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

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.

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

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 2047-2047 ◽  
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.

scholarly journals The INTREST registry: protocol of a multicenter prospective cohort study of predictors of women’s response to integrative breast cancer treatment

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Heidemarie Haller ◽  
Petra Voiß ◽  
Holger Cramer ◽  
Anna Paul ◽  
Mattea Reinisch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Cohort Study ◽  
Cancer Treatment ◽  
Prospective Cohort Study ◽  
Cancer Care ◽  
Prospective Cohort ◽  
Breast Cancer Treatment ◽  
Cancer Data ◽  
Patient Reported

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.

Financial Toxicity During Breast Cancer Treatment: A Qualitative Analysis to Inform Strategies for Mitigation

2021 ◽  
pp. OP.21.00182
Author(s):  
Laila A. Gharzai ◽  
Kerry A. Ryan ◽  
Lauren Szczygiel ◽  
Susan Goold ◽  
Grace Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Treatment ◽  
Financial Distress ◽  
Ductal Carcinoma ◽  
Financial Burden ◽  
Unmet Need ◽  
Metastatic Breast ◽  
Financial Toxicity ◽  
Knowledge Gaps ◽  
Treatment Expectations

PURPOSE: Financial toxicity from cancer treatment is a growing concern. Its impact on patients requires refining our understanding of this phenomenon. We sought to characterize patients' experiences of financial toxicity in the context of an established framework to identify knowledge gaps and strategies for mitigation. METHODS: Semistructured interviews with patients with breast cancer who received financial aid from a philanthropic organization during treatment were conducted from February to May 2020. Interviews were transcribed and coded until thematic saturation was reached, and findings were contextualized within an existing financial toxicity framework. RESULTS: Thirty-two patients were interviewed, of whom 58% were non-Hispanic White. The mean age was 46 years. Diagnoses ranged from ductal carcinoma in situ to metastatic breast cancer. Concordant with an established framework, we found that direct and indirect costs determined objective financial burden and subjective financial distress stemmed from psychosocial, behavioral, and material impact of diagnosis and treatment. We identified expectations as a novel theme affecting financial toxicity. We identified knowledge gaps in treatment expectations, provider conversations, identification of resources, and support-finding and offer strategies for mitigating financial toxicity on the basis of participant responses, such as leveraging support from decision aids and allied providers. CONCLUSION: This qualitative study confirms an existing framework for understanding financial toxicity and identifies treatment expectations as a novel theme affecting both objective financial burden and subjective financial distress. Four knowledge gaps are identified, and strategies for mitigating financial toxicity are offered. Mitigating patients' financial toxicity is an important unmet need in optimizing cancer treatment.

scholarly journals Identification and Management of Lymphedema in Patients With Breast Cancer

2019 ◽  
Vol 15 (5) ◽  
pp. 255-262 ◽  
Author(s):  
Pavankumar Tandra ◽  
Avyakta Kallam ◽  
Jairam Krishnamurthy
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Treatment ◽  
Early Stage ◽  
Financial Burden ◽  
Breast Cancer Treatment ◽  
Initial Diagnosis ◽  
Axillary Surgery ◽  
Incidence Risk ◽  
End Stage

Breast cancer–related lymphedema (BCRL) is a potentially debilitating and often irreversible complication of breast cancer treatment. Risk of BCRL is proportional to the extent of axillary surgery and radiation. Other risk factors include obesity and infections. Given the 5-year survival rate of 90% and its potential impact on the quality of life of survivors of breast cancer, BCRL has become a significant financial burden on the health care system. Minimizing axillary surgery and radiation has been proven to reduce the risk of BCRL. Comprehensive multidisciplinary assessment at the time of initial diagnosis; early referral to physical therapy after surgery; and patient education regarding weight loss, skin, and nail care are cornerstones of the management of early-stage lymphedema. End-stage lymphedema may benefit from referral to a plastic surgeon specializing in lymphedema surgery. In this review, we attempt to review the incidence, risk factors, staging, prevention, and management of this complication of breast cancer treatment. We also describe our multidisciplinary approach for the prevention of this complication at the time of initial diagnosis.

