Endoscopic Treatment of T1 Colorectal Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Endoscopic Evaluation of Colorectal Lesions—Not Always Perfect
3. T1 CRC—Not All Criteria for ‘Curative’ vs. ‘Noncurative’ Resection Are Equally Important
4. Optimizing the Prediction of Lymph-Node Metastasis Risk of T1 CRC
5. Endoscopic Resection Techniques and Specimen Quality
5.1. ESD
5.2. Endoscopic Intermuscular Dissection (EID)
5.3. Endoscopic Full Thickness Resection (EFTR)
5.4. Endoscopic Submucosal Resection (ESR)
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Dedication
References
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Study/Year | Population | LNM (per Total Number of Cases) | LNM (sm Infiltration >1000 µm in Isolation) |
---|---|---|---|
Yasue et al., 2022 [28] | Endoscopic, surgical | 74/864 (8.7%) | 1.6% |
Rönnow et al., 2022 [29] | Surgical | 150/1439 (10%) | 7.4% |
Yamaoka et al., 2022 [30] | Surgical | 70/519 (13.5%) | 3.8% |
Morini et al., 2022 [31] | Surgical | 15/122 (12.5%) | 2.9% |
Santos-Antunes et al., 2023 [32] | Endoscopic | 26/96 (27%) | 0.0% |
Study | Method | Number | Result (Prediction LNM Risk) |
---|---|---|---|
Yasue et al. [28] | Histology | N = 846 | LVI, budding and differentiation: OR 8.09, 1.89, 2.09 |
Piao et al. [37] | Histology | N = 271 | LVI and grading: 0.8% vs. 66.6% LNM |
Miyazaki et al. [38] | Exosomal miRNAs from preoperative serum | N = 200 | AUC 0.84 for LNM |
Kajiwara et al. [34] | Nomogram | N = 4673 | SMI, budding, LVI, grading, location, sex |
Takamatsu et al. [39] | AI/deep learning from H&E histology | N = 783 | AUC 0.76 for LNM |
Song et al. [40] | AI/deep learning from H&E histology | N = 400 | AUC 0.76 for LNM |
Kasahara et al. [41] | AI/deep learning from H&E histology | N = 146 | Accuracy 81.8–86.3% |
Yan et al. [42] | Nomogram | N = 141 | LVI, location, grading AUC 0.89 |
Wada et al. [36] | Liquid biopsy Micro RNA, mRNA | N = 330 | AUC 0.90 |
Tang et al. [43] | Multivariate analysis | N = 476 | Early-onset CRC with higher LNM rate |
Kudo et al. [35] | AI/deep learning with various risk factors | N = 3134 | AUC 0.83 for cases with initial endoscopic resection (n = 517) |
Kang et al. [44] | Learning algorithm with various factors | N = 316 | AUC 0.76 |
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Metter, K.; Weißinger, S.E.; Várnai-Händel, A.; Grund, K.-E.; Dumoulin, F.L. Endoscopic Treatment of T1 Colorectal Cancer. Cancers 2023, 15, 3875. https://doi.org/10.3390/cancers15153875
Metter K, Weißinger SE, Várnai-Händel A, Grund K-E, Dumoulin FL. Endoscopic Treatment of T1 Colorectal Cancer. Cancers. 2023; 15(15):3875. https://doi.org/10.3390/cancers15153875
Chicago/Turabian StyleMetter, Klaus, Stephanie Ellen Weißinger, Alinda Várnai-Händel, Karl-Ernst Grund, and Franz Ludwig Dumoulin. 2023. "Endoscopic Treatment of T1 Colorectal Cancer" Cancers 15, no. 15: 3875. https://doi.org/10.3390/cancers15153875
APA StyleMetter, K., Weißinger, S. E., Várnai-Händel, A., Grund, K. -E., & Dumoulin, F. L. (2023). Endoscopic Treatment of T1 Colorectal Cancer. Cancers, 15(15), 3875. https://doi.org/10.3390/cancers15153875