Use of Symmetry Assessment Methods in the Context of Breast Surgery

Error assessment of subjective estimates of linear breast dimensions versus the objective method

Objective

The study aimed to investigate the subjective method of estimating linear breast dimensions in comparison to the objective method.

Methods

The reproducibility and accuracy of the subjective method of estimating linear breast dimensions during a simplified breast shape analysis were examined. Four linear breast dimensions including the distance from the sternal notch to the nipple, distance from the nipple to the inframammary fold, distance from the nipple to the midline and under-breast width were evaluated based on subjective estimates. Images from 100 women with natural breasts and without any history of breast surgery were reviewed by two examiners three times each. The cases were obtained from a large database of breast images captured using the Vectra Camera System (Canfield Scientific Inc., USA). The subjective data were then compared with the objective linear data from the Vectra Camera System in the automated analysis. Statistical evaluation was conducted between the three repeated estimates of each examiner, between the two examiners and between the objective and subjective data.

Results

The intra-individual variations of the three subjective estimates were significantly greater in one examiner than in the other. This trend was consistent across all eight parameters in the majority of the comparisons of the standard deviations and variation coefficients, and the differences were significant in 14 out of 16 comparisons (p<0.05). Conversely, in the comparison between the subjective and objective data, the estimates were closer to the measurements in one examiner than the other. In contrast to the reproducibility observed, the assessment of the accuracy revealed that the examiner who previously presented with less reproducibility of the estimated data overall showed better accuracy in comparison to the objective data. The overall differences were inconsistent, with some being positive and others being negative. Regarding the distances from the sternal notch to the nipple and breast width, both examiners underestimated the values. However, the deviations were at different levels, particularly when considering the objective data from the Vectra Camera System as the gold standard data for comparison. Regarding the distance from the nipple to the inframammary fold, one examiner underestimated the distance, while the other overestimated it. An opposite trend was noted for the distance from the nipple to the midline. There were no differences in the estimates between the right and left sides of the breasts. The correlations between the measured and estimated distances were positive: as the objective distances increased, the subjective distances also increased. In all cases, the correlations were significant. However, the correlation for the breast width was notably weaker than that for the other distances.

Conclusions

The error assessment of the subjective method reveals that it varies significantly and unsystematically between examiners. This is true when assessing the reproducibility as well as the accuracy of the method in comparison to the objective data obtained with an automated system.

A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients

INTRODUCTION

Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc.

MATERIALS AND METHODS

We combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis.

RESULTS

Our model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans.

CONCLUSIONS

Our AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Identification of Key Breast Features Using a Neural Network: Applications of Machine Learning in the Clinical Setting of Plastic Surgery

BACKGROUND

In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network.

METHODS

An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery.

RESULTS

The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds.

CONCLUSIONS

The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.

Evaluation of Vectra® XT 3D Surface Imaging Technology in Measuring Breast Symmetry and Breast Volume

BACKGROUND

Breast symmetry is an essential component of breast cosmesis. The Harvard Cosmesis scale is the most widely adopted method of breast symmetry assessment. However, this scale lacks reproducibility and reliability, limiting its application in clinical practice. The VECTRA® XT 3D (VECTRA®) is a novel breast surface imaging system that, when combined with breast contour measuring software (Mirror®), aims to produce a more accurate and reproducible measurement of breast contour to aid operative planning in breast surgery.

OBJECTIVES

This study aims to compare the reliability and reproducibility of subjective (Harvard Cosmesis scale) with objective (VECTRA®) symmetry assessment on the same cohort of patients.

METHODS

Patients at a tertiary institution had 2D and 3D photographs of their breasts. Seven assessors scored the 2D photographs using the Harvard Cosmesis scale. Two independent assessors used Mirror® software to objectively calculate breast symmetry by analysing 3D images of the breasts.

