Prediction of Posthepatectomy Liver Failure in Narrow Resection Margins HCC: A Model Based on Iodine Map Histogram Analysis of Nontumorous Liver Parenchyma.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
154 patients with NRM-HCC who underwent hepatectomy at our center, with patients randomly divided into a 7:3 ratio into a training cohort (n=107) and an internal validation cohort (n=47).
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Calibration curve and DCA demonstrated good consistency and clinical utility of the model in both the training and validation cohorts. [CONCLUSION] A novel comprehensive model combining iodine map histogram parameter Kurtosis of nontumorous liver parenchyma, SFLVR, and clinical features facilitates early prediction of PHLF in NRM-HCC patients.
[RATIONALE AND OBJECTIVES] Posthepatectomy liver failure (PHLF) is a severe postoperative complication.
- 표본수 (n) 107
- 95% CI 0.80-0.94
APA
Xu Y, Liu B, et al. (2025). Prediction of Posthepatectomy Liver Failure in Narrow Resection Margins HCC: A Model Based on Iodine Map Histogram Analysis of Nontumorous Liver Parenchyma.. Academic radiology, 32(9), 5219-5230. https://doi.org/10.1016/j.acra.2025.05.002
MLA
Xu Y, et al.. "Prediction of Posthepatectomy Liver Failure in Narrow Resection Margins HCC: A Model Based on Iodine Map Histogram Analysis of Nontumorous Liver Parenchyma.." Academic radiology, vol. 32, no. 9, 2025, pp. 5219-5230.
PMID
40436712 ↗
Abstract 한글 요약
[RATIONALE AND OBJECTIVES] Posthepatectomy liver failure (PHLF) is a severe postoperative complication. This study aims to develop and validate a model combining iodine map histogram parameters of nontumorous liver parenchyma and clinical characteristics to predict early PHLF in patients with narrow resection margins-hepatocellular carcinoma (NRM-HCC).
[MATERIALS AND METHODS] A retrospective analysis was conducted on 154 patients with NRM-HCC who underwent hepatectomy at our center, with patients randomly divided into a 7:3 ratio into a training cohort (n=107) and an internal validation cohort (n=47). Iodine map histogram parameters of nontumorous liver parenchyma during the portal venous phase of spectral CT were measured. Standardized Future Residual Liver Volume Ratio (SFLVR) was calculated based on Future Liver Remnant Volume. Based on training cohort data, logistic regression analysis was performed to identify predictors and construct a model for predicting PHLF. The model's performance was evaluated by using receiver operating characteristic curve analysis, calibration curves, and decision curve analyses (DCA).
[RESULTS] In the training cohort, univariate and multivariate logistic regression analyses identified Albumin-bilirubin score, intraoperative blood loss (L), Kurtosis, and SFLVR as independent risk factors for PHLF. A comprehensive model combining these independent risk factors yielded an area under the curve of 0.87 (95% CI: 0.80-0.94) for predicting PHLF, outperforming each individual risk factor. Calibration curve and DCA demonstrated good consistency and clinical utility of the model in both the training and validation cohorts.
[CONCLUSION] A novel comprehensive model combining iodine map histogram parameter Kurtosis of nontumorous liver parenchyma, SFLVR, and clinical features facilitates early prediction of PHLF in NRM-HCC patients.
[MATERIALS AND METHODS] A retrospective analysis was conducted on 154 patients with NRM-HCC who underwent hepatectomy at our center, with patients randomly divided into a 7:3 ratio into a training cohort (n=107) and an internal validation cohort (n=47). Iodine map histogram parameters of nontumorous liver parenchyma during the portal venous phase of spectral CT were measured. Standardized Future Residual Liver Volume Ratio (SFLVR) was calculated based on Future Liver Remnant Volume. Based on training cohort data, logistic regression analysis was performed to identify predictors and construct a model for predicting PHLF. The model's performance was evaluated by using receiver operating characteristic curve analysis, calibration curves, and decision curve analyses (DCA).
[RESULTS] In the training cohort, univariate and multivariate logistic regression analyses identified Albumin-bilirubin score, intraoperative blood loss (L), Kurtosis, and SFLVR as independent risk factors for PHLF. A comprehensive model combining these independent risk factors yielded an area under the curve of 0.87 (95% CI: 0.80-0.94) for predicting PHLF, outperforming each individual risk factor. Calibration curve and DCA demonstrated good consistency and clinical utility of the model in both the training and validation cohorts.
[CONCLUSION] A novel comprehensive model combining iodine map histogram parameter Kurtosis of nontumorous liver parenchyma, SFLVR, and clinical features facilitates early prediction of PHLF in NRM-HCC patients.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
- Humans
- Male
- Female
- Hepatectomy
- Liver Neoplasms
- Carcinoma
- Hepatocellular
- Retrospective Studies
- Middle Aged
- Liver Failure
- Aged
- Postoperative Complications
- Tomography
- X-Ray Computed
- Margins of Excision
- Contrast Media
- Liver
- Iodine
- Hepatocellular carcinoma
- Histogram analysis
- Iodine map
- Narrow resection margins
- Posthepatectomy liver failure
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