1
|
Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water. Abdom Radiol (NY) 2024; 49:1892-1904. [PMID: 38526597 DOI: 10.1007/s00261-024-04230-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVES Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. RESULTS The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). CONCLUSION IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy.
Collapse
Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| |
Collapse
|
2
|
Zhang Y, Chen J, Yang C, Dai Y, Zeng M. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging. Eur Radiol 2024; 34:3215-3225. [PMID: 37853175 DOI: 10.1007/s00330-023-10339-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 07/27/2023] [Accepted: 08/20/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). RESULTS MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. CONCLUSIONS DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. CLINICAL RELEVANCE STATEMENT The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. KEY POINTS • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion.
Collapse
Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jiejun Chen
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 200032, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| |
Collapse
|
3
|
Zhang Y, Sheng R, Dai Y, Yang C, Zeng M. The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1154-1164. [PMID: 38311671 DOI: 10.1007/s00261-023-04168-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 02/06/2024]
Abstract
PURPOSE Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. RESULTS MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. CONCLUSION VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.
Collapse
Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| |
Collapse
|
4
|
Guo R, Lu F, Lin J, Fu C, Liu M, Yang S. Multi-b-value DWI to evaluate the synergistic antiproliferation and anti-heterogeneity effects of bufalin plus sorafenib in an orthotopic HCC model. Eur Radiol Exp 2024; 8:43. [PMID: 38467904 PMCID: PMC10928042 DOI: 10.1186/s41747-024-00448-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/06/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Multi-b-value diffusion-weighted imaging (DWI) with different postprocessing models allows for evaluating hepatocellular carcinoma (HCC) proliferation, spatial heterogeneity, and feasibility of treatment strategies. We assessed synergistic effects of bufalin+sorafenib in orthotopic HCC-LM3 xenograft nude mice by using intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), a stretched exponential model (SEM), and a fractional-order calculus (FROC) model. METHODS Twenty-four orthotopic HCC-LM3 xenograft mice were divided into bufalin+sorafenib, bufalin, sorafenib treatment groups, and a control group. Multi-b-value DWI was performed using a 3-T scanner after 3 weeks' treatment to obtain true diffusion coefficient Dt, pseudo-diffusion coefficient Dp, perfusion fraction f, mean diffusivity (MD), mean kurtosis (MK), distributed diffusion coefficient (DDC), heterogeneity index α, diffusion coefficient D, fractional order parameter β, and microstructural quantity μ. Necrotic fraction (NF), standard deviation (SD) of hematoxylin-eosin staining, and microvessel density (MVD) of anti-CD31 staining were evaluated. Correlations of DWI parameters with histopathological results were analyzed, and measurements were compared among four groups. RESULTS In the final 22 mice, f positively correlated with MVD (r = 0.679, p = 0.001). Significantly good correlations of MK (r = 0.677), α (r = -0.696), and β (r= -0.639) with SD were observed (all p < 0.010). f, MK, MVD, and SD were much lower, while MD, α, β, and NF were higher in bufalin plus sorafenib group than control group (all p < 0.050). CONCLUSION Evaluated by IVIM, DKI, SEM, and FROC, bufalin+sorafenib was found to inhibit tumor proliferation and angiogenesis and reduce spatial heterogeneity in HCC-LM3 models. RELEVANCE STATEMENT Multi-b-value DWI provides potential metrics for evaluating the efficacy of treatment in HCC. KEY POINTS • Bufalin plus sorafenib combination may increase the effectiveness of HCC therapy. • Multi-b-value DWI depicted HCC proliferation, angiogenesis, and spatial heterogeneity. • Multi-b-value DWI may be a noninvasive method to assess HCC therapeutic efficacy.
