Ultrasound International Open (Aug 2021)

Development of a Standardized Semantic Feature-Based Reporting Proforma for Intraoperative Ultrasound Findings in Brain Tumors and Application in High-Grade Gliomas – A Preliminary Study

  • Prakash Shetty,
  • Vikas Kumar Singh,
  • Amit Choudhari,
  • Aliasgar V Moiyadi

DOI
https://doi.org/10.1055/a-1637-9550
Journal volume & issue
Vol. 07, no. 02
pp. E55 – E63

Abstract

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Purpose A semantic feature-based reporting proforma for intraoperative ultrasound findings in brain tumors was devised to standardize reporting. It was applied as a pilot study on a cohort of histologically confirmed high-grade supratentorial gliomas (Grade 3 and 4) for internal validation. Materials and Methods This intraoperative semantic ultrasound proforma was used to evaluate 3D ultrasound volumes using Radiant DICOM software by 3 surgeons. The ultrasound semantic features were correlated with histological features like tumor grade, IDH status, and MIB index. Results 68 patients were analyzed using the semantic proforma. Irregular crenated was the most common margin (63.2%) and lesions were heterogeneously hyperechoic (95.6%). Necrosis was commonly seen and noted as single (67.6%) or multiple (13.2%) in over 80% cases. A separate perilesional zone, which was predominantly hyperechoic in 41.8% and both hypo and hyperechoic in 12.7%, could be identified in 54.5% of cases. Grade 4 tumors were more likely to have an irregular crenated margin (71.2%) with a single large area of necrosis, while Grade 3 tumors were likely to have smooth (31.3%) or non-characterizable margins (31.2%) with no or multiple areas of necrosis. IDH-negative tumors were more likely to have a single large focus of necrosis. Among the GBMs (52 cases), MIB labelling index of>15% was associated with poorly delineated, uncharacterizable margins, when compared with MIB labelling index<15% (23.5 vs. 0%), (p=0.046). Conclusion A detailed semantic proforma was developed for brain tumors and was internally validated. A few ultrasound sematic features were identified correlating with histological features in high-grade gliomas. It will require further external validation for refinement and acceptability.

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