Application of Texture Analysis to Study Small Vessel Disease and Blood–Brain Barrier Integrity

artículo 327Evaluamos el uso alternativo del análisis de textura para evaluar el papel de la barrera hematoencefálica (BBB) en la enfermedad de pequeños vasos (SVD). Utilizamos imágenes de resonancia magnética cerebral de 204 pacientes con accidente cerebrovascular, adquiridas antes y 20 minutos después de la administración intravenosa de gadolinio. Segmentamos tejidos, hiperintensidades de la materia blanca (WMH) y aplicamos puntuaciones visuales validadas. Medimos las características de la textura en todos los tejidos antes y después del contraste y utilizamos ANCOVA para evalúe el efecto de los indicadores de SVD en el cambio anterior / posterior al contraste, Kruskal-Wallis para determinar la importancia entre los grupos de pacientes y los modelos lineales mixtos para las variaciones anteriores / posteriores al contraste en el líquido cefalorraquídeo (LCR) con puntuaciones Fazekas. El aumento de la "homogeneidad" textural en los tejidos normales con mayor presencia de indicadores de la EVP fue consecuentemente más evidente que en los tejidos anormales. La “homogeneidad” textural aumentó con la edad, las puntuaciones de los espacios perivasculares de los ganglios basales (p <0,01) y las puntuaciones de la EVP (p <0,05) y fue significativamente mayor en los pacientes hipertensos (p <0,002) y el ictus lacunar (p = 0,04). La hipertensión (74% de los pacientes), la carga de WMH (mediana = 1.5 ± 1.6% del volumen intracraneal) y la edad (media = 65.6 años, SD = 11.3) predijeron el cambio pre / post-contraste en la sustancia blanca normal, WMH e índice Lesión de trazo. Señal CSF Incremento con el aumento de SVD post-contraste. Un patrón general consistente de aumento de la homogeneidad de la textura "con el aumento de la SVD y el cambio posterior al contraste en el LCR con el aumento de WMH sugiere que el análisis de textura puede ser útil para el estudio de la integridad de BBB.S

Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Intraoperative Quantification of Bone Perfusion in Lower Extremity Injury Surgery

Orthopaedic surgery is one of the most common surgical categories. In particular, lower extremity injuries sustained from trauma can be complex and life-threatening injuries that are addressed through orthopaedic trauma surgery. Timely evaluation and surgical debridement following lower extremity injury is essential, because devitalized bones and tissues will result in high surgical site infection rates. However, the current clinical judgment of what constitutes “devitalized tissue” is subjective and dependent on surgeon experience, so it is necessary to develop imaging techniques for guiding surgical debridement, in order to control infection rates and to improve patient outcome. In this thesis work, computational models of fluorescence-guided debridement in lower extremity injury surgery will be developed, by quantifying bone perfusion intraoperatively using Dynamic contrast-enhanced fluorescence imaging (DCE-FI) system. Perfusion is an important factor of tissue viability, and therefore quantifying perfusion is essential for fluorescence-guided debridement. In Chapters 3-7 of this thesis, we explore the performance of DCE-FI in quantifying perfusion from benchtop to translation: We proposed a modified fluorescent microsphere quantification technique using cryomacrotome in animal model. This technique can measure bone perfusion in periosteal and endosteal separately, and therefore to validate bone perfusion measurements obtained by DCE-FI; We developed pre-clinical rodent contaminated fracture model to correlate DCE-FI with infection risk, and compare with multi-modality scanning; Furthermore in clinical studies, we investigated first-pass kinetic parameters of DCE-FI and arterial input functions for characterization of perfusion changes during lower limb amputation surgery; We conducted the first in-human use of dynamic contrast-enhanced texture analysis for orthopaedic trauma classification, suggesting that spatiotemporal features from DCE-FI can classify bone perfusion intraoperatively with high accuracy and sensitivity; We established clinical machine learning infection risk predictive model on open fracture surgery, where pixel-scaled prediction on infection risk will be accomplished. In conclusion, pharmacokinetic and spatiotemporal patterns of dynamic contrast-enhanced imaging show great potential for quantifying bone perfusion and prognosing bone infection. The thesis work will decrease surgical site infection risk and improve successful rates of lower extremity injury surgery