Feasibility of 1 H MR spectroscopy in malignant pulmonary lesions at 3 Tesla: phantom study and clinical verification
Bibliographic record
Abstract
Abstract Purpose: Primary lung malignancies are the leading cause of cancer death worldwide. In addition to primary lung malignancy, the presence of pulmonary metastases has a critical role in patient prognostication and management. Recently, cellular and molecular imaging, particularly proton magnetic resonance spectroscopy (1H MRS), has been employed for early tumor detection. This article aims to survey the role of 1H-MRS as a virtual biopsy technique to determine the biological make-up of lung lesions. Methods: NEMA IEC phantom analysis was utilized to acquire free induction decay (FID) of several tumor markers including choline, lactate, and creatine on single-voxel spectroscopy (SVS) at 3 Tesla (T) MRI. Various protocol modifications were performed on acquisition and processing steps. Preliminary in vivo verifications were subsequently performed on MRS datasets of 4 patients with malignant lung pathologies. Results: 1H MRS of phantom revealed detectable peaks of choline, lactate and creatine in concentrations above the threshold of 1 µ Molar (µM) in patients with lung malignancies. Our result demonstrate that the phantom sphere diameter is the most influential factor on peak detectability with minimum reasonable value of 13mm; with the least acceptable in vitro voxel size of 1 cm3 and in vivo equivalent of 8 cm3. Trading off between acquisition time and signal to noise ratio (SNR), the appreciable number of acquisition (NAS) was equal to 64. A quantitative formula was derived to calculate metabolite concentrations (AUC=0.73). Concordant results were obtained on pilot clinical assessments. Conclusion: This preliminary study provides a one-stop-shop solution for SVS through optimizing acquisition and processing parameters as well as absolute quantification of choline, lactate, and creatine concentrations in malignant pulmonary lesions, with successful clinical verification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".