Machine learning increasingly supports physician decisions by making it easier to detect patterns in data as a means of predicting patient outcomes. In breast cancer, that now could apply to every stage of the experience, from diagnostics to mastectomy and breast reconstruction. At the annual meeting of the American Society of Clinical Oncology — which was virtual this year, due to the ongoing coronavirus pandemic — a consortium of researchers presented an abstract detailing how machine learning algorithms were able to correctly predict how individual patients would feel about their breast reconstruction.1 Using this tool in a clinical setting could help physicians guide patients through the recovery process in a way that better anticipates, and subsequently supports, their emotional reaction to this intensely personal medical procedure.
Physician-researchers across 11 institutions in the United States and Canada trained 4 different types of machine learning algorithms — regularized regression, Support Vector Machine, Neural Network, Regression Tree — to predict with 95% accuracy whether a specific patient would be satisfied or dissatisfied with their breast reconstruction 2 years after their operation.
The algorithms were trained on data collected during a 5-year clinical trial of 4436 patients that assessed patient-reported outcomes in response to different techniques in breast reconstruction — crucially, it included both patients who underwent reconstruction jointly with mastectomy and those who held off on a secondary, postmastectomy reconstruction procedure until a later date.
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Allowing machine learning to pick up where the clinical trial left off is the next step in the trial’s mission: “In order to actively participate in the reconstruction decision-making process, breast cancer survivors need objective, up-to-date information on breast reconstruction outcomes from the patient’s perspective.”
Increasingly, aiming for breast conservation is the gold standard in primary therapy for breast cancer, but for those who ultimately must undergo mastectomy, the postmastectomy options can be hard for patients to wade through. The choices are multifold. One option for many patients, of course, is to stop at the point of mastectomy. Not every patient medically requires a breast reconstruction; some instead choose to wear bras outfitted with breast prostheses, while others go without them altogether.
For patients who do consider breast reconstruction critical to their psychological well-being, sense of self, and quality of life, however, a second level of choices abounds for which reconstruction technique will yield the best results for them: expander/implant, latissimus dorsi/implant (LD), pedicle transverse rectus abdominis musculocutaneous (PTRAM), free TRAM (FTRAM), deep inferior epigastric perforator (DIEP), superficial inferior epigastric artery (SIEA), superior gluteal artery perforator (SGAP), or inferior gluteal artery perforator (IGAP).
