European Radiology: Application of AI-based automatic spleen segmentation in risk stratification of liver cancer patients with TACE

Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world and the second leading cause of cancer-related death.

In addition to impairing hepatic protein synthesis, cirrhosis also causes progressive changes in splenic circulatio.

With the development in the field of artificial intelligence, this technology offers the possibility of automatic organ segmentation and volume assessment for the clinic, which can be easily integrated into the clinical workflow in real tim.

Recently, a study published in European Radiology established a deep learning algorithm for automatic spleen volume assessment based on CT images, and validated the clinical value of total spleen volume as a new predictor of survival in imaging student.

This retrospective study included 327 treatment-resistant HCC patients who underwent initial TACE at our institution's tertiary care center between 2010 and 2020. Convolutional neural networks for spleen segmentation are trained and validated on the first 100 consecutive case.

The algorithm showed a Sørensen Dice score of 96 during training and validatio.

This study demonstrates that the deep learning algorithm proposed in this study can perform fully automated spleen volume assessment in HCC patients undergoing TAC.

Original source:

Lukas Müller, Roman Kloeckner, Aline Mähringer-Kunz, et a.