Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer

The establishment of the precision diagnosis and treatment system and the advent of the digital intelligence era have not only deepened people's understanding of liver cancer but also continuously improved the diagnosis and treatment methods of liver cancer. Cutting-edge computer technology represented by artificial intelligence (AI) has been used in the prediction, screening, diagnosis, treatment, and rehabilitation of liver cancer. The rise of AI has given new vitality to liver surgery, as well as individualized treatment experience and greater healing opportunities for patients. We focus on summarizing the latest applications and developments of AI in liver cancer diagnosis and treatment from six aspects: virtual assistants, medical imaging diagnosis, adjuvant therapy, risk and treatment response prediction, drug development and testing, and postoperative rehabilitation management. Especially in the two major aspects of medical imaging diagnosis and adjuvant therapy, the development and achievements of AI are gratifying. Finally, we put forward a view on the current challenges of AI in the precise diagnosis and treatment of liver cancer and how to promote its development, and we have a prospect for the future development direction.

[1]  A. Madabhushi,et al.  Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.

[2]  P. Schirmacher,et al.  Hepatocellular carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[3]  J. Xing,et al.  Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma , 2020, EBioMedicine.

[4]  Jie Tian,et al.  Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound , 2020, European Radiology.

[5]  Jaewoo Lim,et al.  Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare , 2019, Nanomaterials.

[6]  Muhammad Attique Khan,et al.  Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM , 2020, Microscopy research and technique.

[7]  Xingyu Zhao,et al.  Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images , 2020, Frontiers in Oncology.

[8]  W. Lu,et al.  Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data , 2020, Frontiers in Oncology.

[9]  Yixin Chen,et al.  Liver disease screening based on densely connected deep neural networks , 2019, Neural Networks.

[10]  E. Goceri,et al.  Deep learning based classification of facial dermatological disorders , 2020, Comput. Biol. Medicine.

[11]  Jing Zhang,et al.  Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. , 2019, Journal of hepatology.

[12]  L. Xing,et al.  Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy. , 2020, Medical physics.

[13]  Hiroshi Yagi,et al.  Organ reengineering through development of a transplantable recellularized liver graft using decellularized liver matrix , 2010, Nature Medicine.

[14]  Xuequn Shang,et al.  Deep learning‐based classification and mutation prediction from histopathological images of hepatocellular carcinoma , 2020, Clinical and translational medicine.

[15]  G. Rosman,et al.  Artificial Intelligence in Surgery: Promises and Perils , 2018, Annals of surgery.

[16]  J. Balibrea,et al.  5G‐assisted telementored surgery , 2019, The British journal of surgery.

[17]  O. Abe,et al.  Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. , 2017, Radiology.

[18]  L. Schwartz,et al.  Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules , 2019, European Radiology.

[19]  Jung-Hwan Yoon,et al.  Real-time US-CT/MR fusion imaging for percutaneous radiofrequency ablation of hepatocellular carcinoma. , 2017, Journal of hepatology.

[20]  Bulat Ibragimov,et al.  Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT , 2018, Medical physics.

[21]  D. Azoulay,et al.  Robot-assisted laparoscopic liver resection: A review. , 2016, Journal of visceral surgery.

[22]  John Salvatier,et al.  When Will AI Exceed Human Performance? Evidence from AI Experts , 2017, ArXiv.

[23]  Evgin Goceri,et al.  CapsNet topology to classify tumours from brain images and comparative evaluation , 2020, IET Image Process..

[24]  Samar Damiati,et al.  Acoustic and hybrid 3D-printed electrochemical biosensors for the real-time immunodetection of liver cancer cells (HepG2). , 2017, Biosensors & bioelectronics.

[25]  M. Sugimoto,et al.  Intraoperative 3D Hologram Support With Mixed Reality Techniques in Liver Surgery. , 2019, Annals of surgery.

[26]  Michael L. Volk,et al.  Practice Management. , 2018, The American journal of gastroenterology.

