The Patient Feedback Response Framework – understanding why UK hospital staff find it difficult to make improvements based on patient feedback: A qualitative study

Patients are increasingly being asked for feedback about their healthcare experiences. However, healthcare staff often find it difficult to act on this feedback in order to make improvements to services. This paper draws upon notions of legitimacy and readiness to develop a conceptual framework (Patient Feedback Response Framework – PFRF) which outlines why staff may find it problematic to respond to patient feedback. A large qualitative study was conducted with 17 ward based teams between 2013 and 2014, across three hospital Trusts in the North of England. This was a process evaluation of a wider study where ward staff were encouraged to make action plans based on patient feedback. We focus on three methods here: i) examination of taped discussion between ward staff during action planning meetings ii) facilitators notes of these meetings iii) telephone interviews with staff focusing on whether action plans had been achieved six months later. Analysis employed an abductive approach. Through the development of the PFRF, we found that making changes based on patient feedback is a complex multi-tiered process and not something that ward staff can simply ‘do’. First, staff must exhibit normative legitimacy – the belief that listening to patients is a worthwhile exercise. Second, structural legitimacy has to be in place – ward teams need adequate autonomy, ownership and resource to enact change. Some ward teams are able to make improvements within their immediate control and environment. Third, for those staff who require interdepartmental co-operation or high level assistance to achieve change, organisational readiness must exist at the level of the hospital otherwise improvement will rarely be enacted. Case studies drawn from our empirical data demonstrate the above. It is only when appropriate levels of individual and organisational capacity to change exist, that patient feedback is likely to be acted upon to improve services

Blended learning of radiology improves medical students’ performance, satisfaction, and engagement

Purpose To evaluate the impact of blended learning using a combination of educational resources (flipped classroom and short videos) on medical students’ (MSs) for radiology learning. Material and methods A cohort of 353 MSs from 2015 to 2018 was prospectively evaluated. MSs were assigned to four groups (high, high-intermediate, low-intermediate, and low achievers) based on their results to a 20-MCQs performance evaluation referred to as the pretest. MSs had then free access to a self-paced course totalizing 61 videos based on abdominal imaging over a period of 3 months. Performance was evaluated using the change between posttest (the same 20 MCQs as pretest) and pretest results. Satisfaction was measured using a satisfaction survey with directed and spontaneous feedbacks. Engagement was graded according to audience retention and attendance on a web content management system. Results Performance change between pre and posttest was significantly different between the four categories (ANOVA, P = 10−9): low pretest achievers demonstrated the highest improvement (mean ± SD, + 11.3 ± 22.8 points) while high pretest achievers showed a decrease in their posttest score (mean ± SD, − 3.6 ± 19 points). Directed feedback collected from 73.3% of participants showed a 99% of overall satisfaction. Spontaneous feedback showed that the concept of “pleasure in learning” was the most cited advantage, followed by “flexibility.” Engagement increased over years and the number of views increased of 2.47-fold in 2 years. Conclusion Learning formats including new pedagogical concepts as blended learning, and current technologies allow improvement in medical student’s performance, satisfaction, and engagement

Relevance of Dynamic ¹⁸F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

This study evaluates the relevance of 18F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic 18F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBRmean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values

Role of Positron Emission Tomography in Primary Central Nervous System Lymphoma

The incidence of primary central nervous system lymphoma has increased over the past two decades in immunocompetent patients and the prognosis remains poor. A diagnosis and complete evaluation of the patient is needed without delay, but histologic evaluation is not always available and PCNSL can mimic a variety of brain lesions on MRI. In this article, we review the potential role of 18F-FDG PET for the diagnosis of PCNSL in immunocompetent and immunocompromised patients. Its contribution to systemic assessment at the time of diagnosis has been well established by expert societies over the past decade. In addition, 18F-FDG provides valuable information for differential diagnosis and outcome prediction. The literature also shows the potential role of 18F-FDG as a therapeutic evaluation tool during the treatment and the end of the treatment. Finally, we present several new radiotracers that may have a potential role in the management of PCNSL in the future

Simultaneously acquired PET and ASL imaging biomarkers may be helpful in differentiating progression from pseudo-progression in treated gliomas

International audienceThe aim of this work was investigating the methods based on coupling cerebral perfusion (ASL) and amino acid metabolism ([18F]DOPA-PET) measurements to evaluate the diagnostic performance of PET/MRI in glioma follow-up

Ultra-low-dose in brain 18F-FDG PET/MRI in clinical settings

International audienceAbstract We previously showed that the injected activity could be reduced to 1 MBq/kg without significantly degrading image quality for the exploration of neurocognitive disorders in 18F-FDG-PET/MRI. We now hypothesized that injected activity could be reduced ten-fold. We simulated a 18F-FDG-PET/MRI ultra-low-dose protocol (0.2 MBq/Kg, PET ULD ) and compared it to our reference protocol (2 MBq/Kg, PET STD ) in 50 patients with cognitive impairment. We tested the reproducibility between PET ULD and PET STD using SUVratios measurements. We also assessed the impact of PET ULD for between-group comparisons and for visual analysis performed by three physicians. The intra-operator agreement between visual assessment of PET STD and PET ULD in patients with severe anomalies was substantial to almost perfect (kappa > 0.79). For patients with normal metabolism or moderate hypometabolism however, it was only moderate to substantial (kappa > 0.53). SUV ratios were strongly reproducible (SUVratio difference ± SD = 0.09 ± 0.08). Between-group comparisons yielded very similar results using either PET ULD or PET STD . 18F-FDG activity may be reduced to 0.2 MBq/Kg without compromising quantitative measurements. The visual interpretation was reproducible between ultra-low-dose and standard protocol for patients with severe hypometabolism, but less so for those with moderate hypometabolism. These results suggest that a low-dose protocol (1 MBq/Kg) should be preferred in the context of neurodegenerative disease diagnosis

