Patient reported outcomes of satisfaction with bilateral breast reduction surgery for breast cancer treatment

Abstract Background Cancer registries usually assess data of conventional treatments and/or patient survival. Beyond that, little is known about the influence of other predictors of treatment response related to the use of complementary therapies (CM) and lifestyle factors affecting patients’ quality and quantity of life. Methods INTREST is a prospective cohort study collecting register data at multiple German certified cancer centers, which provide individualized, integrative, in- and outpatient breast cancer care. Patient-reported outcomes and clinical cancer data of anticipated N = 715 women with pTNM stage I-III breast cancer are collected using standardized case report forms at the time of diagnosis, after completing neo−/adjuvant chemotherapy, after completing adjuvant therapy (with the exception of endocrine therapy) as well as 1, 2, 5, and 10 years after baseline. Endpoints for multivariable prediction models are quality of life, fatigue, treatment adherence, and progression-based outcomes/survival. Predictors include the study center, sociodemographic characteristics, histologic cancer and comorbidity data, performance status, stress perception, depression, anxiety, sleep quality, spirituality, social support, physical activity, diet behavior, type of conventional treatments, use of and belief in CM treatments, and participation in a clinical trial. Safety is recorded following the Common Terminology Criteria for Adverse Events. Discussion This trial is currently recruiting participants. Future analyses will allow to identify predictors of short- and long-term response to integrative breast cancer treatment in women, which, in turn, may improve cancer care as well as quality and quantity of life with cancer. Trial registration German Clinical Trial Register DRKS00014852. Retrospectively registered at July 4th, 2018.

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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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Patient reported insomnia after breast cancer treatment in breast cancer patients

European Journal of Cancer ◽

10.1016/s0959-8049(18)30322-8 ◽

2018 ◽

Vol 92 ◽

pp. S32

Author(s):

M. Gregorowitsch ◽

A. Swart ◽

D. Young Afat ◽

D. Van den Bongard ◽

H. Verkooijen

Keyword(s):

Breast Cancer ◽

Cancer Treatment ◽

Cancer Patients ◽

Breast Cancer Treatment ◽

Breast Cancer Patients ◽

Patient Reported

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Impact of Breast Cancer Treatment on Employment: Results of a Multicenter Prospective Cohort Study (CANTO)

Journal of Clinical Oncology ◽

10.1200/jco.19.01726 ◽

2020 ◽

Vol 38 (7) ◽

pp. 734-743 ◽

Cited By ~ 6

Author(s):

Agnes Dumas ◽

Ines Vaz Luis ◽

Thomas Bovagnet ◽

Mayssam El Mouhebb ◽

Antonio Di Meglio ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Treatment ◽

Psychological Symptoms ◽

Depression Scale ◽

Breast Cancer Diagnosis ◽

Breast Cancer Treatment ◽

European Organization ◽

Logistic Regression Models ◽

Arm Morbidity ◽

Patient Reported

PURPOSE Adverse effects of breast cancer treatment can negatively affect survivors’ work ability. Previous reports lacked detailed clinical data or health-related patient-reported outcomes (PROs) and did not prospectively assess the combined impact of treatment and related sequelae on employment. METHODS We used a French prospective clinical cohort of patients with stage I-III breast cancer including 1,874 women who were working and ≥ 5 years younger than legal retirement age (≤ 57 years) at breast cancer diagnosis. Our outcome was nonreturn to work (non-RTW) 2 years after diagnosis. Independent variables included treatment characteristics as well as toxicities (Common Toxicity Criteria Adverse Events [CTCAE] v4) and PROs (European Organization for Research and Treatment of Cancer [EORTC] Quality of life Questionnaires, Breast cancer module [QLQ-BR23] and Fatigue module [QLQ-FA12], Hospital Anxiety and Depression Scale) collected 1 year after diagnosis. Logistic regression models assessed correlates of non-RTW, adjusting for age, stage, comorbidities, and socioeconomic covariates. RESULTS Two years after diagnosis, 21% of patients had not returned to work. Odds of non-RTW were significantly increased among patients treated with combinations of chemotherapy and trastuzumab (odds ratio [OR] v chemotherapy-hormonotherapy: for chemotherapy-trastuzumab, 2.01; 95% CI, 1.18 to 3.44; for chemotherapy-trastuzumab-hormonotherapy, 1.62; 95% CI, 1.10 to 2.41). Other significant associations with non-RTW included grade ≥ 3 CTCAE toxicities (OR v no, 1.59; 95% CI, 1.15 to 2.18), arm morbidity (OR v no, 1.59; 95% CI, 1.19 to 2.13), anxiety (OR v no, 1.47; 95% CI, 1.02 to 2.11), and depression (OR v no, 2.29; 95% CI, 1.34 to 3.91). CONCLUSION Receipt of systemic therapy combinations including trastuzumab was associated with increased odds of non-RTW. Likelihood of unemployment was also higher among patients who reported severe physical and psychological symptoms. This comprehensive study identifies potentially vulnerable patients and warrants supportive interventional strategies to facilitate their RTW.

