Comparison of Cinematic Rendering and maximum intensity projection in abdominal-based autologous breast reconstruction

Introduction: The transverse myocutaneous gracilis (TMG) flap has become a popular and reliable alternative for autologous breast reconstruction. Initially described as a valuable tissue source for women with low body-mass index, indications nowadays have widely expanded. The Western civilization demographic development with its aging population and the steady growing average BMI has led to increasing breast reconstructions with TMG flaps in overweight and aged individuals. Patients and Methods: A total of 300 TMG free flaps for unilateral autologous breast reconstruction were evaluated in the form of a retrospective double center cohort study. Data extraction, study group formation and statistical analysis (One-way analysis of variance (ANOVA), Pearson’s chi-squared statistical analysis and relative risk calculation) were done specifically to evaluate age and BMI as risk factors for postoperative complications and outcome. Results: No significant differences in patients’ age and BMI in the complication groups compared to the no-complication group could be found. No significant difference regarding the occurrence of complications could be found in any of the formed risk-groups. No significant increase of minor-, major- or overall complications, flap loss or revision surgeries were found in the elderly patient groups or for patients with overweight. Conclusion: Age and overweight do not significantly increase the risk for postoperative complications after breast reconstructions with free TMG flaps. The findings of this study support the fact that microsurgical breast reconstruction with a free TMG flap should not solely be reserved for younger patients and females with a lower BMI.

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How Do We Raise the Bar in Autologous Breast Reconstruction? The Use of Progressive Tension Sutures for Donor Site Closure

Plastic & Reconstructive Surgery Global Open ◽

10.1097/01.gox.0000584316.54908.08 ◽

2019 ◽

Vol 7 ◽

pp. 22-23

Author(s):

Kristy L. Hamilton ◽

Andres F. Doval ◽

Kieran S. Boochoon ◽

Aldona J. Spiegel

Keyword(s):

Breast Reconstruction ◽

Donor Site ◽

Autologous Breast Reconstruction ◽

Progressive Tension Sutures

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Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽

10.3390/diagnostics10050330 ◽

2020 ◽

Vol 10 (5) ◽

pp. 330

Author(s):

Mio Adachi ◽

Tomoyuki Fujioka ◽

Mio Mori ◽

Kazunori Kubota ◽

Yuka Kikuchi ◽

...

Keyword(s):

Artificial Intelligence ◽

Magnetic Resonance ◽

Diagnostic Performance ◽

Maximum Intensity Projection ◽

Breast Mri ◽

Magnetic Resonance Images ◽

Maximum Intensity ◽

Validation Data ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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