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An artificial intelligence-assisted framework for fast and automatic radiofrequency ablation planning of liver tumors in CT images

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Chinese Journal of Academic Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop and validate an artificial intelligence (AI)-assisted framework for fast and automatic radiofrequency ablation (RFA) planning of liver tumors from CT images.

Materials and methods

This framework consisted of three steps. First, the abdominal multi-organs related to RFA planning were automatically segmented from CT images using a modified nnU-Net with class-weighted loss function. Then, utilizing the segmented liver as a location prior, the liver tumors and hepatic vessels were further segmented by a sensitivity-enhanced segmentation network. Finally, a clinically acceptable RFA plan was generated by a fully automatic planning method based on the segmented organs and tumors. Experiments were conducted on two public segmentation datasets and patients from two different centers to evaluate the proposed framework.

Results

The proposed abdominal multi-organ segmentation model achieved an average dice of 87.7 \(\pm\) 8.0% on 15 abdominal organs and the proposed liver tumor and hepatic vessel segmentation model achieved an average dice of 80.7 \(\pm\) 11.2% and 65.3 \(\pm\) 10.2% and an average sensitivity of 87.4 \(\pm\) 12.9% and 76.8 \(\pm\) 14.9% for liver tumor and hepatic vessel, respectively. Finally, the proposed framework generated clinically acceptable RFA plans within a few minutes without human intervention for all patients from two centers.

Conclusion

The proposed AI-assisted framework is fast and can automatically generate clinically acceptable RFA plans for liver tumors from CT images, which can assist interventional radiologists in determining suitable plans and reduce their burden.

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Data availability

Data generated or analyzed during the study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

References

  1. Sun L, Yang Y, Li Y, Zhang B, Shi R. The past, present, and future of liver cancer research in China. Cancer Lett. 2023;2023: 216334. https://doi.org/10.1016/j.canlet.2023.216334.

    Article  CAS  Google Scholar 

  2. Garrean S, Hering J, Saied A, Helton WS, Espat NJ. Radiofrequency ablation of primary and metastatic liver tumors: a critical review of the literature. Am J Surg. 2008;195:508–20. https://doi.org/10.1016/j.amjsurg.2007.06.024.

    Article  PubMed  Google Scholar 

  3. Benson AB 3rd, Abrams TA, Ben-Josef E, et al. NCCN clinical practice guidelines in oncology: hepatobiliary cancers. J Natl Compr Cancer Netw: JNCCN. 2009;7:350–91. https://doi.org/10.6004/jnccn.2009.0027.

    Article  CAS  PubMed  Google Scholar 

  4. Chen MH, Yang W, Yan K, et al. Large liver tumors: protocol for radiofrequency ablation and its clinical application in 110 patients—mathematic model, overlapping mode, and electrode placement process. Radiology. 2004;232:260–71. https://doi.org/10.1148/radiol.2321030821.

    Article  PubMed  Google Scholar 

  5. Schumann C, Rieder C, Bieberstein J, et al. State of the art in computer-assisted planning, intervention, and assessment of liver tumor ablation. Crit Rev™ Biomed Eng. 2010;38:31–52. https://doi.org/10.1615/CritRevBiomedEng.v38.i1.40.

    Article  PubMed  Google Scholar 

  6. Minami Y, Kudo M. Radiofrequency ablation of hepatocellular carcinoma: a literature review. Int J Hepatol. 2011;2011:1–9. https://doi.org/10.4061/2011/104685.

    Article  Google Scholar 

  7. Wang Y, Zhou Y, Shen W, Park S, Fishman EK, Yuille AL. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med Image Anal. 2019;55:88–102. https://doi.org/10.1016/j.media.2019.04.005.

    Article  PubMed  Google Scholar 

  8. Hu P, Wu F, Peng J, Bao Y, Chen F, Kong D. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int J Comput Ass Rad. 2017;12:399–411. https://doi.org/10.1007/s11548-016-1501-5.

    Article  Google Scholar 

  9. Chen H, Wang X, Huang Y, Wu X, Yu Y, Wang L (2019) Harnessing 2D networks and 3D features for automated pancreas segmentation from volumetric CT images. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019, pp 339–347. https://doi.org/10.1007/978-3-030-32226-7_38

  10. Gul S, Khan MS, Bibi A, Khandakar A, Ayari MA, Chowdhury ME. Deep learning techniques for liver and liver tumor segmentation: a review. Comput Biol Med. 2022;147: 105620. https://doi.org/10.1016/j.compbiomed.2022.105620.

    Article  PubMed  Google Scholar 

  11. Huang YJ, Dou Q, Wang ZX, et al. 3-D RoI-aware U-net for accurate and efficient colorectal tumor segmentation. IEEE T Cybern. 2020;51(11):5397–408. https://doi.org/10.1109/TCYB.2020.2980145.