Collapse
Affiliation(s)
- Ran Guo
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Middle Zhi-jiang Road, Shanghai, 200071, People's Republic of China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People's Republic of China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, 518057, People's Republic of China
| | - Mengxiao Liu
- MR scientific Marketing, Diagnostic Imaging, Siemens Healthineers Ltd, Shanghai, 201318, People's Republic of China
| | - Shuohui Yang
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Middle Zhi-jiang Road, Shanghai, 200071, People's Republic of China.
| |
Collapse
|
5
|
Zhou L, Chen Y, Li Y, Wu C, Xue C, Wang X. Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 13:1323534. [PMID: 38234405 PMCID: PMC10792117 DOI: 10.3389/fonc.2023.1323534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Background Radiomics 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. Methods Original 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. Results We 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 (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased. Conclusion Radiomics 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 Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.
Collapse
Affiliation(s)
- Lu Zhou
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yiheng Chen
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yan Li
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Chaoyong Wu
- Shenzhen Hospital of Beijing University of Chinese Medicine, Shenzhen, China
| | - Chongxiang Xue
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Xihong Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China
| |
Collapse
|
6
|
Chen Y, Chen J, Yang C, Wu Y, Wei H, Duan T, Zhang Z, Long L, Jiang H, Song B. Preoperative prediction of cholangiocyte phenotype hepatocellular carcinoma on contrast-enhanced MRI and the prognostic implication after hepatectomy. Insights Imaging 2023; 14:190. [PMID: 37962669 PMCID: PMC10645671 DOI: 10.1186/s13244-023-01539-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) expressing cytokeratin (CK) 7 or CK19 has a cholangiocyte phenotype that stimulates HCC proliferation, metastasis, and sorafenib therapy resistance This study aims to noninvasively predict cholangiocyte phenotype-positive HCC and assess its prognosis after hepatectomy. METHODS Between January 2010 and May 2022, preoperative contrast-enhanced MRI was performed on consecutive patients who underwent hepatectomy and had pathologically confirmed solitary HCC. Two abdominal radiologists separately assessed the MRI features. A predictive model for cholangiocyte phenotype HCC was created using logistic regression analysis and five-fold cross-validation. A receiver operating characteristic curve was used to calculate the model performance. Kaplan-Meier and log-rank methods were used to evaluate survival outcomes. RESULTS In total, 334 patients were included in this retrospective study. Four contrast-enhanced MRI features, including "rim arterial phase hyperenhancement" (OR = 5.9, 95% confidence interval [CI]: 2.9-12.0, 10 points), "nodule in nodule architecture" (OR = 3.5, 95% CI: 2.1-5.9, 7 points), "non-smooth tumor margin" (OR = 1.6, 95% CI: 0.8-2.9, 3 points), and "non-peripheral washout" (OR = 0.6, 95% CI: 0.3-1.0, - 3 points), were assigned to the cholangiocyte phenotype HCC prediction model. The area under the curves for the training and independent validation set were 0.76 and 0.73, respectively. Patients with model-predicted cholangiocyte phenotype HCC demonstrated lower rates of recurrence-free survival (RFS) and overall survival (OS) after hepatectomy, with an estimated median RFS and OS of 926 vs. 1565 days (p < 0.001) and 1504 vs. 2960 days (p < 0.001), respectively. CONCLUSIONS Contrast-enhanced MRI features can be used to predict cholangiocyte phenotype-positive HCC. Patients with pathologically confirmed or MRI model-predicted cholangiocyte phenotype HCC have a worse prognosis after hepatectomy. CRITICAL RELEVANCE STATEMENT Four contrast-enhanced MRI features were significantly associated with cholangiocyte phenotype HCC and a worse prognosis following hepatectomy; these features may assist in predicting prognosis after surgery and improve personalized treatment decision-making. KEY POINTS • Four contrast-enhanced MRI features were significantly associated with cholangiocyte phenotype HCC. • A noninvasive cholangiocyte phenotype HCC predictive model was established based on MRI features. • Patients with cholangiocyte phenotype HCC demonstrated a worse prognosis following hepatic resection.
