Original Research Article

Article volume = 2024 and issue = 1

Pages: 81–97

Article publication Date: December 23, 2023

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Deep Learning Techniques for Liver Cancer: A Survey on Early Prediction, Detection, and Prognosis of Metastasis and Survival

Arefe Pourmajidian and Javad Vahidi

(a) Mazandaran University of Science and Technology, Department of Computer Engineering, Babol, Iran.

(b) Iran University of Science and Technology, Department of Computer Science, Tehran, Iran.

Abstract:

Background: The liver, an essential organ for digestion and removing toxins, is vulnerable to conditions such as liver cancer. Frequently, these ailments aren't identified until they've progressed significantly due to their understated initial symptoms. Although contemporary imaging techniques occasionally overlook these preliminary indications, the advent of advanced tech solutions is increasingly recognized as pivotal. The urgency of early detection is underscored by the severe repercussions of liver cancer. While traditional diagnostic measures have their limitations, nascent technologies, notably Deep Learning (DL) and especially Convolutional Neural Networks (CNN), are exhibiting remarkable diagnostic prowess, occasionally outpacing human expertise. Leveraging these DL methodologies and state-of-the-art tools has the potential to revolutionize early liver cancer detection, thus mitigating patient mortality rates and subsequently trimming both the costs and duration of treatments. Methodology: This survey assessed 84 notable articles from top journals over the last five years. Selection prioritized articles showcasing precise computations with evaluable outcomes. Our focus was on the potential of DL in early detection of precancerous liver lesions, liver cancer diagnosis, and survival rate predictions. Results: DL proved highly effective in early lesion detection and liver cancer diagnosis, achieving near-perfect accuracy of 100% in specific datasets. Prognoses concerning metastasis and survival reached an accuracy of 89.1%. Conclusion: Our survey highlights the advantage of merging neural networks with other techniques for better image classification. However, data-related constraints limit its wider application, especially due to the lack of a global standard for AI in biomedical imaging. Collaborative efforts are needed to curate extensive datasets, and more research is essential on precancerous lesions and liver cancer prognosis.

Keywords:

Artificial Intelligence, Deep Learning, Early Detection, Liver Cancer, Prediction, Prognosis.

