Dynamic contrast-enhanced ultrasound (DCE-US) has been used in single-center studies to evaluate tumor response to antiangiogenic treatments: the change of area under the perfusion curve (AUC), a criterion linked to blood volume, was consistently correlated with the Response Evaluation Criteria in Solid Tumors response. The main objective here was to do a multicentric validation of the use of DCE-US to evaluate tumor response in different solid tumor types treated by several antiangiogenic agents. A secondary objective was to evaluate the costs of the procedure. This prospective study included patients from 2007 to 2010 in 19 centers (8 teaching hospitals and 11 comprehensive cancer centers). All patients treated with antiangiogenic therapy were eligible. Dynamic contrast-enhanced ultrasound examinations were performed at baseline as well as on days 7, 15, 30, and 60. For each examination, a perfusion curve was recorded during 3 minutes after injection of a contrast agent. Change from baseline at each time point was estimated for each of 7 fitted criteria. The main end point was freedom from progression (FFP). Criterion/time-point combinations with the strongest correlation with FFP were analyzed further to estimate an optimal cutoff point. A total of 1968 DCE-US examinations in 539 patients were analyzed. The median follow-up was 1.65 years. Variations from baseline were significant at day 30 for several criteria, with AUC having the most significant association with FFP (P = 0.00002). Patients with a greater than 40% decrease in AUC at day 30 had better FFP (P = 0.005) and overall survival (P = 0.05). The mean cost of each DCE-US was 180&OV0556;, which corresponds to $250 using the current exchange rate. Dynamic contrast-enhanced ultrasound is a new functional imaging technique that provides a validated criterion, namely, the change of AUC from baseline to day 30, which is predictive of tumor progression in a large multicenter cohort. Because of its low cost, it should be considered in the routine evaluation of solid tumors treated with antiangiogenic therapy.

In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.

Carbonic Anhydrase IX (CAIX) is a well-described enzyme in renal cell carcinoma, with its expression being regulated by the hypoxia-inducible factor 1 alpha, it is known for interfering with hypoxia processes. Renal carcinoma encompasses a broad spectrum of histological entities and is also described as a heterogeneous malignant tumor. Recently, various combinations of checkpoint inhibitors and targeted therapies have been validated to manage this disease. Reliable markers to confirm the diagnosis, estimate the prognosis, predict or monitor the treatment response are required. Molecular imaging developments allow a comprehensive analysis of the tumor, overcoming the spatial heterogeneity issue. CAIX, being highly expressed at the tumor cell surfaces of clear cell renal carcinoma, also represents a potential treatment target. In this manuscript we reviewed the current knowledge from the literature on the pathophysiological interactions between renal cell carcinoma and CAIX, the role of CAIX as a marker for diagnosis, prognosis, treatment monitoring and molecular imaging, and the potential target for therapeutic strategies.

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.