Pub Date : 2024-07-18DOI: 10.35713/aic.v5.i1.96690
P. Tombesi, Andrea Cutini, Valentina Grasso, Francesca Di Vece, Ugo Politti, Eleonora Capatti, Florence Labb, Stefano Petaccia, Sergio Sartori
The first ablation procedures for small hepatocellular carcinomas were percutaneous ethanol injection under ultrasound (US) guidance. Later, radiofrequency ablation was shown to achieve larger coagulation areas than percutaneous ethanol injection and became the most used ablation technique worldwide. In the past decade, microwave ablation systems have achieved larger ablation areas than radiofrequency ablation, suggesting that the 3-cm barrier could be broken in the treatment of liver tumors. Likewise, US techniques to guide percutaneous ablation have seen important progress. Contrast-enhanced US (CEUS) can define and target the tumor better than US and can assess the size of the ablation area after the procedure, which allows immediate retreatment of the residual tumor foci. Furthermore, fusion imaging fuses real-time US images with computed tomography or magnetic resonance imaging with significant improvements in detecting and targeting lesions with low conspicuity on CEUS. Recently, software powered by artificial intelligence has been developed to allow three-dimensional segmentation and reconstruction of the anatomical structures, aiding in procedure planning, assessing ablation completeness, and targeting the residual viable foci with greater precision than CEUS. Hopefully, this could lead to the ablation of tumors up to 5-7 cm in size.
小肝细胞癌的首批消融术是在超声波（US）引导下进行的经皮乙醇注射。后来，射频消融被证明比经皮乙醇注射能获得更大的凝固区域，并成为全球最常用的消融技术。在过去的十年中，微波消融系统比射频消融获得了更大的消融面积，这表明在治疗肝脏肿瘤时，3厘米的障碍可能会被打破。同样，用于引导经皮消融的 US 技术也取得了重大进展。对比增强 US（CEUS）能比 US 更好地确定和瞄准肿瘤，并能在术后评估消融区域的大小，从而可以立即对残余肿瘤灶进行再治疗。此外，融合成像将实时 US 图像与计算机断层扫描或磁共振成像融合在一起，在检测和定位 CEUS 上不明显的病灶方面有显著改善。最近开发的人工智能软件可以对解剖结构进行三维分割和重建，从而帮助制定手术计划、评估消融的完整性，并比 CEUS 更精确地定位残留病灶。希望这能帮助消融 5-7 厘米大小的肿瘤。
{"title":"Past, present, and future perspectives of ultrasound-guided ablation of liver tumors: Where could artificial intelligence lead interventional oncology?","authors":"P. Tombesi, Andrea Cutini, Valentina Grasso, Francesca Di Vece, Ugo Politti, Eleonora Capatti, Florence Labb, Stefano Petaccia, Sergio Sartori","doi":"10.35713/aic.v5.i1.96690","DOIUrl":"https://doi.org/10.35713/aic.v5.i1.96690","url":null,"abstract":"The first ablation procedures for small hepatocellular carcinomas were percutaneous ethanol injection under ultrasound (US) guidance. Later, radiofrequency ablation was shown to achieve larger coagulation areas than percutaneous ethanol injection and became the most used ablation technique worldwide. In the past decade, microwave ablation systems have achieved larger ablation areas than radiofrequency ablation, suggesting that the 3-cm barrier could be broken in the treatment of liver tumors. Likewise, US techniques to guide percutaneous ablation have seen important progress. Contrast-enhanced US (CEUS) can define and target the tumor better than US and can assess the size of the ablation area after the procedure, which allows immediate retreatment of the residual tumor foci. Furthermore, fusion imaging fuses real-time US images with computed tomography or magnetic resonance imaging with significant improvements in detecting and targeting lesions with low conspicuity on CEUS. Recently, software powered by artificial intelligence has been developed to allow three-dimensional segmentation and reconstruction of the anatomical structures, aiding in procedure planning, assessing ablation completeness, and targeting the residual viable foci with greater precision than CEUS. Hopefully, this could lead to the ablation of tumors up to 5-7 cm in size.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lakshmi Nagendra, Joseph M Pappachan, C. Fernandez
The diagnosis and management of thyroid cancer is fraught with challenges despite the advent of innovative diagnostic, surgical, and chemotherapeutic modalities. Challenges like inaccuracy in prognostication, uncertainty in cytopathological diagnosis, trouble in differentiating follicular neoplasms, intra-observer and inter-observer variability on ultrasound imaging preclude personalised treatment in thyroid cancer. Artificial intelligence (AI) is bringing a paradigm shift to the healthcare, powered by quick advancement of the analytic techniques. Several recent studies have shown remarkable progress in thyroid cancer diagnostics based on AI-assisted algorithms. Application of AI techniques in thyroid ultrasonography and cytopathology have shown remarkable impro-vement in sensitivity and specificity over the traditional diagnostic modalities. AI has also been explored in the development of treatment algorithms for indeterminate nodules and for prognostication in the patients with thyroid cancer. The benefits of high repeatability and straightforward implementation of AI in the management of thyroid cancer suggest that it holds promise for clinical application. Limited clinical experience and lack of prospective validation studies remain the biggest drawbacks. Developing verified and trustworthy algorithms after extensive testing and validation using prospective, multi-centre trials is crucial for the future use of AI in the pipeline of precision medicine in the management of thyroid cancer.
