Comparative analysis of volumetric changes between resection volume of oral tongue cancer and post operative volume of radial forearm flaps.
Abstract
[OBJECTIVES] This study investigates the relationship between the total volume of oral tongue cancer pre-operatively and the RFFF volume post-operatively.
[MATERIALS AND METHODS] A total of 52 DICOM imaging datasets (CT or MRI) of 26 patients were included in this study. The volume of the desired structure was quantified using semi-automatic segmentation using the software ITK-SNAP. All extracted measurements were validated by two further clinicians at separate instances.
[RESULTS] The variation of MeanVolTu can be predicted by MeanVolFlap moderately reliable with 59.1% confidence (R-Qua: 0.591). ANOVA Testing to represent how well the regression line fits the data, resulted in the overall regression model being statistically significant in predicting the MeanVolTu (p < 0.001). The flap volume may be predicted using the following algorithm: MeanVolFlap0 = 3241,633 + 1, 322 * MeanVolTu.
[CONCLUSION] The results of this study show positive correlation between tumor volume and flap volume, highlighting the significance of efficient flap planning with increasing tumor volume. A larger extraction volume of the radial forearm free flap from the donor site compromises the forearm more, thus increasing the probability of post-operative complications.
[CLINICAL RELEVANCE] Radial forearm free flap design in accordance with its corresponding 3D tumor volume.
[MATERIALS AND METHODS] A total of 52 DICOM imaging datasets (CT or MRI) of 26 patients were included in this study. The volume of the desired structure was quantified using semi-automatic segmentation using the software ITK-SNAP. All extracted measurements were validated by two further clinicians at separate instances.
[RESULTS] The variation of MeanVolTu can be predicted by MeanVolFlap moderately reliable with 59.1% confidence (R-Qua: 0.591). ANOVA Testing to represent how well the regression line fits the data, resulted in the overall regression model being statistically significant in predicting the MeanVolTu (p < 0.001). The flap volume may be predicted using the following algorithm: MeanVolFlap0 = 3241,633 + 1, 322 * MeanVolTu.
[CONCLUSION] The results of this study show positive correlation between tumor volume and flap volume, highlighting the significance of efficient flap planning with increasing tumor volume. A larger extraction volume of the radial forearm free flap from the donor site compromises the forearm more, thus increasing the probability of post-operative complications.
[CLINICAL RELEVANCE] Radial forearm free flap design in accordance with its corresponding 3D tumor volume.
추출된 의학 개체 (NER)
| 유형 | 영어 표현 | 한국어 / 풀이 | UMLS CUI | 출처 | 등장 |
|---|---|---|---|---|---|
| 시술 | flap
|
피판재건술 | dict | 3 | |
| 시술 | free flap
|
피판재건술 | dict | 2 | |
| 해부 | RFFF
|
scispacy | 1 | ||
| 해부 | line
|
scispacy | 1 | ||
| 해부 | forearm
|
scispacy | 1 | ||
| 약물 | [OBJECTIVES]
|
scispacy | 1 | ||
| 질환 | cancer
|
C0006826
Malignant Neoplasms
|
scispacy | 1 | |
| 질환 | fits
|
C0036572
Seizures
|
scispacy | 1 | |
| 질환 | tumor
|
C0027651
Neoplasms
|
scispacy | 1 | |
| 질환 | oral tongue cancer
|
scispacy | 1 | ||
| 질환 | 3D tumor
|
scispacy | 1 | ||
| 기타 | patients
|
scispacy | 1 |
MeSH Terms
Humans; Tongue Neoplasms; Forearm; Male; Female; Middle Aged; Magnetic Resonance Imaging; Tumor Burden; Tomography, X-Ray Computed; Aged; Free Tissue Flaps; Adult; Algorithms
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