Patient reported insomnia after breast cancer treatment in breast cancer patients

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

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

2020 ◽  
Vol 38 (7) ◽  
pp. 734-743 ◽  
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.

scholarly journals OUT OF POCKETS SPENDING AMONG BREAST CANCER WOMEN RECEIVING OUT-PATIENT TREATMENT IN A TERTIARY TEACHING HOSPITAL IN KUALA LUMPUR MALAYSIA

2021 ◽  
Vol 21 (3) ◽  
pp. 240-249
Author(s):  
Jamilah Yusoff ◽  
Aniza Ismail
Keyword(s):  
Breast Cancer ◽  
Adjuvant Therapy ◽  
Cancer Treatment ◽  
Financial Burden ◽  
Breast Cancer Treatment ◽  
Patient Treatment ◽  
Cost Calculation ◽  
Cost Component ◽  
Cross Sectional ◽  
Tertiary Teaching

There is lack of information about the estimates of out-of-pocket (OOP) spending among women diagnosed with breast cancer and effects on their family’s financial situation in Malaysia. This study is to determine estimates of out-of-pocket spending among women with breast cancer in HCTM and identify factors that significantly associated with high OOP spending. This cross-sectional study assessed out of pockets spending among breast cancer women in HCTM for breast cancer as out-patient. Sampling unit is a woman who has been diagnosed breast cancer and receiving treatment and follows up in HCTM. A 189 breast cancer women were identified for a 6 months period between July to December 2017. Only those patients who received at least 6 months treatment in HCTM were recruited. Out of pocket spending for treatments for adjuvant therapy and follow up, consultations with other practitioners, care taker fee, equipment and traditional/complementary treatment were collected by face-to-face interview using structured questionnaires. The dependent variable was OOP, analyzed using SSPS v23 for descriptive analysis and both univariate and multivariate analysis.173 out of 186 women were recruited and only 172 were considered for cost calculation (participation rate, 93.0 %) in this study. Total out of pocket spending for breast cancer treatment in 2017 was RM 1,037,446.23 with women’s total means out of pocket spending was RM6031.66 (SD =/- 495.58). The highest percentage of cost component contributed by cost of adjuvant therapy, RM334,056.00 (30.2%) followed by traditional/complementary medicine, RM268,278.00 (24.3%), other expenses/private hospital, RM224,887.00 (20.4%), transportation RM 79,772.52 (7.2%), chemotherapy RM67,300.00 (6.1%), dressing RM54,850.00 (5.0%), equipment/prosthesis RM50,550.00 (4.6%) and care taker cost RM24,661.43 (2.2%). Out of pockets costs from breast cancer in HCTM for 2017 are a burden for women and their family. Financial support provides assistance to the out-of-pocket spending in breast cancer treatment and is able to reduce the financial burden among patients and families.  

Patient-reported benefit from proposed interventions to reduce financial hardship during cancer treatment.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7080-7080
Author(s):  
Emeline Aviki ◽  
Fumiko Chino ◽  
Julia Ramirez ◽  
Victoria Susana Blinder ◽  
Jennifer Jean Mueller ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Cancer Patients ◽  
Financial Burden ◽  
Gynecologic Cancer ◽  
Financial Hardship ◽  
Care Providers ◽  
Financial Toxicity ◽  
The Past ◽  
Patient Reported ◽  
The Impact

7080 Background: Awareness of cancer patients’ financial toxicity (FT) has increased substantially over the past decade; however, interventions to minimize financial burden remain underdeveloped and understudied. This survey-based study explores patient beliefs on which potential mitigating strategies could improve their financial hardship during cancer treatment. Methods: Interviewer-administered surveys were conducted with consecutive patients in an outpatient, urban, private academic Gynecologic Cancer clinic waiting room for 2 weeks in August 2019. The survey items included patient demographics, disease characteristics, the Comprehensive Score for Financial Toxicity (COST) tool (validated measure of FT with score 0-44; lower scores indicate worse FT), assessment of cost-coping strategies, and patient-reported anticipated benefit from described potential interventions (items that were feasible and relevant to implement in clinic). Results: Of 101 patients who initiated the survey, 87 (86%) completed it and were included in this analysis. The median age was 66 (range, 32-87). Thirty-eight patients (44%) had ovarian, 29 (33%) uterine, 5 (6%) cervical, and 15 (17%) an “other” gynecologic cancer. The median COST score was 32 (range, 6-44). Twenty-nine patients (33%) had COST scores ≤25 and 16 (18%) had COST scores ≤18. The most frequent cost-coping strategy reported was reducing leisure activities (n = 36, 41%) and using savings to pay for medical bills (n = 34, 39%). Six patients (7%) reported not taking a prescribed medication in the past 12 months due to the inability to pay and 0 reported skipping a recommended imaging study. When it came to interventions patients anticipated would improve their current financial hardships, 34 (39%) indicated access to transportation assistance to and from appointments, 31 (36%) said “knowing up front how much I’m going to have to pay for my healthcare”, 29 (33%) indicated “minimizing wait time associated with appointments, which keeps me away from work”, and 22 (25%) indicated “access to free food during/around appointments and treatments”. Only 26 (30%) noted they were not experiencing financial hardship. Conclusions: For an outpatient population of gynecologic cancer patients, several focused, feasible interventions could be implemented to potentially decrease patient FT. Our study can help health care providers in the design of interventions to create meaningful improvements in patient financial burden. Next steps should assess the impact of targeted interventions on patient outcomes.

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