Multiple instance learning for differentiating neoplastic and non-neoplastic brain lesions using MR spectroscopy.
Authors
Affiliations (3)
Affiliations (3)
- Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt am Main, 60528, Germany.
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, 60438, Germany.
- Independent Researcher, Germany.
Abstract
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. 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. 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). 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.