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Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC.

Lukas MüllerRoman KloecknerAline Mähringer-KunzFabian StoehrChristoph DüberGordon ArnholdSimon Johannes GairingFriedrich FoersterArndt WeinmannPeter Robert GalleJens MittlerDaniel Pinto Dos SantosFelix Hahn
Published in: European radiology (2022)
• Splenic volume is a relevant prognostic factor for prediction of survival in patients with HCC undergoing TACE, and should be preferred over two-dimensional surrogates for splenic size. • Besides overall survival, progression-free survival and hepatic decompensation were significantly associated with splenic volume, making splenic volume a currently underappreciated prognostic factor prior to TACE. • Splenic volume can be fully automatically assessed using deep-learning methods; thus, it is a promising imaging biomarker easily integrable into daily radiological routine.
Keyphrases
  • prognostic factors
  • free survival
  • deep learning
  • artificial intelligence
  • machine learning
  • high resolution
  • physical activity
  • convolutional neural network
  • high throughput