References:
  • [1] R. L. Siegel, K. D. Miller, N. S. Wagle and A. Jemal, ” Cancer statistics, 2023,” A Cancer Journal for Clinicians, pp. 17-48, 2023. 1
  • [2] S. Qin, G.-J. Liu, M. Huang, J. Huang, Y. Luo, Y. Wen, Y. Wang and L. Chen, ” The local efficacy and influencing factors of ultrasound-guided percutaneous microwave ablation in colorectal liver metastases: a review of a 4-year experience at a single center,” International Journal of Hyperthermia, vol. 36, no. 1, pp. 36-43, 2019. 1
  • [3] J. Keyl, R. Hosch, A. Berger, O. Ester, T. Greiner, S. Bogner, J. Treckmann, S. Ting, B. Schumacher, D. Albers, P. Markus, M. Wiesweg, M. Forsting, F. Nensa, M. Schuler, S. Kasper and J. Kleesiek, ” Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer,” Journal of Cachexia, Sarcopenia and Muscle, vol. 14, no. 1, pp. 545-552, 2022. 1
  • [4] Q.-x. Zhao, X.-l. He, K. Wang, Z.-g. Cheng, Z.-y. Han, F.-y. Liu, X.-l. Yu, Z. Hui, J. Yu, A. Chao and P. Liang, ” Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis,” European Radiology, no. 3, 2023. 3.1, 1
  • [5] J. Liang, W. Zhang, J. Yang, M. Wu, Q. Dai, H. Yin, Y. Xiao and L. Kong, ” Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer,” Nature Machine Intelligence, pp. 408-420, 2023. 3.1, 1
  • [6] Y. Han, J. Akhtar, G. Liu, C. Li and G. Wang, ” Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning,” Computational and Structural Biotechnology Journal, vol. 21, pp. 3478-3489, 2023. 3.1, 1
  • [7] Q. Chen, S. Fang, Y. Yuchen, R. Li, R. Deng, Y. Chen, D. Ma, H. Lin and F. Yan, ” Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value,” European Journal of Radiology, vol. 168, 2023. 3.2, 2
  • [8] R. A. Khan, M. Fu, B. Burbridge, Y. Luo and F.-X. Wu, ” A multi-modal deep neural network for multi-class liver cancer diagnosis,” Neural Networks, vol. 165, pp. 553-561, 2023. 3.2, 2
  • [9] R. V. Manjunath, A. Ghanshala and K. Kwadiki, ” Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images,” Springer Link, 2023. 3.2, 2
  • [10] K. Sridhar, K. C, W.-C. Lai and B. P. Kavin, ” Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model,” Biomedicines, 2023. 3.2, 2
  • [11] D. Bhattacharyya, N. S. Joshua, N. T. Rao, A. Kumar, M. Bhushan, J. A. Galindo, L. Garg and Y.-C. Hu, ” Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques,” in Machine Intelligence, Big Data Analytics, and IoT in Image Processing: Practical Applications, Scrivener Publishing LLC, 2023. 3.2, 2
  • [12] N. K. Trivedi, R. G. Tiwari, A. Anand, V. Gautam, D. Witarsyah and A. Misra, ” Application of Machine Learning for Diagnosis of Liver Cancer,” in International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), 2022. 3.2, 2
  • [13] A. Sirco, A. Almisreb, N. M. Tahir and J. Bakri, ” Liver Tumour Segmentation based on ResNet Technique,” in IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE), 2022. 3.2, 2
  • [14] H. Rahman, T. F. Naik Bukht, A. Imran, J. Tariq, S. Tu and A. Alzahrani, ” A Deep Learning Approach for Liver and Tumour Segmentation in CT Images Using ResUNet,” Bioengineering, 2022. 3.2, 2
  • [15] C. Chen, C. Chen, M. Ma, X. Ma, X. Lv, X. Dong, Z. Yan, M. Zhu and J. Chen, ” Classification of multidifferentiated liver cancer pathological images based on deep learning attention mechanism,” BMC Medical Informatics and Decision Making, 2022. 3.2, 2
  • [16] B. Das and S. Toraman, ” Deep transfer learning for automated liver cancer gene recognition using spectrogram images of digitized DNA sequences,” Biomedical Signal Processing and Control, vol. 72, 2022. 3.2, 2
  • [17] A. A. Aatresh, K. Alabhya, S. Lal, J. Kini and P. P. Saxena, ” LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images,” International Journal of Computer Assisted Radiology and Surgery, 2021. 3.2, 2
  • [18] N. Cheng, J. Fu, D. Chen, S. Chen and H. Wang, ” An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning,” NanoImpact, vol.21, 2021. 3.2, 3.2
  • [19] S. Lal, D. Das, K. Alabhya, A. Kanfade, A. Kumar and J. Kini, ” NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images,” Computers in Biology and Medicine, vol. 128, 2021. 3.2, 3.2
  • [20] D.-V. Phan, C.-L. Chan, A.-H. A. Li, T.-Y. Chien and V.-C. Nguyen, ” Liver cancer prediction in a viral hepatitis cohort: A deep learning approach,” International Journal of Cancer (IJC), vol. 147, no. 10, pp. 2871-2878, 2020. 3.2, 3.2
  • [21] X. Dong, Y. Zhou, L. Wang, J. Peng, Y. Lou and Y. Fan, ” Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework,” IEEE Access, vol. 8, pp. 129889 - 129898, 2020. 3.2, 3.2
  • [22] Q. Yang, J. Wei, X. Hao, D. Kong, X. Yu, T. Jiang, J. Xi, W. Cai, Y. Luo, X. Jing, Y. Yang, Z. Cheng, J. Wu, H. Zhang, J. Liao, P. Zhou, Y. Song, Y. Zhang, Z. Han, W. Cheng, L. Tang, F. Liu, J. Dou, R. Zheng, J. Yu, J. Tian and P. Liang, ” Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study,” EBioMedicine, vol. 56, 2020. 3.2, 3.2