Collapse
Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Zhen Zhang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China
| | - Liling Long
- Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Road No. 37, Chengdu, 610041, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
| |
Collapse
|
7
|
Song M, Wang Q, Feng H, Wang L, Zhang Y, Liu H. Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers. Bioengineering (Basel) 2023; 10:1298. [PMID: 38002422 PMCID: PMC10669695 DOI: 10.3390/bioengineering10111298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Mengyu Song
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Qi Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Hui Feng
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Lijia Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai 201800, China
| | - Hui Liu
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| |
Collapse
|
8
|
Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2023; 10:1659-1671. [PMID: 37799828 PMCID: PMC10547827 DOI: 10.2147/jhc.s433948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 09/21/2023] [Indexed: 10/07/2023] Open
Abstract
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 (Dt) from bi-IVIM, as well as fast-diffusion coefficients (Df) and slow-diffusion coefficients (Ds) 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 (Ds and Df) yielded higher diagnostic accuracy (AUC = 0.808) compared to bi-IVIM (AUC = 0.741). A predictive model based on a nomogram was constructed using Ds, Df, 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.
Collapse
Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 200032, People’s Republic of China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| |
Collapse
|
9
|
Zhang Y, Yang C, Sheng R, Dai Y, Zeng M. Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging. Acad Radiol 2023; 30 Suppl 1:S30-S39. [PMID: 37442719 DOI: 10.1016/j.acra.2023.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
RATIONALE AND OBJECTIVES To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI). MATERIALS AND METHODS 62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f1 (fraction of restricted diffusion), f2 (fraction of hindered diffusion), f3 (fraction of free diffusion), and f1f2 (the multiply of f1 and f2) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy. RESULTS MVI-positive HCC showed significantly higher f1 and lower ADC values (ADC: 1.549 ± 0.228 ×10-3 vs 1.365 ± 0.239 ×10-3 mm2/s, P = .003; f1: 0.1633 ± 0.0341 vs 0.2221 ± 0.0491, P < .001). Tumor size and f1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f1. Nomogram-based model (area under ROC curve [AUC]= 0.856) yielded the best diagnostic accuracy followed by f1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f1 and nomogram model were significantly higher than that of ADC. CONCLUSION RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management.
Collapse
Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, China (Y.D.)
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.).
| |
Collapse
|
10
|
Zhao Y, Tan X, Chen J, Tan H, Huang H, Luo P, Liang Y, Jiang X. Preoperative prediction of cytokeratin-19 expression for hepatocellular carcinoma using T1 mapping on gadoxetic acid-enhanced MRI combined with diffusion-weighted imaging and clinical indicators. Front Oncol 2023; 12:1068231. [PMID: 36741705 PMCID: PMC9893005 DOI: 10.3389/fonc.2022.1068231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/19/2022] [Indexed: 01/20/2023] Open
Abstract
Objectives To explore the value of T1 mapping on gadoxetic acid-enhanced magnetic resonance imaging (MRI) in preoperative predicting cytokeratin 19 (CK19) expression for hepatocellular carcinoma (HCC). Methods This retrospective study included 158 patients from two institutions with surgically resected treatment-native solitary HCC who underwent preoperative T1 mapping on gadoxetic acid-enhanced MRI. Patients from institution I (n = 102) and institution II (n = 56) were assigned to training and test sets, respectively. univariable and multivariable logistic regression analyses were performed to investigate the association of clinicoradiological variables with CK19. The receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the performance for CK19 prediction. Then, a prediction nomogram was developed for CK19 expression. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. Results Multivariable logistic regression analysis showed that AFP>400ng/ml (OR=4.607, 95%CI: 1.098-19.326; p=0.037), relative apparent diffusion coefficient (rADC)≤0.71 (OR=3.450, 95%CI: 1.126-10.567; p=0.030), T1 relaxation time in the 20-minute hepatobiliary phase (T1rt-HBP)>797msec (OR=4.509, 95%CI: 1.301-15.626; p=0.018) were significant independent predictors of CK19 expression. The clinical-quantitative model (CQ-Model) constructed based on these significant variables had the best predictive performance with an area under the ROC curve of 0.844, an area under the PR curve of 0.785 and an F1 score of 0.778. The nomogram constructed based on CQ-Model demonstrated satisfactory performance with C index of 0.844 (95%CI: 0.759-0.908) and 0.818 (95%CI: 0.693-0.902) in the training and test sets, respectively. Conclusions T1 mapping on gadoxetic acid-enhanced MRI has good predictive efficacy for preoperative prediction of CK19 expression in HCC, which can promote the individualized risk stratification and further treatment decision of HCC patients.
