Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study

Zhao, Jian; Zhang, Wei; Fan, Chenglin; Zhang, Jun; Yuan, Fang; Liu, Si-Yun; Li, Fuyu; Song, Bin

doi:10.1016/j.ejrad.2021.109631

Cited by 12 publications

(19 citation statements)

References 45 publications

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“…Radiologists were aware that patients had pCCA, but they were blinded to the exact pathological type and TNM stage. The standard of diagnosing lymph nodes metastasis, invasions of hepatic artery and portal vein were in accordance with a previous study 23 . For enhancement mode, obvious enhancement means that the degree of enhancement of the tumor was higher than 20% of liver parenchyma.…”

Section: Methodssupporting

confidence: 84%

“…Four hundred and two radiomics features were extracted from each ROI, including 42 histogram features, 15 form factor features, 144 grey level co‐occurrence matrix (GLCM) features, 180 run length matrix (RLM) features, 11 grey level size zone matrix (GLSZM) features, and 10 Haralick features 26 . Please refer to previous study for the description of radiomics features used to develop radiomics model 23 …”

Section: Methodsmentioning

confidence: 99%

“…The standard of diagnosing lymph nodes metastasis, invasions of hepatic artery and portal vein were in accordance with a previous study. 23 For enhancement mode, obvious enhancement means that the degree of enhancement of the tumor was higher than 20% of liver parenchyma. Moderate enhancement means that the degree of enhancement of the tumor was less than AE20% compared with liver parenchyma.…”

Section: Mri Protocolmentioning

confidence: 99%

“…26 Please refer to previous study for the description of radiomics features used to develop radiomics model. 23…”

Section: Region-of-interest Segmentation and Radiomics Feature Extrac...mentioning

confidence: 99%

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Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Zhao

Zhang²,

Zhu³

et al. 2021

Magnetic Resonance Imaging

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Background Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. Purpose To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. Study Type Retrospective. Population One hundred eighty‐four patients (median age, 61.0 years; interquartile range: 53.0–66.8 years) including 115 men and 69 women. Field Strength/Sequence A 1.5 T; volumetric interpolated breath‐hold examination (VIBE) sequence. Assessment The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast‐enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic‐radiomics nomogram (Modelcombine). Statistical Tests Chi‐squared (χ2) test or Fisher's exact test, Mann–Whitney U‐test or t‐test, Delong test. Two tailed P < 0.05 was considered statistically significant. Results Based on the comparison of area under the curves (AUC) using Delong test, Modelclinic and Modelcombine had significantly better performance than Modelradiomic and tumor‐node‐metastasis (TNM) system in training set. In the testing set, both Modelclinic and Modelcombine had significantly better performance than TNM system, whereas only Modelcombine was significantly superior to Modelradiomic. However, the AUC values were not significantly different between Modelclinic and Modelcombine (P = 0.156 for training set and P = 0.439 for testing set). Data Conclusion A noninvasive model combining the MRI‐based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA. Level of Evidence 3 Technical Efficacy Stage 4

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Section: Methodssupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Section: Mri Protocolmentioning

confidence: 99%

“…26 Please refer to previous study for the description of radiomics features used to develop radiomics model. 23…”

Section: Region-of-interest Segmentation and Radiomics Feature Extrac...mentioning

confidence: 99%

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Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Zhao

Zhang²,

Zhu³

et al. 2021

Magnetic Resonance Imaging

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“…Consequently, MRI has garnered attention in recent years as a predictive tool for postoperative survival in ECC patients [12,13].…”

Section: Introductionmentioning

confidence: 99%

Survival analysis of patients with extrahepatic cholangiocarcinoma: A nomogram for clinical and MRI features

Zeng

Wang

et al. 2023

Preprint

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Background This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. Methods A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. Results Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion’s position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions Conclusions Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

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CT Texture Analysis of Perihilar Cholangiocarcinoma—Associations With Tumor Grading, Tumor Markers and Clinical Outcome

Leonhardi,

Sabanov,

Höhn

et al. 2024

Cancer Reports

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BackgroundTexture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis.AimsThe present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors.MethodsThis retrospective study included 22 patients (10 females, 45%) with a mean age of 71.8 ± 8.7 years. Texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. Survival and clinical parameters were used as primary study outcomes.ResultsIn discrimination analysis, “S(1,1)SumVarnc” was statistically significantly different between patients with long‐term survival and nonlong‐term survival (mean 275.8 ± 32.6 vs. 239.7 ± 26.0, p = 0.01). The first‐order parameter “skewness” was associated with the tumor marker “carcinoembryonic antigen” (CEA) (r = −0.7, p = 0.01). A statistically significant correlation of the texture parameter “S(5,0)SumVarnc” with tumor grading was identified (r = −0.6, p < 0.01). Several other texture features correlated with tumor markers CA‐19‐9 and AFP, as well as with T and N stage of tumors.ConclusionSeveral texture features derived from CT images were associated with tumor characteristics and survival in patients with perihilar cholangiocarcinomas. CT texture features could be used as valuable novel imaging markers in clinical routine.

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Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study

Cited by 12 publications

References 45 publications

Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Survival analysis of patients with extrahepatic cholangiocarcinoma: A nomogram for clinical and MRI features

CT Texture Analysis of Perihilar Cholangiocarcinoma—Associations With Tumor Grading, Tumor Markers and Clinical Outcome

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