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01-08-2018 | Head and Neck

Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network

Authors: Jinjin Liu, Yongchun Chen, Li Lan, Boli Lin, Weijian Chen, Meihao Wang, Rui Li, Yunjun Yang, Bing Zhao, Zilong Hu, Yuxia Duan

Published in: European Radiology | Issue 8/2018

Abstract

Objectives

Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN).

Materials and method

594 ACOM aneurysms, 54 unruptured and 540 ruptured, were reviewed. A two-layer feed-forward ANN was designed for ACOM aneurysm rupture-risk analysis. To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Seventeen parameters (13 morphological parameters of ACOM aneurysm measured from these patients' CT angiography (CTA) images, two demographic factors, and hypertension and smoking histories) were adopted as ANN input.

Results

Age, vessel size, aneurysm height, perpendicular height, aneurysm neck size, aspect ratio, size ratio, aneurysm angle, vessel angle, aneurysm projection, A1 segment configuration, aneurysm lobulations and hypertension were significantly different between the ruptured and unruptured groups. Areas under the ROC curve for training, validating, testing and overall data sets were 0.953, 0.937, 0.928 and 0.950, respectively. Overall prediction accuracy for raw 594 samples was 94.8 %.

Conclusion

This ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms.

Key Points

• A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms.

• Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired.

• An ADASYN sampling approach was used to improve ANN quality.

• Overall prediction accuracy of 94.8 % for the raw samples was achieved.

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Title: Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network
Authors: Jinjin Liu
Yongchun Chen
Li Lan
Boli Lin
Weijian Chen
Meihao Wang
Rui Li
Yunjun Yang
Bing Zhao
Zilong Hu
Yuxia Duan
Publication date: 01-08-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 8/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5300-3

At a glance: The STEP trials

Springer Medicine

Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network

Abstract

Objectives

Materials and method

Results

Conclusion

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Materials and method

Results

Conclusion

Key Points

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