{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:49:39Z","timestamp":1782103779239,"version":"3.54.5"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>TP53 mutation is associated with poor prognosis and resistance to androgen deprivation therapy in prostate cancer (PCa). This study investigated whether stretched exponential model (SEM) and diffusion kurtosis imaging (DKI) could serve as a non-invasive predictor of TP53 mutation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 84 patients with PCa who underwent radical prostatectomy. Clinical, clinicopathological, and quantitative imaging parameters were compared between the two groups using the independent-samples t test, chi-square test, or Fisher\u2019s exact test, as appropriate. Univariate and multivariate binary logistic regression analyses were performed to identify factors associated with TP53 mutation. Five logistic regression models were constructed: model 1 (based on DKI), model 2 (based on SEM), model 3 (based on mono-exponential model), model 4 (based on DKI, SEM and mono-exponential model) and model 5 (based on mean kurtosis (MK) value). Receiver operating characteristic analysis, DeLong test, Akaike information criterion (AIC), decision curve analysis (DCA), calibration curves, Hosmer\u2013Lemeshow test, and bootstrap internal validation were used to evaluate model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared with the TP53 wild-type group, the TP53 mutated group showed significantly lower apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC), and mean diffusivity (MD) values and a significantly higher MK value (all\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05). In univariate logistic regression, MK, MD, DDC, and ADC values were significantly associated with TP53 mutation (all\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05). In multivariate analysis, only MK value (OR\u00a0=\u00a019.329,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.004) remained independent predictors. Among the four models, Model 4 achieved the highest AUC (0.889, 95% CI: 0.821\u20130.958), followed by Model 1 (0.881, 95% CI: 0.811\u20130.952) and Model 5 (0.860, 95% CI: 0.784\u20130.937). However, no significant difference was observed among Models 1, 4 and 5 (all\n                      <jats:italic>p<\/jats:italic>\n                      &amp;gt;0.05). The AIC of model 5 was 79.739, which was well calibrated. The net benefit of DCA was robust, and the AUC of optimistic correction was 0.858, which indicated good internal stability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MK value is an independent predictor of TP53 mutation in PCa. MK value-based model demonstrated good diagnostic performance and outperformed the SEM and mono-exponential model, suggesting that MK value may serve as a promising preoperative, noninvasive tool for assessing TP53 mutation status in PCa.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1854578","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:32:32Z","timestamp":1782102752000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Noninvasive prediction of TP53 mutation in prostate cancer based on advanced diffusion weighted imaging"],"prefix":"10.3389","volume":"16","author":[{"given":"Juan","family":"Chen","sequence":"first","affiliation":[{"name":"Medical Imaging Center, Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital)","place":["Shenzhen, 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