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Contains fulltext : 238654.pdf (Publisher’s version ) (Open Access), Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: * Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the da

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality

The updated version of the EFSUMB guidelines on the application of non-hepatic contrast-enhanced ultrasound (CEUS) deals with the use of microbubble ultrasound contrast outside the liver in the many established and emerging applications.

Item does not contain fulltext, CONTEXT: The introduction of targeted agents for the treatment of renal cell carcinoma (RCC) has resulted in new challenges for assessing response to therapy, and conventional response criteria using computed tomography (CT) are limited. It is widely recognised that targeted therapies may lead to significant necrosis without significant reduction in tumour size. In addition, the vascular effects of antiangiogenic therapy may occur long before there is any reduction in tumour size. OBJECTIVE: To perform a systematic review of conventional and novel imaging methods for the assessment of response to targeted agents in RCC and to discuss their use from a clinical perspective. EVIDENCE ACQUISITION: Relevant databases covering the period January 2006 to April 2013 were searched for studies reporting on the use of anatomic and functional imaging techniques to predict response to targeted therapy in RCC. Inclusion criteria were randomised trials, nonrandomised controlled studies, retrospective case series, and cohort studies. Reviews, animal and preclinical studies, case reports, and commentaries were excluded. A narrative synthesis of the evidence is presented. EVIDENCE SYNTHESIS: A total of 331 abstracts and 76 full-text articles were assessed; 34 studies met the inclusion criteria. Current methods of response assessment in RCC include anatomic methods-based on various criteria including Choi, size and attenuation CT, and morphology, attenuation, size, and structure-and functional techniques including dynamic contrast-enhanced (DCE) CT, DCE-magnetic resonance imaging, DCE-ultrasonography, positron emission tomography, and approaches utilising radiolabelled monoclonal antibodies. CONCLUSIONS: Functional imaging techniques are promising surrogate biomarkers of response in RCC and may be more appropriate than anatomic CT-based methods. By enabling quantification of tumour vascularisation, functional techniques can directly and rapidly detect the biologic effects of antiangio

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a "virtual population" from which "virtual individuals" can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the "virtual individuals" that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models.

Linda Chami1,2, Nathalie Lassau1,2, Mohamed Chebil1, Caroline Robert31Imaging Department Institut Gustave Roussy, Villejuif, France; 2IR4M (UMR8081), University Paris-Sud-CNRS Orsay, France; 3Dermatology, Institut Gustave Roussy, Villejuif, FranceAbstract: High-frequency ultrasound (8–14 MHz) is routinely used to display cutaneous melanomas. Maximum thickness measurement (Breslow index) has been shown to be well correlated to histologic findings for lesions of more than 0.75 mm. Some morphological criteria (strong delineation, hypoechoic texture, homogeneity) have been reported to help differentiate between malignant and benign pigmented blue lesions, but remain insufficient. Vascular ultrasound analysis using Doppler mode provides additional information and showed good specificity for malignancy (90%–100%), but variable sensitivity (34%–100%). Recent advances in ultrasound imaging allow functional evaluation. Likewise, dynamic contrast-enhanced ultrasound using contrast medium injection and specific perfusion and quantification software showed promising results in clinical and preclinical trials for early prediction of tumor response to target treatments.Keywords: melanoma, ultrasound, dynamic contrast-enhanced ultrasound

Background: The optimal dose of TNF-α delivered by isolated limb perfusion (ILP) in patients with locally advanced soft tissue sarcoma is still unknown. Patients and methods: Randomised phase II trial comparing hyperthermic ILP (38-40°) with melphalan and one of the four assigned doses of TNF-α: 0.5 mg, 1 mg, 2 mg, and 3/4 mg upper/lower limb. The main end point was objective tumour response on MRI. Secondary end points were histological response, rate of amputation and toxicity. Resection of the remnant tumour was performed 2-3 months after ILP. The sample size was calculated assuming a linear increase of 10% in the objective response rates between each dose level group. Results: One hundred patients (25 per arm) were included. Thirteen per cent of patients had a systemic leakage with a cardiac toxicity in six patients correlated with high doses of TNF-α. Objective tumour responses were: 68%, 56%, 72% and 64% in the 0.5 mg, 1 mg, 2 mg and 3 or 4 mg arms, respectively (NS). Sixteen per cent of patients were not operated, 71% had a conservative surgery and 13% were amputated with no difference between the groups. With a median follow-up of 24 months, the 2 year overall and disease-free survival rates (95% CI) were 82% (73% to 89%) and 49% (39% to 59%), respectively. Conclusion: At the range of TNF-α doses tested, there was no dose effect detected for the objective tumour response, but systemic toxicity was significantly correlated with higher TNF-α doses. Efficacy and safety of low-dose TNF-α could greatly facilitate ILP procedures in the near future

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.