{"title":"Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions","authors":"Lakshmi Nagendra, Joseph M Pappachan, C. Fernandez","doi":"10.35713/aic.v4.i1.1","DOIUrl":"https://doi.org/10.35713/aic.v4.i1.1","url":null,"abstract":"The diagnosis and management of thyroid cancer is fraught with challenges despite the advent of innovative diagnostic, surgical, and chemotherapeutic modalities. Challenges like inaccuracy in prognostication, uncertainty in cytopathological diagnosis, trouble in differentiating follicular neoplasms, intra-observer and inter-observer variability on ultrasound imaging preclude personalised treatment in thyroid cancer. Artificial intelligence (AI) is bringing a paradigm shift to the healthcare, powered by quick advancement of the analytic techniques. Several recent studies have shown remarkable progress in thyroid cancer diagnostics based on AI-assisted algorithms. Application of AI techniques in thyroid ultrasonography and cytopathology have shown remarkable impro-vement in sensitivity and specificity over the traditional diagnostic modalities. AI has also been explored in the development of treatment algorithms for indeterminate nodules and for prognostication in the patients with thyroid cancer. The benefits of high repeatability and straightforward implementation of AI in the management of thyroid cancer suggest that it holds promise for clinical application. Limited clinical experience and lack of prospective validation studies remain the biggest drawbacks. Developing verified and trustworthy algorithms after extensive testing and validation using prospective, multi-centre trials is crucial for the future use of AI in the pipeline of precision medicine in the management of thyroid cancer.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123858101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anupama Ramachandran, Deeksha Bhalla, K. Rangarajan, R. Pramanik, Subhashis Banerjee, Chetan Arora
{"title":"Building and evaluating an artificial intelligence algorithm: A practical guide for practicing oncologists","authors":"Anupama Ramachandran, Deeksha Bhalla, K. Rangarajan, R. Pramanik, Subhashis Banerjee, Chetan Arora","doi":"10.35713/aic.v3.i3.42","DOIUrl":"https://doi.org/10.35713/aic.v3.i3.42","url":null,"abstract":"","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao-Ming Hung, Hongfeng Shi, Po-Huang Lee, Chao-Sung Chang, K. Rau, Hui-Ming Lee, Cheng-Hao Tseng, S. Pei, K. Tsai, C. Chiu
{"title":"Potential and role of artificial intelligence in current medical healthcare","authors":"Chao-Ming Hung, Hongfeng Shi, Po-Huang Lee, Chao-Sung Chang, K. Rau, Hui-Ming Lee, Cheng-Hao Tseng, S. Pei, K. Tsai, C. Chiu","doi":"10.35713/aic.v3.i1.1","DOIUrl":"https://doi.org/10.35713/aic.v3.i1.1","url":null,"abstract":"","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121194294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Burati, F. Tagliabue, Adriana Lomonaco, M. Chiarelli, M. Zago, Gerardo Cioffi, U. Cioffi
{"title":"Artificial intelligence as a future in cancer surgery","authors":"M. Burati, F. Tagliabue, Adriana Lomonaco, M. Chiarelli, M. Zago, Gerardo Cioffi, U. Cioffi","doi":"10.35713/aic.v3.i1.11","DOIUrl":"https://doi.org/10.35713/aic.v3.i1.11","url":null,"abstract":"","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133252170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications. applications the field of This mini-review to open a window on the attempts being made on the application of artificial intelligence in the scientific and clinical research of colorectal cancer by summarizing the most evident results. Our aim is not to draw definitive conclusions but to stimulate the interest of researchers in the application of these new technologies, which seem to be able to offer valuable help in the near future.
{"title":"Artificial intelligence in colorectal cancer management","authors":"P. Cianci, E. Restini","doi":"10.35713/aic.v2.i6.79","DOIUrl":"https://doi.org/10.35713/aic.v2.i6.79","url":null,"abstract":"Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications. applications the field of This mini-review to open a window on the attempts being made on the application of artificial intelligence in the scientific and clinical research of colorectal cancer by summarizing the most evident results. Our aim is not to draw definitive conclusions but to stimulate the interest of researchers in the application of these new technologies, which seem to be able to offer valuable help in the near future.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134360974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence reveals roles of gut microbiota in driving human colorectal cancer evolution","authors":"Xueting Wan","doi":"10.35713/aic.v2.i5.69","DOIUrl":"https://doi.org/10.35713/aic.v2.i5.69","url":null,"abstract":"","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122550295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation (LT) for liver cancer and cirrhosis. Artificial neural network (ANN) has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology, where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure, the donor (graft characteristics), and the recipient comorbidities. In the specific case of LT, ANN models have been developed mainly to predict survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis, highlighting potential strengths of the method to forecast this serious postoperative complication.
{"title":"Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma","authors":"L. Bredt, L. Peres","doi":"10.35713/aic.v2.i5.51","DOIUrl":"https://doi.org/10.35713/aic.v2.i5.51","url":null,"abstract":"Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation (LT) for liver cancer and cirrhosis. Artificial neural network (ANN) has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology, where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure, the donor (graft characteristics), and the recipient comorbidities. In the specific case of LT, ANN models have been developed mainly to predict survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis, highlighting potential strengths of the method to forecast this serious postoperative complication.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125047116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.
{"title":"Repairing the human with artificial intelligence in oncology","authors":"Ian Morilla","doi":"10.35713/aic.v2.i5.60","DOIUrl":"https://doi.org/10.35713/aic.v2.i5.60","url":null,"abstract":"Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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