Role of artificial intelligence in gastrointestinal surgery

Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.

Past, present, and future perspectives of ultrasound-guided ablation of liver tumors: Where could artificial intelligence lead interventional oncology?

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.

Artificial intelligence in rectal cancer: What is the future?

Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.

Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions

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.

Building and evaluating an artificial intelligence algorithm: A practical guide for practicing oncologists

The use of machine learning and deep learning has enabled many applications, previously thought of as being impossible. Among all medical fields, cancer care is arguably the most significantly impacted, with precision medicine now truly being a possibility. The effect of these technologies, loosely known as artificial intelligence, is particularly striking in fields involving images (such as radiology and pathology) and fields involving large amounts of data (such as genomics). Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients. Underst-anding these new claims and technologies requires a deep understanding of the field. In this review, we provide an overview of the basis of deep learning. We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects, as well as evaluate algorithms presented to them.

Usefulness of artificial intelligence in early gastric cancer

Gastric cancer (GC) is a major cancer worldwide, with high mortality and morbidity. Endoscopy, important for the early detection of GC, requires trained skills, high-quality technologies, surveillance and screening programs. Early diagnosis allows a better prognosis, through surgical or curative endoscopic therapy. Magnified endoscopy with virtual chromoendoscopy remarkably improve the detection of early gastric cancer (EGC) when endoscopy is performed by expert endoscopists. Artificial intelligence (AI) has also been introduced to GC diagnostics to increase diagnostic efficiency. AI improves the early detection of gastric lesions because it supports the non-expert and experienced endoscopist in defining the margins of the tumor and the depth of infiltration. AI increases the detection rate of EGC, reduces the rate of missing tumors, and characterizes EGCs, allowing clinicians to make the best therapeutic decision, that is, one that ensures curability. AI has had a remarkable evolution in medicine in recent years, moving from the research phase to clinical practice. In addition, the diagnosis of GC has markedly progressed. We predict that AI will allow great evolution in the diagnosis and treatment of EGC by overcoming the variability in performance that is currently a limitation of chromoendoscopy.

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

BACKGROUND

In their everyday life, clinicians face an overabundance of biological indicators potentially helpful during a disease therapy. In this context, to be able to reliably identify a reduced number of those markers showing the ability of optimising the classification of treatment outcomes becomes a factor of vital importance to medical prognosis. In this work, we focus our interest in inflammatory bowel disease (IBD), a long-life threaten with a continuous increasing prevalence worldwide. In particular, IBD can be described as a set of autoimmune conditions affecting the gastrointestinal tract whose two main types are Crohn’s disease and ulcerative colitis.

AIM

To identify the minimal signature of microRNA (miRNA) associated with colorectal cancer (CRC) in patients with one chronic IBD.

METHODS

We provide a framework of well-established statistical and computational learning methods wisely adapted to reconstructing a CRC network leveraged to stratify these patients.

RESULTS

Our strategy resulted in an adjusted signature of 5 miRNAs out of approximately 2600 in Crohn’s Disease (resp. 8 in Ulcerative Colitis) with a percentage of success in patient classification of 82% (resp. 81%).

CONCLUSION

Importantly, these two signatures optimally balance the proportion between the number of significant miRNAs and their percentage of success in patients’ stratification.

Potential and role of artificial intelligence in current medical healthcare

Artificial intelligence (AI) is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings. The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services; and current advancements have led to a dramatic change in the healthcare system. However, many efficient applications are still in their initial stages, which need further evaluations to improve and develop these applications. Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services; but for this to be possible, a significant revision of medical education is needed to provide future leaders with the required competencies. This article reviews the potential and limitations of AI in healthcare, as well as the current medical application trends including healthcare administration, clinical decision assistance, patient health monitoring, healthcare resource allocation, medical research, and public health policy development. Also, future possibilities for further clinical and scientific practice were also summarized.

Artificial intelligence as a future in cancer surgery

Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making. Machine learning and deep learning (DL) are subfields of AI that are able to learn from experience in order to complete tasks. AI and its subfields, in particular DL, have been applied in numerous fields of medicine, especially in the cure of cancer. Computer vision (CV) system has improved diagnostic accuracy both in histopathology analyses and radiology. In surgery, CV has been used to design navigation system and robotic-assisted surgical tools that increased the safety and efficiency of oncological surgery by minimizing human error. By learning the basis of AI, surgeons can take part in this revolution to optimize surgical care of oncologic disease.

Artificial intelligence in colorectal cancer management

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.

Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma

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.

