Association of short duration from initial symptoms to specialist consultation with poor survival in soft-tissue sarcomas

Cancer diagnostic tools to aid decision-making in primary care: mixed-methods systematic reviews and cost-effectiveness analysis

BACKGROUND

Tools based on diagnostic prediction models are available to help general practitioners diagnose cancer. It is unclear whether or not tools expedite diagnosis or affect patient quality of life and/or survival.

OBJECTIVES

The objectives were to evaluate the evidence on the validation, clinical effectiveness, cost-effectiveness, and availability and use of cancer diagnostic tools in primary care.

METHODS

Two systematic reviews were conducted to examine the clinical effectiveness (review 1) and the development, validation and accuracy (review 2) of diagnostic prediction models for aiding general practitioners in cancer diagnosis. Bibliographic searches were conducted on MEDLINE, MEDLINE In-Process, EMBASE, Cochrane Library and Web of Science) in May 2017, with updated searches conducted in November 2018. A decision-analytic model explored the tools' clinical effectiveness and cost-effectiveness in colorectal cancer. The model compared patient outcomes and costs between strategies that included the use of the tools and those that did not, using the NHS perspective. We surveyed 4600 general practitioners in randomly selected UK practices to determine the proportions of general practices and general practitioners with access to, and using, cancer decision support tools. Association between access to these tools and practice-level cancer diagnostic indicators was explored.

RESULTS

Systematic review 1 - five studies, of different design and quality, reporting on three diagnostic tools, were included. We found no evidence that using the tools was associated with better outcomes. Systematic review 2 - 43 studies were included, reporting on prediction models, in various stages of development, for 14 cancer sites (including multiple cancers). Most studies relate to QCancer^® (ClinRisk Ltd, Leeds, UK) and risk assessment tools.

DECISION MODEL

In the absence of studies reporting their clinical outcomes, QCancer and risk assessment tools were evaluated against faecal immunochemical testing. A linked data approach was used, which translates diagnostic accuracy into time to diagnosis and treatment, and stage at diagnosis. Given the current lack of evidence, the model showed that the cost-effectiveness of diagnostic tools in colorectal cancer relies on demonstrating patient survival benefits. Sensitivity of faecal immunochemical testing and specificity of QCancer and risk assessment tools in a low-risk population were the key uncertain parameters.

SURVEY

Practitioner- and practice-level response rates were 10.3% (476/4600) and 23.3% (227/975), respectively. Cancer decision support tools were available in 83 out of 227 practices (36.6%, 95% confidence interval 30.3% to 43.1%), and were likely to be used in 38 out of 227 practices (16.7%, 95% confidence interval 12.1% to 22.2%). The mean 2-week-wait referral rate did not differ between practices that do and practices that do not have access to QCancer or risk assessment tools (mean difference of 1.8 referrals per 100,000 referrals, 95% confidence interval -6.7 to 10.3 referrals per 100,000 referrals).

LIMITATIONS

There is little good-quality evidence on the clinical effectiveness and cost-effectiveness of diagnostic tools. Many diagnostic prediction models are limited by a lack of external validation. There are limited data on current UK practice and clinical outcomes of diagnostic strategies, and there is no evidence on the quality-of-life outcomes of diagnostic results. The survey was limited by low response rates.

CONCLUSION

The evidence base on the tools is limited. Research on how general practitioners interact with the tools may help to identify barriers to implementation and uptake, and the potential for clinical effectiveness.

FUTURE WORK

Continued model validation is recommended, especially for risk assessment tools. Assessment of the tools' impact on time to diagnosis and treatment, stage at diagnosis, and health outcomes is also recommended, as is further work to understand how tools are used in general practitioner consultations.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017068373 and CRD42017068375.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 24, No. 66. See the NIHR Journals Library website for further project information.

Soft tissue sarcoma of the extremity: Characterizing symptom duration and outcomes

BACKGROUND

We sought to investigate how the interval between symptom onset and diagnosis of soft tissue sarcoma (STS) of the extremity was associated with survival.

METHODS

Patients treated for extremity STS years 2006-2015 were stratified by symptom duration: at least two, six or twelve months between symptom onset and diagnosis. Chi-square tests compared patient and tumor-related characteristics based on symptom duration. Survival analysis included Cox regression and Kaplan-Meier estimates.

RESULTS

Of 113 patients included, mean age was 56.7 years, 52.2% were male, and 75.2% were white. Median tumor size was 75 mm, 48.7% were grade 3, and 38.1% were stage I. With symptom duration of either at least 6 or 12 months, a greater proportion of patients who experienced the specified symptom duration had lower grade tumors (p < 0.01 and p = 0.01, respectively) and lower stage disease (p < 0.01 and p = 0.02, respectively) than those who did not. Among all patients, survival estimates were similar between those who experienced a symptom duration of 2 (p = 0.12), 6 (p = 0.18) or 12 (p = 0.61) months and those who did not.

CONCLUSION

Patients with extremity STS who tolerated a longer symptom duration had less advanced disease. Reasons for prolonged symptom duration and methods to address these factors warrant further investigation.

Routes to Diagnosis for Suspected Sarcoma: The Impact of Symptoms and Clinical Findings on the Diagnostic Process

Background and Objectives. Sarcoma patients often experience delay before diagnosis. We examined the association between presenting symptoms/signs and time intervals for suspected sarcoma patients. Methods. 545 consecutive patients suspected for sarcoma referred over a one-year period were included. Median time intervals in routes to diagnosis were collected from medical records and questionnaires. Results. 102 patients (18.7%) had a sarcoma; 68 (12.5%) had other malignancies. Median interval for the patient (time from first symptom to first doctor visit), primary care, local hospital, sarcoma center, diagnostic, and total interval for sarcoma patients were 77, 17, 29, 17, 65, and 176 days, respectively. Sarcoma patients visited more hospital departments and had longer median primary care (+10 days) and diagnostic intervals (+19 days) than patients with benign conditions. Median primary care (−19 days) and sarcoma center (−4 days) intervals were shorter for patients with a lump versus no lump. Median patient (+40 days), primary care (+12 days), diagnostic (+17 days), and total intervals (+78 days) were longer for patients presenting with pain versus no pain. GP suspicion of malignancy shortened local hospital (−20 days) and total intervals (−104 days). Conclusions. The main part of delay could be attributed to the patient and local hospitals. Length of time intervals was associated with presenting symptoms/signs and GP suspicion.

Postoperative brain metastases in soft tissue sarcomas

Brain metastases (BMs) from soft tissue sarcoma (STS) are rare but lethal. We reviewed 187 consecutive patients with STS treated with definitive surgery in Nagoya University Hospital from 2004 to 2014. There were 10 patients with neurofibromatosis-1 (NF-1). We investigated estimated brain metastasis free survival (BMFS) after surgery and overall survival (OS) after BMs in STS. The factors that affected BMFS were also investigated. Eight of 187 patients (4.3%) developed BM with a median period of 18.2 (range 8.8-42.6) months after surgery. Seven of 8 BM patients had metastases at other sites. Estimated 5 year BMFS rate after surgery was 95.2%, and 3 month OS rate after BM was 25.0%. NF-1 (p<0.0001), histological subtype of MPNST (p=0.008), and primary tumor size≥5 cm (p=0.021) were significantly associated with increasing incidence of BM. In this study, postoperative BMs were common in patients with NF-1, MPNST, and large tumors. Considering the impact of NF-1 on BMFS, careful follow up for BM is necessary for NF-1 patients with metastases at other sites.

Prognostic factors for local recurrence and mortality in adult soft tissue sarcoma of the extremities and trunk wall: a cohort study of 922 consecutive patients

BACKGROUND AND PURPOSE

Previous studies of soft tissue sarcoma (STS) have identified a number of possible prognostic factors; however, the majority of these include highly selected populations, with unclear validation of data and insufficient statistical methods. We identified prognostic factors in a validated, population-based 30-year series of STS treated at a single institution, using an advanced statistical approach.

PATIENTS AND METHODS

Between 1979 and 2008, 922 adult patients from western Denmark were treated at the Aarhus Sarcoma Center for non-metastatic STS in the extremities or trunk. The endpoints were local recurrence (LR) and disease-specific mortality (DSM). Prognostic factors were analyzed using a proportional hazard model, including continuous variables as cubic splines. Directed acyclic graphs were used to depict the causal structure.

RESULTS

The 5-year LR was 16% and the 5-year DSM was 24%. Important prognostic factors for both LR and DSM were age, duration of symptoms, tumor size, grade, margin, and radiotherapy, while anatomical location (upper, lower extremity, trunk) was prognostic for DSM.

INTERPRETATION

In this population-based series of adult, non-metastatic STS, we included directed acyclic graphs, cubic splines, and a competing risk model in order to minimize bias, and demonstrated that these statistical methods are feasible. Using these statistical methods on a large, validated dataset, we excluded depth as a prognostic factor and established that age, duration of symptoms, size, grade, margin, and radiotherapy were important prognostic factors for both local recurrence and disease-specific mortality.