3D imaging of upper tract urothelial carcinoma
This Learning Wednesday paper note highlights “Three-dimensional imaging of upper tract urothelial carcinoma improves diagnostic yield and accuracy.” The study is relevant to 3D pathology approaches to diagnosing upper tract urothelial carcinoma, with a focus on how three-dimensional tissue context can alter what researchers observe and measure.
Selected notes from the paper
“Upper tract urothelial carcinoma (UTUC) is a rare form of urothelial cancer with a high incidence of recurrence and a low survival rate. Almost two-thirds of UTUCs are invasive at the time of diagnosis; therefore, improving diagnostic methods is key to increasing survival rates.”
“The diagnosis and grading of UTUC are heavily reliant on 2-dimensional (2D) imaging, which requires thinly sliced, stained, and mounted tumor samples.”
“Microscopy, which cannot visualize 3D structures, such as the vasculature, results in the loss of crucial spatial information about the tumor environment. This can be resolved by utilizing DIPCO (diagnosing immunolabeled paraffin-embedded cleared organs), an imaging pipeline that captures images of intact tumor samples by 3D light-sheet microscopy.”
“Here, we used volumetric 3-dimensional (3D) imaging to explore the inner landscape of clinical UTUC biopsies, without sectioning, revealing that 3D analysis of phosphorylated ribosomal protein S6 (pS6) could predict tumor grade and prognosis with improved accuracy.”
“By visualizing the tumor vasculature, we discovered that pS6+ cells were localized near blood vessels at significantly higher levels in high-grade tumors than in low-grade tumors.”
“We next interrogated our 3D data and stratified the cohort into high versus low clustered pS6+ cell ratios. Patients with a low clustered pS6+ cell ratio exhibited significantly longer relapse-free survival.”
“We then performed a comparative study between 3D and 2D image analyses used in daily clinical practice. Two of the five 2D data sets failed to discriminate between the percentage of pS6+ cells in high- and low-grade tumors, whereas this discrimination was notably more pronounced in 3D.”
“Calculation of the area under the curve (AUC) revealed that 3D analysis provided the highest accuracy in stratifying high- and low-grade UTUC.”
“These findings indicate that 3D analysis of the tumor microenvironment could serve as a tool to predict recurrence.”
“In the current clinical practice, diagnosis and treatment strategies are determined based on limited 2D information from biopsy and cytology samples. In this study, we revealed that 3D image analysis of minute biopsies identified key tumor features that could aid in UTUC diagnosis, grading, and prediction, which were distorted or lost in 2D image analysis.”
“3D analysis [...] , may become a tool for clinical pathologists when facing difficult samples that are challenging to stage or grade.”
From an Alpenglow perspective, this paper is useful because it connects 3D pathology approaches for upper tract urothelial carcinoma diagnosis with a broader need in 3D spatial biology, measuring tissue architecture across depth while preserving context for quantitative analysis.