[27]  T. Decaens,et al.  Liver immunotolerance and hepatocellular carcinoma: Patho-physiological mechanisms and therapeutic perspectives. , 2017, European journal of cancer.

[28]  Rui Yao,et al.  Three-dimensional printing: review of application in medicine and hepatic surgery , 2016, Cancer biology & medicine.

[29]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[30]  Jijo Paul,et al.  Image-guided microwave thermoablation of hepatic tumours using novel robotic guidance: an early experience , 2015, European Radiology.

[31]  J. Duncan,et al.  Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI , 2019, European Radiology.

[32]  Kartik Shankar,et al.  Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier , 2019, Comput. Electr. Eng..

[33]  Jhi-Joung Wang,et al.  Comparison of Models for Predicting Quality of Life After Surgical Resection of Hepatocellular Carcinoma: a Prospective Study , 2018, Journal of Gastrointestinal Surgery.

[34]  H. Greenspan,et al.  CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites: Initial Results. , 2017, Academic radiology.

[35]  A. Tsung,et al.  Prediction of Perioperative Mortality of Cadaveric Liver Transplant Recipients During Their Evaluations , 2014, Transplantation.

[36]  J. Mitani,et al.  Novel 3-dimensional virtual hepatectomy simulation combined with real-time deformation. , 2015, World journal of gastroenterology.

[37]  Anubhav,et al.  GA and PSO hybrid algorithm for ANN training with application in Medical Diagnosis , 2019, 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS).

[38]  S. Hayami,et al.  Indocyanine green fluorescence imaging techniques and interventional radiology during laparoscopic anatomical liver resection (with video) , 2018, Surgical Endoscopy.

[39]  N. de’Angelis,et al.  Laparoscopic vs. Open Liver Resection for Hepatocellular Carcinoma of Cirrhotic Liver: A Case–Control Study , 2014, World Journal of Surgery.

[40]  J. Ioannidis,et al.  Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies , 2020, BMJ.

[41]  Puja Bharti,et al.  Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model , 2018, Ultrasonic imaging.

[42]  Hari Mohan Pandey,et al.  Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection , 2020, Neural Computing and Applications.

[43]  Michael Eisenstein,et al.  Artificial organs: Honey, I shrunk the lungs , 2015, Nature.

[44]  C. Liang,et al.  Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast‐enhanced MR images , 2017, Journal of magnetic resonance imaging : JMRI.

[45]  A. Luciani,et al.  Diagnosis of focal liver lesions from ultrasound using deep learning. , 2019, Diagnostic and interventional imaging.

[46]  P Neuhaus,et al.  Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis , 2001, Hepatology.

[47]  Fucang Jia,et al.  Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks , 2015 .

[48]  Kumardeep Chaudhary,et al.  Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer , 2017, Clinical Cancer Research.

[49]  Manal M. Hassan,et al.  A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. , 2019, Radiology. Artificial intelligence.

[50]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[51]  Dinggang Shen,et al.  Correction to "Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning" , 2017, IEEE Trans. Biomed. Eng..

[52]  Hafiz Tayyab Rauf,et al.  Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks , 2021, Personal and ubiquitous computing.

[53]  D. Calvisi,et al.  Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma , 2020, Nature Reviews Gastroenterology & Hepatology.

[54]  Muhammad Sharif,et al.  Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework , 2021, Pattern Recognit. Lett..

[55]  Hung-Wen Chiu,et al.  Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation. , 2017, Journal of the Formosan Medical Association = Taiwan yi zhi.

[56]  Cristiano Quintini,et al.  Three‐dimensional print of a liver for preoperative planning in living donor liver transplantation , 2013, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[57]  A. Zhu,et al.  Systemic Therapy for Advanced Hepatocellular Carcinoma: ASCO Guideline. , 2020, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[58]  W. Jia,et al.  Precise hepatectomy in the intelligent digital era , 2020, International journal of biological sciences.

