Keywords: Urogenital, Urogenital, 3D reduced-FOV CUBE T2WI, deep learning, muscle invasion, upper tract urothelial carcinoma
Motivation: Accurate imaging assessment of muscle invasion in upper urinary tract urothelial carcinoma (UTUC) is crucial for surgical planning.
Goal(s): To better display the anatomy between tumor and adjacent urinary wall using 3D reduced-FOV (rFOV) CUBE T2 with deep learning reconstruction (DLR), thus improving muscle invasion prediction in UTUC.
Approach: The structural clarity of CUBE T2 images with and without DLR were scored and compared. The irregular surface of tumor attachment was assessed using a novel evaluation method.
Results: CUBE T2 with DLR better depicted irregular surfaces of tumor attachment. The proposed method showed promising specificity and negative predictive value for muscle invasion prediction.
Impact: The visualization of irregular attachment surfaces using 3D rFOV CUBE T2 with DLR offers a new perspective for predicting muscle invasion in UTUC. This could assist patients without irregular attachment surfaces in avoiding radical nephroureterectomy.
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