2021
DOI: 10.1007/s11547-021-01431-y
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Automatic PI-RADS assignment by means of formal methods

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Cited by 23 publications
(17 citation statements)
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“…This type of learning requires large amounts of training data which has been pre-labeled ("curated") by a human operator. Once the training of the model is completed, a different dataset is used to test its performance (testing data) [264][265][266][267][268][269][270][271][272][273][274][275][276][277][278]. In unsupervised learning, the model classifies noncurated data by using the algorithm to identify features within the dataset that can be grouped and analyzed further to reach a specific outcome [279][280][281][282][283][284][285][286][287][288].…”
Section: Artificial Intelligence Radiomics and Pancreatic Cancermentioning
confidence: 99%
“…This type of learning requires large amounts of training data which has been pre-labeled ("curated") by a human operator. Once the training of the model is completed, a different dataset is used to test its performance (testing data) [264][265][266][267][268][269][270][271][272][273][274][275][276][277][278]. In unsupervised learning, the model classifies noncurated data by using the algorithm to identify features within the dataset that can be grouped and analyzed further to reach a specific outcome [279][280][281][282][283][284][285][286][287][288].…”
Section: Artificial Intelligence Radiomics and Pancreatic Cancermentioning
confidence: 99%
“…The assessment of textural characteristics, obtained by radiological images, which depend on mathematical analysis, such as histogram analysis, is called radiomics [ 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 ]. This approach is captivating since it should allow to extract biological data from the radiological images [ 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 ] without an invasive approach, reducing costs and time and avoiding any risk for the patients. For several tumors, radiomic analyses have already provided an accurate evaluation of biology, allowing the identification of indices correlated with clinical outcomes [ 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 ].…”
Section: Common Postoperative Complicationsmentioning
confidence: 99%
“…Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status [ 9 , 10 , 11 , 12 , 13 , 14 ]. Using standard of care images that are usually obtained in a clinical setting, Radiomics analysis is a cost-effective and highly feasible implement for clinical decision support, providing prognostic and/or predictive biomarkers which enables a fast, low-cost, and repeatable tool for longitudinal monitoring [ 15 , 16 , 17 , 18 , 19 , 20 ]. Even though individual features may correlate with genomic data, so-called radiogenomics, or clinical outcomes, the impact of radiomics is increased when the data are processed using machine learning techniques.…”
Section: Introductionmentioning
confidence: 99%