrimination. This model had an area under the curve (AUC) of 0.736 (95% CI: 0.655-0.817). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. The average accuracy of the tenfold cross validation was 71.6% for high-TMB tumors. Conclusions: Our results indicated that a distinct OS disadvantage was associated with the high TMB group. In addition, extranodular growth, nonfrontal lobe tumors and length-width ratio ≥ median can be conveniently used to facilitate the prediction of high-TMB tumors. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Nomogram; glioma; radiogenomics; tumor mutational burden

Year:  2022        PMID: 35284257      PMCID: PMC8899970          DOI: 10.21037/qims-21-300

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  42 in total

1.  Diffuse low-grade gliomas.

Authors:  Asgeir Store Jakola; Geirmund Unsgård; Roar Kloster; Ole Solheim
Journal:  J Neurosurg       Date:  2013-08-23       Impact factor: 5.115

2.  An MRI-based radiomics signature as a pretreatment noninvasive predictor of overall survival and chemotherapeutic benefits in lower-grade gliomas.

Authors:  Jingtao Wang; Xuejun Zheng; Jinling Zhang; Hao Xue; Lijie Wang; Rui Jing; Shuo Chen; Fengyuan Che; Xueyuan Heng; Gang Li; Fuzhong Xue
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

Review 3.  Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future.

Authors:  Nicholas McGranahan; Charles Swanton
Journal:  Cell       Date:  2017-02-09       Impact factor: 41.582

Review 4.  The etiopathogenesis of diffuse low-grade gliomas.

Authors:  Amélie Darlix; Catherine Gozé; Valérie Rigau; Luc Bauchet; Luc Taillandier; Hugues Duffau
Journal:  Crit Rev Oncol Hematol       Date:  2016-11-27       Impact factor: 6.312

Review 5.  Clinical Presentation, Natural History, and Prognosis of Diffuse Low-Grade Gliomas.

Authors:  Anja Smits; Asgeir S Jakola
Journal:  Neurosurg Clin N Am       Date:  2018-11-01       Impact factor: 2.509

6.  Immune Checkpoint Inhibition for Hypermutant Glioblastoma Multiforme Resulting From Germline Biallelic Mismatch Repair Deficiency.

Authors:  Eric Bouffet; Valérie Larouche; Brittany B Campbell; Daniele Merico; Richard de Borja; Melyssa Aronson; Carol Durno; Joerg Krueger; Vanja Cabric; Vijay Ramaswamy; Nataliya Zhukova; Gary Mason; Roula Farah; Samina Afzal; Michal Yalon; Gideon Rechavi; Vanan Magimairajan; Michael F Walsh; Shlomi Constantini; Rina Dvir; Ronit Elhasid; Alyssa Reddy; Michael Osborn; Michael Sullivan; Jordan Hansford; Andrew Dodgshun; Nancy Klauber-Demore; Lindsay Peterson; Sunil Patel; Scott Lindhorst; Jeffrey Atkinson; Zane Cohen; Rachel Laframboise; Peter Dirks; Michael Taylor; David Malkin; Steffen Albrecht; Roy W R Dudley; Nada Jabado; Cynthia E Hawkins; Adam Shlien; Uri Tabori
Journal:  J Clin Oncol       Date:  2016-03-21       Impact factor: 44.544

7.  Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden.

Authors:  Zachary R Chalmers; Caitlin F Connelly; David Fabrizio; Laurie Gay; Siraj M Ali; Riley Ennis; Alexa Schrock; Brittany Campbell; Adam Shlien; Juliann Chmielecki; Franklin Huang; Yuting He; James Sun; Uri Tabori; Mark Kennedy; Daniel S Lieber; Steven Roels; Jared White; Geoffrey A Otto; Jeffrey S Ross; Levi Garraway; Vincent A Miller; Phillip J Stephens; Garrett M Frampton
Journal:  Genome Med       Date:  2017-04-19       Impact factor: 11.117

8.  Comprehensive evaluation of treatment and outcomes of low-grade diffuse gliomas.

Authors:  Catherine R Garcia; Stacey A Slone; Thomas Pittman; William H St Clair; Donita D Lightner; John L Villano
Journal:  PLoS One       Date:  2018-09-20       Impact factor: 3.240

Review 9.  Radiogenomics and Radiomics in Liver Cancers.

Authors:  Aman Saini; Ilana Breen; Yash Pershad; Sailendra Naidu; M Grace Knuttinen; Sadeer Alzubaidi; Rahul Sheth; Hassan Albadawi; Malia Kuo; Rahmi Oklu
Journal:  Diagnostics (Basel)       Date:  2018-12-27

10.  TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.

Authors:  Mario Zanfardino; Katia Pane; Peppino Mirabelli; Marco Salvatore; Monica Franzese
Journal:  Int J Mol Sci       Date:  2019-11-29       Impact factor: 5.923

View more
  1 in total

1.  A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas.

Authors:  Luu Ho Thanh Lam; Ngan Thy Chu; Thi-Oanh Tran; Duyen Thi Do; Nguyen Quoc Khanh Le
Journal:  Cancers (Basel)       Date:  2022-07-18       Impact factor: 6.575

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.