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Assessments from 537 readers interpreting 239 normal mammograms were used to categorise cases as 120 <jats:italic>difficult-to-interpret<\/jats:italic> and 119 <jats:italic>easy-to-interpret<\/jats:italic> based on cases having the highest and lowest difficulty scores, respectively. Using lattice- and squared-based approaches, 34 handcrafted GRFs per image were extracted and normalised. Three classifiers were constructed: (i) <jats:italic>CC<\/jats:italic> and (ii) <jats:italic>MLO<\/jats:italic> using the GRFs from corresponding craniocaudal and mediolateral oblique images only, based on the random forest technique for distinguishing <jats:italic>difficult-<\/jats:italic> from <jats:italic>easy-to-interpret<\/jats:italic> NCs, and (iii) <jats:italic>CC<\/jats:italic>\u2009+\u2009<jats:italic>MLO<\/jats:italic> using the median predictive scores from both <jats:italic>CC<\/jats:italic> and <jats:italic>MLO<\/jats:italic> models. Useful GRFs for the <jats:italic>CC<\/jats:italic> and <jats:italic>MLO<\/jats:italic> models were recognised using a scree test. The <jats:italic>CC<\/jats:italic> and <jats:italic>MLO<\/jats:italic> models were trained and validated using the leave-one-out-cross-validation. The models\u2019 performances were assessed by the AUC and compared using the DeLong test. A Kruskal\u2013Wallis test was used to examine if the 34 GRFs differed between <jats:italic>difficult-<\/jats:italic> and <jats:italic>easy-to-interpret<\/jats:italic> NCs and if difficulty level based on the traditional breast density (BD) categories differed among 115 <jats:italic>low-BD<\/jats:italic> and 124 <jats:italic>high<\/jats:italic>-<jats:italic>BD<\/jats:italic> NCs. The <jats:italic>CC<\/jats:italic>\u2009+\u2009<jats:italic>MLO<\/jats:italic> model achieved higher performance (0.71 AUC) than the individual <jats:italic>CC<\/jats:italic> and <jats:italic>MLO<\/jats:italic> model alone (0.66 each), but statistically non-significant difference was found (all <jats:italic>p<\/jats:italic>\u2009&gt;\u2009<jats:italic>0.05<\/jats:italic>). Six GRFs were identified to be valuable in describing <jats:italic>difficult-to-interpret<\/jats:italic> NCs. Twenty features, when compared between <jats:italic>difficult-<\/jats:italic> and <jats:italic>easy-to-interpret<\/jats:italic> NCs, differed significantly (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). No statistically significant difference was observed in difficulty between <jats:italic>low-<\/jats:italic> and <jats:italic>high-BD<\/jats:italic> NCs (<jats:italic>p<\/jats:italic>\u2009=\u20090.709). GRF mammographic analysis can predict <jats:italic>difficult-to-interpret<\/jats:italic> NCs.\n<\/jats:p>","DOI":"10.1007\/s10278-023-00836-7","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:02:17Z","timestamp":1685466137000},"page":"1541-1552","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2891-9217","authenticated-orcid":false,"given":"Somphone","family":"Siviengphanom","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziba","family":"Gandomkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah J.","family":"Lewis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick C.","family":"Brennan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"836_CR1","doi-asserted-by":"crossref","unstructured":"Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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