Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

Kirienko, Margarita; Sollini, Martina; Silvestri, Giorgia; Mognetti, Serena; Voulaz, Emanuele; Antunovic, Lidija; Rossi, Alexia; Antiga, Luca; Chiti, Arturo

doi:10.1155/2018/1382309

Cited by 68 publications

(40 citation statements)

References 15 publications

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“…Also, an unbiased reference standard, not always easy to obtain, should be chosen to ensure AI model reliability. The combined radiomics/AI strategy is at its early stages [179–183] and the complementary role of radiomics and AI techniques should be addressed [184]. Which is the best image mining approach is still an open question.…”

Section: Discussionmentioning

confidence: 99%

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Sollini

Antunovic

Chiti

et al. 2019

Eur J Nucl Med Mol Imaging

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211

156

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PurposeThe aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.MethodsAmong the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.ResultsOverall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.ConclusionsThe results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.Electronic supplementary materialThe online version of this article (10.1007/s00259-019-04372-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Sollini

Antunovic

Chiti

et al. 2019

Eur J Nucl Med Mol Imaging

Self Cite

211

156

View full text Add to dashboard Cite

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“…In particular, medical image analysis using DL opened a new door into CAD. In recent years, convolutional neural networks (CNNs) have been used to detect and classify a range of diseases from cancer to neurological disorders [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Yağış

García

Citi

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateof-the-art convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimer's Disease (AD) and Parkinson's Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying cross-validation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.

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“…A CNN algorithm based on FDG PET/CT images was developed to automatically classify Tstaging of lung cancer in 94 patients, with an accuracy of 87 % and 69 % in the training and test sets, respectively. The poor classification performance might result from the insufficient sample size and the choice of using clinical staging instead of the pathological one [74].…”

Section: Thoracic Neoplasmsmentioning

confidence: 99%

Artiﬁcial intelligence applications for oncological positron emission tomography imaging

Liu

Cheng

et al. 2021

European Journal of Radiology

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Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PETbased AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

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Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

Cited by 68 publications

References 15 publications

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Artiﬁcial intelligence applications for oncological positron emission tomography imaging

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