Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study

Myung, Yujin; Jeon, Sungmi; Heo, Chan Yeong; Kim, Eun‐Kyu; Kang, Eunyoung; Shin, Hee-Chul; Yang, Eun Joo; Jeong, Jae Hoon

doi:10.1038/s41598-021-85155-z

Cited by 17 publications

(9 citation statements)

References 27 publications

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“…Several studies facilitated GAN-based anomaly detection to diagnoses anomalous lesions in ultrasound images of breast 16 and digital breast tomosynthesis 17 . Myung et al 21 newly published machine learning approaches for predicting complication in reconstructed breast cancer patients, though it did not provide cosmetic evaluation. To our knowledge, no research has been found to evaluate cometic outcome using by the GAN-based approach and its association with major complication after breast reconstruction and PMRT.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

et al. 2022

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The aim of this study is to evaluate cosmetic outcomes of the reconstructed breast in breast cancer patients, using anomaly score (AS) detected by generative adversarial network (GAN) deep learning algorithm. A total of 251 normal breast images from patients who underwent breast-conserving surgery were used for training anomaly GAN network. GAN-based anomaly detection was used to calculate abnormalities as an AS, followed by standardization by using z-score. Then, we reviewed 61 breast cancer patients who underwent mastectomy followed by reconstruction with autologous tissue or tissue expander. All patients were treated with adjuvant radiation therapy (RT) after reconstruction and computed tomography (CT) was performed at three-time points with a regular follow-up; before RT (Pre-RT), one year after RT (Post-1Y), and two years after RT (Post-2Y). Compared to Pre-RT, Post-1Y and Post-2Y demonstrated higher AS, indicating more abnormal cosmetic outcomes (Pre-RT vs. Post-1Y, P = 0.015 and Pre-RT vs. Post-2Y, P = 0.011). Pre-RT AS was higher in patients having major breast complications (P = 0.016). Patients with autologous reconstruction showed lower AS than those with tissue expander both at Pre-RT (2.00 vs. 4.19, P = 0.008) and Post-2Y (2.89 vs. 5.00, P = 0.010). Linear mixed effect model revealed that days after baseline were associated with increased AS (P = 0.007). Also, tissue expander was associated with steeper rise of AS, compared to autologous tissue (P = 0.015). Fractionation regimen was not associated with the change of AS (P = 0.389). AS detected by deep learning might be feasible in predicting cosmetic outcomes of RT-treated patients with breast reconstruction. AS should be validated in prospective studies.

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Section: Discussionmentioning

confidence: 99%

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

et al. 2022

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“…Patients and healthcare providers have been looking for new strategies to improve patient satisfaction and quality of life as the survival rate after breast cancer diagnosis has grown over the last several decades [11][12][13].…”

Section: Discussionmentioning

confidence: 99%

A Novel, Deep Learning-Based, Automatic Photometric Analysis Software for Breast Aesthetic Scoring

Park,

Baek,

Heo

et al. 2023

Arch Plast Surg

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Background: Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods: S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and cm based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results: S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared to physical measurements. However, the nipple-to-inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusions: S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

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“… 27 More recently, a neural network model was shown to accurately predict abdominal donor site complications after autologous breast reconstruction in a 2021 retrospective cohort study from Seoul, Korea. 23 Although a publicly accessible machine learning–based calculator has yet to become available, these early indicators of improved accuracy and prediction capability suggest that this technology may play a major role in the risk calculators of the future.…”

Section: Discussionmentioning

confidence: 99%

An Ounce of Prediction is Worth a Pound of Cure: Risk Calculators in Breast Reconstruction

Oleck

Keshri

Shammas

et al. 2022

Plastic and Reconstructive Surgery - Global Open

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Background: Preoperative risk calculators provide individualized risk assessment and stratification for surgical patients. Recently, several general surgery-derived models have been applied to the plastic surgery patient population, and several plastic surgery-specific calculators have been developed. In this scoping review, the authors aimed to identify and critically appraise risk calculators implemented in postmastectomy breast reconstruction. Methods: A systematic review of the literature was conducted. Included studies described the development of a novel risk calculator, or validation of an existing calculator, in postmastectomy breast reconstruction. Results: In total, 4641 studies met criteria for title and abstract screening. Fortyseven were eligible for full-text review, and 28 met final inclusion criteria. The most common risk calculators included the Breast Reconstruction Risk Assessment score (n = 6 studies), modified frailty index (n = 3), Caprini score (n = 3), and ACS NSQIP calculator (n = 2). Calculators were applied to institutional data (n = 17), NSQIP (n = 6), and Tracking Outcomes in Plastic Surgery (n = 1) databases. Predicted outcomes included general postoperative complications (n = 17), venous thromboembolism/pulmonary embolism (n = 4), infection (n = 2), and patient reported outcomes (n = 2). Model accuracy was reported in 18 studies, and it varied significantly (accurate risk calculator 0.49-0.85). Conclusions: This is the first study to provide a systematic review of available risk calculators for breast reconstruction. Models vary significantly in their statistical basis, predicted outcomes, and overall accuracy. Risk calculators are valuable tools that may aid in individualized risk assessments, preoperative counseling, and expectation management in breast reconstruction.

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Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study

Cited by 17 publications

References 27 publications

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

Feasibility of anomaly score detected with deep learning in irradiated breast cancer patients with reconstruction

A Novel, Deep Learning-Based, Automatic Photometric Analysis Software for Breast Aesthetic Scoring

An Ounce of Prediction is Worth a Pound of Cure: Risk Calculators in Breast Reconstruction

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