ArticleAnnals of surgical oncology2026
CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
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Authors and funding
14 authors.
Funding
Abstract
backgroundThe 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). PATIENTS AND
methodsPatients from three institutions were allocated to a training cohort (n = 372) and an internal validation cohort (n = 160) at a 7:3 ratio. An external validation cohort (n = 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.
resultsThe 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) (P < 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 (P < 0.05).
conclusionsThe CT-based nested habitats analysis effectively captures intratumoral heterogeneity and accurately predicts early recurrence in HCC. This technique enables precise postoperative risk stratification.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.