Thin-Slice Pituitary MRI with Deep Learning–based Reconstruction: Diagnostic Performance in a Postoperative Setting
Bibliographic record
Abstract
Background Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning–based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice MRI. Purpose To assess the diagnostic performance of 1-mm slice thickness MRI with deep learning–based reconstruction (DLR) (hereafter, 1-mm MRI+DLR) compared with 3-mm slice thickness MRI (hereafter, 3-mm MRI) for identifying residual tumor and cavernous sinus invasion in the evaluation of postoperative pituitary adenoma. Materials and Methods This single-institution retrospective study included 65 patients (mean age ± standard deviation, 54 years ± 10; 26 women) who underwent a combined imaging protocol including 3-mm MRI and 1-mm MRI+DLR for postoperative evaluation of pituitary adenoma between August and October 2019. Reference standards for correct diagnosis were established by using all available imaging resources, clinical histories, laboratory findings, surgical records, and pathology reports. The diagnostic performances of 3-mm MRI, 1-mm slice thickness MRI without DLR (hereafter, 1-mm MRI), and 1-mm MRI+DLR for identifying residual tumor and cavernous sinus invasion were evaluated by two readers and compared between the protocols. Results The performance of 1-mm MRI+DLR in the identification of residual tumor was comparable to that of 3-mm MRI (area under the receiver operating characteristic curve [AUC], 0.89–0.92 vs 0.85–0.89, respectively; P ≥ .09). In the identification of cavernous sinus invasion, the diagnostic performance of 1-mm MRI+DLR was higher than that of 3-mm MRI (AUC, 0.95–0.98 vs 0.83–0.87, respectively; P ≤ .02). Conventional 1-mm MRI (AUC, 0.82–0.83) showed comparable diagnostic performance to 3-mm MRI (AUC, 0.83–0.87) (P ≥ .38). With 1-mm MRI+DLR, residual tumor was diagnosed in 20 patients and cavernous sinus invasion was diagnosed in 14 patients, in whom these diagnoses were not made with 3-mm MRI. Conclusion In the postoperative evaluation of pituitary adenoma, 1-mm slice thickness MRI with deep learning–based reconstruction showed higher diagnostic performance than 3-mm slice thickness MRI in the identification of cavernous sinus invasion and comparable diagnostic performance to 3-mm slice thickness MRI in the identification of residual tumor. © RSNA, 2020 Online supplemental material is available for this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".