Efficacy of da Vinci robot‐assisted lymph node surgery than conventional axillary lymph node dissection in breast cancer – A comparative study
Background da Vinci robot‐assisted axillary lymph node dissection (dVALND) can be a minimally invasive technique to minimize post‐operative complications. Objective To explore the clinical efficacy of dVALND in breast cancer (BC) patients for mitigating the postoperative complications than conventio...
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Published in | The international journal of medical robotics + computer assisted surgery Vol. 17; no. 6 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hamilton
Wiley Subscription Services, Inc
01.12.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Background
da Vinci robot‐assisted axillary lymph node dissection (dVALND) can be a minimally invasive technique to minimize post‐operative complications.
Objective
To explore the clinical efficacy of dVALND in breast cancer (BC) patients for mitigating the postoperative complications than conventional ALND.
Methods
Total 60 female patients with BC were admitted to our hospitals since September 2018, and these patients segregated into two groups of 30 patients each. Modified radical mastectomy for BC was performed to the patients in both groups. In Group 1 (control group), ALND was performed using conventional mode of axillary lymph node surgery. In Group 2 (Test group), the dVALND was performed using da Vinci robot‐assisted surgery. Wound healing, aesthetic effect and patient's satisfaction were evaluated after conventional method and dVALND.
Results
Postoperative complications viz., wound infection (1/30 (3.33%), p < 0.05), fat necrosis (3/30 (10%), p < 0.05) and lymphedema of upper limbs (2/30 (6.67%), p < 0.05) were observed in dVALND than conventional surgery. Local recurrence or metastasis was minimized and overall aesthetic effect not observed during follow‐up.
Conclusion
dVALND improved the overall patient's quality of life by mitigating postoperative complications than ALND. |
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Bibliography: | Narasimha M. Beeraka and Kuo Chen contributed equally to this work. |
ISSN: | 1478-5951 1478-596X |
DOI: | 10.1002/rcs.2307 |