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Development of Machine Learning Algorithms for the Prediction of Financial Toxicity in Localized Breast Cancer Following Surgical Treatment

JCO Clinical Cancer Informatics ◽

10.1200/cci.20.00088 ◽

2021 ◽

pp. 338-347

Author(s):

Chris Sidey-Gibbons ◽

André Pfob ◽

Malke Asaad ◽

Stefanos Boukovalas ◽

Yu-Li Lin ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cancer Treatment ◽

Linear Model ◽

Financial Burden ◽

Breast Cancer Treatment ◽

Support Vector ◽

Financial Toxicity ◽

Operating Characteristics ◽

Patient Reported

PURPOSE Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We sought to develop and test a tool to accurately predict an individual's risk of financial toxicity based on clinical, demographic, and patient-reported data prior to initiation of breast cancer treatment. PATIENTS AND METHODS We surveyed 611 patients undergoing breast cancer therapy at MD Anderson Cancer Center. We collected data using the validated COmprehensive Score for financial Toxicity (COST) patient-reported outcome measure alongside other financial indicators (credit score, income, and insurance status). We also collected clinical and perioperative data. We trained and tested an ensemble of machine learning (ML) algorithms (neural network, regularized linear model, support vector machines, and a classification tree) to predict financial toxicity. Data were randomly partitioned into training and test samples (2:1 ratio). Predictive performance was assessed using area-under-the-receiver-operating-characteristics-curve (AUROC), accuracy, sensitivity, and specificity. RESULTS In our test sample (N = 203), 48 of 203 women (23.6%) reported significant financial burden. The algorithm ensemble performed well to predict financial burden with an AUROC of 0.85, accuracy of 0.82, sensitivity of 0.85, and specificity of 0.81. Key clinical predictors of financial burden from the linear model were neoadjuvant therapy (βregularized, .11) and autologous, rather than implant-based, reconstruction (βregularized, .06). Notably, radiation and clinical tumor stage had no effect on financial burden. CONCLUSION ML models accurately predicted financial toxicity related to breast cancer treatment. These predictions may inform decision making and care planning to avoid financial distress during cancer treatment or enable targeted financial support. Further research is warranted to validate this tool and assess applicability for other types of cancer.

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Patient-Reported Outcomes in Breast Oncology: A Review of Validated Outcome Instruments

Tumori Journal ◽

10.1177/030089161209800602 ◽

2012 ◽

Vol 98 (6) ◽

pp. 678-688 ◽

Cited By ~ 37

Author(s):

Anastasios Kanatas ◽

Galina Velikova ◽

Brenda Roe ◽

Kieran Horgan ◽

Naseem Ghazali ◽

...

Keyword(s):

Breast Cancer ◽

Quality Of Life ◽

Body Image ◽

Functional Assessment ◽

Patient Reported Outcomes ◽

Breast Cancer Treatment ◽

Body Image Scale ◽

Patient Reported ◽

Related Quality

Aims and background Patient-reported outcomes (PROs) include areas of health-related quality of life but also broader concepts such as patient satisfaction with care. The aim of this review is to give an account of all instruments with potential use in patients with a history of treatment for breast cancer (including surgery, chemotherapy and/or radiotherapy) with evidence of validation in the breast cancer population. Methods All instruments included in this review were identified as PRO measures measuring breast-related quality of life and/or satisfaction that had undergone development and validation with breast oncology patients. We specifically looked for PRO measures examining patient satisfaction and/or quality of life after breast cancer treatment. Following an evaluation of 323 papers, we identified 15 instruments that were able to satisfy our inclusion criteria. Results These instruments are the EORTC QOL-C30 and QLQ-BR23 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Breast Cancer Module), the FACT-B (Functional Assessment of Cancer Therapy-Breast Cancer), the SLDS-BC (Satisfaction with Life Domains Scale for Breast Cancer), the BIBCQ (Body Image after Breast Cancer Questionnaire), the HIBS (Hopwood Body Image Scale), the PBIS (Polivy Body Image Scale), the MBROS (Michigan Breast Reconstruction Outcomes Study) Satisfaction and Body Image Questionnaires, the BREAST-Q, the BCTOS (Breast Cancer Treatment Outcome Scale), the BCQ, the FACT-ES (Functional Assessment of Cancer Therapy-Endocrine System), the MAS (Mastectomy Attitude Scale), and the Breast Cancer Prevention Trial Symptom Checklist (BCPT). Conclusions Suggestions for future directions include (1) to use and utilize validated instruments tailored to clinical practice; (2) to develop a comprehensive measurement of surgical outcome requiring the combination of objective and subjective measures; (3) to aim for a compromise between these two competing considerations in the form of a scale incorporating both generalizability in cancer-related QOL and specificity in breast cancer issues.

