AI Article Synopsis

  • The study aimed to evaluate the effectiveness of artificial neural networks (ANN) in distinguishing between brain tumors (both low- and high-grade gliomas) and non-tumor lesions (like tuberculomas and abscesses) using proton magnetic resonance spectroscopy (1H MRS).
  • Researchers collected spectral data from 138 participants, categorizing them into five groups, and built two ANN models—one focused on separating tumors from infections and the other on differentiating all five types of lesions.
  • The ANN showed high accuracy: diagnosing low-grade gliomas at 73%, high-grade at 98%, and correctly identifying 89% of tuberculomas and 83% of abscesses, with overall diagnostic specificity ranging from 92% to

Article Abstract

Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data.

Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes.

Results: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively.

Conclusion: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.

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Source
http://dx.doi.org/10.1007/s004320050284DOI Listing

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