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World Journal of Gastroenterology Nov 2020Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial intelligence (AI) is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables. AI and deep learning are increasingly employed in several topics of liver cancer research, including diagnosis, pathology, and prognosis.
AIM
To assess the role of AI in the prediction of survival following HCC treatment.
METHODS
A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords "artificial intelligence", "deep learning" and "hepatocellular carcinoma" (and synonyms). The specific research question was formulated following the patient (patients with HCC), intervention (evaluation of HCC treatment using AI), comparison (evaluation without using AI), and outcome (patient death and/or tumor recurrence) structure. English language articles were retrieved, screened, and reviewed by the authors. The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. Data were extracted and collected in a database.
RESULTS
Among the 598 articles screened, nine papers met the inclusion criteria, six of which had low-risk rates of bias. Eight articles were published in the last decade; all came from eastern countries. Patient sample size was extremely heterogenous ( = 11-22926). AI methodologies employed included artificial neural networks (ANN) in six studies, as well as support vector machine, artificial plant optimization, and peritumoral radiomics in the remaining three studies. All the studies testing the role of ANN compared the performance of ANN with traditional statistics. Training cohorts were used to train the neural networks that were then applied to validation cohorts. In all cases, the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.
CONCLUSION
AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis. Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.
Topics: Artificial Intelligence; Carcinoma, Hepatocellular; Humans; Liver Neoplasms; Neoplasm Recurrence, Local; Prognosis; Reproducibility of Results
PubMed: 33268955
DOI: 10.3748/wjg.v26.i42.6679 -
International Journal of Molecular... Aug 2020Melanoma is the fourth most common type of cancer diagnosed in Australians after breast, prostate, and colorectal cancers. While there has been substantial progress in...
Melanoma is the fourth most common type of cancer diagnosed in Australians after breast, prostate, and colorectal cancers. While there has been substantial progress in the treatment of cancer in general, malignant melanoma, in particular, is resistant to existing medical therapies requiring an urgent need to develop effective treatments with lesser side effects. Several studies have shown that "cannabinoids", the major compounds of the plant, can reduce cell proliferation and induce apoptosis in melanoma cells. Despite prohibited use of in most parts of the world, in recent years there have been renewed interests in exploiting the beneficial health effects of the plant-derived compounds. Therefore, the aim of this study was in the first instance to review the evidence from in vivo studies on the effects of cannabinoids on melanoma. Systematic searches were carried out in PubMed, Embase, Scopus, and ProQuest Central databases for relevant articles published from inception. From a total of 622 potential studies, six in vivo studies assessing the use of cannabinoids for treatment of melanoma were deemed eligible for the final analysis. The findings revealed cannabinoids, individually or combined, reduced tumor growth and promoted apoptosis and autophagy in melanoma cells. Further preclinical and animal studies are required to determine the underlying mechanisms of cannabinoids-mediated inhibition of cancer-signaling pathways. Well-structured, randomized clinical studies on cannabinoid use in melanoma patients would also be required prior to cannabinoids becoming a viable and recognized therapeutic option for melanoma treatment in patients.
Topics: Animals; Antineoplastic Agents, Phytogenic; Apoptosis; Cannabinoids; Cell Proliferation; Clinical Trials as Topic; Disease Models, Animal; Humans; Melanocytes; Melanoma; Mice; Skin Neoplasms; Survival Analysis; Tumor Burden; Tumor Cells, Cultured; Melanoma, Cutaneous Malignant
PubMed: 32839414
DOI: 10.3390/ijms21176040 -
Drug Resistance Updates : Reviews and... May 2020Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving,...
Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving, showing that this area of biomedical research attracts great interest in the scientific community. The current review explores promising recent approaches that have been developed with the aim of circumventing or overcoming MDR. Encouraging results have been obtained in the investigation of the MDR-modulating properties of various classes of natural compounds and their analogues. Inhibition of P-gp or downregulation of its expression have proven to be the main mechanisms by which MDR can be surmounted. The use of hybrid molecules that are able to simultaneously interact with two or more cancer cell targets is currently being explored as a means to circumvent drug resistance. This strategy is based on the design of hybrid compounds that are obtained either by merging the structural features of separate drugs, or by conjugating two drugs or pharmacophores via cleavable/non-cleavable linkers. The approach is highly promising due to the pharmacokinetic and pharmacodynamic advantages that can be achieved over the independent administration of the two individual components. However, it should be stressed that the task of obtaining successful multivalent drugs is a very challenging one. The conjugation of anticancer agents with nitric oxide (NO) donors has recently been developed, creating a particular class of hybrid that can combat tumor drug resistance. Appropriate NO donors have been shown to reverse drug resistance via nitration of ABC transporters and by interfering with a number of metabolic enzymes and signaling pathways. In fact, hybrid compounds that are produced by covalently attaching NO-donors and antitumor drugs have been shown to elicit a synergistic cytotoxic effect in a variety of drug resistant cancer cell lines. Another strategy to circumvent MDR is based on nanocarrier-mediated transport and the controlled release of chemotherapeutic drugs and P-gp inhibitors. Their pharmacokinetics are governed by the nanoparticle or polymer carrier and make use of the enhanced permeation and retention (EPR) effect, which can increase selective delivery to cancer cells. These systems are usually internalized by cancer cells via endocytosis and accumulate in endosomes and lysosomes, thus preventing rapid efflux. Other modalities to combat MDR are described in this review, including the pharmaco-modulation of acridine, which is a well-known scaffold in the development of bioactive compounds, the use of natural compounds as means to reverse MDR, and the conjugation of anticancer drugs with carriers that target specific tumor-cell components. Finally, the outstanding potential of in silico structure-based methods as a means to evaluate the ability of antitumor drugs to interact with drug transporters is also highlighted in this review. Structure-based design methods, which utilize 3D structural data of proteins and their complexes with ligands, are the most effective of the in silico methods available, as they provide a prediction regarding the interaction between transport proteins and their substrates and inhibitors. The recently resolved X-ray structure of human P-gp can help predict the interaction sites of designed compounds, providing insight into their binding mode and directing possible rational modifications to prevent them from becoming P-gp drug substrates. In summary, although major efforts were invested in the search for new tools to combat drug resistant tumors, they all require further implementation and methodological development. Further investigation and progress in the abovementioned strategies will provide significant advances in the rational combat against cancer MDR.
Topics: ATP Binding Cassette Transporter, Subfamily B, Member 1; ATP-Binding Cassette Transporters; Acridines; Antineoplastic Agents; Antineoplastic Agents, Immunological; Antineoplastic Combined Chemotherapy Protocols; Drug Design; Drug Resistance, Neoplasm; Glycoconjugates; Humans; Nanoparticles; Neoplasms; Nitric Oxide; Plant Preparations; Polymers; Technology, Pharmaceutical
PubMed: 32087558
DOI: 10.1016/j.drup.2020.100682 -
Frontiers in Pharmacology 2019Glucomannan, long recognized as the active ingredient of the traditional Chinese medicinal herb Konjac glucomannan, is a naturally occurring polysaccharide existing in... (Review)
Review
Glucomannan, long recognized as the active ingredient of the traditional Chinese medicinal herb Konjac glucomannan, is a naturally occurring polysaccharide existing in certain plant species and fungi. Due to its special property to also serve as a dietary supplement, glucomannan has been widely applied in clinic to lower body weight and circulation cholesterol level and to treat constipation, diabetes, and arterial sclerosis. Besides the regulatory role engaged with gastroenterological and metabolic syndrome, recently, its therapeutic effect and the underlying mechanisms in treating cancerous diseases have been appreciated by mounting researches. The present review aims to emphasize the multifaceted aspects of how glucomannan exerts its anti-tumor function.
PubMed: 31507423
DOI: 10.3389/fphar.2019.00930