Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Avanzo, Michele; Porzio, Massimiliano; Lorenzon, Leda; Milan, Lisa; Sghedoni, Roberto; Russo, Giorgio; Massafra, Raffaella; Fanizzi, Annarita; Barucci, Andrea; Ardu, V.; Branchini, M.; Giannelli, Marco; Gallio, E.; Cilla, Savino; Tangaro, Sabina; Lombardi, Angela; Pirrone, G.; Martin, E. De; Giuliano, Alessia; Belmonte, Gina; Russo, S.; Rampado, Osvaldo; Mettivier, Giovanni

doi:10.1016/j.ejmp.2021.04.010

Cited by 54 publications

(35 citation statements)

References 175 publications

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“…Since the beginning of the digital era, the amount of data produced has considerably increased, even in the field of medicine [5]. At the same time, the interest in the application of ML and DL approaches in medical data analysis has grown enormously [6][7][8][9]. A specific difficulty that is encountered in developing ML and DL analysis tools for medical applications is that the training processes often require large amounts of annotated data.…”

Section: Introductionmentioning

confidence: 99%

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Ubaldi

Valenti²,

Borgese

et al. 2021

Physica Medica

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Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC).We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra-and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets.We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine).According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

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Section: Introductionmentioning

confidence: 99%

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Ubaldi

Valenti²,

Borgese

et al. 2021

Physica Medica

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“…Cancer was the subject of 25% of AI studies and MRI was the most commonly used diagnostic method for radiomics. The authors note a significant increase in interest regarding AI in medical imaging, especially after 2018 [50].…”

Section: Radiomics In Breast Cancer Researchmentioning

confidence: 99%

Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

Mireștean

Volovăţ

Iancu

et al. 2022

JCM

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In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator’s eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.

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“…Avanzo et al [5] presented a survey of research articles in AI applications in medical imaging produced in 2015-2020 by authors with scientific affiliation in Italy, also in collaboration with the task group "AI for Medical Physics" of the Italian Association of Medical Physics (AIFM). This represents the first attempt to review this research field in Italy by the medical physics community, with an analysis of 168 studies.…”

Section: The Medical Physicist Curricular and Professional Programme To Include Artificial Intelligencementioning

confidence: 99%

Focus issue: Artificial intelligence in medical physics

Zanca¹,

Avanzo

Colgan

et al. 2021

Physica Medica

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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Cited by 54 publications

References 175 publications

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

Focus issue: Artificial intelligence in medical physics

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