Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Unraveling the complexities of colorectal cancer and its promising therapies - An updated review

Colorectal cancer (CRC) continues to be a global health concern, necessitating further research into its complex biology and innovative treatment approaches. The etiology, pathogenesis, diagnosis, and treatment of colorectal cancer are summarized in this thorough review along with recent developments. The multifactorial nature of colorectal cancer is examined, including genetic predispositions, environmental factors, and lifestyle decisions. The focus is on deciphering the complex interactions between signaling pathways such as Wnt/β-catenin, MAPK, TGF-β as well as PI3K/AKT that participate in the onset, growth, and metastasis of CRC. There is a discussion of various diagnostic modalities that span from traditional colonoscopy to sophisticated molecular techniques like liquid biopsy and radiomics, emphasizing their functions in early identification, prognostication, and treatment stratification. The potential of artificial intelligence as well as machine learning algorithms in improving accuracy as well as efficiency in colorectal cancer diagnosis and management is also explored. Regarding therapy, the review provides a thorough overview of well-known treatments like radiation, chemotherapy, and surgery as well as delves into the newly-emerging areas of targeted therapies as well as immunotherapies. Immune checkpoint inhibitors as well as other molecularly targeted treatments, such as anti-epidermal growth factor receptor (anti-EGFR) as well as anti-vascular endothelial growth factor (anti-VEGF) monoclonal antibodies, show promise in improving the prognosis of colorectal cancer patients, in particular, those suffering from metastatic disease. This review focuses on giving readers a thorough understanding of colorectal cancer by considering its complexities, the present status of treatment, and potential future paths for therapeutic interventions. Through unraveling the intricate web of this disease, we can develop a more tailored and effective approach to treating CRC.

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Revolutionizing Gastrointestinal Disorder Management: Cutting-Edge Advances and Future Prospects

In recent years, remarkable strides have been made in the management of gastrointestinal disorders, transforming the landscape of patient care and outcomes. This article explores the latest breakthroughs in the field, encompassing innovative diagnostic techniques, personalized treatment approaches, and novel therapeutic interventions. Additionally, this article emphasizes the use of precision medicine tailored to individual genetic and microbiome profiles, and the application of artificial intelligence in disease prediction and monitoring. This review highlights the dynamic progress in managing conditions such as inflammatory bowel disease, gastroesophageal reflux disease, irritable bowel syndrome, and gastrointestinal cancers. By delving into these advancements, we offer a glimpse into the promising future of gastroenterology, where multidisciplinary collaborations and cutting-edge technologies converge to provide more effective, patient-centric solutions for individuals grappling with gastrointestinal disorders.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.

Establishment of disulfidptosis-related LncRNA signature as biomarkers in colon adenocarcinoma

PURPOSE

Metabolic reprogramming is a hallmark of cancer and plays a key role in precision oncology treatment. Long non-coding RNAs (lncRNAs) regulate cancer cell behavior, including metabolism. Disulfidptosis, a newly identified form of regulated cell death triggered by glucose starvation, has yet to be fully understood in colon adenocarcinoma (COAD). This study aimed to confirm the existence and role of disulfidptosis in COAD and identify disulfidptosis-related lncRNAs that may be targeted to induce disulfidptosis in COAD.

METHODS

PI and F-actin staining were used to observe disulfidptosis in COAD cell lines. Disulfidptosis-related lncRNAs were identified based on the expression of disulfidptosis-associated genes in the TCGA-COAD database. A four-lncRNA signature for disulfidptosis was established. Subsequently, loss-of-function assays explored the roles of AC013652.1 and MCM3AP-AS1 in disulfidptosis.

RESULTS

Disulfidptosis was observed in COAD cells under glucose starvation and could be reversed by agents that prevent disulfide stress, such as dithiothreitol (DTT) and tris-(2-carboxyethyl)-phosphine (TCEP). The prognostic value of disulfidptosis-associated genes in COAD patients was confirmed, with higher expression indicating longer survival. A disulfidptosis-related lncRNA signature comprising four lncRNAs was established based on the expression of these genes. Among these, AC013652.1 and MCM3AP-AS1 predicted worse prognoses. Furthermore, inhibiting AC013652.1 or MCM3AP-AS1 increased disulfidptosis-associated gene expression and cellular death, which could be reversed by DTT and TCEP.

CONCLUSIONS

This study provides hitherto undocumented evidence of the existence of disulfidptosis and the prognostic value of disulfidptosis-associated genes in COAD. Importantly, we identified lncRNAs AC013652.1 and MCM3AP-AS1, which suppress disulfidptosis and may serve as potential therapeutic targets for COAD.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps

(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Negative Samples for Improving Object Detection-A Case Study in AI-Assisted Colonoscopy for Polyp Detection

Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).

Intraprocedure Artificial Intelligence Alert System for Colonoscopy Examination

Colonoscopy is a valuable tool for preventing and reducing the incidence and mortality of colorectal cancer. Although several computer-aided colorectal polyp detection and diagnosis systems have been proposed for clinical application, many remain susceptible to interference problems, including low image clarity, unevenness, and low accuracy for the analysis of dynamic images; these drawbacks affect the robustness and practicality of these systems. This study proposed an intraprocedure alert system for colonoscopy examination developed on the basis of deep learning. The proposed system features blurred image detection, foreign body detection, and polyp detection modules facilitated by convolutional neural networks. The training and validation datasets included high-quality images and low-quality images, including blurred images and those containing folds, fecal matter, and opaque water. For the detection of blurred images and images containing folds, fecal matter, and opaque water, the accuracy rate was 96.2%. Furthermore, the study results indicated a per-polyp detection accuracy of 100% when the system was applied to video images. The recall rates for high-quality image frames and polyp image frames were 95.7% and 92%, respectively. The overall alert accuracy rate and the false-positive rate of low quality for video images obtained through per-frame analysis were 95.3% and 0.18%, respectively. The proposed system can be used to alert colonoscopists to the need to slow their procedural speed or to perform flush or lumen inflation in cases where the colonoscope is being moved too rapidly, where fecal residue is present in the intestinal tract, or where the colon has been inadequately distended.