[59]  X. Qiu,et al.  [Bioinformatics on vascular invasion markers in hepatocellular carcinoma via Big-Data analysis]. , 2017, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[60]  Muhammad Attique Khan,et al.  Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine , 2020, Computers & Electrical Engineering.

[61]  Seifedine Kadry,et al.  Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection , 2020, IEEE Access.

[62]  Evgin Goceri,et al.  Analysis of Deep Networks with Residual Blocks and Different Activation Functions: Classification of Skin Diseases , 2019, 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[63]  Evgin Goceri,et al.  A comparative performance evaluation of various approaches for liver segmentation from SPIR images , 2015 .

[64]  Muhammad Shaheen,et al.  Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review , 2020, Computer Modeling in Engineering & Sciences.

[65]  Sang Joon Park,et al.  Personalized 3D-Printed Transparent Liver Model Using the Hepatobiliary Phase MRI: Usefulness in the Lesion-by-Lesion Imaging-Pathologic Matching of Focal Liver Lesions—Preliminary Results , 2019, Investigative radiology.

[66]  Waqar Mehmood,et al.  Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review. , 2020, Current medical imaging.

[67]  Ryanne A. Brown,et al.  Impact of a deep learning assistant on the histopathologic classification of liver cancer , 2020, npj Digital Medicine.

[68]  N. Halama Machine learning for tissue diagnostics in oncology: brave new world , 2019, British Journal of Cancer.

[69]  Dan Stoianovici,et al.  Robotic Assisted Radio-Frequency Ablation of Liver Tumors - Randomized Patient Study , 2005, MICCAI.

[70]  Maria Klara Wolters,et al.  Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia , 2016, Health Informatics J..

[71]  Tanzila Saba,et al.  Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture , 2020, Microscopy research and technique.

[72]  M. Lee,et al.  Fusion imaging of real-time ultrasonography with CT or MRI for hepatic intervention , 2014, Ultrasonography.

[73]  Wei Zhao,et al.  Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging , 2019, European Radiology.

[74]  Claire M. Lochner,et al.  Monitoring of Vital Signs with Flexible and Wearable Medical Devices , 2016, Advanced materials.

[75]  Oumeima Laifa,et al.  Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides , 2020, Hepatology.

[76]  F. Zhu,et al.  Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study , 2019, EBioMedicine.

[77]  Steven J. M. Jones,et al.  Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma , 2017, Cell.

[78]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[79]  Sang-Hoon Lee,et al.  3D liver models on a microplatform: well-defined culture, engineering of liver tissue and liver-on-a-chip. , 2015, Lab on a chip.

[80]  F. Piscaglia,et al.  Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. , 2010, Journal of hepatology.

[81]  Merlijn Hutteman,et al.  The clinical use of indocyanine green as a near‐infrared fluorescent contrast agent for image‐guided oncologic surgery , 2011, Journal of surgical oncology.

[82]  Rafal Scherer,et al.  Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists , 2020, Diagnostics.

[83]  Evgin Göçeri,et al.  Artificial Neural Network Based Abdominal Organ Segmentations: A Review , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[84]  M. Kersten,et al.  Erratum: Waldenström's macroglobulinaemia: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up (Annals of oncology : official journal of the European Society for Medical Oncology (2018) 29 Suppl 4 (iv41-iv50)) , 2018 .

[85]  Wei Zhu,et al.  3D bioprinting of functional tissue models for personalized drug screening and in vitro disease modeling. , 2018, Advanced drug delivery reviews.

[86]  Jie Tian,et al.  Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. , 2019, European journal of radiology.

[87]  Ryanne A. Brown,et al.  Impact of a deep learning assistant on the histopathologic classification of liver cancer. , 2020, NPJ digital medicine.

[88]  M. Bachtiar,et al.  Comprehensive review of Hepatitis B Virus‐associated hepatocellular carcinoma research through text mining and big data analytics , 2018, Biological reviews of the Cambridge Philosophical Society.

