Predicting Clinical Efficacy of Vascular Disrupting Agents in Rodent Models of Primary and Secondary Liver Cancers: An Overview with Imaging-Histopathology Correlation
Abstract
:1. Introduction
2. Animal Models of Primary and Secondary Liver Cancers Usable for VDAs Studies
2.1. Primary Liver Cancer Model in Rats
2.2. Complex Model of Primary and Secondary Liver Tumors for Comparative VDA Studies
2.3. Clinical Relevance of Rodent Models to Human Patients on the Use of VDAs
3. Multiparametric MRI on Target/Receiver Selection and Efficacy Evaluation of VDA Therapy
3.1. Hepatobiliary CAs
3.2. Nonspecific MRI CA
3.3. Dynamic Contrast Enhancement (DCE) MRI
3.4. Diffusion-Weighted Imaging (DWI)
4. Update of Antitumor Effects of CA4P in Primary and Secondary Liver Cancers
4.1. CA4P Dose-Related Efficacy
4.2. Vascularity and Differentiation of HCCs in Relation to CA4P Efficacies
4.3. Distinct Volume-Efficacy Relation between Micro-HCCs and Macro-HCCs
4.4. Superior Efficacy in Secondary Malignancies Shown by Intraindividual Comparison
5. Study Limitations and Practical Challenges
6. Future Perspectives of VDA in Experimental Liver Cancers
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Lower Dose [26] | Higher Dose [24,25] | p Value * | |
---|---|---|---|
Animal number | |||
Recruited rats | 16 | 30 | |
Lost rats during induction | 0 | 5 | |
DENA toxicity | 0 | 3 | |
Tumor hemorrhage | 0 | 2 | |
Protocol of gavage | |||
DENA dose (mg/kg/day) | 5 | 10 | |
DENA exposure period (week) | 14 | 8 | |
Period of carcinogenesis (week) | |||
Recognizable nodule (Ø > 1mm) | 16–23 | 7–11 | |
Ready for CA4P therapy (maximum Ø ≥ 8 mm) | 21–31 | 14–22 | |
Tumor number | 61 | 108 | |
Primary HCC lesion (89.9%) | 56 | 96 | |
HCC differentiation § | <0.0001 | ||
Well-differentiated (a) | 15 (26.8%) | 12 (12.5%) | |
Moderately-differentiated (b) | 26 (46.4%) | 28 (29.2%) | |
Poorly-differentiated (c) | 13 (23.2%) | 44 (45.8%) | |
Undifferentiated (d) | 2 (3.6%) | 12 (12.5%) | |
(a + b) vs (c + d) | 73.2% vs 26.8% | 41.7% vs 58.3% | <0.0001 |
Tumoral vascularity ¶ | =0.1256 | ||
+ | 34 (60.7%) | 41 (42.7%) | |
++ | 13 (23.2%) | 26 (27.1%) | |
+++ | 7 (12.5%) | 19 (19.8%) | |
++++ (angioma-like HCCs) | 2 (3.6%) | 10 (10.4%) | |
Primary ICC (2.4%) | 0 | 4 | |
Combined HCC-ICC (7.7%) | 5 | 8 | |
Liver cirrhosis | slightly | moderate - severe |
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Liu, Y.; Wang, S.; Zhao, X.; Feng, Y.; Bormans, G.; Swinnen, J.; Oyen, R.; Huang, G.; Ni, Y.; Li, Y. Predicting Clinical Efficacy of Vascular Disrupting Agents in Rodent Models of Primary and Secondary Liver Cancers: An Overview with Imaging-Histopathology Correlation. Diagnostics 2020, 10, 78. https://doi.org/10.3390/diagnostics10020078
Liu Y, Wang S, Zhao X, Feng Y, Bormans G, Swinnen J, Oyen R, Huang G, Ni Y, Li Y. Predicting Clinical Efficacy of Vascular Disrupting Agents in Rodent Models of Primary and Secondary Liver Cancers: An Overview with Imaging-Histopathology Correlation. Diagnostics. 2020; 10(2):78. https://doi.org/10.3390/diagnostics10020078
Chicago/Turabian StyleLiu, Yewei, Shuncong Wang, Xiaohui Zhao, Yuanbo Feng, Guy Bormans, Johan Swinnen, Raymond Oyen, Gang Huang, Yicheng Ni, and Yue Li. 2020. "Predicting Clinical Efficacy of Vascular Disrupting Agents in Rodent Models of Primary and Secondary Liver Cancers: An Overview with Imaging-Histopathology Correlation" Diagnostics 10, no. 2: 78. https://doi.org/10.3390/diagnostics10020078
APA StyleLiu, Y., Wang, S., Zhao, X., Feng, Y., Bormans, G., Swinnen, J., Oyen, R., Huang, G., Ni, Y., & Li, Y. (2020). Predicting Clinical Efficacy of Vascular Disrupting Agents in Rodent Models of Primary and Secondary Liver Cancers: An Overview with Imaging-Histopathology Correlation. Diagnostics, 10(2), 78. https://doi.org/10.3390/diagnostics10020078