A Prognostic Model for Colon Adenocarcinoma Patients Based on Ten Amino Acid Metabolism Related Genes

Background

Amino acid metabolism plays a vital role in cancer biology. However, the application of amino acid metabolism in the prognosis of colon adenocarcinoma (COAD) has not yet been explored. Here, we construct an amino acid metabolism-related risk model to predict the survival outcome of COAD and improve clinical decision making.

Methods

The RNA-sequencing-based transcriptome for 524 patients with COAD from The Cancer Genome Atlas (TCGA) was selected as a training set. The integrated Gene Expression Omnibus (GEO) dataset with 1,430 colon cancer samples was used for validation. Differential expression of amino acid metabolism-related genes (AAMRGs) was identified for prognostic gene selection. Univariate cox regression analysis, LASSO-penalized Cox regression analysis, and multivariate Cox regression analysis were applied to construct a prognostic risk model. Moreover, the correlation between risk score and microsatellite instability, immunotherapy response, and drug sensitivity were analyzed.

Results

A prognostic signature was constructed based on 10 AAMRGs, including ASPG, DUOX1, GAMT, GSR, MAT1A, MTAP, PSMD12, RIMKLB, RPL3L, and RPS17. Patients with COAD were divided into high-risk and low-risk group based on the medianrisk score. Univariate and multivariate Cox regression analysis revealed that AAMRG-related signature was an independent risk factor for COAD. Moreover, COAD patients in the low-risk group were more sensitive to immunotherapy targeting PD-1 and CTLA-4.

Conclusion

Our study constructed a prognostic signature based on 10 AAMRGs, which could be used to build a novel prognosis model and identify potential drug candidates for the treatment of COAD.

Magnetic Levitation of Personalized Nanoparticle-Protein Corona as an Effective Tool for Cancer Detection

Unprecedented opportunities for early stage cancer detection have recently emerged from the characterization of the personalized protein corona (PC), i.e., the protein cloud that surrounds nanoparticles (NPs) upon exposure to a patients’ bodily fluids. Most of these methods require “direct characterization” of the PC., i.e., they necessitate protein isolation, identification, and quantification. Each of these steps can introduce bias and affect reproducibility and inter-laboratory consistency of experimental data. To fulfill this gap, here we develop a nanoparticle-enabled blood (NEB) test based on the indirect characterization of the personalized PC by magnetic levitation (MagLev). The MagLev NEB test works by analyzing the levitation profiles of PC-coated graphene oxide (GO) NPs that migrate along a magnetic field gradient in a paramagnetic medium. For the test validation, we employed human plasma samples from 15 healthy individuals and 30 oncological patients affected by four cancer types, namely breast cancer, prostate cancer, colorectal cancer, and pancreatic ductal adenocarcinoma (PDAC). Over the last 15 years prostate cancer, colorectal cancer, and PDAC have continuously been the second, third, and fourth leading sites of cancer-related deaths in men, while breast cancer, colorectal cancer, and PDAC are the second, third and fourth leading sites for women. This proof-of-concept investigation shows that the sensitivity and specificity of the MagLev NEB test depend on the cancer type, with the global classification accuracy ranging from 70% for prostate cancer to an impressive 93.3% for PDAC. We also discuss how this tool could benefit from several tunable parameters (e.g., the intensity of magnetic field gradient, NP type, exposure conditions, etc.) that can be modulated to optimize the detection of different cancer types with high sensitivity and specificity.

Performance of Convolutional Neural Networks for Polyp Localization on Public Colonoscopy Image Datasets

Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten public colonoscopy image datasets and analyze them in the context of the results of other 20 state-of-the-art publications using the same datasets. The F1-score of our recently published model was 0.88 when evaluated on a private test partition, i.e., intra-dataset testing, but it decayed, on average, by 13.65% when tested on ten public datasets. In the published research, the average intra-dataset F1-score is 0.91, and we observed that it also decays in the inter-dataset setting to an average F1-score of 0.83.

Artificial intelligence applications in gastroenterology: steps ahead

Artificial intelligence (AI) applications are used in gastroenterology for automatic imaging diagnostic methods such as ultrasonography, computer tomography, magnetic resonance imaging, but also in endoscopy, capsule endoscopy and biopsy followed by automatic digital pathology evaluation. The accuracy of AI-based systems is superior to human expertise. Furthermore, in reality, a very small percentage of the patients are being investigated by a human expert in endoscopy, so implementing AI in this investigation would only increase the diagnostic accuracy. The existence of an unimaginable number of digital images and different types of medical information made possible the analysis and training of convolutional neural network (CNN), which consists of multilayers of artificial neural networks (ANN) with step-by-step minimal processing, creating a fundamental resource for any AI-based system to learn by itself how to automatically perform medical tasks, which were performed only by human experts in the past. The main objectives for AI applications used in gastroenterology are to improve the medical procedures with enhanced precision, to reduce the number of medical errors and to perform repetitive tasks.