PurposeA number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET) or computed tomography (CT) images.MethodsFor study identification PubMed and Scopus were searched (1/2000–9/2013) using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies.ResultsFifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34–99%) was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis.ConclusionsWe found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these image-derived biomarkers should be supported by appropriate biological and statistical evidence before their association with patient outcome is investigated in prospective studies.

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Imaging or tissue biomarker evidence has been introduced into the core diagnostic pathway for Alzheimer’s disease (AD). PET using 18F-labelled beta-amyloid PET tracers has shown promise for the early diagnosis of AD. However, most studies included only small numbers of participants and no consensus has been reached as to which radiotracer has the highest diagnostic accuracy. First, we performed a systematic review of the literature published between 1990 and 2014 for studies exploring the diagnostic accuracy of florbetaben, florbetapir and flutemetamol in AD. The included studies were analysed using the QUADAS assessment of methodological quality. A meta-analysis of the sensitivity and specificity reported within each study was performed. Pooled values were calculated for each radiotracer and for visual or quantitative analysis by population included. The systematic review identified nine studies eligible for inclusion. There were limited variations in the methods between studies reporting the same radiotracer. The meta-analysis results showed that pooled sensitivity and specificity values were in general high for all tracers. This was confirmed by calculating likelihood ratios. A patient with a positive ratio is much more likely to have AD than a patient with a negative ratio, and vice versa. However, specificity was higher when only patients with AD were compared with healthy controls. This systematic review and meta-analysis found no marked differences in the diagnostic accuracy of the three beta-amyloid radiotracers. All tracers perform better when used to discriminate between patients with AD and healthy controls. The sensitivity and specificity for quantitative and visual analysis are comparable to those of other imaging or biomarker techniques used to diagnose AD. Further research is required to identify the combination of tests that provides the highest sensitivity and specificity, and to identify the most suitable position for the tracer in the clinical pathway.Electronic supplementary materialThe online version of this article (doi:10.1007/s00259-015-3228-x) contains supplementary material, which is available to authorized users.

Implications of all the available evidenceThis evaluation study strengthens the available evidence in the literature supporting the use of SABR in appropriately selected patients with metachronous extracranial oligometastases and resulted in routine commissioning of SABR for treating patients with oligometastatic disease by NHS England in 2019 (4).

ObjectivePrecision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies.MethodsWe performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research.ResultsWe identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and ‘omics’-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects.DiscussionInnovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.Electronic supplementary materialThe online version of this article (10.1007/s40273-018-0686-6) contains supplementary material, which is available to authorized users.

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Background:Amyloid PET (aPET) imaging could improve patient outcomes in clinical practice, but the extent of impact needs quantification.Objective:To provide an aggregated quantitative analysis of the value added by aPET in cognitively impaired subjects.Methods:Systematic literature searches were performed in Embase and Medline until January 2017. 1,531 cases over 12 studies were included (1,142 cases over seven studies in the primary analysis where aPET was the key biomarker; the remaining cases included as defined groups in the secondary analysis). Data was abstracted by consensus among two observers and assessed for bias. Clinical utility was measured by diagnostic change, diagnostic confidence, and patient management before and after aPET. Three groups were further analyzed: control patients for whom feedback of aPET scan results was delayed; aPET Appropriate Use Criteria (AUC+) cases; and patients undergoing additional FDG/CSF testing.Results:For 1,142 cases with only aPET, 31.3% of diagnoses were revised, whereas 3.2% of diagnoses changed in the delayed aPET control group (p < 0.0001). Increased diagnostic confidence following aPET was found for 62.1% of 870 patients. Management changes with aPET were found in 72.2% of 740 cases and in 55.5% of 299 cases in the control group (p < 0.0001). The diagnostic value of aPET in AUC+ patients or when FDG/CSF were additionally available did not substantially differ from the value of aPET alone in the wider population.Conclusions:Amyloid PET contributed to diagnostic revision in almost a third of cases and demonstrated value in increasing diagnostic confidence and refining management plans.

Images of DaTscan (ioflupane [123I] SPECT) have been used as an adjunct to clinical diagnosis to facilitate the differential diagnosis of neurodegenerative (ND) Parkinsonian Syndrome (PS) vs. non-dopamine deficiency aetiologies of Parkinsonism. Despite several systematic reviews having summarised the evidence on diagnostic accuracy, the impact of imaging results on clinical utility has not been systematically assessed. Our objective was to examine the available evidence on the clinical utility of DaTscan imaging in changing diagnosis and subsequent management of patients with suspected PS. We performed a systematic review of published studies of clinical utility from 2000 to 2019 without language restrictions. A meta-analysis of change in diagnosis and management rates reported from each study was performed using a random-effects model and logit transformation. Sub-group analysis, meta-regression and sensitivity analysis was performed to explore heterogeneity. Twenty studies met the inclusion criteria. Thirteen of these contributed to the meta-analyses including 950 and 779 patients with a reported change in management and change in diagnosis, respectively. The use of DaTscan imaging resulted in a change in management in 54% (95% CI: 47–61%) of patients. Change in diagnosis occurred in 31% (95% CI: 22–42%) of patients. The two pooled analyses were characterised by high levels of heterogeneity. Our systematic review and meta-analysis show that imaging with DaTscan was associated with a change in management in approximately half the patients tested and the diagnosis was modified in one third. Regardless of time from symptom onset to scan results, these changes were consistent. Further research focusing on specific patient subgroups could provide additional evidence on the impact on clinical outcomes.