An overview of the use of artificial neural networks in lung cancer research

Bertolaccini, Luca; Solli, Piergiorgio; Pardolesi, Alessandro; Pasini, Antonello

doi:10.21037/jtd.2017.03.157

Cited by 67 publications

(54 citation statements)

References 19 publications

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“…The efficiency of ANNs is also proved in [17], where the authors present an overview regarding the use of ANNs in lung cancer research. They also underline that even if the literature shows that these tools are suitable for clinical decision support, a strict cooperation between physician and biostatistician is mandatory in order to avoid inaccurate use of ANNs.…”

Section: Related Workmentioning

confidence: 99%

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Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

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The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.

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Section: Related Workmentioning

confidence: 99%

“…weighting matrices 0  , and  is the Euclidean norm of vector  . The second term in the objective function in (17), which weights vector U j , is added in order to prevent over-fitting.…”

mentioning

confidence: 99%

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

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show abstract

“…This allows them to classify cases based on data by assigning a numerical weight value to each input and adjusting them as they sample the data, effectively learning the optimal solution. The main advantages of ANNs include their high fault and failure tolerance, scalability and consistent generalisation ability, all of which allow them to effectively predict or classify new, fuzzy and unlearned data [100,101]. Additionally, they have been recently used to create panels of biomarkers that, when used in conjunction with each other, predict breast cancer [102].…”

Section: Supervised Learningmentioning

confidence: 99%

Discovery and application of immune biomarkers for hematological malignancies

Zafeiris

Vadakekolathu

Wagner

et al. 2017

Expert Review of Molecular Diagnostics

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Hematological malignancies originate and progress in primary and secondary lymphoid organs, where they establish a uniquely immune-suppressive tumour microenvironment. Although high-throughput transcriptomic and proteomic approaches are being employed to interrogate immune surveillance and escape mechanisms in patients with solid tumours, and to identify actionable targets for immunotherapy, our knowledge of the immunological landscape of hematological malignancies, as well as our understanding of the molecular circuits that underpin the establishment of immune tolerance, is not comprehensive. Areas covered: This article will discuss how multiplexed immunohistochemistry, flow cytometry/mass cytometry, proteomic and genomic techniques can be used to dynamically capture the complexity of tumour-immune interactions. Moreover, the analysis of multi-dimensional, clinically annotated data sets obtained from public repositories such as Array Express, TCGA and GEO is crucial to identify immune biomarkers, to inform the rational design of immune therapies and to predict clinical benefit in individual patients. We will also highlight how artificial neural network models and alternative methodologies integrating other algorithms can support the identification of key molecular drivers of immune dysfunction. Expert commentary: High-dimensional technologies have the potential to enhance our understanding of immune-cancer interactions and will support clinical decision making and the prediction of therapeutic benefit from immune-based interventions.

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“…Authors claimed the follows parameters of the model: sensitivity 98.3%, the specificity 99.5% and the accuracy 96.9%. However, as was underlined by [3] the use of ANN presented in the medical literature is not always performed in an accurate manner. In the effect, reliability of the results presented raises doubts.…”

Section: Res Med Eng Scimentioning

confidence: 99%

“…Therefore, it is extremely important to be influenced by the most recent achievements in broadly understood medicine. First of all, patient care based on evidence-based medicine [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%