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Machine learning algorithms to predict financial toxicity associated with breast cancer treatment.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.2047 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 2047-2047 ◽

Cited By ~ 1

Author(s):

Chris Sidey-Gibbons ◽

Malke Asaad ◽

André Pfob ◽

Stefanos Boukovalas ◽

Yu-Li Lin ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Decision Making ◽

Cancer Treatment ◽

Financial Burden ◽

Breast Cancer Treatment ◽

Support Vector ◽

Financial Toxicity ◽

Operating Characteristics ◽

Patient Reported

2047 Background: Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but objective identification of individuals in need is difficult. Accurate predictions of an individual’s risk of financial toxicity prior to initiation of breast cancer treatment may facilitate informed clinical decision making, reduce financial burden, and improve patient outcomes. Methods: We retrospectively surveyed 611 patients who had undergone breast cancer therapy at MD Anderson Cancer Center to assess the financial impact of their care. All patients were over 18 and received either a lumpectomy or a mastectomy. We collected data using the FACT-COST patient-reported outcome measures alongside other financial indicators including income and insurance status. We extracted clinical and perioperative data from the electronic health record. Missing data were imputed using multiple imputation. We used this data to train and validate a neural network, LASSO-regularized linear model, and support vector machines. Data were randomly partitioned into training and validation samples (3:1 ratio). Analyses were informed by international PROBAST recommendations for developing multivariate predictors. We combined algorithms into a voting ensemble and assessed predictive performance using area under the receiver operating characteristics curve (AUROC), accuracy, sensitivity, and specificity. Results: In our validation sample, 48 of 203 (23.6%) women reported FACT-COST scores commensurate with significant financial burden. The algorithm predicted significant financial burden relating to cancer treatment with high accuracy (Accuracy = .83, AUROC = .82, sensitivity = .81, specificity = .82). Key clinical predictors of financial burden from linear models were neo-adjuvant therapy (βregularized 0.12) and autologous, rather than implant-based, reconstruction (βregularized 0.10). Conclusions: Machine learning models were able to accurately predict the occurrence of financial toxicity related to breast cancer treatment. These predictions may be used to inform decision making and care planning to avoid financial distress during cancer treatment or to enable targeted financial support for individuals. Further research is warranted to further improve this tool and assess applicability for other types of cancer.

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Patient-reported outcomes in breast cancer FDA drug labels and review documents

Journal of Patient-Reported Outcomes ◽

10.1186/s41687-021-00308-y ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Kyungwan Hong ◽

Kayleigh R. Majercak ◽

Ester Villalonga-Olives ◽

Eleanor M. Perfetto

Keyword(s):

Breast Cancer ◽

Patient Reported Outcomes ◽

Breast Cancer Treatment ◽

Cancer Drug ◽

Medical Review ◽

Cancer Drugs ◽

Patient Reported ◽

Approved Drugs ◽

Breast Cancer Drugs ◽

Fda Approved Drugs

Abstract Background Patient-reported outcomes (PROs) can provide valuable information about drug benefit-risk tradeoffs from the patient perspective and are particularly important to patients with breast cancer due to its symptoms and adverse events from breast cancer treatments. The United States Food and Drug Administration (U.S. FDA) has acknowledged PROs as important approval endpoints used in clinical trials of cancer drugs. However, previous studies found that PROs are rarely mentioned in cancer drug labels, a widely used and trusted source of information about drugs. Our objectives were to compare PRO data reported in FDA labeling versus FDA medical review documents for breast cancer drugs approved in the U.S. between 2000 and 2019 to identify possible causes for PRO-data labeling exclusions. Methods We included new molecular entities (NMEs) and biologic license applications (BLAs) initially approved for breast cancer treatment by the FDA between 1/1/2000 and 12/31/2019. Product labeling and FDA medical review documents were collected from the FDA-Approved Drugs database (Drugs@FDA). From these resources, details on PRO measures used in trials, design of trials using PRO measures, PRO-endpoint status, analytical methods, and FDA reviewer comments regarding PRO measurement were extracted. Results Of 633 FDA-approved drugs, 13 were indicated for breast cancer treatment; none of their prescribing information contained information about PROs. However, 11 of 13 (85%) included PRO measures and endpoint information in FDA medical review documents. PRO measures were used in 14 different clinical trials, and FDA reviewers’ comments regarding PRO measurement were related to lack of meaningfulness and clinical significance, lack of content validity, and inadequate analytical methods. Conclusions Despite the importance of PROs to patients with breast cancer, PRO measures were only described in FDA medical review documents of breast cancer drugs, but not in drug product labeling. Therefore, it appears that PRO data are often collected in breast cancer trials, but have not been methodologically acceptable to FDA reviewers. Collaborative efforts between the FDA and industry are warranted to increase the number of breast cancer drug applications with appropriate use of PRO measures and endpoints.

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