  • [23] C. Sun, A. Xu, D. Liu, Z. Xiong, F. Zhao and W. Ding, ” Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 6, 2019. 3.2, 3.2
  • [24] S.-h. Zhen, M. Cheng, Y.-b. Tao, Y.-f. Wang, S. Juengpanich, Z.-y. Jiang, Y.-k. Jiang, Y.-y. Yan, W. Lu, J.-m. Lue, J.-h. Qian, Z.-y. Wu, J.-h. Sun, H. Lin and X.-j. Cai, ” Deep Learning for Accurate Diagnosis of Liver Tumour Based on Magnetic Resonance Imaging and Clinical Data,” Cancer Imaging and Image-directed Interventions, vol. 10, 2020. 3.2, 3.2
  • [25] M. Chen, B. Zhang, W. Topatana, J. Cao, H. Zhu, S. Juengpanich, Q. Mao, H. Yu and X. Cai, ” Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning,” npj precision oncology, 2020. 3.2, 3.2
  • [26] A. Kiani, B. Uyumazturk, P. Rajpurkar, A. Wang, R. Gao, E. Jones, Y. Yu, C. P. Langlotz, R. L. Ball, T. J. Montine, B. A. Martin, G. J. Berry, M. G. Ozawa, F. K. Hazard, R. A. Brown, S. B. Chen, M. Wood, L. S. Allard, L. Ylagan, A. Y. Ng and J. Shen, ” Impact of a deep learning assistant on the histopathologic classification of liver cancer,” npj precision oncology, 2020. 3.2, 3.2
  • [27] M. Yamakawa, T. Shiina, N. Nishida and M. Kudo, ” Computer aided diagnosis system developed for ultrasound diagnosis of liver lesions using deep learning,” in IEEE Symposium (IUS) Ultrasonics, Glasgow, UK, 2019. 3.2, 3.2
  • [28] A. Das, U. R. Acharya, S. S. Panda and S. Sabut, ” Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques,” Cognitive Systems Research, vol. 54, pp. 165-175, 2019. 3.2, 3.2
  • [29] D. I. Tsilimigras, P. Brodt, P.-A. Clavien, R. J. Muschel, M. I. D’Angelica, I. Endo, R. W. Parks, M. Doyle, E. d. Santibañes and T. M. Pawlik , ” Liver metastases,” National Library of Medicine, 2021. 3.3
  • [30] S. Zhou, D. Sun, W. Mao, Y. Liu, W. Cen, L. Ye, F. Liang, J. Xu, H. Shi, Y. Ji, L. Wang and W. Chang, ”Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study,” eClinicalMedicine, vol. 65, 2023. 3.3, 3
  • [31] X. Zhang, M. Dutton, R. Liu, A. A. Ali and F. Sherbeny, ” Deep Learning–Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver,” Transplantation Proceedings, 2023. 3.3
  • [32] C. T. Jensen, V. K. Wong, N. A. Wagner-Bartak, X. Liu, R. P. Nair Sobha, J. Sun, G. S. Likhari and S. Gupta, ”Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction,” European Journal of Radiology, vol. 168, 2023. 3.3, 3
  • [33] Z. H. Foda, A. V. Annapragada, K. Boyapati, D. C. Bruhm, N. A. Vulpescu, J. E. Medina, D. Mathios, S.Cristiano, N. Niknafs, H. T. Luu, M. G. Goggins, R. A. Anders, J. Sun, S. H. Meta, D. L. Thomas, G. D. Kirk, V. Adleff, J. Phallen, R. B. Scharpf, A. K. Kim and V. E. Velculescu, ” Detecting Liver Cancer Using Cell-Free DNA Fragmentomes,” Cancer Discovery, 2023. 3.3, 3
  • [34] F. Heinemann, P. Gross, S. Zeveleva, H. S. Qian, J. Hill, A. Höfer, D. Jonigk, A. M. Diehl, M. Abdelmalek, M. C. Lenter, S. S. Pullen, P. Guarnieri and B. Stierstorfer, ” Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies,” Scientific Reports, 2022. 3.3
  • [35] D. Bertsimas, G. A. Margonis, S. Sujichantararat, T. Boerner, Y. Ma, J. Wang, C. Kamphues, K. Sasaki, S. Tang, J. Gagniere, A. Dupré, M. I. Løes, D. Wagner, G. Stasinos, A. Macher-Beer, R. Burkhart, D. Morioka, K. Imai, V. Ardiles, J. M. O’Connor, T. M. Pawlik, G. Poultsides, H. Seeliger, K. Beyer, K. Kaczirek, P. Kornprat, F. N. Aucejo, E. d. Santibañes, H. Baba, I. Endo, P. E. Lønning, M. E. Kreis, M. J. Weiss, C. L. Wolfgang and M. D’Angelica, ” Using Artificial Intelligence to Find the Optimal Margin Width in Hepatectomy for Colorectal Cancer Liver Metastases,” JAMA Surgery, 2022. 3.3, 3
  • [36] M. Ranjan, A. Shukla, K. Soni, S. Varma, M. Kuliha and U. Singh, ” Cancer Prediction Using Random Forest and Deep Learning Techniques,” in International Conference on Communication Systems and Network Technologies (CSNT), Indore, India, 2022. 3.3, 3
  • [37] T. Han, J. Zhu, X. Chen, R. Chen, Y. Jiang, S. Wang, D. Xu, G. Shen, J. Zheng and C. Xu, ” Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer,”Cancer Cell International, vol. 22, 2022. 3.3, 3
  • [38] J. Wei, J. Cheng, D. Gu, F. Chai, N. Hong, Y. Wang and J. Tian, ” Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases,” Medical Physics, 2021. 3.3, 3
  • [39] T. He, J. N. Fong, L. W. Moore, C. F. Ezeana, D. Victor, M. Divatia, M. Vasquez, M. Ghobrial and S. T. Wong, ”An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer,” Computerized Medical Imaging and Graphics, vol. 89, 2021. 3.3, 3
Cite this article as:
  • Arefe Pourmajidian and Javad Vahidi, Deep Learning Techniques for Liver Cancer: A Survey on Early Prediction, Detection, and Prognosis of Metastasis and Survival, Communications in Combinatorics, Cryptography & Computer Science, 2024(1), PP.81–97, 2023
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