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Abstract #1070

Prediction of muscle invasion in upper urinary tract urothelial carcinoma using 3D reduced-FOV CUBE T2 with deep learning reconstruction

Hanmei Zhang1, Yi Wang1, Qin Wang1, Hanyu Zheng1, Yuan Yuan1, Liping Deng1, Yi-Ge Bao2, Bo Zhang3, Huilou Liang3, and Bin Song1,4
1Radiology, Sichuan University West China Hospital, Chengdu, China, 2Urology, Sichuan University West China Hospital, Chengdu, China, 3GE Healthcare, MR Research, Beijing, China, 4Sanya People’s Hospital, Sanya, China

Synopsis

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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Keywords