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Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

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Abstract

Purpose

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

Methods

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10−3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

Results

VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

Conclusion

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

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Funding

Grant Support: S.H.P.: RSNA Research Scholar Grant (RSCH1819).

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Authors and Affiliations

  1. Department of Radiation Oncology, University of Virginia, Charlottesville, VA, 22908, USA

    Eric Aliotta & Sunil W. Dutta

  2. Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA

    Nicholas J. Tustison, Prem P. Batchala, T. Jason Druzgal, Sugoto Mukherjee & Sohil H. Patel

  3. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA

    Xue Feng

  4. Departments of Neurology, Neurological Surgery, and Medicine, University of Virginia, Charlottesville, VA, 22908, USA

    David Schiff

  5. Departments of Pathology (Neuropathology) and Neurological Surgery, University of Virginia, Charlottesville, VA, 22908, USA

    M. Beatriz Lopes

  6. Departments of Radiology and Neurosurgery, NYU-Langone Medical Center, New York, NY, 10016, USA

    Rajan Jain

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  1. Eric Aliotta
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  2. Sunil W. Dutta
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Aliotta, E., Dutta, S.W., Feng, X. et al. Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol 149, 325–335 (2020). https://doi.org/10.1007/s11060-020-03611-8

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