Can magnetic resonance spectroscopy differentiate malignant and benign causes of lymphadenopathy? An in-vitro approach
Bibliographic record
Abstract
Lymphadenopathy continues to be a common problem to radiologists and treating physicians because of the difficulty in confidently categorizing a node as being benign or malignant using standard diagnostic techniques. The goal of our research was to assess whether magnetic resonance (MR) spectroscopy contains the necessary information to allow differentiation of benign from malignant lymph nodes in an in-vitro approach using a modern pattern recognition method. Tissue samples from a tissue bank were analyzed on a nuclear magnetic resonance (NMR) spectrometer. A total of 69 samples were studied. The samples included a wide variety of malignant and benign etiologies. Using 45 samples, we initially created a model which was able to predict if a certain spectrum originates from benign or malignant lymph nodes using a pattern-recognition technique which takes into account the entire magnetic spectrum rather than single peaks alone. The remaining 24 samples were blindly loaded in the model to assess its performance. We obtained an excellent accuracy in differentiating benign and malignant lymphadenopathy using the model. It correctly differentiated as malignant or benign, in a blinded fashion, all of the malignant samples (13 of 13) and 10 out of the 11 benign samples. We thus showed that magnetic spectroscopy is able to differentiate benign from malignant causes of lymphadenopathy. Additional experiments were performed to verify that the differentiating abilities of our model were not due to differential tissue decay in between benign and malignant tissues. If future experiments demonstrate that a similar approach could be executed with standard MR imaging, this technique could be useful as a problem-solving tool when assessing lymphadenopathy in general. Alternatively, our in-vitro technique could also be useful to pathologists faced with indeterminate pathologies of the lymph nodes after validating our results with a larger sample size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".