[89]  Sohee Jeon,et al.  Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective , 2020, Progress in Retinal and Eye Research.

[90]  J. Shindoh,et al.  How Has Virtual Hepatectomy Changed the Practice of Liver Surgery?: Experience of 1194 Virtual Hepatectomy Before Liver Resection and Living Donor Liver Transplantation , 2017, Annals of surgery.

[91]  Bulat Ibragimov,et al.  Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes , 2019, IEEE Journal of Biomedical and Health Informatics.

[92]  Muhammad Younus Javed,et al.  Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review. , 2020, Current medical imaging.

[93]  Evgin Göçeri,et al.  A Neural Network Based Kidney Segmentation from MR Images , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[94]  Sergey Plis,et al.  Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. , 2016, Molecular pharmaceutics.

[95]  M. Viergever,et al.  Automatic classification of focal liver lesions based on MRI and risk factors , 2019, PloS one.

[96]  Jun Zhang,et al.  Application of three-dimensional visualization technique in preoperative planning of progressive hilar cholangiocarcinoma. , 2018, American journal of translational research.

[97]  James A Scott,et al.  Neural network evaluation of PET scans of the liver: a potentially useful adjunct in clinical interpretation. , 2011, Radiology.

[98]  S. Fan,et al.  Long-Term Survival Analysis of Pure Laparoscopic Versus Open Hepatectomy for Hepatocellular Carcinoma in Patients With Cirrhosis: A Single-Center Experience , 2013, Annals of surgery.

[99]  Mohammed Elmogy,et al.  Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images , 2017, Arabian Journal for Science and Engineering.

[100]  Chandra Thapa,et al.  Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy , 2020, Comput. Biol. Medicine.

[101]  Y. Yen,et al.  Pan-Asian adapted ESMO Clinical Practice Guidelines for the management of patients with intermediate and advanced/relapsed hepatocellular carcinoma: a TOS-ESMO initiative endorsed by CSCO, ISMPO, JSMO, KSMO, MOS and SSO. , 2020, Annals of oncology : official journal of the European Society for Medical Oncology.

[102]  Takeaki Ishizawa,et al.  Real‐time identification of liver cancers by using indocyanine green fluorescent imaging , 2009, Cancer.

[103]  T. Rawson,et al.  Artificial intelligence can improve decision-making in infection management , 2019, Nature Human Behaviour.

[104]  Wen-Hsien Ho,et al.  Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery , 2012, PloS one.

[105]  Wei Wang,et al.  CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation , 2019, Cancer Imaging.

[106]  Dong Li 5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare? , 2019, Precision clinical medicine.

[107]  U. Rajendra Acharya,et al.  Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features , 2018, Comput. Biol. Medicine.

[108]  Xiao Zheng,et al.  A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. , 2018, Clinical hemorheology and microcirculation.

[109]  Feipei Lai,et al.  Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods , 2014, Comput. Methods Programs Biomed..

[110]  G. Klintmalm,et al.  Recipient characteristics and morbidity and mortality after liver transplantation. , 2018, Journal of hepatology.

[111]  May D. Wang,et al.  –Omic and Electronic Health Record Big Data Analytics for Precision Medicine , 2017, IEEE Transactions on Biomedical Engineering.

[112]  Yin Zongyi,et al.  Immunotherapy for hepatocellular carcinoma. , 2019, Cancer letters.

[113]  V. Laudone,et al.  Robotic Liver Resection: A Case-Matched Comparison , 2016, World Journal of Surgery.

[114]  Deepak Gaur,et al.  An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine , 2020, Int. J. Syst. Assur. Eng. Manag..

[115]  J. Duncan,et al.  Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features , 2019, European Radiology.

[116]  Christian Schuetz,et al.  Regeneration and orthotopic transplantation of a bioartificial lung , 2010, Nature Medicine.

