Maximizing surgical precision in gracilis muscle reconstruction: anatomical Variants, advanced Imaging, and novel surgical strategies.
Abstract
[BACKGROUND] The gracilis muscle (GM) is a reliable flap in reconstructive microsurgery due to its consistent anatomy and low donor-site morbidity. However, morphological, vascular, and neural variations may complicate surgical planning and outcomes.
[METHODS] A narrative review was conducted based on anatomical studies, imaging reports, and surgical case series sourced from PubMed and Scopus (up to 2025), with emphasis on anatomical variants, imaging modalities, and reconstructive strategies.
[RESULTS] Key variants include supernumerary heads, accessory slips, multiple vascular pedicles, and diverse innervation patterns. These factors affect flap design, harvesting, and reinnervation success. Advanced imaging techniques such as CTA, MRA, Doppler ultrasound, and ICG angiography enable precise preoperative mapping. We propose two clinical frameworks—the Gracilis Functional Flap Classification (GFFC) and the Gracilis Innervation-Based Surgical Planning Algorithm (GISPA)—to integrate anatomical variation into individualized surgical planning.
[CONCLUSIONS] GM anatomical variability significantly influences reconstructive outcomes. The integration of GFFC and GISPA, supported by imaging and emerging technologies, enhances surgical precision and functional results in microsurgical applications.
[METHODS] A narrative review was conducted based on anatomical studies, imaging reports, and surgical case series sourced from PubMed and Scopus (up to 2025), with emphasis on anatomical variants, imaging modalities, and reconstructive strategies.
[RESULTS] Key variants include supernumerary heads, accessory slips, multiple vascular pedicles, and diverse innervation patterns. These factors affect flap design, harvesting, and reinnervation success. Advanced imaging techniques such as CTA, MRA, Doppler ultrasound, and ICG angiography enable precise preoperative mapping. We propose two clinical frameworks—the Gracilis Functional Flap Classification (GFFC) and the Gracilis Innervation-Based Surgical Planning Algorithm (GISPA)—to integrate anatomical variation into individualized surgical planning.
[CONCLUSIONS] GM anatomical variability significantly influences reconstructive outcomes. The integration of GFFC and GISPA, supported by imaging and emerging technologies, enhances surgical precision and functional results in microsurgical applications.
추출된 의학 개체 (NER)
| 유형 | 영어 표현 | 한국어 / 풀이 | UMLS CUI | 출처 | 등장 |
|---|---|---|---|---|---|
| 시술 | flap
|
피판재건술 | dict | 3 | |
| 시술 | microsurgery
|
미세수술 | dict | 1 | |
| 해부 | gracilis muscle
|
scispacy | 1 | ||
| 해부 | neural
|
scispacy | 1 | ||
| 약물 | GFFC
→ Gracilis Functional Flap Classification
|
scispacy | 1 | ||
| 약물 | [BACKGROUND] The
|
scispacy | 1 | ||
| 약물 | [RESULTS]
|
scispacy | 1 | ||
| 약물 | ICG
|
scispacy | 1 | ||
| 약물 | [CONCLUSIONS] GM
|
scispacy | 1 | ||
| 질환 | gracilis muscle
|
scispacy | 1 | ||
| 기타 | vascular
|
scispacy | 1 |
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