Health economics of targeted intraoperative radiotherapy (TARGIT-IORT) for early breast cancer: a cost-effectiveness analysis in the United Kingdom

Objective: The clinical effectiveness of targeted intraoperative radiotherapy (TARGIT-IORT) has been confirmed in the randomised TARGIT-A (targeted intraoperative radiotherapy-alone) trial to be similar to a several weeks’ course of whole-breast external-beam radiation therapy (EBRT) in patients with early breast cancer. This study aims to determine the cost effectiveness of TARGIT-IORT to inform policy decisions about its wider implementation. Setting TARGIT-A randomised clinical trial (ISRCTN34086741) which compared TARGIT with traditional EBRT and found similar breast cancer control, particularly when TARGIT was given simultaneously with lumpectomy. Methods: Cost-utility analysis using decision analytic modelling by a Markov model. A cost-effectiveness Markov model was developed using TreeAge Pro V.2015. The decision analytic model compared two strategies of radiotherapy for breast cancer in a hypothetical cohort of patients with early breast cancer based on the published health state transition probability data from the TARGIT-A trial. Analysis was performed for UK setting and National Health Service (NHS) healthcare payer’s perspective using NHS cost data and treatment outcomes were simulated for both strategies for a time horizon of 10 years. Model health state utilities were drawn from the published literature. Future costs and effects were discounted at the rate of 3.5%. To address uncertainty, one-way and probabilistic sensitivity analyses were performed. Main outcome measures: Quality-adjusted life-years (QALYs). Results: In the base case analysis, TARGIT-IORT was a highly cost-effective strategy yielding health gain at a lower cost than its comparator EBRT. Discounted TARGITIORT and EBRT costs for the time horizon of 10 years were £12 455 and £13 280, respectively. TARGIT-IORT gained 0.18 incremental QALY as the discounted QALYs gained by TARGIT-IORT were 8.15 and by EBRT were 7.97 showing TARGIT-IORT as a dominant strategy over EBRT. Model outputs were robust to one-way and probabilistic sensitivity analyses. Conclusions: TARGIT-IORT is a dominant strategy over EBRT, being less costly and producing higher QALY gain

TUmor-volume to breast-volume RAtio for improving COSmetic results in breast cancer patients (TURACOS); a randomized controlled trial

Background: Cosmetic result following breast conserving surgery (BCS) for cancer influences quality of life and psychosocial functioning in breast cancer patients. A preoperative prediction of expected cosmetic result following BCS is not (yet) standard clinical practice and therefore the choice for either mastectomy or BCS is still subjective. Recently, we showed that tumour volume to breast volume ratio as well as tumour location in the breast are independent predictors of superior cosmetic result following BCS. Implementation of a prediction model including both factors, has not been studied in a prospective manner. This study aims to improve cosmetic outcome by implementation of a prediction model in the treatment decision making for breast cancer patients opting for BCS. Methods/design: Multicentre, single-blinded, randomized controlled trial comparing standard preoperative work-up to a preoperative work-up with addition of the prediction model. Tumour volume to bre

A model building exercise of mortality risk for Taiwanese women with breast cancer

Abstract Background The accurate estimation of outcome in patients with malignant disease is an essential component of the optimal treatment, decision-making and patient counseling processes. The prognosis and disease outcome of breast cancer patients can differ according to geographic and ethnic factors. To our knowledge, to date these factors have never been validated in a homogenous loco-regional patient population, with the aim of achieving accurate predictions of outcome for individual patients. To clarify this topic, we created a new comprehensive prognostic and predictive model for Taiwanese breast cancer patients based on a range of patient-related and various clinical and pathological-related variables. Methods Demographic, clinical, and pathological data were analyzed from 1 137 patients with breast cancer who underwent surgical intervention. A survival prediction model was used to allow analysis of the optimal combination of variables. Results The area under the receiver operating characteristic (ROC) curve, as applied to an independent validation data set, was used as the measure of accuracy. Results were compared by comparing the area under the ROC curve. Conclusions our model building exercise of mortality risk was able to predict disease outcome for individual patients with breast cancer. This model could represent a highly accurate prognostic tool for Taiwanese breast cancer patients.</p

Bridging the age gap: a prognostic model that predicts survival and aids in primary treatment decisions for older women with oestrogen receptor‐positive early breast cancer

