Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgery Article Swipe
Fangliang Guo
,
Cong Xia
,
Zongheng Wang
,
Ruiqi Wang
,
Jianfeng Gao
,
Yue Meng
,
Jiahao Pan
,
Qianshi Zhang
,
Shuangyi Ren
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1303686
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1303686
Background Total mesorectal excision (TME), represents a key technique in radical surgery for rectal cancer. This study aimed to construct a preoperative nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision (L-TME) and to investigate whether there were potential benefits of robotic TME (R-TME) for patients with technically challenging rectal cancer. Methods Consecutive midlow rectal cancer patients receiving total mesorectal excision were included. A preoperative nomogram to predict the surgical difficulty of L-TME was established and validated. Patients with technically challenging rectal cancer were screened by calculating the prediction score of the nomogram. Then patients with technically challenging rectal cancer who underwent different types of surgery, R-TME or L-TME, were analyzed for comparison. Results A total of 533 consecutive patients with midlow rectal cancer who underwent TME at a single tertiary medical center between January 2018 and January 2021 were retrospectively enrolled. Multivariable analysis demonstrated that mesorectal fat area, intertuberous distance, tumor size, and tumor height were independent risk factors for surgical difficulty. Subsequently, these variables were used to construct the nomogram model to predict the surgical difficulty of L-TME. The area under the receiver operating characteristic curve of the nomogram was 0.827 (95% CI 0.745 - 0.909) and 0.809 (95% CI 0.674- 0.944) in the training and validation cohort, respectively. For patients with technically challenging rectal cancer, R-TME was associated with a lower diverting ileostomy rate (p = 0.003), less estimated blood loss (p < 0.043), shorter procedure time (p = 0.009) and shorter postoperative hospital stay (p = 0.037). Conclusion In this study, we established a preoperative nomogram to predict the surgical difficulty of L-TME. Furthermore, this study also indicated that R-TME has potential technical advantages for patients with technically challenging rectal cancer.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2024.1303686
- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1303686/pdf
- OA Status
- gold
- Cited By
- 2
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391316754
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391316754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fonc.2024.1303686Digital Object Identifier
- Title
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Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgeryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-26Full publication date if available
- Authors
-
Fangliang Guo, Cong Xia, Zongheng Wang, Ruiqi Wang, Jianfeng Gao, Yue Meng, Jiahao Pan, Qianshi Zhang, Shuangyi RenList of authors in order
- Landing page
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https://doi.org/10.3389/fonc.2024.1303686Publisher landing page
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1303686/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1303686/pdfDirect OA link when available
- Concepts
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Nomogram, Medicine, Total mesorectal excision, Colorectal cancer, Surgery, Cancer, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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