Is pulsed saturation transfer sufficient for differentiating radiation necrosis from tumor progression in brain metastases?

Background: Stereotactic radiosurgery (SRS) for the treatment of brain metastases delivers a high dose of radiation with excellent local control but comes with the risk of radiation necrosis (RN), which can be difficult to distinguish from tumor progression (TP). Magnetization transfer (MT) and chemical exchange saturation transfer (CEST) are promising techniques for distinguishing RN from TP in brain metastases. Previous studies used a 2D continuous-wave (ie, block radiofrequency [RF] saturation) MT/CEST approach. The purpose of this study is to investigate a 3D pulsed saturation MT/CEST approach with perfusion MRI for distinguishing RN from TP in brain metastases. Methods: The study included 73 patients scanned with MT/CEST MRI previously treated with SRS or fractionated SRS who developed enhancing lesions with uncertain diagnoses of RN or TP. Perfusion MRI was acquired in 49 of 73 patients. Clinical outcomes were determined by at least 6 months of follow-up or via pathologic confirmation (in 20% of the lesions). Results: Univariable logistic regression resulted in significant variables of the quantitative MT parameter 1/(RA·T2A), with 5.9 ±â€…2.7 for RN and 6.5 ±â€…2.9 for TP. The highest AUC of 75% was obtained using a multivariable logistic regression model for MT/CEST parameters, which included the CEST parameters of AREXAmide,0.625µT (P = .013), AREXNOE,0.625µT (P = .008), 1/(RA·T2A) (P = .004), and T1 (P = .004). The perfusion rCBV parameter did not reach significance. Conclusions: Pulsed saturation transfer was sufficient for achieving a multivariable AUC of 75% for differentiating between RN and TP in brain metastases, but had lower AUCs compared to previous studies that used a block RF approach.

Deuterium MRS of early treatment-induced changes in tumour lactate in vitro.

Elevated production of lactate is a key characteristic of aberrant tumour cell metabolism and can be non-invasively measured as an early marker of tumour response using deuterium (2 H) MRS. Following treatment, changes in the 2 H-labelled lactate signal could identify tumour cell death or impaired metabolic function, which precede morphological changes conventionally used to assess tumour response. In this work, the association between apoptotic cell death, extracellular lactate concentration, and early treatment-induced changes in the 2 H-labelled lactate signal was established in an in vitro tumour model. Experiments were conducted at 7 T on acute myeloid leukaemia (AML) cells, which had been treated with 10 µg/mL of the chemotherapeutic agent cisplatin. At 24 and 48 h after cisplatin treatment the cells were supplied with 20 mM of [6,6'-2 H2 ]glucose and scanned over 2 h using a two-dimensional 2 H MR spectroscopic imaging sequence. The resulting signals from 2 H-labelled glucose, lactate, and water were quantified using a spectral fitting algorithm implemented on the Oxford Spectroscopy Analysis MATLAB toolbox. After scanning, the cells were processed for histological stains (terminal deoxynucleotidyl transferase UTP nick end labelling and haematoxylin and eosin) to assess apoptotic area fraction and cell morphology respectively, while a colorimetric assay was used to measure extracellular lactate concentrations in the supernatant. Significantly lower levels of 2 H-labelled lactate were observed in the 48 h treated cells compared with the untreated and 24 h treated cells, and these changes were significantly correlated with an increase in apoptotic fraction and a decrease in extracellular lactate. By establishing the biological processes associated with treatment-induced changes in the 2 H-labelled lactate signal, these findings suggest that 2 H MRS of lactate may be valuable in evaluating early tumour response.

FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy.

Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python.