Radiotherapy utilization rates for cancer vary widely internationally. It has previously been suggested that approximately 50% of all cancer patients should receive radiation. However, this estimate was not evidence-based. The aim of this study was to estimate the ideal proportion of new cases of cancer that should receive radiotherapy at least once during the course of their illness based on the best available evidence. An optimal radiotherapy utilization tree was constructed for each cancer based upon indications for radiotherapy taken from evidence-based treatment guidelines. The proportion of patients with clinical attributes that indicated a possible benefit from radiotherapy was obtained by adding epidemiologic data to the radiotherapy utilization tree. The optimal proportion of patients with cancer that should receive radiotherapy was then calculated using TreeAge (TreeAge Software, Williamstown, MA) software. Sensitivity analyses using univariate analysis and Monte Carlo simulations were performed. The proportion of patients with cancer in whom external beam radiotherapy is indicated according to the best available evidence was calculated to be 52%. Monte Carlo analysis indicated that the 95% confidence limits were from 51.7% to 53.1%. The tightness of the confidence interval suggests that the overall estimate is robust. Comparison with actual radiotherapy utilization data suggests a shortfall in actual radiotherapy delivery.This methodology allows comparison of optimal rates with actual rates to identify areas where improvements in the evidence-based use of radiotherapy can be made. It provides valuable data for radiotherapy service planning. Actual rates need to be addressed to ensure better radiotherapy utilization. Cancer 2005;104: 1129 -37.

No abstract

The 5-year and 10-year survival rates varied according to molecular subtype. Although this approach provides additional information to predict time to IBTR, LRR, DDFS, and death from breast cancer, its predictive power is less than that of traditional pathologic indices. This information may be useful in discussing outcomes and planning management with patients after BCT.

The exquisite soft-tissue contrast of magnetic resonance imaging (MRI) has meant that the technique is having an increasing role in contouring the gross tumor volume (GTV) and organs at risk (OAR) in radiation therapy treatment planning systems (TPS). MRI-planning scans from diagnostic MRI scanners are currently incorporated into the planning process by being registered to CT data. The soft-tissue data from the MRI provides target outline guidance and the CT provides a solid geometric and electron density map for accurate dose calculation on the TPS computer. There is increasing interest in MRI machine placement in radiotherapy clinics as an adjunct to CT simulators. Most vendors now offer 70 cm bores with flat couch inserts and specialised RF coil designs. We would refer to these devices as MR-simulators. There is also research into the future application of MR-simulators independent of CT and as in-room image-guidance devices. It is within the background of this increased interest in the utility of MRI in radiotherapy treatment planning that this paper is couched. The paper outlines publications that deal with standard MRI sequences used in current clinical practice. It then discusses the potential for using processed functional diffusion maps (fDM) derived from diffusion weighted image sequences in tracking tumor activity and tumor recurrence. Next, this paper reviews publications that describe the use of MRI in patient-management applications that may, in turn, be relevant to radiotherapy treatment planning. The review briefly discusses the concepts behind functional techniques such as dynamic contrast enhanced (DCE), diffusion-weighted (DW) MRI sequences and magnetic resonance spectroscopic imaging (MRSI). Significant applications of MR are discussed in terms of the following treatment sites: brain, head and neck, breast, lung, prostate and cervix. While not yet routine, the use of apparent diffusion coefficient (ADC) map analysis indicates an exciting future application for functional MRI. Although DW-MRI has not yet been routinely used in boost adaptive techniques, it is being assessed in cohort studies for sub-volume boosting in prostate tumors.

The purpose of this study was to assess the safety and efficacy of progressive resistance training (PRT) in breast cancer. Randomized controlled trials (RCTs) published to November 2013 that reported on the effects of PRT (>6 weeks) on breast cancer-related lymphedema (BCRL) (incidence/exacerbation, arm volume, and symptom severity), physical functioning (upper and lower body muscular strength), and health-related quality of life (HRQoL) in breast cancer patients were included. Of 446 citations retrieved, 15 RCTs in 1,652 patients were included and yielded five studies on BCRL incidence/exacerbation (N = 647), four studies on arm volume (N = 384) and BCRL symptom severity (N = 479), 11 studies on upper body muscular strength (N = 1,252), nine studies on lower body muscular strength (N = 1,079), and seven studies on HRQoL (N = 823). PRT reduced the risk of BCRL versus control conditions [OR = 0.53 (95% CI 0.31-0.90); I2 = 0%] and did not worsen arm volume or symptom severity (both SMD = -0.07). PRT significantly improved upper [SMD = 0.57 (95% CI 0.37-0.76); I2 = 58.4%] and lower body muscular strength [SMD = 0.48 (95% CI 0.30-0.67); I2 = 46.7%] but not HRQoL [SMD = 0.17 (95% CI -0.03 to 0.38); I2 = 47.0%]. The effect of PRT on HRQoL became significant in our sensitivity analysis when two studies conducted during adjuvant chemotherapy [SMD = 0.30 (95% CI 0.04-0.55), I2 = 37.0%] were excluded. These data indicate that PRT improves physical functioning and reduces the risk of BCRL. Clinical practice guidelines should be updated to inform clinicians on the benefits of PRT in this cohort.

BackgroundDespite accumulating evidence indicating that collecting patient-reported outcomes (PROs) and transferring results to the treating health professional in real time has the potential to improve patient well-being and cancer outcomes, this practice is not widespread.ObjectiveThe aim of this study was to test the feasibility and acceptability of PROMPT-Care (Patient Reported Outcome Measures for Personalized Treatment and Care), a newly developed electronic health (eHealth) system that facilitates PRO data capture from cancer patients, data linkage and retrieval to support clinical decisions and patient self-management, and data retrieval to support ongoing evaluation and innovative research.MethodsWe developed an eHealth system in consultation with content-specific expert advisory groups and tested it with patients receiving treatment or follow-up care in two hospitals in New South Wales, Australia, over a 3-month period. Participants were recruited in clinic and completed self-report Web-based assessments either just before their upcoming clinical consultation or every 4 weeks if in follow-up care. A mixed methods approach was used to evaluate feasibility and acceptability of PROMPT-Care; data collected throughout the study informed the accuracy and completeness of data transfer procedures, and extent of missing data was determined from participants’ assessments. Patients participated in cognitive interviews while completing their first assessment and completed evaluation surveys and interviews at study-end to assess system acceptability and usefulness of patient self-management resources, and oncology staff were interviewed at study-end to determine the acceptability and perceived usefulness of real-time PRO reporting.ResultsA total of 42 patients consented to the study; 7 patients were withdrawn before starting the intervention primarily because of changes in eligibility. Overall, 35 patients (13 on treatment and 22 in follow-up) completed 67 assessments during the study period. Mean completeness of patient-reported data was 93%, with 100% accuracy of data transfer. Ten patients completed cognitive interviews, 28 completed evaluation surveys, and 14 completed evaluation interviews at study-end. PROMPT-Care patient acceptability was high—100% (28/28) reported the time to complete the Web-based assessments (average 15 min) as about right, most willing to answer more questions (79%, 22/28 yes), 96% (27/28) found the Web-based assessment easier or same as completing a paper copy, and they valued the self-management resources . Oncology staff (n=5) also reported high acceptability and potential feasibility of the system.ConclusionsPatients and oncology staff found the PROMPT-Care system to be highly acceptable, and the results suggest that it would be feasible to implement it into an oncology setting. Suggested modifications to the patient assessment survey, clinician access to the reports, and system requirements will be made as part of the next stage of large-scale testing and future implementation of the...

Treatment patterns were in broad concordance with present national guidelines. Nevertheless, a significant proportion of lung cancer patients did not receive cancer-specific therapy. Treatment decisions should be multidisciplinary and decision-makers should include experienced lung cancer specialists.

BackgroundProposed causes for increased mortality following weekend admission (the ‘weekend effect’) include poorer quality of care and sicker patients. The aim of this study was to analyse the 7 days post-admission time patterns of excess mortality following weekend admission to identify whether distinct patterns exist for patients depending upon the relative contribution of poorer quality of care (care effect) or a case selection bias for patients presenting on weekends (patient effect).MethodsEmergency department admissions to all 501 hospitals in New South Wales, Australia, between 2000 and 2007 were linked to the Death Registry and analysed. There were a total of 3 381 962 admissions for 539 122 patients and 64 789 deaths at 1 week after admission. We computed excess mortality risk curves for weekend over weekday admissions, adjusting for age, sex, comorbidity (Charlson index) and diagnostic group.ResultsWeekends accounted for 27% of all admissions (917 257/3 381 962) and 28% of deaths (18 282/64 789). Sixteen of 430 diagnosis groups had a significantly increased risk of death following weekend admission. They accounted for 40% of all deaths, and demonstrated different temporal excess mortality risk patterns: early care effect (cardiac arrest); care effect washout (eg, pulmonary embolism); patient effect (eg, cancer admissions) and mixed (eg, stroke).ConclusionsThe excess mortality patterns of the weekend effect vary widely for different diagnostic groups. Recognising these different patterns should help identify at-risk diagnoses where quality of care can be improved in order to minimise the excess mortality associated with weekend admission.