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العنوان
Scarless versus conventional latissimus dorsi flap in post- mastectomy implant based breast reconstruction /
الناشر
Sherif Nasser Taha ,
المؤلف
Sherif Nasser Taha
هيئة الاعداد
باحث / Sherif Nasser Taha
مشرف / Magdy Ibrahim Elsherbiny
مشرف / Ahmed Mohamed Farahat
مشرف / Tarek Mohamed Hashem
تاريخ النشر
2021
عدد الصفحات
106 P . :
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
علم الأورام
تاريخ الإجازة
1/1/2021
مكان الإجازة
جامعة القاهرة - معهد الأورام القومى - Department of Oncology (Surgical)
الفهرس
Only 14 pages are availabe for public view

from 130

from 130

Abstract

The incidence of breast cancer world-wide is increasing, resulting in an increase in the number of total mastectomies. Breast reconstruction is becoming a popular preference for women who undergo a mastectomy. The latissimus dorsi muscle has been the workhorse of reconstructive surgery. It allows surgeons to reconstruct larger and more natural looking breasts to mimic as much as possible the contralateral side and provides good lower pole implant coverage to prevent migration or displacement at low cost. The main hypothesis of this study is that, in breast reconstruction after total mastectomy, the scarless Latissimus Dorsi muscle harvesting technique is at least non-inferior to the conventional technique regarding rate of development of complications and is superior to it regarding the aesthetic outcome.51 patients were enrolled over the period from Jan 2015 to Jan 2019 in this prospective, non-randomized, controlled, interventional, safety/efficacy trial. All patients were scheduled for implant-based post mastectomy reconstruction. 24 patients underwent the scarless LD harvesting technique and were compared to 27 patients that had the conventional LD harvesting technique in reconstruction. All patients were followed up for a minimum of one year for a primary outcome of superiority for aesthetic outcome and noninferiority for complications. Aesthetic outcome was evaluated by either physician assessed photographic evaluation and by computed photographic assessment using BCCT.core 3.0 artificial intelligence program