Background <p>The purpose of this study was to develop and validate a computed tomography (CT)–based nested habitats analysis for identifying aggressive tumor subregions and predicting early recurrence in patients with hepatocellular carcinoma (HCC).</p> Patients and Methods <p>Patients from three institutions were allocated to a training cohort (<i>n</i> = 372) and an internal validation cohort (<i>n</i> = 160) at a 7:3 ratio. An external validation cohort (<i>n</i> = 169) from a fourth institution was included. Venous-phase CT images underwent nested habitats analysis to locate aggressive subregions. First, a support vector machine classified tumors on the basis of global radiomic features. Then, local features were extracted to construct probability maps, from which aggressive micro-regions were identified using k-means clustering. Features from the highest-risk micro-regions were integrated to generate a nested habitats score. Model performance was evaluated with the area under the curve (AUC) and Kaplan–Meier survival analysis.</p> Results <p>The nested habitats score demonstrated strong predictive ability for early recurrence, achieving AUCs of 0.832 (95% CI 0.778–0.885) in the training cohort, 0.896 (95% CI 0.833–0.959) in the internal validation cohort, and 0.833 (95% CI 0.762–0.905) in the external validation cohort. In multivariable Cox regression, the nested habitats score remained an independent predictor of recurrence-free survival (RFS) (<i>P</i> &lt; 0.05), along with alkaline phosphatase, macrotrabecular-massive HCC, sex, and intratumoral tertiary lymphoid structures. Kaplan–Meier analysis further confirmed significantly shorter RFS among patients with high nested habitats scores or high nomogram-predicted risk (<i>P</i> &lt; 0.05).</p> Conclusions <p>The CT-based nested habitats analysis effectively captures intratumoral heterogeneity and accurately predicts early recurrence in HCC. This technique enables precise postoperative risk stratification.</p>