Mobile SRS imaging for real-time histological assessment in bladder cancer (Conference Presentation)

Bladder cancer (BC) treatment is costly due to high recurrence and revision surgeries. If a resected tumor is diagnosed as high-risk by post-operative histological assessment, revision surgeries are mandatory. We present a fully integrated, clinically compatible SRS imaging device for real-time histological assessment of BC during surgery. We demonstrate excellent agreement between acquired SRS images and classic H&E-stained images. Our device paves the way for classifying BC risk levels, enabling intraoperative therapy stratification. Additionally, our device spans the full Raman spectrum, offering rapid, detailed molecular profiling and potentially reducing reliance on advanced diagnostic tools.

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Publication Details

Published
2025-03-20
DOI
https://doi.org/10.1117/12.3042698
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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Mobile SRS imaging for real-time histological assessment in bladder cancer (Conference Presentation)

Thomas Würthwein, Sven Dobner, Niklas Lüpken, Maximilian Brinkmann et al.
Radiomics and Machine Learning in Medical Imaging
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Mobile SRS imaging for real-time histological assessment in bladder cancer (Conference Presentation)

Thomas Würthwein, Sven Dobner, Niklas Lüpken, Maximilian Brinkmann, Ramon Droop, Tim Hellwig, F. Neumann, Steffen Ullmann, Anke Bonse
article en

Abstract

Bladder cancer (BC) treatment is costly due to high recurrence and revision surgeries. If a resected tumor is diagnosed as high-risk by post-operative histological assessment, revision surgeries are mandatory. We present a fully integrated, clinically compatible SRS imaging device for real-time histological assessment of BC during surgery. We demonstrate excellent agreement between acquired SRS images and classic H&E-stained images. Our device paves the way for classifying BC risk levels, enabling intraoperative therapy stratification. Additionally, our device spans the full Raman spectrum, offering rapid, detailed molecular profiling and potentially reducing reliance on advanced diagnostic tools.

Openalex Percentile: Top 92%
Radiomics and Machine Learning in Medical Imaging
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