Radiomics and deep learning in lung cancer

Avanzo, Michele; Stancanello, Joseph; Pirrone, G.; Sartor, G.

doi:10.1007/s00066-020-01625-9

Cited by 181 publications

(118 citation statements)

References 81 publications

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“…In previous studies, many researchers also said that ANN model was used to analyze different clinical data, among which convolution neural network was used to analyze medical imaging and pathology [39][40][41][42]. However, there are few studies using ANN model to analyze bioinformatics data.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma

Zhanhu¹,

d²,

Akakuru³

et al. 2021

Preprint

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The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma

Zhanhu¹,

d²,

Akakuru³

et al. 2021

Preprint

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“…Despite the importance and success of radiomics in the fields discussed in the previous sections, the development of the alternative deep learning based methods performing feature extraction similar to radiomics is still a main research focus for many groups. Avanzo et al (82) pointed out that deep learning can facilitate automated radiomic feature extraction without the need to design a set of handcrafted radiomic features. However, they also noted that the explainability of deep learning models should be taken into account during model development, and further research should be conducted in that area.…”

Section: Radiomics As a Tool For Predicting Therapeutic Responsementioning

confidence: 99%

Radiomics and artificial intelligence in lung cancer screening

Bińczyk¹,

Prazuch²,

Bozek³

et al. 2021

Transl Lung Cancer Res

107

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Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first-and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions.Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

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“…In the relatively recent radiomics approach, quantitative analysis of radiological images (mainly CT [37][38][39], magnetic resonance imaging (MRI) [40][41][42], and positron emission tomography (PET) [43] images, but also ultrasounds [44], mammograms [45], and radiography) by extraction of a large number of image features (up to a few hundred or thousands) can be combined with ML classifiers to produce prognostic and predictive models [39].…”

Section: Imagingmentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

Self Cite

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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Radiomics and deep learning in lung cancer

Cited by 181 publications

References 81 publications

Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma

Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma

Radiomics and artificial intelligence in lung cancer screening

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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