Background A prognostic model was developed and validated using cancer registry data. This underpins an online decision support tool, informing primary treatment choice for women aged 70 years or older with hormone receptor‐positive early breast cancer. Methods Data from women diagnosed between 2002 and 2010 in the English Northern and Yorkshire and West Midlands regions were used to develop the model. Primary treatment options of surgery with adjuvant endocrine therapy or primary endocrine therapy were compared. Models predicting the hazard of breast cancer‐specific mortality and hazard of other‐cause mortality were combined to derive survival probabilities. The model was validated externally using data from the Eastern Cancer Registration and Information Centre. Results The model was developed using data from 23 842 women, and validated externally on a data set from 14 526 patients. The overall model calibration was good. At 2 and 5 years, predicted mortality from breast cancer and other causes differed from the observed rate by less than 1 per cent. At 5 years, there were slight overpredictions in breast cancer mortality (2629 predicted versus 2556 observed deaths; P  = 0·142) and mortality from all causes (6399 versus 6320 respectively; P  = 0·583). The discrepancy varied between subgroups. Model discrimination was 0·75 or above for all mortality measures. Conclusion A prognostic model for older women with oestrogen receptor‐positive early breast cancer was developed and validated in the present study. This forms a basis for an online decision support tool (https://agegap.shef.ac.uk/)

Measuring decision quality: psychometric evaluation of a new instrument for breast cancer chemotherapy

Background: Women diagnosed with early stage (I or II) breast cancer face a highly challenging decision – whether or not to undergo adjuvant chemotherapy. We developed a decision quality instrument for chemotherapy for early stage breast cancer and sought to evaluate its performance. Methods: Cross-sectional, mailed survey of recent breast cancer survivors, providers, and healthy controls and a retest survey of survivors. The decision quality instrument includes questions on knowledge and personal goals. It results in a knowledge score and concordance score, which reflects the percentage of patients who received treatments that match their goals. Hypotheses related to acceptability, feasibility, validity, and reliability of the survey instrument were examined. Results: Responses were received from 352 patients, 89 providers and 35 healthy controls. The decision quality instrument was feasible to implement with few missing data. The knowledge scores had good retest reliability (intraclass correlation coefficient (ICC) =0.75). Knowledge scores discriminated between providers and patients (mean difference 31.1%, 95% CI 26.9, 35.3) and between patients and healthy controls (mean difference 11.2, 95% CI 5.4, 17.1). Most providers reported that the knowledge items covered essential content. Two of the five goal items had a ceiling effect, and one goal had low content validity. The goal items had moderate retest reliability (ICC’s 0.57 to 0.78). In the multivariable model of treatment, none of the patient goals was associated with receipt of chemotherapy. Age and hormone receptor status were the only variables independently associated with chemotherapy. Most patients (77.6%) had treatment concordant with that predicted by the model. Patients who had concordant treatment had similar levels of confidence and regret as those who did not. Conclusions: The Decision Quality Instrument is a reliable and valid measure of patient knowledge about chemotherapy, but its ability to measure concordance with patient goals is limited. In this sample, patient goals were not associated with treatment, and most patients reported they were not asked their preference, suggesting that goals were not adequately considered in decision making

Personalisation of breast cancer follow-up: a time-dependent prognostic nomogram for the estimation of annual risk of locoregional recurrence in early breast cancer patients

The objective of this study was to develop and validate a time-dependent logistic regression model for prediction of locoregional recurrence (LRR) of breast cancer and a web-based nomogram for clinical decision support. Women first diagnosed with early breast cancer between 2003 and 2006 in all Dutch hospitals were selected from the Netherlands Cancer Registry (n = 37,230). In the first 5 years following primary breast cancer treatment, 950 (2.6 %) patients developed a LRR as first event. Risk factors were determined using logistic regression and the risks were calculated per year, conditional on not being diagnosed with recurrence in the previous year. Discrimination and calibration were assessed. Bootstrapping was used for internal validation. Data on primary tumours diagnosed between 2007 and 2008 in 43 Dutch hospitals were used for external validation of the performance of the nomogram (n = 12,308). The final model included the variables grade, size, multifocality, and nodal involvement of the primary tumour, and whether patients were treated with radio-, chemo- or hormone therapy. The index cohort showed an area under the ROC curve of 0.84, 0.77, 0.70, 0.73 and 0.62, respectively, per subsequent year after primary treatment. Model predictions were well calibrated. Estimates in the validation cohort did not differ significantly from the index cohort. The results were incorporated in a web-based nomogram (http://​www.​utwente.​nl/​mira/​influence). This validated nomogram can be used as an instrument to identify patients with a low or high risk of LRR who might benefit from a less or more intensive follow-up after breast cancer and to aid clinical decision making for personalised follow-up

