Search

Purpose: The purpose of this study is to identify predictive factors on baseline [18F]NaF positron emission tomography (PET)/computed tomography (CT) of early response to radium-223 dichloride after 3 cycles of treatment in metastatic castration-resistant prostate cancer patients.Procedures: Analysis of 152 metastases was performed in six consecutive patients who underwent [18F]NaF PET/CT at baseline and for early monitoring after 3 cycles of radium-223 dichloride. All metastases depicted on whole-body [18F]NaF PET/CT were contoured and CT (density in Hounsfield units, sclerotic, mixed, or lytic appearance) as well as [18F]NaF [maximum standardized uptake value (SUVmax), SUVmean, and lesion volume (V18F-NaF)] patterns were recorded. Tumor response was defined as percentage change in SUVmax and SUVmean between baseline and post-treatment PET. Bone lesions were defined as stable, responsive, or progressive, according to thresholds derived from a recent multicentre test-retest study in [18F]NaF PET/CT. Total [18F]NaF uptake in metastases, defined as MATV × SUVmean, was correlated to uptake of radium-223 on biodistribution scintigraphy performed 7 days after the first cycle of treatment.Results: Among metastases, 116 involved the axial skeleton and 36 the appendicular skeleton. Lesions were sclerotic in 126 cases and mixed in 26 cases. No lytic lesion was depicted. ROC analysis showed that SUVmax and SUVmean were better predictors of lesion response than V18F-NaF and density on CT (P < 0.0001 and P = 0.001, respectively). SUVmax and SUVmean were predictors of individual tumor response in separate multivariate models (P = 0.01 and P = 0.02, respectively). CT pattern (mixed versus sclerotic) and lesion density were independent predictors only when assessing response with delta SUVmax (P = 0.002 and 0.007, respectively). A good correlation between total [18F]NaF uptake within metastases and their relative radium-223 uptake assessed by two observers 7 days after treatment (r = 0.72 and 0.77, P < 0.0001) was found.Conclusions: SUVmax and SUVmean on baseline [18F]NaF PET/CT are independent predictors of bone lesions' response to 3 cycles of radium-223 dichloride, supporting the use of NaF to select patients more likely to respond to treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters. [ABSTRACT FROM AUTHOR]

Proton therapy is a radiotherapy, based on the use of protons, charged subatomic particles that stop at a given depth depending on their initial energy (pristine Bragg peak), avoiding any output beam, unlike the photons used in most of the other modalities of radiotherapy. Proton therapy has been used for 60 years, but has only become ubiquitous in the last decade because of recent major advances in particle accelerator technology. This article reviews the history of clinical implementation of protons, the nature of the technological advances that now allows its expansion at a lower cost. It also addresses the technical and physical specificities of proton therapy and the clinical situations for which proton therapy may be relevant but requires evidence. Different proton therapy techniques are possible. These are explained in terms of their clinical potential by explaining the current terminology (such as cyclotrons, synchrotrons or synchrocyclotrons, using superconducting magnets, fixed line or arm rotary with passive diffusion delivery or active by scanning) in basic words. The requirements associated with proton therapy are increased due to the precision of the depth dose deposit. The learning curve of proton therapy requires that clinical indications be prioritized according to their associated uncertainties (such as range uncertainties and movement in lung tumors). Many clinical indications potentially fall under proton therapy ultimately. Clinical strategies are explained in a paralleled manuscript., (Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: The purpose of this study was to analyze the detector responses in non-equilibrium small photon fields., Methods: Five detectors (PTW 31014 ionization chamber, PTW 60016, PTW 60017 and Sun Nuclear EDGE diodes and PTW 60003 diamond detector) and one passive dosimeter (Harshaw micro-LiF) as well as a 1000MU/min CyberKnife were modeled with the PENELOPE Monte Carlo code. Field factors, [Formula: see text] were calculated and perturbations due to volume averaging effect, active material effect and coating effect were quantified for the five detectors and passive dosimeter., Results: The PTW 31014 ionization chamber under-response is mainly due to the fluence perturbation caused by the presence of air as detecting material. Regarding diodes, the high density materials used in their active volume and in their coating is responsible for their over-response. Regarding the PTW 60003 diamond, its under-response for the 5mm field size is due to a large volume averaging effect whereas for largest field sizes a nearly perfect compensation between the volume averaging effect and the material effect due to the diamond density occurs. Despite its small size, a volume averaging effect was observed for the micro-LiF for the 5mm field size., Conclusion: Perturbations due to volume averaging effect, active material effect and coating effect were investigated and quantified for five active detectors. Since these perturbations can cause opposite effects, wrong conclusions may be drawn regarding the radiological water-equivalence of detectors. Thus, we recommend performing such a study for each novel detector available on the market., (Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)