Showing posts with label Machine learning. Show all posts
Showing posts with label Machine learning. Show all posts

Thursday, 10 February 2022

 

Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy

 

by Naoum, George E.; Ho, Alice Y.; Shui, Amy; Salama, Laura; Goldberg, Saveli; Arafat, Waleed; Winograd, Jonathan; Colwell, Amy; Smith, Barbara L.; Taghian, Alphonse G. 

 

Plastic and Reconstructive Surgery: January 2022 - Volume 149 - Issue 1 - p 1e-12e

 

Background: 

The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications with or without postmastectomy radiation therapy.

Methods: 

Between 1997 and 2017, 1617 breast cancer patients undergoing mastectomy and breast reconstruction were analyzed. Those with autologous, tissue expander/implant, and single-stage direct-to-implant reconstruction were included. Postmastectomy radiation therapy was delivered either with three-dimensional conformal photon or proton therapy. Complication endpoints were defined based on surgical reintervention operative notes as infection/necrosis requiring débridement. For implant-based patients, complications were defined as capsular contracture requiring capsulotomy and implant failure. For each complication endpoint, least absolute shrinkage and selection operator–penalized regression was used to select the subset of predictors associated with the smallest prediction error from 10-fold cross-validation. Nomograms were built using the least absolute shrinkage and selection operator–selected predictors, and internal validation using cross-validation was performed.

Results: 

Median follow-up was 6.6 years. Among 1617 patients, 23 percent underwent autologous reconstruction, 39 percent underwent direct-to-implant reconstruction, and 37 percent underwent tissue expander/implant reconstruction. Among 759 patients who received postmastectomy radiation therapy, 8.3 percent received proton-therapy to the chest wall and nodes and 43 percent received chest wall boost. Internal validation for each model showed an area under the receiver operating characteristic curve of 73 percent for infection, 75 percent for capsular contracture, 76 percent for absolute implant failure, and 68 percent for overall implant failure. Periareolar incisions and complete implant muscle coverage were found to be important predictors for infection and capsular contracture, respectively. In a multivariable analysis, we found that protons compared to no postmastectomy radiation therapy significantly increased capsular contracture risk (OR, 15.3; p < 0.001). This was higher than the effect of photons with electron boost versus no postmastectomy radiation therapy (OR, 2.5; p = 0.01).

Conclusion: 

Using machine learning, these nomograms provided prediction of postmastectomy breast reconstruction complications with and without radiation therapy.

CLINICAL QUESTION/LEVEL OF EVIDENCE: 

Risk, III.

 

 

Thursday, 7 October 2021

 

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

 

by André Pfob, Babak J. Mehrara, Jonas A. Nelson, Edwin G. Wilkins, Andrea L. Pusic, Chris Sidey-Gibbons 

 

The Breast: VOLUME 60, P111-122, DECEMBER 01, 2021

 

Background

Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer.

Methods

We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristics-curve (AUC) was the primary outcome measure.

Results

Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n = 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86–0.87 and for decreased satisfaction AUCs were 0.84–0.85.

Conclusion

Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life.