RESULTS

Intra-observer agreement ranged from none to moderate (kappa - 0.005-0.7) amongst the assessors using the Harvard Cosmesis scale. Inter-observer agreement was weak (kappa 0.078-0.454) amongst Harvard scores compared to VECTRA® measurements. Kappa values ranged 0.537-0.674 for intra-observer agreement (p < 0.001) with Root Mean Square (RMS) scores. RMS had a moderate correlation with the Harvard Cosmesis scale (r_s = 0.613). Furthermore, absolute volume difference between breasts had poor correlation with RMS (R² = 0.133).

CONCLUSION

VECTRA® and Mirror® software have potential in clinical practice as objectifying breast symmetry, but in the current form, it is not an ideal test.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

An Intraoperative Measurement Method of Breast Symmetry Using Three-Dimensional Scanning Technique in Reduction Mammaplasty

BACKGROUND

Intraoperative symmetrical assessment plays a decisive role in the aesthetic results of reduction mammaplasty, but it depends mostly on the surgeons' experience that may be biased by individual subjective factors. This study was intended to propose an objective method based on a hand-held three-dimensional (3D) scanner to assist intraoperative symmetrical assessment, aiming to achieve better aesthetic results in reduction mammaplasty.

METHODS

Sixty patients were enrolled in the study from April 2018 to January 2020. Intraoperative 3D scanning was routinely performed on 29 patients (study group) to assist symmetrical adjustments during breast shaping. 3D surface scanning data of both groups were obtained at 3 months postoperatively to objectively assess breast symmetry. Postoperative symmetry scores in five aspects, including nipple-areolar complex position, inframammary-fold height, breast size, shape, and footprint, were rated by six independent observers based on anonymized photographs to subjectively evaluate pre- and postoperative breast symmetry of the two groups.

RESULTS

The bilateral breast volume difference of the study group was significantly smaller than the control group (39.1 vs. 113.3 cm³, p = 0.001), as well as the difference in nipple to inframammary-fold distance (2.79 vs. 7.43 mm, p = 0.01). The observer-reported results showed that breast reduction significantly improved postoperative symmetry in all five aspects compared with preoperative symmetry in the study group (P<0.001). Furthermore, postoperative symmetrical ratings of all five aspects in the study group were statistically better than the control group (P<0.05).

CONCLUSIONS

Intraoperative 3D scanning provided a reliable method to assist symmetry adjustments and ensure better postoperative breast symmetry in reduction mammaplasty.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Evaluation of aesthetic outcomes of breast-conserving surgery by the surgeon, nurse, and patients: An analysis

BACKGROUND

Evaluation of the cosmetic outcome after breast-conserving surgery (BCS) differs depending on the evaluator. We performed a clinical trial to examine the differences between assessments of cosmetic outcomes performed by a surgeon, patients, and a nurse as a third party after BCS; the evaluation was performed two times (at 3 months and 9 months after surgery). Similarly, we identified factors most significantly affecting the overall cosmetic outcomes.

METHODS

Sixty-eight patients with primary breast cancer who had undergone BCS between September 2017 and December 2018 were consecutively enrolled in the study. Breast shape, symmetry, hardness, scarring, and overall outcomes were evaluated by a surgeon, patients, and a nurse via a questionnaire.

RESULTS

Intraclass correlation coefficients (ICCs) for the 3- to 9-month comparisons of the surgeon, patients, and nurse were 0.73, 0.64, and 0.29, respectively. The ICCs for the surgeon-patient, nurse-patient, and surgeon-nurse comparisons (3 months/9 months) were 0.49/0.44, 0.34/0.10, and 0.41/0.51, respectively. The partial regression coefficient for shape was 0.45 (p = 0.003)/0.61 (p = 0.001), 0.37 (p = 0.005)/0.50 (p < 0.001), and -0.08 (p = 0.48)/0.58 (p < 0.001) for evaluations performed by the surgeon, patients, and nurse, 3 months and 9 months, respectively.

CONCLUSION

With reproducibility, only moderate agreement was observed between the surgeon and the patients. Breast shape was identified as the most important factor affecting cosmetic outcomes.