Preoperative identification of cytokeratin 19 status of hepatocellular carcinoma based on diffusion kurtosis imaging

Chen, Jiejun; Guo, Yansu; Zhang, Yunfei; Guo, Yunxue; Jiang, Mengmeng; Dai, Yongming; Wang, Lisheng

doi:10.1007/s00261-022-03736-6

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…A total of 4300 samples were involved (2002 samples in Ki-67 models, 2298 samples in CK-19 models) in the 34 included studies. Among the included studies, 18 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37) focused on Ki-67 and 16 (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53) focused on CK-19. The samples of the included studies came from different hospitals, with six studies from multiple centers and the rest from single centers (33,37,42,44,48,53).…”

Section: Study Characteristicsmentioning

confidence: 99%

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis

Zhou,

Chen,

et al. 2024

Front. Oncol.

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BackgroundRadiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC.MethodsOriginal studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression.ResultsWe identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86–0.92) in the training set, and 0.87 (95% CI: 0.82–0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81–0.90) in the training set, and 0.79 (95% CI: 0.73–0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased.ConclusionRadiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.

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Section: Study Characteristicsmentioning

confidence: 99%

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis

Zhou,

Chen,

et al. 2024

Front. Oncol.

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“…As a non-invasive and cost-effective imaging technique, diffusion weighted magnetic resonance imaging (DW-MRI) has shown tremendous clinical potential for cancer diagnosing, and cancer grading, as well as therapeutic response prediction [10,11]. Moreover, numerous DWI models, including the conventional DWI (mono-exponential DWI), IVIM (intravoxel incoherent motions), DKI (diffusion kurtosis imaging), and so on, have been proposed for characterizing tumor from different perspectives [12][13][14][15][16][17][18][19][20][21]. However, the biological insights provided by a lot of DWI-derived parameters mainly include cellularity, vascularity and micro-structural heterogeneity [12].…”

Section: Introductionmentioning

confidence: 99%

Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers

Song,

Wang,

Feng

et al. 2023

Bioengineering

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Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann–Whitney U tests or independent Student’s t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754–1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer.

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“…6,7 Various intriguing DWI techniques have been developed and implemented in clinical imaging. [8][9][10][11][12][13][14][15][16][17] Among these techniques, Intra-voxel incoherent motion (IVIM) MRI stands out as one of the most widely-used approaches. Since the introduction of IVIM theory in 1986, 18 over 1300 published studies have extensively showcased its clinical potential across a wide range of clinical scenarios (Data accessed from PubMed, May 2023).…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma

Zhang,

Sheng,

Yang

et al. 2023

JHC

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Purpose To assess the effectiveness of tri-exponential Intra-Voxel Incoherent Motion (tri-IVIM) MRI in preoperatively identifying microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Patients and Methods In this prospective study, 67 patients with HCC were included. Metrics from bi-exponential IVIM (bi-IVIM) and tri-IVIM were calculated. Subgroup comparisons were analyzed using the independent Student’s t -test or Mann–Whitney U -test. Logistic regression was performed to explore clinical risk factors. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis. Results MVI-positive HCCs exhibited significantly lower true diffusion coefficient (D t ) from bi-IVIM, as well as fast-diffusion coefficients (D f ) and slow-diffusion coefficients (D s ) from tri-IVIM, compared to MVI-negative HCCs (p < 0.05). Tumor size and alpha-fetoprotein (AFP) were identified as risk factors. The combination of tri-IVIM-derived metrics (D s and D f ) yielded higher diagnostic accuracy (AUC = 0.808) compared to bi-IVIM (AUC = 0.741). A predictive model based on a nomogram was constructed using D s , D f , tumor size, and AFP, resulting in the highest diagnostic accuracy (AUC = 0.859). Decision curve analysis indicated that the constructed model, provided the highest net benefit by accurately stratifying the risk of MVI, followed by tri-IVIM and bi-IVIM. Conclusion Tri-IVIM can provide information on perfusion and diffusion for evaluating MVI in HCC. Additionally, tri-IVIM outperformed bi-IVIM in identifying MVI-positive HCC. By integrating clinical risk factors and metrics from tri-IVIM, a predictive nomogram exhibited the highest diagnostic accuracy, potentially aiding in the noninvasive and preoperative assessment of MVI.

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Preoperative identification of cytokeratin 19 status of hepatocellular carcinoma based on diffusion kurtosis imaging

Cited by 7 publications

References 28 publications

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis

Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers

The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma

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