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Related Experiment Video

Updated: Jun 18, 2025

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

Deep learning identifies histopathologic changes in bladder cancers associated with smoke exposure status.

Okyaz Eminaga¹, Hubert Lau^2,3, Eugene Shkolyar⁴

¹AI Vobis, Palo Alto, California, United States of America.

|July 31, 2024

Summary

Deep learning models can predict smoking status from bladder cancer histology images. This technology may offer insights into a patient's smoke exposure history.

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Published on: October 30, 2013

Area of Science:

Oncology
Pathology
Artificial Intelligence

Background:

Smoking is a known risk factor for bladder cancer (BC).
The relationship between BC histology and smoking status is not well understood.

Purpose of the Study:

To investigate if bladder cancer histology images can predict smoking exposure status.
To develop and validate a deep learning model for this prediction.

Main Methods:

A deep learning model was trained on whole-slide histology images from 66 BC cases.
The model was externally validated on 94 BC cases, assessing prediction accuracy using AUC.
Multivariate analyses controlled for BC grade, gender, age, and time to diagnosis.

Main Results:

The model achieved an AUC of 0.67 (95% CI: 0.58-0.76) on external validation, indicating non-random classification.
The model independently predicted smoking exposure status with an odds ratio of 1.710 (95% CI: 1.148-2.54).
Histologic patterns predictive of smoking status were identified.

Conclusions:

Deep learning can identify histopathologic features in bladder cancer that correlate with smoking exposure.
Histology-based prediction of smoking status may provide valuable clinical information.