Artificial intelligence reveals roles of gut microbiota in driving human colorectal cancer evolution

With the rapid development of high-throughput sequencing and artificial intelligence (AI) techniques, gut mucosal microbiota begins to be recognized as critical drivers of human colorectal cancer (CRC). Various AI approaches have been designed to obtain effective information from enormous numbers of microbial cells residing in gut mucosal as well as cancer cells. These mainly include detection of microbial markers for early clinical diagnosis of stage-specific CRC, characterization of pathogenic bacterial activities via genomic and transcriptomic analyses, and prediction of interplay between bacterial drivers and host immune systems. Here I review the current progresses of AI applications in profiling gut microbiomes linked to CRC initiation and development. I further look forward to future AI research for improving our understanding of the roles of gut microbiota in CRC evolution.

Repairing the human with artificial intelligence in oncology

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.

How is artificial intelligence applied in solid tumor imaging?

How is artificial intelligence (AI) applied in solid tumor imaging? What is the essential value of AI for tumor precision diagnosis and can it wholly replace the human beings? Some opinions in this letter should be considered.

Application of retroperitoneal laparoscopy and robotic surgery in complex adrenal tumors

As a surgical method for the treatment of adrenal surgical diseases, laparoscopy has the advantages of small trauma, short operation time, less bleeding, and fast postoperative recovery. It is considered as the gold standard for the treatment of adrenal surgical diseases. Retroperitoneal laparoscopy is widely used because it does not pass through the abdominal cavity, does not interfere with internal organs, and has little effect on gastrointestinal function. However, complex adrenal tumors have the characteristics of large volume, compression of adjacent tissues, and invasion of surrounding tissues, so they are rarely treated by retroperitoneal laparoscopy. In recent years, with the development of laparoscopic technology and the progress of surgical technology, robotic surgery has been gradually applied to the surgical treatment of complex adrenal tumors. This paper reviews the clinical application of retroperitoneal laparoscopic surgery and robotic surgery in the treatment of complex adrenal tumors.

Therapeutic tumor vaccines — a rising star to benefit cancer patients

Malignant tumors are still a worldwide threat to human health. Tumor treatment strategies are constantly evolving, and the advent of tumor immunotherapy has brought up hope to many types of tumors, especially for those that are refractory to conventional therapies including surgery, radiotherapy, and chemotherapy. Tumor vaccines can initiate or amplify an anti-tumor immune response in tumor patients through active immunization, and therefore occupy an important position in tumor immunotherapy. The main types of tumor vaccines include tumor cell vaccines, dendritic cell vaccines, polypeptide vaccines and nucleic acid vaccines. Due to factors such as poor antigen selection and suppressive tumor microenvironment, earliest tumor vaccines on clinical trials failed to achieve satisfactory clinical effects. However, with the development of second-generation genome sequencing technologies and bioinformatics tools, it is possible to predict neoantigens generated by tumor-specific mutations and therefore prepare personalized vaccines. This article summarizes the global efforts in developing tumor vaccines and highlights several representative tumor vaccines in each category.

Advances in the application of artificial intelligence in solid tumor imaging

Early diagnosis and timely treatment are crucial in reducing cancer-related mortality. Artificial intelligence (AI) has greatly relieved clinical workloads and changed the current medical workflows. We searched for recent studies, reports and reviews referring to AI and solid tumors; many reviews have summarized AI applications in the diagnosis and treatment of a single tumor type. We herein systematically review the advances of AI application in multiple solid tumors including esophagus, stomach, intestine, breast, thyroid, prostate, lung, liver, cervix, pancreas and kidney with a specific focus on the continual improvement on model performance in imaging practice.

Artificial intelligence and colorectal cancer: How far can you go?

Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.

Cancer recognition of artificial intelligence

The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding AI neural networks and their application in therapeutics. A number of multilayered neural networks can recognize cancer through deep learning. It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated, which is one of the main points in this editorial. The automatic detection of cancer with computer-aided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks. The subtypes and stages of cancer, in terms of progression and metastasis, should be classified with AI for optimized therapeutics. The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.

Artificial intelligence for modeling uveal melanoma

Understanding of the cellular signaling pathways involved in cancer disease is of great importance. These complex biological mechanisms can be thoroughly revealed by their structure, dynamics, and control methods. Artificial intelligence offers rule-based models that favor the research of human signaling processes. In this paper, we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma. A language is described that allows us: (1) To define the system states and elements with their alterations; (2) To model the dynamics of the cellular system; and (3) To perform inference-based analysis with the logical tools of the language.

How can artificial intelligence and humans work together to fight against cancer?

