CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
환자: hepatocellular carcinoma (HCC)
I · Intervention 중재 / 시술
nested habitats analysis to locate aggressive subregions
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSIONS] The CT-based nested habitats analysis effectively captures intratumoral heterogeneity and accurately predicts early recurrence in HCC. This technique enables precise postoperative risk stratification.
[BACKGROUND] 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
- 표본수 (n) 372
- p-value P < 0.05
- 95% CI 0.778-0.885
APA
Zhao J, Zhou M, et al. (2026). CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.. Annals of surgical oncology. https://doi.org/10.1245/s10434-026-19361-2
MLA
Zhao J, et al.. "CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.." Annals of surgical oncology, 2026.
PMID
41807840
Abstract
[BACKGROUND] 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).
[PATIENTS AND METHODS] Patients 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.
[RESULTS] 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) (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).
[CONCLUSIONS] The CT-based nested habitats analysis effectively captures intratumoral heterogeneity and accurately predicts early recurrence in HCC. This technique enables precise postoperative risk stratification.
[PATIENTS AND METHODS] Patients 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.
[RESULTS] 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) (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).
[CONCLUSIONS] The CT-based nested habitats analysis effectively captures intratumoral heterogeneity and accurately predicts early recurrence in HCC. This technique enables precise postoperative risk stratification.
같은 제1저자의 인용 많은 논문 (5)
- The Effect of CDT-Based Rehabilitation Nursing on Breast Cancer-related Lymphedema.
- Prognostic value of BMI, prognostic nutritional index, and CRP in patients with lymphoma after autologous hematopoietic stem cell transplantation.
- Analysis of independent prognostic factors for the occurrence and severity of heart failure after chemotherapy in patients with small cell lung cancer.
- Silymarin sensitizes human colorectal cancer cells to 5-ALA-mediated photodynamic therapy by enhancing cytotoxicity and apoptotic signaling in vitro.
- Exploratory Investigation Into Perioperative Treatment Strategies for Potentially Resectable Stage III-N2 Driver Gene-Negative Non-Small Cell Lung Cancer in the Immunotherapy Era.