{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:08:49Z","timestamp":1789013329443,"version":"build-2803163510"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"15","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,8,4]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">We investigated whether radiomic scores (RadScores), computed from high\u2010dimensional quantitative features extracted from cardiac magnetic resonance parametric mapping images, can predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">This retrospective analysis included 74 patients with nonischemic dilated cardiomyopathy who received optimized medical therapy and underwent cardiac magnetic resonance. LVRR was defined as a left ventricular ejection fraction \u226540% with \u226510% increase at least 180\u2009days after cardiac magnetic resonance. RadScores were calculated for each case as linear combinations of selected radiomic features, with coefficients derived from least absolute shrinkage and selection operator\u2013penalized logistic regression on native longitudinal relaxation time (T1) and extracellular volume (ECV) fraction maps. Predictive performance for LVRR was evaluated using logistic regression and receiver operating characteristic analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Patients with LVRR (n=31, 42%) had higher T1\u2010RadScore (0.72\u00b11.52 versus \u22121.54\u00b12.66;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.0001), higher ECV\u2010RadScore (0.24\u00b10.65 versus \u22120.16\u00b10.64;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =0.005), lower mean\u2010ECV (31.5%\u00b13.8% versus 34.9%\u00b16.0%;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =0.004), and lower extent of late gadolinium enhancement (0.19\u00b10.58 versus 2.99\u00b16.5;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =0.047) than those without LVRR. The area under the receiver operating characteristic curve for predicting LVRR was 0.834 (95% CI, 0.716\u20130.909) for the T1\u2010RadScore and 0.718 (95% CI, 0.573\u20130.827) for the ECV\u2010RadScore. Adding T1\u2010 and ECV\u2010RadScore combination to the extent of late gadolinium enhancement model significantly improved the area under the receiver operating characteristic curve from 0.618 to 0.903 (\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Radiomic scores extracted from native T1 and ECV maps may enhance LVRR prediction and provide incremental value beyond traditional late gadolinium enhancement assessment in patients with dilated cardiomyopathy.<\/jats:p>\n                  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