Collapse
Affiliation(s)
- Yue Zhao
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China,Department of Radiology, Guangzhou First People’s Hospital, Guangzhou, China
| | - Xiaoliang Tan
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jingmu Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongweng Tan
- Department of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, China
| | - Huasheng Huang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Peng Luo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yongsheng Liang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China,Department of Radiology, Guangzhou First People’s Hospital, Guangzhou, China,*Correspondence: Xinqing Jiang,
| |
Collapse
|
11
|
Zhang L, Zhou H, Zhang X, Ding Z, Xu J. A radiomics nomogram for predicting cytokeratin 19-positive hepatocellular carcinoma: a two-center study. Front Oncol 2023; 13:1174069. [PMID: 37182122 PMCID: PMC10174303 DOI: 10.3389/fonc.2023.1174069] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/13/2023] [Indexed: 05/16/2023] Open
Abstract
Objectives We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid-enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19-positive (+) hepatocellular carcinoma (HCC) and patients' prognosis. Methods A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan-Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort. Results By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (-) group were 81% and 77%, and 80% and 74%, respectively. Kaplan-Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients. Conclusion The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment.
Collapse
Affiliation(s)
- Liqing Zhang
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Heshan Zhou
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoqian Zhang
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University, Shulan International Medical College, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zhongxiang Ding, ; Jianfeng Xu,
| | - Jianfeng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University, Shulan International Medical College, Hangzhou, China
- *Correspondence: Zhongxiang Ding, ; Jianfeng Xu,
| |
Collapse
|
12
|
Zhou Y, Zheng J, Yang C, Peng J, Liu N, Yang L, Zhang XM. Application of intravoxel incoherent motion diffusion-weighted imaging in hepatocellular carcinoma. World J Gastroenterol 2022; 28:3334-3345. [PMID: 36158259 PMCID: PMC9346463 DOI: 10.3748/wjg.v28.i27.3334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/26/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
The morbidity and mortality of hepatocellular carcinoma (HCC) rank 6th and 4th, respectively, among malignant tumors worldwide. Traditional diffusion-weighted imaging (DWI) uses the apparent diffusion coefficient (ADC) obtained by applying the monoexponential model to reflect water molecule diffusion in active tissue; however, the value of ADC is affected by microcirculation perfusion. Using a biexponential model, intravoxel incoherent motion (IVIM)-DWI quantitatively measures information related to pure water molecule diffusion and microcirculation perfusion, thus compensating for the shortcomings of DWI. The number of studies examining the application of IVIM-DWI in patients with HCC has gradually increased over the last few years, and many results show that IVIM-DWI has vital value for HCC differentiation, pathological grading, and predicting and evaluating the treatment response. The present study principally reviews the principle of IVIM-DWI and its research progress in HCC differentiation, pathological grading, predicting and evaluating the treatment response, predicting postoperative recurrence and predicting gene expression prediction.
Collapse
Affiliation(s)
- Yi Zhou
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Cui Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Juan Peng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, Sichuan Provincial People's Hospital Jinniu Hospital, Chengdu Jinniu District People's Hospital, Chengdu 610007, Sichuan Province, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| |
Collapse
|