This editorial will focus on and discuss growing artificial intelligence (AI) and the utilization of AI in human cancer therapy. The databases and big data related to genomes, genes, proteins and molecular networks are rapidly increasing all worldwide where information on human diseases, including cancer and infection resides. To overcome diseases, prevention and therapeutics are being developed with the abundant data analyzed by AI. AI has so much potential for handling considerable data, which requires some orientation and ambition. Appropriate interpretation of AI is essential for understanding disease mechanisms and finding targets for prevention and therapeutics. Collaboration with AI to extract the essence of cancer data and model intelligent networks will be explored. The utilization of AI can provide humans with a predictive future in disease mechanisms and treatment as well as prevention.

Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine

Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to convert raw input data into decision-making actions, like humans. AI programs are designed to make decisions, often using deep learning and computer-guided programs that analyze and process raw data into clinical decision making for effective treatment. New techniques for predicting cancer at an early stage are needed as conventional methods have poor accuracy and are not applicable to personalized medicine. AI has the potential to use smart, intelligent computer systems for image interpretation and early diagnosis of cancer. AI has been changing almost all the areas of the medical field by integrating with new emerging technologies. AI has revolutionized the entire health care system through innovative digital diagnostics with greater precision and accuracy. AI is capable of detecting cancer at an early stage with accurate diagnosis and improved survival outcomes. AI is an innovative technology of the future that can be used for early prediction, diagnosis and treatment of cancer.

Artificial intelligence and omics in cancer

Cancer is a major public health problem worldwide. Current predictions suggest that 13 million people will die each year from cancer by 2030. Thus, new ideas are urgently needed to change paradigms in the global fight against cancer. Over the last decades, artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline. Although emerging, a great potential is appreciated in AI to improve cancer diagnosis and prognosis, as well as to identify relevant therapeutics in the current era of personalized medicine. Developing pipelines connecting patient-generated health data easily translatable into clinical practice to assist clinicians in decision making represents a challenging but fascinating task. AI algorithms are mainly fueled by multi omics data which, in the case of cancer research, have been largely derived from international cancer programs, including The Cancer Genome Atlas (TCGA). Here, I briefly review some examples of supervised and unsupervised big data derived from TCGA programs and comment on how AI algorithms have been applied to improve the management of patients with cancer. In this context, Artificial Intelligence in Cancer journal was specifically launched to promote the development of this discipline, by serving as a forum to publish high-quality basic and clinical research articles in various fields of AI in oncology.

Management of cancer patients during the COVID-19 pandemic: A comprehensive review

The novel 2019 corona virus disease also called severe acute respiratory syndrome coronavirus 2 has caused a global pandemic and more than 2.5 million people have been affected globally with over 100000 deaths. The disease has caused an escalation in hospitalization with growing need for hospital beds and intensive care unit for severe cases. Recent evidence has shown that a significant proportion of cancer patients affected by the corona virus present with severe respiratory pneumonia-like illness with need for subsequent intensive care unit ventilation and higher mortality risk. This susceptibility may be due to the immunosuppressive state of patients with malignancy confounded by chemotherapy, immunotherapy and targeted therapy. Many solid tumors (lung cancer, pancreatic cancer) as well as hematological malignancies (leukemias) may require prompt diagnosis and treatment based on the disease aggression and progression. Many centers lack clear guideline on the management of cancer during the pandemic. The objective of this review is to synthesize the available literature and provide recommendations on the management of various soft tissue and hematological malignancies. The review will also assess the management guidelines for hospitalized cancer patients; cancer patients in the outpatient setting as well as available modalities for follow-up.

Impact of blurs on machine-learning aided digital pathology image analysis

BACKGROUND

Digital pathology image (DPI) analysis has been developed by machine learning (ML) techniques. However, little attention has been paid to the reproducibility of ML-based histological classification in heterochronously obtained DPIs of the same hematoxylin and eosin (HE) slide.

AIM

To elucidate the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs.

METHODS

We created paired DPIs by scanning 298 HE stained slides containing 584 tissues twice with a virtual slide scanner. The paired DPIs were analyzed by our ML-aided classification model. We defined non-flipped and flipped groups as the paired DPIs with concordant and discordant classification results, respectively. We compared differences in color and blur between the non-flipped and flipped groups by L1-norm and a blur index, respectively.

RESULTS

We observed discordant classification results in 23.1% of the paired DPIs obtained by two independent scans of the same microscope slide. We detected no significant difference in the L1-norm of each color channel between the two groups; however, the flipped group showed a significantly higher blur index than the non-flipped group.

CONCLUSION

Our results suggest that differences in the blur - not the color - of the paired DPIs may cause discordant classification results. An ML-aided classification model for DPI should be tested for this potential cause of the reduced reproducibility of the model. In a future study, a slide scanner and/or a preprocessing method of minimizing DPI blur should be developed.