LUCID：脂质特征尿液细胞学智能检测平台用于膀胱癌无创早期诊断

Abstract

Leveraging its non-invasive and convenient nature, urinary cytology offers a way for the early diagnosis and surveillance of bladder cancer (BCa). However, its sensitivity remains substantially inferior to the histopathological gold standard, rendering it inadequate to support precise clinical diagnosis. Here, we developed Lipid-featured Urine Cytology with Intelligent Detection (LUCID), an automated platform integrating single-cell stimulated Raman scattering (SRS) imaging and explainable AI model for accurate BCa detection. Following a newly developed standardized urine-preprocessing protocol, 19,678 urinary exfoliated cells from 26 BCa patients and 54 non-BCa participants were imaged using two-color SRS microscopy to simultaneously visualize lipid and protein distributions. For cell-level analysis, a customized deep-supervised Cellpose segmentation algorithm and a YOLO-based detection model were developed to enable precise single-cell segmentation and intracellular lipid droplet (LD) identification, respectively. Totally 20 quantitative morphological and compositional features per cell were extracted and 10 related principal components were obtained via principal component analysis. These features were subsequently integrated into a multiple-instance learning (MIL)-XGBoosting model, which achieved robust diagnostic performance, with 92.31% sensitivity, 87.04% specificity, and an AUC of 0.940. We found that morphological and compositional features provided complementary diagnostic information, while LD-associated features provided primary discriminative information, supporting intracellular LD phenotypes as potential urinary cellular markers of BCa. More importantly, LUCID demonstrated exceptional accuracy in detecting early-stage and small tumors, and showed higher concordance than FISH in a preliminary paired subset (5/6 vs. 2/6). Together, LUCID establishes a chemical imaging-AI paradigm that transforms molecular urine cytology.

Competing Interest Statement

The authors have declared no competing interest.

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This study was approved by the Institutional Review Boards of Beijing Cancer Hospital and Beihang University, and written informed consent was obtained from all participants.

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Funder Information Declared

National Key R&D Program of China, No. 2025YFF0517900, No. 2023YFC2415500

PKU-Baidu Fund, No. 2020BD033

Peking University First Hospital Scientific and Technological Achievement Transformation Incubation Guidance Fund, No. 2022CX02

Fundamental Research Funds for the Central Universities, JKF-2025086684620

National Natural Science Foundation of China, No. 82672586