Evaluation of the role of the breast care nurse at Toowoomba Base Hospital

[Executive Summary]: The Supporting Rural Women with Breast Cancer Project started in Toowoomba in January 2005 with a Breast Care Nurse in a full time position in July 2005. The aim of the project is to develop and implement a revised multidisciplinary model of care resulting in the reorganisation and enhanced coordination of breast care services provided by Toowoomba Health Service District. A key deliverable under the service agreement with the Australian Government is the implementation of an evaluation plan and the compilation of an evaluation report. A decision on continuation of project initiatives will be informed in part by the results of the evaluation reported herein which was conducted by the Centre for Rural and Remote Area Health (CRRAH) based at the University of Southern Queensland. Structured questionnaires were used for both patient and stakeholder feedback. Fifty-one former breast cancer patients were interviewed by telephone. Twenty questions polled patients’ views on their access to the Breast Care Nurse and the nurse’s role in coordinating care, referral to other health professionals, and in providing information and psychosocial, emotional and practical support. Stakeholders received the questionnaire through the Toowoomba Health Services internal email system and returned completed questionnaires by reply paid mail to CRRAH. The questions were designed to provide views on the support that the Breast Care Nurse had made to a multi disciplinary treatment regimen. Views on the reasons for success or failure of the programme were also elicited. Widespread knowledge of the Breast Care Nurse prior to breast cancer treatment was poor; patients were unaware of the Breast Care Nurse until their first contact with her which was usually at the Surgical Outpatients Clinic held at the BreastScreen Toowoomba Service. More information about the position and role could be made available through GPs. Results from the patients revealed enormous gratitude for the support that they received from the Breast Care Nurse. There was overwhelming agreement that the timing of contact, ease of accessibility, information provided and support offered was extremely valuable in making their treatment and recovery easier. The vast majority of participants would recommend to their friends that they should attend hospitals with a Breast Care Nurse. Similar sentiments about the value of the Breast Care Nurse were received from stakeholders who recognised the benefit of the position not only to patients but also to the multidisciplinary team members in terms of coordination and liaison. However stakeholders did believe that a multidisciplinary team approach had not yet been fully achieved. The importance of maintaining a full time position of Breast Care Nurse was noted by both patients and stakeholders as accessibility of the nurse to patients was a key feature of the success of the programme. The study was in agreement with several other Australian reports all of which have demonstrated the success of dedicated Breast Care Nurses. The recommendation from the evaluation team is that the position of a full time Breast Care Nurse should be maintained. The Breast Care Nurse model is one that could be used successfully to support other medical condition

Personalized Decision Modeling for Intervention and Prevention of Cancers

Personalized medicine has been utilized in all stages of cancer care in recent years, including the prevention, diagnosis, treatment and follow-up. Since prevention and early intervention are particularly crucial in reducing cancer mortalities, personalizing the corresponding strategies and decisions so as to provide the most appropriate or optimal medical services for different patients can greatly improve the current cancer control practices. This dissertation research performs an in-depth exploration of personalized decision modeling of cancer intervention and prevention problems. We investigate the patient-specific screening and vaccination strategies for breast cancer and the cancers related to human papillomavirus (HPV), representatively. Three popular healthcare analytics techniques, Markov models, regression-based predictive models, and discrete-event simulation, are developed in the context of personalized cancer medicine. We discuss multiple possibilities of incorporating patient-specific risk into personalized cancer prevention strategies and showcase three practical examples. The first study builds a Markov decision process model to optimize biopsy referral decisions for women who receives abnormal breast cancer screening results. The second study directly optimizes the annual breast cancer screening using a regression-based adaptive decision model. The study also proposes a novel model selection method for logistic regression with a large number of candidate variables. The third study addresses the personalized HPV vaccination strategies and develops a hybrid model combining discrete-event simulation with regression-based risk estimation. Our findings suggest that personalized screening and vaccination benefit patients by maximizing life expectancies and minimizing the possibilities of dying from cancer. Preventive screening and vaccination programs for other cancers or diseases, which have clearly identified risk factors and measurable risk, may all benefit from patient-specific policies

