Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 343-349 |
| Number of pages | 7 |
| Journal | Journal of Cancer Research and Clinical Oncology |
| Volume | 125 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1999 |
| MoE publication type | A1 Journal article-refereed |
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Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. / Poptani, Harish; Kaartinen, Jouni; Gupta, Rakesh; Niemitz, Matthias; Hiltunen, Yrjö; Kauppinen, Risto.
In: Journal of Cancer Research and Clinical Oncology, Vol. 125, No. 6, 1999, p. 343-349.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks
AU - Poptani, Harish
AU - Kaartinen, Jouni
AU - Gupta, Rakesh
AU - Niemitz, Matthias
AU - Hiltunen, Yrjö
AU - Kauppinen, Risto
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
U2 - 10.1007/s004320050284
DO - 10.1007/s004320050284
M3 - Article
VL - 125
SP - 343
EP - 349
JO - Journal of Cancer Research and Clinical Oncology
JF - Journal of Cancer Research and Clinical Oncology
SN - 0171-5216
IS - 6
ER -