Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges

Imaging plays a crucial role in the management of oncologic patients, from the initial diagnosis to staging and treatment response monitoring. Recently, it has been suggested that its importance could be further increased by accessing a new layer of previously hidden quantitative data at the pixel level. Using a multi-step process, radiomics extracts potential biomarkers from medical images that could power decision support tools. Despite the growing interest and rising number of research articles being published, radiomics is still far from fulfilling its promise of guiding oncologic imaging toward personalized medicine. This is, at least partly, due to the heterogeneous methodological quality in radiomic research, caused by the complexity of the analysis pipelines. In this review, we aim to disentangle this complexity with a stepwise approach. Specifically, we focus on challenges to face during image preprocessing and segmentation, how to handle imbalanced classes and avoid information leaks, as well as strategies for the proper validation of findings

Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): Guidelines for medical 3D printing and appropriateness for clinical scenarios

Este número da revista Cadernos de Estudos Sociais estava em organização quando fomos colhidos pela morte do sociólogo Ernesto Laclau. Seu falecimento em 13 de abril de 2014 surpreendeu a todos, e particularmente ao editor Joanildo Burity, que foi seu orientando de doutorado na University of Essex, Inglaterra, e que recentemente o trouxe à Fundação Joaquim Nabuco para uma palestra, permitindo que muitos pudessem dialogar com um dos grandes intelectuais latinoamericanos contemporâneos. Assim, buscamos fazer uma homenagem ao sociólogo argentino publicando uma entrevista inédita concedida durante a sua passagem pelo Recife, em 2013, encerrando essa revista com uma sessão especial sobre a sua trajetória

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

[EN] There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.This study was funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 vertical bar RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.Cerdà Alberich, L.; Sangüesa Nebot, C.; Alberich-Bayarri, A.; Carot Sierra, JM.; Martinez De Las Heras, B.; Veiga Canuto, D.; Cañete, A.... (2020). A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers. 12(12):1-14. https://doi.org/10.3390/cancers12123858114121

An investigation into the importance of T2 relaxation and echo time choice for accurate metabolite biomarker quantification

Metabolite concentrations are fundamental biomarkers of disease. With increasing interest in personalised medicine, this work assessed the accuracy of non-invasive metabolite quantification with magnetic resonance spectroscopy (MRS) using a combination of simulations, phantom and in vivo data. No optimal echo time (TE) was found for measuring a range of key metabolites with quantification accuracy generally influenced more by data quality than TE choice. The T2 relaxation times of water and metabolites with MRS dominated by a singlet could be estimated using 2 TEs and were found to be significantly different in paediatric brain tumours compared with normal brain, varying between tumour types. The T2 relaxation times of paediatric brain tumours were significantly shorter at 3T compared with 1.5T. Metabolite concentrations for individual patients were most affected by changes in the T2 relaxation time of water which is quick to measure. A clinical JPRESS protocol was developed which aids assignment of overlapping metabolites using changes of MRS with TE. Overall, measurement of MRS with a short TE reduces inaccuracies associated with variability in metabolite T2 and does not tend to lead to worse quantification of overlapping resonances. Further improvements in concentration accuracy can be obtained by measuring case-specific water T2