Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Conventional MRI (cMRI)
2.3. Tomoelastography
2.4. Histopathological Analysis
2.5. Image Preprocessing
2.6. cMRI-Based DLCR Model
2.7. cMRI-Based DLCR Model with Tomoelastography
2.8. Statistical Analysis
3. Results
3.1. Demographics and Clinical Characteristics
3.2. Optimization of cMRI-Based DLCR Models
3.3. Comparison of DLCR Models with/without Tomoelastography
3.4. Contribution of Predictive Efficacy
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Total (n = 108) | Training (n = 87) | Testing (n = 21) | p-Value |
---|---|---|---|---|
Age (years) | 59.57 ± 10.97 | 59.38 ± 11.13 | 60.38 ± 10.20 | 0.19 |
Sex, n (%) | 93 (87.03%) | 76 (88.51%) | 17 (80.95%) | 0.83 |
BMI (kg/m2) | 23.81 ± 3.01 | 23.63 ± 2.94 | 24.59 ± 3.15 | 0.45 |
Etiology, No. | -- | - | - | <0.05 |
Hepatitis B virus | 83 | 64 | 18 | - |
Hepatitis C virus | 4 | 3 | 1 | - |
Others | 21 | 19 | 2 | - |
AFP level (mg/mL) | - | - | - | <0.05 |
<20 | 50 | 40 | 10 | - |
≥20 | 58 | 47 | 11 | - |
Platelet count (×109/L) | 143.06 ± 64.93 | 141.81 ± 65.41 | 148.40 ± 62.56 | 0.39 |
Prealbumin level (mg/L) | 195.99 ± 60.55 | 195.74 ± 62.60 | 197.05 ± 50.78 | 0.31 |
ALT level (IU/L) | 41.08 ± 60.21 | 41.23 ± 65.27 | 40.45 ± 29.96 | 0.27 |
AST level (IU/L) | 45.25 ± 64.87 | 46.37 ± 71.14 | 40.45 ± 22.68 | 0.25 |
Total bilirubin (μmol/L) | 18.95 ± 12.09 | 18.52 ± 8.02 | 20.77 ± 22.23 | 0.16 |
Direct bilirubin (μmol/L) | 3.89 ± 2.96 | 3.90 ± 2.72 | 3.83 ± 3.84 | 0.35 |
Albumin level (g/L) | 34.77 ± 11.81 | 39.86 ± 5.87 | 40.30 ± 7.70 | 0.27 |
Prothrombin time (s) | 12.54 ± 1.13 | 12.14 ± 1.36 | 12.89 ± 0.91 | 0.44 |
INR | 1.04 ± 0.12 | 1.03 ± 0.12 | 1.10 ± 0.08 | 0.57 |
Ki-67(%) | 27.28 ± 20.47 | 27.55 ± 19.52 | 26.14 ± 23.98 | 0.26 |
Variable | Training (n = 87) | p Value | Validation (n = 21) | p Value | Testing (n = 43) | p Value | |||
---|---|---|---|---|---|---|---|---|---|
High Ki-67 (n = 40) | Low Ki-67 (n = 47) | High Ki-67 (n = 9) | Low Ki-67 (n = 12) | High Ki-67 (n = 17) | Low Ki-67 (n = 26) | ||||
Age (years) | 56.8 ± 11.5 | 65.0 ± 7.9 | 0.07 | 61.8 ± 9.5 | 59.3 ± 10.6 | 0.09 | 59.4 ± 11.7 | 60.7 ± 10.5 | 0.17 |
Sex, n (%) | 35 (87.50%) | 41 (87.23%) | 0.78 | 7 (77.78%) | 10 (83.33%) | 0.81 | 15 (88.24%) | 21 (80.77%) | 0.38 |
BMI (kg/m2) | 23.30 ± 2.83 | 24.32 ± 3.05 | 0.57 | 26.02 ± 2.46 | 23.51 ± 3.19 | 0.67 | 23.18 ± 2.34 | 25.35 ± 4.06 | 0.27 |
Etiology, No. | |||||||||
Hepatitis B virus | 27 (67.50%) | 39 (82.98%) | 8 (88.89%) | 10 (83.33%) | 11 (64.71%) | 18 (69.23%) | |||
Hepatitis C virus | 3 (7.50%) | 1 (2.13%) | 1 (11.11%) | 1 (8.33%) | 2 (11.76%) | 1 (3.85%) | |||
Others | 10 (25.00%) | 7 (14.89%) | 0 (0%) | 1 (8.33%) | 4 (23.53%) | 7 (26.92%) | |||
AFP level (mg/mL) | 0.03 | 0.02 | 0.04 | ||||||
