2020
DOI: 10.3390/app10051718
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Abstract: Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack … Show more

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Cited by 43 publications
(31 citation statements)
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“…Our results indicate the importance that these techniques may have the future in the medical decision making, together with the possibilities in terms of monitoring and early detection of recurrence that are reflected in the results of studies, such as Zhou et al, 2017 [34] and Bianconi et al, 2020 [35]. Therefore, it is essential to establish a common methodological criterion to obtain the best results that are possible and bring radiomics closer to the daily hospital clinic [35]. Regarding the best observed parameters, in our case, the texture features combining wavelet and Haralick's coefficients energy, correlation, contrast, homogeneity, and entropy (WCF dm vector with d = 1 pixels and m = 5) are clearly superior than the other texture descriptors.…”
Section: Discussionsupporting
confidence: 60%
See 1 more Smart Citation
“…Our results indicate the importance that these techniques may have the future in the medical decision making, together with the possibilities in terms of monitoring and early detection of recurrence that are reflected in the results of studies, such as Zhou et al, 2017 [34] and Bianconi et al, 2020 [35]. Therefore, it is essential to establish a common methodological criterion to obtain the best results that are possible and bring radiomics closer to the daily hospital clinic [35]. Regarding the best observed parameters, in our case, the texture features combining wavelet and Haralick's coefficients energy, correlation, contrast, homogeneity, and entropy (WCF dm vector with d = 1 pixels and m = 5) are clearly superior than the other texture descriptors.…”
Section: Discussionsupporting
confidence: 60%
“…Our results indicate the importance that these techniques may have the future in the medical decision making, together with the possibilities in terms of monitoring and early detection of recurrence that are reflected in the results of studies, such as Zhou et al, 2017 [34] and Bianconi et al, 2020 [35]. Therefore, it is essential to establish a common methodological criterion to obtain the best results that are possible and bring radiomics closer to the daily hospital clinic [35].…”
Section: Discussionmentioning
confidence: 67%
“…The recent literature has consistently emphasized the potentially useful role that shape and texture features from PET/CT could play in the characterisation of suspicious pulmonary nodules. Still, the validity and implications of these results need to be understood better before SPN radiomics can be translated into clinical practice [13,16].…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, quantitative extraction of imaging features from medical scans (“radiomics” [ 13 , 14 , 15 ]) has attracted widespread interest as a possible means to discriminate between benign vs. malignant SPN [ 16 ]. The rationale behind radiomics is to leverage on that fraction of image information which may have clinical relevance but go unnoticed to the human eye [ 17 ].…”
Section: Introductionmentioning
confidence: 99%
“…The typical workflow in radiomics is rather independent of the underlying disease and consists of six sequential steps (acquisition, pre-processing, segmentation, feature extraction, post-processing, data analysis) [ 98 ]. Many recent studies have consistently emphasized the potential advantages of FDG-PET/CT radiomics in lung cancer [ 99 ]. The results available in the literature are undoubtedly promising, but they also need to be considered with care.…”
Section: Fdg-pet/ct Radiomics and Radiogenomicsmentioning
confidence: 99%