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Purpose To evaluate an MRI-based strategy for quantifying intra- and peritumoral heterogeneity (ITH and PTH) in hepatocellular carcinoma (HCC) and develop ITH- and PTH-based models for diagnosing microvascular invasion (MVI) and stratifying prognostic risk. Materials and Methods Patients with HCC (≤5 cm) were retrospectively included from three different institutions from March 2012 to September 2023 and divided into internal training, internal testing, and external testing cohorts. Tumor and peritumoral tissues in MR images were categorized into distinct habitats using unsupervised clustering algorithms. High-throughput radiomic features were extracted from each habitat. The degree of feature variation within each habitat was quantified to derive characteristics representing ITH and PTH. Engineered features were developed to train machine learning models for MVI diagnosis. Kaplan-Meier survival curves and Cox regression analysis were used for survival analysis. Results A total of 432 patients (mean age, 54.31 years ± 11.15 [SD]; 371 male) were included. The TH_DNN model, constructed using ITH- and PTH-based quantitative features combined with a deep neural network (DNN), demonstrated the best predictive performance for MVI across the three datasets (area under the receiver operating characteristic curve range = 0.82-0.99). The subgroup predicted as MVI positive with the TH_DNN model exhibited a poorer prognosis than the MVI-negative subgroup. In terms of overall survival and postoperative recurrence, the hazard ratios for MVI diagnosis were 2.79 (95% CI: 1.35, 5.75; P = .006) and 2.17 (95% CI: 1.38, 3.43; P < .001), respectively. Conclusion This study developed a strategy for quantifying ITH and PTH, which was valuable for noninvasive and accurate identification of MVI and prognostic risk in patients with HCC. Keywords: Liver, MRI, Oncology, Hepatocellular Carcinoma, Microvascular Invasion, Tumor habitat, Intratumoral Heterogeneity, Peritumoral Heterogeneity Supplemental material is available for this article. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.