BACKGROUND
Brain metastases carry poor prognosis, and accurate survival prediction is critical for treatment planning. Radiomics offers a means of extracting high-dimensional imaging biomarkers, but its prognostic utility in brain metastasis remains unclear.
PURPOSE
To evaluate whether MRI-derived radiomic features can improve survival prediction in patients with brain metastases.
MATERIALS AND METHODS
This retrospective study developed a T1 postcontrast MRI radiomics survival model in a public brain metastasis cohort of 198 patients and externally validated the fixed radiomics model, without refitting or recalibration, in an independent cohort of 69 patients. Patient-level radiomics features were derived from lesion-level features aggregated across the three largest lesions. An elastic-net Cox model was selected with 10-fold cross-validation. Model discrimination was assessed with Harrell C-index and 2000 bootstrap resamples for 95% CIs.
RESULTS
The final model retained 7 nonzero T1 postcontrast radiomics features after correlation filtering and elastic-net selection. The radiomics score had a C-index of 0.615 (95% CI, 0.543-0.687) in the training cohort and 0.574 (95% CI, 0.491-0.658) in external validation. In the external-validation subset with available Graded Prognostic Assessment, the combined radiomics plus Graded Prognostic Assessment model had a C-index of 0.591 (95% CI, 0.509-0.672).
CONCLUSION
T1 postcontrast MRI radiomics showed limited standalone discrimination for overall survival in patients with brain metastases. These results support cautious use of radiomics as an exploratory imaging biomarker and emphasize the need for integrated prognostic models that include clinical, treatment, molecular, and systemic disease variables.
H. Salim, Evan Calabrese, Ahmed Naeem et al.· AJNR. American journal of ne...· 0 citations
BACKGROUND
Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. Higher doses of GBCAs can improve lesion sensitivity but may have safety implications, particularly in light of recent findings on gadolinium retention and deposition.
PURPOSE
To evaluate the clinical performance of an FDA-cleared deep-learning (DL)-based contrast boosting algorithm in routine clinical brain MRI exams.
STUDY TYPE
Retrospective.
POPULATION
One hundred ten patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) with clinical contrast-enhanced brain MR studies.
FIELD STRENGTH AND SEQUENCES
T1 weighted pre-contrast and post-contrast brain MRI sequences at 0.3 T, 1.5 T, and 3 T.
ASSESSMENT
A multi-center database of contrast-enhanced brain MR images was used to evaluate a DL-based contrast boosting algorithm. Pre-contrast and standard post-contrast (SC) images were processed with the algorithm to obtain contrast boosted (CB) images. CB images were compared to SC images in terms of contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Three board-certified radiologists with 9, 15, and 18 years of experience reviewed CB and SC images side-by-side for qualitative evaluation and rated them on a 4-point Likert scale for lesion contrast enhancement, border delineation, internal morphology, overall image quality, presence of artifacts, and changes in vessel conspicuity. The presence, cause, and severity of any false lesions was recorded.
STATISTICAL TESTS
Wilcoxon signed rank test. A p value < 0.05 was considered significant.
RESULTS
CB images had significantly superior quantitative performance than SC images in terms of CNR (729.17% ± 1576.92%), LBR (87.91% ± 72.12%), and CEP (165.81% ± 42%). In the qualitative assessment, CB images showed significantly better lesion visualization (3.73 vs. 3.16) and had significantly better image quality (3.55 vs. 3.07).
DATA CONCLUSION
In this multi-center, multi-reader study, deep learning-based contrast boosting demonstrates robust improvements in lesion visualization and image quality without increasing contrast dosage.
EVIDENCE LEVEL
4.
TECHNICAL EFFICACY
Stage 3.
Srivathsa Pasumarthi Venkata, Thomas Campbell Arnold, Sonia Colombo Serra et al.· Journal of Magnetic Resonanc...· 0 citations
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