    Article  Google Scholar 

  12. Li R, Huang YJ, Chen H, et al. 3d graph-connectivity constrained network for hepatic vessel segmentation. IEEE J Biomed Health. 2021;26(3):1251–62. https://doi.org/10.1109/JBHI.2021.3118104.

    Article  Google Scholar 

  13. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  14. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11. https://doi.org/10.1038/s41592-020-01008-z.

    Article  CAS  PubMed  Google Scholar 

  15. Chaitanya K, Audigier C, Balascuta LE, Mansi T (2022) Automatic planning of liver tumor thermal ablation using deep reinforcement learning. In: International conference on medical imaging with deep learning, PMLR 172, pp 219–230

  16. Meister F, Audigier C, Passerini T, et al. Fast automatic liver tumor radiofrequency ablation planning via learned physics model. In: Medical image computing and computer assisted intervention. Cham: Springer; 2022. p. 167–76. https://doi.org/10.1007/978-3-031-16449-1_17.

    Chapter  Google Scholar 

  17. Liang L, Cool D, Kakani N, Wang G, Ding H, Fenster A. Automatic radiofrequency ablation planning for liver tumors with multiple constraints based on set covering. IEEE Trans Med Imaging. 2019;39:1459–71. https://doi.org/10.1109/TMI.2019.2950947.

    Article  PubMed  Google Scholar 

  18. Yu P, Fu T, Wu C, Jiang Y, Yang J (2021) Automatic radiofrequency ablation planning for liver tumors: a planning method based on the genetic algorithm with multiple constraints. In: 2021 the 3rd International conference on intelligent medicine and health. Association for Computing Machinery, pp 8–14. https://doi.org/10.1145/3484377.3484379

  19. Li R, An C, Wang S, et al. A heuristic method for rapid and automatic radiofrequency ablation planning of liver tumors. Int J Comput Ass Rad. 2023;2023:1–9. https://doi.org/10.1007/s11548-023-02921-2.

    Article  Google Scholar 

  20. Seror O. Ablative therapies: advantages and disadvantages of radiofrequency, cryotherapy, microwave and electroporation methods, or how to choose the right method for an individual patient? Diagn Interv Imaging. 2015;96:617–24. https://doi.org/10.1016/j.diii.2015.04.007.

    Article  CAS  PubMed  Google Scholar 

  21. Ji Y, Bai H, Ge C, et al. Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Adv Neural Inf Process Syst. 2022;35:36722–32.

    Google Scholar 

  22. Antonelli M, Reinke A, Bakas S, et al. The medical segmentation decathlon. Nat Commun. 2022;13(1):4128. https://doi.org/10.1038/s41467-022-30695-9.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Laimer G, Schullian P, Jaschke N, et al. Minimal ablative margin (MAM) assessment with image fusion: an independent predictor for local tumor progression in hepatocellular carcinoma after stereotactic radiofrequency ablation. Eur Radiol. 2020;30:2463–72. https://doi.org/10.1007/s00330-019-06609-7.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by grants from the National Key Research and Development Program of China (2019YFC0118100), National Natural Science Foundation of China (62171167).

Author information

Author notes

  1. Lisheng Wang and Huijie Jiang have equal contributions to this study.

Authors and Affiliations

  1. Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Ruikun Li, Rui Xin & Lisheng Wang

  2. Deepwise AI Lab, Beijing, 100080, China

    Shuxin Wang

  3. Department of Radiology, Third Medical Centre, Chinese PLA General Hospital, Beijing, 100036, China

    Guisheng Wang

  4. Department of Radiology, Daqing Longnan Hospital, Daqing, 163453, China

    Lifeng Zhao

  5. Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China

    Huijie Jiang

Authors
  1. Ruikun Li
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  2. Rui Xin
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  4. Guisheng Wang
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  5. Lifeng Zhao
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  6. Huijie Jiang
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  7. Lisheng Wang
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Corresponding authors

Correspondence to Huijie Jiang or Lisheng Wang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This retrospective study was conducted following ethical approval from the Institutional Review Board of The Third Medical Center of Chinese PLA General Hospital and Daqing Longnan Hospital, and all CT images are anonymized. This paper does not contain any studies with animals performed by any of the authors.

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Li, R., Xin, R., Wang, S. et al. An artificial intelligence-assisted framework for fast and automatic radiofrequency ablation planning of liver tumors in CT images. Chin J Acad Radiol (2024). https://doi.org/10.1007/s42058-024-00145-0

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  • DOI: https://doi.org/10.1007/s42058-024-00145-0

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