Imaging-guided precision medicine in glioblastoma patients treated with immune checkpoint modulators: research trend and future directions in the field of imaging biomarkers and artificial intelligence

International audienceImmunotherapies that employ immune checkpoint modulators (ICMs) have emerged as an effective treatment for a variety of solid cancers, as well as a paradigm shift in the treatment of cancers. Despite this breakthrough, the median survival time of glioblastoma patients has remained at about 2 years. Therefore, the safety and anti-cancer efficacy of combination therapies that include ICMs are being actively investigated. Because of the distinct mechanisms of ICMs, which restore the immune system's anti-tumor capacity, unconventional immune-related phenomena are increasingly being reported in terms of tumor response and progression, as well as adverse events. Indeed, immunotherapy response assessments for neuro-oncology (iRANO) play a central role in guiding cancer patient management and define a "wait and see strategy" for patients treated with ICMs in monotherapy with progressive disease on MRI. This article deciphers emerging research trends to ameliorate four challenges unaddressed by the iRANO criteria: (1) patient selection, (2) identification of immune-related phenomena other than pseudoprogression (i.e., hyperprogression, the abscopal effect, immune-related adverse events), (3) response assessment in combination therapies including ICM, and (4) alternatives to MRI. To this end, our article provides a structured approach for standardized selection and reporting of imaging modalities to enable the use of precision medicine by deciphering the characteristics of the tumor and its immune environment. Emerging preclinical or clinical innovations are also discussed as future directions such as immune-specific targeting and implementation of artificial intelligence algorithms

Brain Metabolic Alterations in Seropositive Autoimmune Encephalitis: An ¹⁸F-FDG PET Study

Introduction: Autoimmune encephalitis (AE) diagnosis and follow-up remain challenging. Brain 18F-fluoro-deoxy-glucose positron emission tomography (FDG PET) has shown promising results in AE. Our aim was to investigate FDG PET alterations in AE, according to antibody subtype. Methods: We retrospectively included patients with available FDG PET and seropositive AE diagnosed in our center between 2015 and 2020. Brain PET Z-score maps (relative to age matched controls) were analyzed, considering metabolic changes significant if |Z-score| ≥ 2. Results: Forty-six patients were included (49.4 yrs [18; 81]): 13 with GAD autoantibodies, 11 with anti-LGI1, 9 with NMDAR, 5 with CASPR2, and 8 with other antibodies. Brain PET was abnormal in 98% of patients versus 53% for MRI. The most frequent abnormalities were medial temporal lobe (MTL) and/or striatum hypermetabolism (52% and 43% respectively), cortical hypometabolism (78%), and cerebellum abnormalities (70%). LGI1 AE tended to have more frequent MTL hypermetabolism. NMDAR AE was prone to widespread cortical hypometabolism. Fewer abnormalities were observed in GAD AE. Striatum hypermetabolism was more frequent in patients treated for less than 1 month (p = 0.014), suggesting a relation to disease activity. Conclusion: FDG PET could serve as an imaging biomarker for early diagnosis and follow-up in AE

Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas

In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data

Hybrid [18F]-F-DOPA PET/MRI Interpretation Criteria and Scores for Glioma Follow-up After Radiotherapy

International audienceObjective18F‑fluoro-L‑3,4‑dihydroxyphenylalanine positron emission tomography (F‑DOPA PET) is used in glioma follow-up after radiotherapy to discriminate treatment-related changes (TRC) from tumor progression (TP). We compared the performances of a combined PET and MRI analysis with F‑DOPA current standard of interpretation.MethodsWe included 76 consecutive patients showing at least one gadolinium-enhanced lesion on the T1‑w MRI sequence (T1G). Two nuclear medicine physicians blindly analyzed PET/MRI images. In addition to the conventional PET analysis, they looked for F‑DOPA uptake(s) outside T1G-enhanced areas (T1G/PET), in the white matter (WM/PET), for T1G-enhanced lesion(s) without sufficiently concordant F‑DOPA uptake (T1G+/PET), and F‑DOPA uptake(s) away from hemorrhagic changes as shown with a susceptibility weighted imaging sequence (SWI/PET). We measured lesions’ F‑DOPA uptake ratio using healthy brain background (TBR) and striatum (T/S) as references, and lesions’ perfusion with arterial spin labelling cerebral blood flow maps (rCBF). Scores were determined by logistic regression.Results53 and 23 patients were diagnosed with TP and TRC, respectively. The accuracies were 74% for T/S, 76% for TBR, and 84% for rCBF, with best cut-off values of 1.3, 3.7 and 1.25, respectively. For hybrid variables, best accuracies were obtained with conventional analysis (82%), T1G+/PET (82%) and SWI/PET (81%). T1G+/PET, SWI/PET and rCBF ≥ 1.25 were selected to construct a 3-point score. It outperformed conventional analysis and rCBF with an AUC of 0.94 and an accuracy of 87%.ConclusionsOur scoring approach combining F‑DOPA PET and MRI provided better accuracy than conventional PET analyses for distinguishing TP from TRC in our patients after radiation therapy