Towards data-driven decision-making for breast cancer patients undergoing mastectomy and reconstruction: accurate prediction of individual patient-reported outcomes at 2-year follow-up using machine learning

520 Background: Post-surgical satisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction. Current decision making relies on group-level evidence, which may not offer optimal choice of treatment for individuals. We developed and validated machine learning algorithms to predict individual post-surgical breast-satisfaction. We aim to facilitate individualized data-driven decision making in breast cancer. Methods: We collected clinical, perioperative, and patient-reported data from 3058 women who underwent breast reconstruction due to breast cancer across 11 sites in North America. We trained and evaluated four algorithms (regularized regression, Support Vector Machine, Neural Network, Regression Tree) to predict significant changes in satisfaction with breasts at 2-year follow up using the validated BREAST-Q measure. Accuracy and area under the receiver operating characteristics curve (AUC) were used to determine algorithm performance in the test sample. Results: Machine learning algorithms were able to accurately predict changes in women’s satisfaction with breasts (see table). Baseline satisfaction with breasts was the most informative predictor of outcome, followed by radiation during or after reconstruction, nipple-sparing and mixed mastectomy, implant-based reconstruction, chemotherapy, unilateral mastectomy, lower psychological well-being, and obesity. Conclusions: We reveal the crucial role of patient-reported outcomes in determining post-operative outcomes and that Machine Learning algorithms are suitable to identify individuals who might benefit from alternative treatment decisions than suggested by group-level evidence. We provide a web-based tool for individuals considering mastectomy and reconstruction. importdemo.com . Clinical trial information: NCT01723423 . [Table: see text]

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SERAPHINA – Safety, Efficacy and patient Reported outcomes of Advanced breast cancer Patients: tHerapy management wIth NAb-paclitaxel in daily routine (a non-interventional study)

Geburtshilfe und Frauenheilkunde ◽

10.1055/s-0036-1592816 ◽

2016 ◽

Vol 76 (10) ◽

Author(s):

PA Fasching ◽

MP Lux ◽

L Häberle ◽

M Wallwiener ◽

A Schneeweiss ◽

...

Keyword(s):

Breast Cancer ◽

Advanced Breast Cancer ◽

Cancer Patients ◽

Patient Reported Outcomes ◽

Advanced Breast ◽

Daily Routine ◽

Breast Cancer Patients ◽

Interventional Study ◽

Therapy Management ◽

Patient Reported

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ASO Visual Abstract: Early Trajectories of Patient-Reported Outcomes in Breast Cancer Patients Undergoing Lumpectomy Versus Mastectomy

Annals of Surgical Oncology ◽

10.1245/s10434-021-10590-1 ◽

2021 ◽

Author(s):

Victoria Huynh ◽

Kathryn Colborn ◽

Shelby Smith ◽

Levi N. Bonnell ◽

Gretchen Ahrendt ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Patient Reported Outcomes ◽

Breast Cancer Patients ◽

Patient Reported

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The Significance of Patient-Reported Outcomes for Metastatic Breast Cancer Patients

Oncology Research and Treatment ◽

10.1159/000521826 ◽

2022 ◽

Author(s):

Larissa Elisabeth Hillebrand ◽

Ulrike Söling ◽

Norbert Marschner

Keyword(s):

Breast Cancer ◽

Decision Making ◽

Metastatic Breast Cancer ◽

Cancer Treatment ◽

Patient Reported Outcomes ◽

Metastatic Breast ◽

Treatment Decision ◽

Breast Cancer Patients ◽

Treatment Decision Making ◽

Patient Reported

Background: Breast cancer is still the most common malignancy in women worldwide. Once metastasized, breast cancer treatment primarily aims at reducing symptom burden, thereby trying to maintain and improve a patient´s quality of life (QoL), delaying disease progression, and prolonging survival. Curing the disease is not possible in the palliative setting. To better understand metastatic breast cancer patients, their symptoms and wishes, which are important for treatment-decision making and outcome, patient-reported outcomes (PROs) are of great importance, giving an impression of what really matters to and concerns a patient. Summary: Many advances have been made to implicate PROs in clinical trials, non-interventional studies, registries, and clinical routine care of metastatic breast cancer. For example, large phase III trials like PALOMA-3 (NCT01942135), MONALEESA-7 (NCT02278120), HER2CLIMB (NCT02614794), and KEYNOTE-119 (NCT02555657) trials implemented PROs in their trial design to assess the QoL of their trial patients. Also, non-interventional studies on metastatic breast cancer, like e.g., the NABUCCO study (IOM-02240), and prospective non-interventional, multicenter registries e.g., the tumor registry breast cancer (NCT01351584) or the breast cancer registry platform OPAL (NCT03417115), have implemented PROs to assess QoL during the anti-cancer treatment periods of the patients. Key Message: Using PROs in metastatic breast cancer can support shared treatment-decision making and management of symptoms, eventually leading to an improvement in QoL. Progressively, regulatory authorities take PROs into consideration for the approval of new drugs. Hence, the implication of PROs in cancer treatment, and especially in MBC, is of significant value.

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