<20 | 6 (15.00%) | 38 (80.85%) | 3 (33.33%) | 8 (66.67%) | 4 (23.53%) | 15 (57.69%) | |||
≥20 | 34 (87.50%) | 9 (19.15%) | 6 (66.67%) | 4 (33.33%) | 13 (76.47%) | 11 (42.31%) | |||
Platelet count (×109/L) | 142.32 ± 70.08 | 140.70 ± 53.78 | 0.35 | 133.50 ± 40.53 | 158.33 ± 71.99 | 0.34 | 156.00 ± 89.57 | 152.32 ± 73.11 | 0.36 |
Prealbumin level (mg/L) | 187.86 ± 54.95 | 212.96 ± 73.84 | 0.24 | 194.13 ± 45.34 | 199.00 ± 54.01 | 0.31 | 177.18 ± 58.22 | 178.44 ± 53.98 | 0.27 |
ALT level (IU/L) | 45.32 ± 77.39 | 32.30 ± 19.06 | 0.35 | 53.25 ± 36.98 | 31.92 ± 20.05 | 0.34 | 35.24 ± 22.25 | 33.40 ± 17.74 | 0.45 |
AST level (IU/L) | 50.76 ± 84.47 | 36.78 ± 19.83 | 0.15 | 46.63 ± 27.50 | 36.33 ± 17.63 | 0.12 | 49.53 ± 41.14 | 36.80 ± 13.22 | 0.12 |
Total bilirubin (μmol/L) | 18.23 ± 7.51 | 19.18 ± 9.00 | 0.36 | 15.35 ± 4.38 | 24.38 ± 27.90 | 0.17 | 17.09 ± 5.09 | 16.54 ± 7.70 | 0.24 |
Direct bilirubin (μmol/L) | 3.99 ± 2.90 | 3.70 ± 2.26 | 0.26 | 3.00 ± 1.04 | 4.38 ± 4.81 | 0.28 | 3.79 ± 2.18 | 3.53 ± 2.24 | 0.23 |
Albumin level (g/L) | 39.58 ± 4.68 | 40.48 ± 7.83 | 0.39 | 37.75 ± 2.90 | 42.00 ± 9.27 | 0.41 | 38.65 ± 4.73 | 38.92 ± 4.07 | 0.72 |
Prothrombin time (s) | 12.21 ± 1.33 | 12.01 ± 1.39 | 0.81 | 12.66 ± 0.52 | 13.04 ± 1.07 | 0.89 | 12.47 ± 0.76 | 12.56 ± 1.11 | 0.82 |
INR | 1.04 ± 0.12 | 1.02 ± 0.12 | 0.67 | 1.08 ± 0.05 | 1.11 ± 0.10 | 0.57 | 1.06 ± 0.07 | 1.07 ± 0.10 | 0.67 |
c (rad) | 2.45 ± 0.65 | 2.26 ± 0.66 | 0.17 | 2.38 ± 0.85 | 2.23 ± 0.97 | 0.05 | 2.07 ± 0.58 | 2.11 ± 0.61 | 0.11 |
φ (m/s) | 1.14 ± 0.25 | 1.05 ± 0.24 | 0.09 | 1.20 ± 0.24 | 0.99 ± 0.20 | 0.71 | 1.03 ± 0.22 | 1.02 ± 0.25 | 0.20 |
Model | Inception-Resnet | Xception | Inception | Resnet | VGG16 | VGG19 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC | 0.71 ± 0.04 | 0.61 ± 0.03 | 0.80 ± 0.03 | 0.71 ± 0.02 | 0.65 ± 0.03 | 0.56 ± 0.03 | 0.70 ± 0.04 | 0.62 ± 0.03 | 0.62 ± 0.03 | 0.53 ± 0.03 | 0.65 ± 0.03 | 0.55 ± 0.05 |
(0.70–0.72) | (0.60–0.62) | (0.79–0.81) | (0.70–0.72) | (0.64–0.66) | (0.55–0.57) | (0.69–0.71) | (0.61–0.63) | (0.61–0.63) | (0.52–0.54) | (0.64–0.66) | (0.54–0.57) | |
Accuracy | 0.71 ± 0.05 | 0.61 ± 0.04 | 0.77 ± 0.04 | 0.68 ± 0.03 | 0.66 ± 0.05 | 0.57 ± 0.04 | 0.70 ± 0.04 | 0.61 ± 0.04 | 0.62 ± 0.04 | 0.53 ± 0.03 | 0.64 ± 0.04 | 0.55 ± 0.03 |
(0.70–0.72) | (0.60–0.62) | (0.76–0.78) | (0.67–0.69) | (0.65–0.67) | (0.56–0.58) | (0.69–0.71) | (0.60–0.62) | (0.61–0.63) | (0.52–0.54) | (0.63–0.65) | (0.54–0.56) | |
Sensitivity | 0.68 ± 0.05 | 0.60 ± 0.03 | 0.76 ± 0.06 | 0.67 ± 0.04 | 0.65 ± 0.04 | 0.57 ± 0.05 | 0.67 ± 0.05 | 0.59 ± 0.03 | 0.59 ± 0.03 | 0.53 ± 0.04 | 0.66 ± 0.04 | 0.57 ± 0.02 |
(0.67–0.69) | (0.59–0.61) | (0.75–0.77) | (0.66–0.68) | (0.65–0.67) | (0.55–0.58) | (0.66–0.68) | (0.58–0.60) | (0.58–0.60) | (0.52–0.54) | (0.65–0.67) | (0.56–0.58) | |