[117]  C. Yeong,et al.  Robotic-assisted thermal ablation of liver tumours , 2015, European Radiology.

[118]  J. Milbrandt,et al.  Typed Versus Voice Recognition for Data Entry in Electronic Health Records: Emergency Physician Time Use and Interruptions , 2014, The western journal of emergency medicine.

[119]  Evgin Goceri,et al.  Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning , 2019, 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[120]  Ho‐Seong Han,et al.  Laparoscopic liver resection for hepatocellular carcinoma in cirrhotic patients: 10-year single-center experience , 2016, Surgical Endoscopy.

[121]  Esther Durá,et al.  A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction , 2017, Pattern Analysis and Applications.

[122]  G. Torzilli,et al.  Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence , 2020, Liver international : official journal of the International Association for the Study of the Liver.

[123]  B. Song,et al.  Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8+ T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography , 2019, Annals of Surgical Oncology.

[124]  Jun Mitani,et al.  A novel three-dimensional print of liver vessels and tumors in hepatectomy , 2017, Surgery Today.

[125]  Ilias Gatos,et al.  A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound. , 2015, Medical physics.

[126]  Julius Chapiro,et al.  The Role of Artificial Intelligence in Interventional Oncology: A Primer. , 2019, Journal of vascular and interventional radiology : JVIR.

[127]  P. Galle,et al.  Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study , 2020, Liver international : official journal of the International Association for the Study of the Liver.

[128]  J. Marrero,et al.  Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma , 2013, The American Journal of Gastroenterology.

[129]  Aaron C. Abajian,et al.  Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept. , 2018, Journal of vascular and interventional radiology : JVIR.

[130]  Jens Rittscher,et al.  Precision immunoprofiling by image analysis and artificial intelligence , 2018, Virchows Archiv.

[131]  A. A. Terentiev,et al.  Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine , 2021, Biomedicines.

[132]  Sung Min Kim,et al.  Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. , 2015, Bio-medical materials and engineering.

[133]  Muhammad Attique Khan,et al.  Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification , 2021, Comput. Electr. Eng..

[134]  D. Gu,et al.  Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT , 2019, European Radiology.

[135]  Sima Ajami Use of speech-to-text technology for documentation by healthcare providers. , 2016, The National medical journal of India.

[136]  R. Zheng,et al.  Fusion imaging techniques and contrast-enhanced ultrasound for thermal ablation of hepatocellular carcinoma – A prospective randomized controlled trial , 2019, International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group.

[137]  Weiqi Zhang,et al.  Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China , 2020, World Journal of Surgery.

[138]  Costin Teodor Streba,et al.  Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. , 2012, World journal of gastroenterology.

[139]  S. Fong,et al.  Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. , 2019, Cancer letters.

[140]  A. Vahrmeijer,et al.  Image-guided cancer surgery using near-infrared fluorescence , 2013, Nature Reviews Clinical Oncology.

[141]  Jan Sylwester Witowski,et al.  Cost-effective, personalized, 3D-printed liver model for preoperative planning before laparoscopic liver hemihepatectomy for colorectal cancer metastases , 2017, International Journal of Computer Assisted Radiology and Surgery.

[142]  Imran Ashraf,et al.  StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification , 2020, IEEE Access.

[143]  Evgin Göçeri,et al.  A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function , 2013 .

[144]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[145]  Samuel Kadoury,et al.  Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases. , 2019, Radiology. Artificial intelligence.

[146]  Suchi Saria,et al.  Better medicine through machine learning: What’s real, and what’s artificial? , 2018, PLoS medicine.

[147]  J. Piette,et al.  Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management , 2015, Circulation.

[148]  Thomas H. Payne,et al.  Using voice to create hospital progress notes: Description of a mobile application and supporting system integrated with a commercial electronic health record , 2018, J. Biomed. Informatics.

[149]  Leo Joskowicz,et al.  Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies , 2017, International Journal of Computer Assisted Radiology and Surgery.