Bladder Cancer Recurrence Prediction: Using CT Scans and Convolutional Neural Networks with Transfer Learning (Preprint)

Xing Liu; Jian Guo; Hao Lu; Kai Liang; Xinlei Wang; Ru Wang; Yu‐Fei Fu; Xue Liang; Yibing Shi; Long Shi; Qing Liang; Zhenduo Shi; Conghui Han

doi:10.2196/preprints.41910

Verified authors • Institutional access • DOI aware

50,000+ researchers120,000+ datasets90% satisfaction

Preprint

English

2022

Bladder Cancer Recurrence Prediction: Using CT Scans and Convolutional Neural Networks with Transfer Learning (Preprint)

0 Datasets

0 Files

English

2022

DOI: 10.2196/preprints.41910

Get instant academic access to this publication’s datasets.

Create free account How it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic access Learn more

✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration

BACKGROUND Deep convolutional neural networks have been applied to predict bladder cancer recurrence by analysing CT scans. However, such models suffer from a limited number of images collected from hospitals, leading to unstable performance. The size of medical datasets is limited by the complexity and high cost of large-scale studies, making it difficult to obtain rich training data in medical research. OBJECTIVE In this study, we aim to investigate 13 mainstream deep convolutional neural networks and build a pipeline of bladder cancer recurrence risk classification using CT scans and pre-trained deep learning convolutional neural networks. METHODS A five-year follow-up dataset from 101 patients with bladder cancer was collected from the Department of Urology at Xuzhou Central Hospital, Jiangsu Province, China. The CT scans of each patient were extracted from the dataset and formed an image set of 492 scans. 5-fold cross-validation were used to assess the performance and general error estimation of deep learning models. 13 mainstream deep convolutional neural networks were built to predict bladder cancer's recurrence risk level. Model performance was assessed by accuracy, precision, recall, F1-score and Area Under the Curve (AUC). RESULTS The pre-trained VGG16 had the best accuracy of 0.954 compared to the other 12 pre-trained models. The model also had a precision of 0.945, recall of 0.946, and F1-score of 0.943. The AUC for short-term recurrence was 0.978, for medium-term recurrence was 0.981, and for long-term recurrence was 0.975. CONCLUSIONS Transfer learning provided a new way to deal with bladder cancer recurrence risk classification with deep learning and small-scale dataset. A pre-trained VGG16 performed the best in predicting the risk of bladder cancer recurrence using CT scans.

Bladder Cancer Recurrence Prediction: Using CT Scans and Convolutional Neural Networks with Transfer Learning (Preprint)

Frequently asked questions

Is access really free for academics and students?

How is my data protected?

Can I request additional materials?

Advance your research today

Bladder Cancer Recurrence Prediction: Using CT Scans and Convolutional Neural Networks with Transfer Learning (Preprint)

Frequently asked questions

Is access really free for academics and students?

How is my data protected?

Can I request additional materials?

Advance your research today

Access Research Data

This PDF is not available in different languages.

Long Shi

Abstract

How to cite this publication

Related publications

Why join Raw Data Library?

Quality

Control

Free for Academia

Publication Details

Join Research Community