Specificity | 0.72 ± 0.04 | 0.63 ± 0.02 | 0.78 ± 0.06 | 0.68 ± 0.04 | 0.67 ± 0.05 | 0.58 ± 0.04 | 0.72 ± 0.03 | 0.58 ± 0.04 | 0.64 ± 0.04 | 0.55 ± 0.03 | 0.62 ± 0.04 | 0.52 ± 0.03 |
(0.71–0.73) | (0.62–0.64) | (0.77–0.79) | (0.67–0.69) | (0.66–0.68) | (0.57–0.59) | (0.71–0.73) | (0.57–0.59) | (0.63–0.65) | (0.54–0.56) | (0.61–0.63) | (0.51–0.53) | |
PPV | 0.69 ± 0.03 | 0.63 ± 0.02 | 0.76 ± 0.03 | 0.65 ± 0.04 | 0.64 ± 0.02 | 0.55 ± 0.04 | 0.67 ± 0.02 | 0.58 ± 0.02 | 0.59 ± 0.04 | 0.52 ± 0.03 | 0.65 ± 0.03 | 0.56 ± 0.04 |
(0.68–0.70) | (0.62–0.64) | (0.75–0.77) | (0.64–0.66) | (0.64–0.65) | (0.54–0.56) | (0.67–0.68) | (0.57–0.59) | (0.58–0.60) | (0.51–0.53) | (0.64–0.66) | (0.55–0.57) | |
NPV | 0.71 ± 0.02 | 0.62 ± 0.03 | 0.77 ± 0.04 | 0.68 ± 0.03 | 0.68 ± 0.03 | 0.59 ± 0.04 | 0.72 ± 0.01 | 0.55 ± 0.03 | 0.64 ± 0.03 | 0.55 ± 0.02 | 0.63 ± 0.02 | 0.54 ± 0.03 |
(0.71–0.72) | (0.62–0.64) | (0.76–0.78) | (0.67–0.69) | (0.67–0.69) | (0.58–0.60) | (0.72–0.73) | (0.54–0.56) | (0.63–0.65) | (0.54–0.56) | (0.63–0.64) | (0.53–0.55) |
Cohort | Parameter Combinations | Evaluation | |||||
---|---|---|---|---|---|---|---|
AUC | Accuracy | Sensitivity | Specificity | PPV | NPV | ||
Internal validation cohort | cMRI + AFP | 0.84 ± 0.03 | 0.81 ± 0.04 | 0.80 ± 0.06 | 0.82 ± 0.06 | 0.78 ± 0.06 | 0.80 ± 0.03 |
(0.83–0.85) | (0.80–0.82) | (0.79–0.81) | (0.81–0.83) | (0.77–0.79) | (0.79–0.81) | ||
cMRI + AFP + MRE | 0.90 ± 0.03 | 0.87 ± 0.05 | 0.86 ± 0.04 | 0.93 ± 0.02 | 0.84 ± 0.03 | 0.87 ± 0.02 | |
(0.89–0.91) | (0.86–0.88) | (0.85–0.87) | (0.93–0.94) | (0.83–0.85) | (0.87–0.88) | ||
Independent testing cohort | cMRI + AFP | 0.74 ± 0.02 | 0.72 ± 0.03 | 0.72 ± 0.05 | 0.72 ± 0.04 | 0.68 ± 0.05 | 0.71 ± 0.03 |
(0.73–0.75) | (0.71–0.73) | (0.71–0.74) | (0.71–0.73) | (0.67–0.70) | (0.70–0.72) | ||
cMRI + AFP + MRE | 0.83 ± 0.03 | 0.83 ± 0.02 | 0.80 ± 0.03 | 0.86 ± 0.01 | 0.78 ± 0.02 | 0.80 ± 0.03 | |
(0.82–0.84) | (0.82–0.84) | (0.79–0.81) | (0.86–0.87) | (0.77–0.79) | (0.79–0.81) |
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Hu, X.; Zhou, J.; Li, Y.; Wang, Y.; Guo, J.; Sack, I.; Chen, W.; Yan, F.; Li, R.; Wang, C. Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model. Cancers 2022, 14, 2575. https://doi.org/10.3390/cancers14112575
Hu X, Zhou J, Li Y, Wang Y, Guo J, Sack I, Chen W, Yan F, Li R, Wang C. Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model. Cancers. 2022; 14(11):2575. https://doi.org/10.3390/cancers14112575
Chicago/Turabian StyleHu, Xumei, Jiahao Zhou, Yan Li, Yikun Wang, Jing Guo, Ingolf Sack, Weibo Chen, Fuhua Yan, Ruokun Li, and Chengyan Wang. 2022. "Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model" Cancers 14, no. 11: 2575. https://doi.org/10.3390/cancers14112575