Early Detection of Breast Cancer Using Machine Learning Techniques

Cancer is the second cause of death in the world. 8.8 million patients died due to cancer in 2015. Breast cancer is the leading cause of death among women. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Most of the studies concentrated on mammogram images. However, mammogram images sometimes have a risk of false detection that may endanger the patient’s health. It is vital to find alternative methods which are easier to implement and work with different data sets, cheaper and safer, that can produce a more reliable prediction. This paper proposes a hybrid model combined of several Machine Learning (ML) algorithms including Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Decision Tree (DT) for effective breast cancer detection. This study also discusses the datasets used for breast cancer detection and diagnosis. The proposed model can be used with different data types such as image, blood, etc

A systematic review of the role of bisphosphonates in metastatic disease

Objectives: To identify evidence for the role of bisphosphonates in malignancy for the treatment of hypercalcaemia, prevention of skeletal morbidity and use in the adjuvant setting. To perform an economic review of current literature and model the cost effectiveness of bisphosphonates in the treatment of hypercalcaemia and prevention of skeletal morbidity Data sources: Electronic databases (1966-June 2001). Cochrane register. Pharmaceutical companies. Experts in the field. Handsearching of abstracts and leading oncology journals (1999-2001). Review methods: Two independent reviewers assessed studies for inclusion, according to predetermined criteria, and extracted relevant data. Overall event rates were pooled in a meta-analysis, odds ratios ( OR) were given with 95% confidence intervals (CI). Where data could not be combined, studies were reported individually and proportions compared using chi- squared analysis. Cost and cost-effectiveness were assessed by a decision analytic model comparing different bisphosphonate regimens for the treatment of hypercalcaemia; Markov models were employed to evaluate the use of bisphosphonates to prevent skeletal-related events (SRE) in patients with breast cancer and multiple myeloma. Results: For acute hypercalcaemia of malignancy, bisphosphonates normalised serum calcium in >70% of patients within 2-6 days. Pamidronate was more effective than control, etidronate, mithramycin and low-dose clodronate, but equal to high dose clodronate, in achieving normocalcaemia. Pamidronate prolongs ( doubles) the median time to relapse compared with clodronate or etidronate. For prevention of skeletal morbidity, bisphosphonates compared with placebo, significantly reduced the OR for fractures (OR [95% CI], vertebral, 0.69 [0.57-0.84], non-vertebral, 0.65 [0.54-0.79], combined, 0.65 [0.55-0.78]) radiotherapy 0.67 [0.57-0.79] and hypercalcaemia 0.54 [0.36-0.81] but not orthopaedic surgery 0.70 [0.46-1.05] or spinal cord compression 0.71 [0.47-1.08]. However, reduction in orthopaedic surgery was significant in studies that lasted over a year 0.59 [0.39-0.88]. Bisphosphonates significantly increased the time to first SRE but did not affect survival. Subanalyses were performed for disease groups, drugs and route of administration. Most evidence supports the use of intravenous aminobisphosphonates. For adjuvant use of bisphosphonates, Clodronate, given to patients with primary operable breast cancer and no metastatic disease, significantly reduced the number of patients developing bone metastases. This benefit was not maintained once regular administration had been discontinued. Two trials reported significant survival advantages in the treated groups. Bisphosphonates reduce the number of bone metastases in patients with both early and advanced breast cancer. Bisphosphonates are well tolerated with a low incidence of side-effects. Economic modelling showed that for acute hypercalcaemia, drugs with the longest cumulative duration of normocalcaemia were most cost-effective. Zoledronate 4 mg was the most costly, but most cost-effective treatment. For skeletal morbidity, Markov models estimated that the overall cost of bisphosphonate therapy to prevent an SRE was pound250 and pound1500 per event for patients with breast cancer and multiple myeloma, respectively. Bisphosphonate treatment is sometimes cost-saving in breast cancer patients where fractures are prevented. Conclusions: High dose aminobisphosphonates are most effective for the treatment of acute hypercalcaemia and delay time to relapse. Bisphosphonates significantly reduce SREs and delay the time to first SRE in patients with bony metastatic disease but do not affect survival. Benefit is demonstrated after administration for at least 6-12 months. The greatest body of evidence supports the use of intravenous aminobisphosphonates. Further evidence is required to support use in the adjuvant setting