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We aimed to evaluate three-dimensional, multiscale, multimodel DWI parameters within intratumoral and peritumoral volumes of interest for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Seventy patients with HCC, including 42 with MVI, were prospectively enrolled. We calculated parameters from monoexponential, intravoxel incoherent motion, diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), and continuous-time random-walk models within the tumor and in 3-mm (P3) and 5-mm (P5) peritumoral rims. Features showing differences on univariable analysis were further screened using logistic regression with variance inflation factor filtering to build combined models. Diagnostic performance was assessed by areas under the receiver operating characteristic curve (AUROC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Four intratumoral diffusion-coefficient\u2013related metrics showed significant differences between groups. In the peritumoral regions, 12 parameters differed between groups, including diffusion-coefficient\u2013related indices and heterogeneity-related metrics. Among single parameters, P3_DKI_\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sub>app<\/jats:sub>\n                      achieved the best discrimination for MVI (AUROC\u2009=\u20090.713); P5_DKI_\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sub>app<\/jats:sub>\n                      and Tumor_SEM_DDC were selected as independent imaging predictors. After integrating alpha-fetoprotein (AFP) and tumor size, the combined model (Cli-IntraPeriDWI) yielded the best performance (AUROC\u2009=\u20090.844). SHAP analysis indicated that higher AFP, larger tumor size, higher P5_DKI_\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sub>app<\/jats:sub>\n                      , and lower Tumor_SEM_DDC were associated with increased MVI risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Peritumoral DWI offers multiple biomarkers and outperforms intratumoral metrics for MVI prediction. An interpretable model integrating peritumoral and intratumoral DWI with clinical factors enables accurate, noninvasive preoperative MVI risk stratification in HCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      Can multiscale, multimodel peritumoral DWI metrics improve preoperative prediction of MVI in HCC compared with intratumoral DWI metrics?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      Compared with intratumoral DWI, peritumoral DWI offered more potential imaging biomarkers and superior diagnostic capability for the preoperative diagnosis of MVI in HCC.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Relevance statement<\/jats:italic>\n                      <\/jats:bold>\n                      By integrating intratumoral and peritumoral metrics with clinical risk factors, we developed a model achieving optimal performance and transparent decision interpretability for MVI assessment, supporting preoperative prognostic evaluation and individualized treatment planning in HCC.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00817-9","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T14:12:45Z","timestamp":1792764765000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preoperative prediction of HCC microvascular invasion by multimodel peritumoral diffusion MRI"],"prefix":"10.1186","volume":"10","author":[{"given":"Yunfei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiejun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianling","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7342-0159","authenticated-orcid":false,"given":"Mengsu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"817_CR1","doi-asserted-by":"publisher","first-page":"785","DOI":"10.4329\/wjr.v8.i9.785","volume":"8","author":"V Baliyan","year":"2016","unstructured":"Baliyan V, Das CJ, Sharma R et al (2016) Diffusion weighted imaging: technique and applications. 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