Improve definition of titanium tandems in MR-guided high dose rate brachytherapy for cervical cancer using proton density weighted MRI

BACKGROUND: For cervical cancer patients treated with MR-guided high dose rate brachytherapy, the accuracy of radiation delivery depends on accurate localization of both tumors and the applicator, e.g. tandem and ovoid. Standard T2-weighted (T2W) MRI has good tumor-tissue contrast. However, it suffers from poor uterus-tandem contrast, which makes the tandem delineation very challenging. In this study, we evaluated the possibility of using proton density weighted (PDW) MRI to improve the definition of titanium tandems. METHODS: Both T2W and PDW MRI images were obtained from each cervical cancer patient. Imaging parameters were kept the same between the T2W and PDW sequences for each patient except the echo time (90 ms for T2W and 5.5 ms for PDW) and the slice thickness (0.5 cm for T2W and 0.25 cm for PDW). Uterus-tandem contrast was calculated by the equation C = (S(u)-S(t))/S(u), where S(u) and S(t) represented the average signal in the uterus and the tandem, respectively. The diameter of the tandem was measured 1.5 cm away from the tip of the tandem. The tandem was segmented by the histogram thresholding technique. RESULTS: PDW MRI could significantly improve the uterus-tandem contrast compared to T2W MRI (0.42±0.24 for T2W MRI, 0.77±0.14 for PDW MRI, p=0.0002). The average difference between the measured and physical diameters of the tandem was reduced from 0.20±0.15 cm by using T2W MRI to 0.10±0.11 cm by using PDW MRI (p=0.0003). The tandem segmented from the PDW image looked more uniform and complete compared to that from the T2W image. CONCLUSIONS: Compared to the standard T2W MRI, PDW MRI has better uterus-tandem contrast. The information provided by PDW MRI is complementary to those provided by T2W MRI. Therefore, we recommend adding PDW MRI to the simulation protocol to assist tandem delineation process for cervical cancer patients

Comparison of optimised endovaginal vs external array coil T2-weighted and diffusion-weighted imaging techniques for detecting suspected early stage (IA/IB1) uterine cervical cancer.

Objective To compare sensitivity and specificity of endovaginal versus external-array coil T2-W and T2-W + DWI for detecting and staging small cervical tumours.Methods Optimised endovaginal and external array coil MRI at 3.0-T was done prospectively in 48 consecutive patients with stage Ia/Ib1 cervical cancer. Sensitivity/specificity for detecting tumour and parametrial extension against histopathology for a reading radiologist were determined on coronal T2-W and T2W + DW images. An independent radiologist also scored T2-W images without and with addition of DWI for the external-array and endovaginal coils on separate occasions >2 weeks apart. Cohen's kappa assessed inter- and intra-observer agreement.Results Median tumour volume in 19/38 cases positive on subsequent histology was 1.75 cm(3). Sensitivity, specificity, PPV, NPV were: reading radiologist 91.3 %, 89.5 %, 91.3 %, 89.5 %, respectively; independent radiologist T2-W 82.6 %, 73.7 %, 79.1 %, 77.8 % for endovaginal, 73.9 %, 89.5 %, 89.5 %, 73.9 % for external-array coil. Adding DWI improved sensitivity and specificity of endovaginal imaging (78.2 %, 89.5 %); adding DWI to external-array imaging improved specificity (94.7 %) but reduced sensitivity (66.7 %). Inter- and intra-observer agreement on T2-W + DWI was good (kappa = 0.67 and 0.62, respectively).Conclusion Endovaginal coil T2-W MRI is more sensitive than external-array coil for detecting tumours <2 cm(3); adding DWI improves specificity of endovaginal imaging but reduces sensitivity of external-array imaging.Key points • Endovaginal more accurate than external-array T2-W MRI for detecting small cervical cancers. • Addition of DWI improves sensitivity and specificity of endovaginal T2-W imaging. • Addition of DWI substantially reduces sensitivity of external-array T2-W imaging

Focal Spot, Fall/Winter 1997

https://digitalcommons.wustl.edu/focal_spot_archives/1077/thumbnail.jp

Quantitative Perfusion-Sensitive Mri Phantoms

Perfusion-sensitive MR methods are increasingly utilized in preclinical and clinical MR research studies with the promise of providing quantitative estimates of parameters that describe in vivo microvasculature. One of these techniques, dynamic contrast enhanced: DCE) MRI, has found particularly common use in oncology for the detection, staging, and monitoring of highly vascularized tumors. DCE-MRI has been qualitatively validated by various studies that show a high correlation between modeled parameters from DCE and histologically measured microvascular density: MVD). However, in the absence of a matching gold-standard technique, DCE-MRI has not yet been quantitatively validated: i.e., the accuracy of the estimated parameters is unknown). Partly because of this inability to determine the accuracy of the measured parameters, there remains debate in the literature about which DCE signal model(s) best reflect(s) experimental data. In order to address these scientific challenges, realistic DCE tissue phantoms have been constructed. These phantoms implement semi-permeable hollow fibers, found commonly in commercial hemodialysis cartridges, to simulate leaky vasculature. Their design and construction are cataloged in this thesis. In addition, the phantoms have been experimentally characterized. In conjunction with these experiments, an interesting example of diffusion driven longitudinal relaxation was observed and is described herein. Lastly, the permeability of the fiber wall with respect to Gd-based contrast agents has been measured independently and compared with values derived from a mock-DCE experiment performed on the phantoms. In general, the results of these experiments support current DCE-MRI methods

Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives.

For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology. KEY POINTS:• Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging