Multiple instance learning for differentiating neoplastic and non-neoplastic brain lesions using MR spectroscopy.
- 1Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt am Main, 60528, Germany.
- 2Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, 60438, Germany.
- 3Independent Researcher, Germany.
Abstract
Objective
To evaluate whether magnetic resonance spectroscopy (MRS) combined with multiple instance learning (MIL) can differentiate neoplastic from non-neoplastic brain lesions by analyzing full spectral patterns rather than isolated metabolite peak ratios. We hypothesized that treating the full spectrum as a metabolic fingerprint, captured through deep learning, would enable more robust tissue classification than conventional single-peak analysis.Methods
We retrospectively analyzed MRS data from 422 patients (7442 spectra total): 6598 spectra for training and 844 spectra in the independent internal hold-out test set. Classification was formulated as a MIL problem to address patient-level weak supervision and variable bag sizes. A deep neural network was trained with data augmentation and evaluated with two permutation-invariant aggregation strategies: min-max-mean pooling and attention-based pooling. Diagnostic performance was benchmarked against experienced neuroradiologists.Results
The best-performing MIL configuration, combining a convolutional neural network (CNN) with 3Pool aggregation and data augmentation (CNN-3Pool + DA), achieved an AUC of 0.82 with a sensitivity of 0.76 and specificity of 0.80 on the independent hold-out test set. In the MRS-only reader comparison, the participating neuroradiologists showed an average sensitivity of 0.54 and specificity of 0.88. Attention-based MIL additionally enabled visualization of the relative contribution of individual spectra, highlighting clinically established spectral features, including lipid and choline peaks, as well as the Glx region (2.2-2.4 ppm).Conclusions
Formulating MRS-based brain tumor classification as a MIL problem enables robust learning from heterogeneous, weakly labeled clinical data. The attention mechanism provides interpretable insights into spectral decision-making and highlights candidate biomarkers for future validation. This framework offers a scalable and interpretable approach to clinical MRS analysis.
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BibTeX
@article{gacheva2026multiple,
title = {{Multiple instance learning for differentiating neoplastic and non-neoplastic brain lesions using MR spectroscopy.}},
author = {Iskra Gacheva and Diyuan Lu and Gerhard Kurz and Jochen Triesch and Elke Hattingen and Nenad Polomac},
journal = {Neuroimage. Reports},
year = {2026},
month = oct,
doi = {10.1016/j.ynirp.2026.100418},
url = {https://europepmc.org/article/MED/42927027},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multiple instance learning for differentiating neoplastic and non-neoplastic brain lesions using MR spectroscopy.
AU - Iskra Gacheva
AU - Diyuan Lu
AU - Gerhard Kurz
AU - Jochen Triesch
AU - Elke Hattingen
AU - Nenad Polomac
PY - 2026
DA - 2026/10/17
JO - Neuroimage. Reports
DO - 10.1016/j.ynirp.2026.100418
UR - https://europepmc.org/article/MED/42927027
AB - Objective To evaluate whether magnetic resonance spectroscopy (MRS) combined with multiple instance learning (MIL) can differentiate neoplastic from non-neoplastic brain lesions by analyzing full spectral patterns rather than isolated metabolite peak ratios. We hypothesized that treating the full spectrum as a metabolic fingerprint, captured through deep learning, would enable more robust tissue classification than conventional single-peak analysis.Methods We retrospectively analyzed MRS data from 422 patients (7442 spectra total): 6598 spectra for training and 844 spectra in the independent internal hold-out test set. Classification was formulated as a MIL problem to address patient-level weak supervision and variable bag sizes. A deep neural network was trained with data augmentation and evaluated with two permutation-invariant aggregation strategies: min-max-mean pooling and attention-based pooling. Diagnostic performance was benchmarked against experienced neuroradiologists.Results The best-performing MIL configuration, combining a convolutional neural network (CNN) with 3Pool aggregation and data augmentation (CNN-3Pool + DA), achieved an AUC of 0.82 with a sensitivity of 0.76 and specificity of 0.80 on the independent hold-out test set. In the MRS-only reader comparison, the participating neuroradiologists showed an average sensitivity of 0.54 and specificity of 0.88. Attention-based MIL additionally enabled visualization of the relative contribution of individual spectra, highlighting clinically established spectral features, including lipid and choline peaks, as well as the Glx region (2.2-2.4 ppm).Conclusions Formulating MRS-based brain tumor classification as a MIL problem enables robust learning from heterogeneous, weakly labeled clinical data. The attention mechanism provides interpretable insights into spectral decision-making and highlights candidate biomarkers for future validation. This framework offers a scalable and interpretable approach to clinical MRS analysis.
SN - 2666-9560
ER -