Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance.

BACKGROUND: We recently conducted a phase 2 trial (NCT028865685) evaluating intracranial efficacy of pembrolizumab for brain metastases (BM) of diverse histologies. Our study met its primary efficacy endpoint and illustrates that pembrolizumab exerts promising activity in a select group of patients with BM. Given the importance of aberrant vasculature in mediating immunosuppression, we explored the relationship between checkpoint inhibitor (ICI) efficacy and vascular architecture in the hopes of identifying potential mechanisms of intracranial ICI response or resistance for BM. METHODS: Using Vessel Architectural Imaging (VAI), a histologically validated quantitative metric for in vivo tumor vascular physiology, we analyzed dual echo DSC/DCE MRI for 44 patients on trial. Tumor and peri-tumor cerebral blood volume/flow, vessel size, arterial- and venous-dominance, and vascular permeability were measured before and after treatment with pembrolizumab. RESULTS: BM that progressed on ICI were characterized by a highly aberrant vasculature dominated by large-caliber vessels. In contrast, ICI-responsive BM possessed a more structurally balanced vasculature consisting of both small and large vessels, and there was a trend towards a decrease in under-perfused tissue, suggesting a reversal of the negative effects of hypoxia. In the peri-tumor region, development of smaller blood vessels, consistent with neo-angiogenesis, was associated with tumor growth before radiographic evidence of contrast enhancement on anatomical MRI. CONCLUSIONS: This study, one of the largest functional imaging studies for BM, suggests that vascular architecture is linked with ICI efficacy. Studies identifying modulators of vascular architecture, and effects on immune activity, are warranted and may inform future combination treatments.

Alloying Effects on Charge-Carrier Transport in Silver-Bismuth Double Perovskites.

Alloying is widely adopted for tuning the properties of emergent semiconductors for optoelectronic and photovoltaic applications. So far, alloying strategies have primarily focused on engineering bandgaps rather than optimizing charge-carrier transport. Here, we demonstrate that alloying may severely limit charge-carrier transport in the presence of localized charge carriers (e.g., small polarons). By combining reflection-transmission and optical pump-terahertz probe spectroscopy with first-principles calculations, we investigate the interplay between alloying and charge-carrier localization in Cs2AgSbxBi1-xBr6 double perovskite thin films. We show that the charge-carrier transport regime strongly determines the impact of alloying on the transport properties. While initially delocalized charge carriers probe electronic bands formed upon alloying, subsequently self-localized charge carriers probe the energetic landscape more locally, thus turning an alloy's low-energy sites (e.g., Sb sites) into traps, which dramatically deteriorates transport properties. These findings highlight the inherent limitations of alloying strategies and provide design tools for newly emerging and highly efficient semiconductors.

Correction to "A Templating Approach to Controlling the Growth of Coevaporated Halide Perovskites".

[This corrects the article DOI: 10.1021/acsenergylett.3c01368.].

A Templating Approach to Controlling the Growth of Coevaporated Halide Perovskites.

Metal halide perovskite semiconductors have shown significant potential for use in photovoltaic (PV) devices. While fabrication of perovskite thin films can be achieved through a variety of techniques, thermal vapor deposition is particularly promising, allowing for high-throughput fabrication. However, the ability to control the nucleation and growth of these materials, particularly at the charge-transport layer/perovskite interface, is critical to unlocking the full potential of vapor-deposited perovskite PV. In this study, we explore the use of a templating layer to control the growth of coevaporated perovskite films and find that such templating leads to highly oriented films with identical morphology, crystal structure, and optoelectronic properties independent of the underlying layers. Solar cells incorporating templated FA0.9Cs0.1PbI3-xClx show marked improvements with steady-state power conversion efficiency over 19.8%. Our findings provide a straightforward and reproducible method of controlling the charge-transport layer/coevaporated perovskite interface, further clearing the path toward large-scale fabrication of efficient PV devices.

Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy.

Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Temperature-Dependent Reversal of Phase Segregation in Mixed-Halide Perovskites.

Understanding the mechanism of light-induced halide segregation in mixed-halide perovskites is essential for their application in multijunction solar cells. Here, photoluminescence spectroscopy is used to uncover how both increases in temperature and light intensity can counteract the halide segregation process. It is observed that, with increasing temperature, halide segregation in CH3 NH3 Pb(Br0.4 I0.6 )3 first accelerates toward ≈290 K, before slowing down again toward higher temperatures. Such reversal is attributed to the trade-off between the temperature activation of segregation, for example through enhanced ionic migration, and its inhibition by entropic factors. High light intensities meanwhile can also reverse halide segregation; however, this is found to be only a transient process that abates on the time scale of minutes. Overall, these observations pave the way for a more complete model of halide segregation and aid the development of highly efficient and stable perovskite multijunction and concentrator photovoltaics.

Solvent-Free Method for Defect Reduction and Improved Performance of p-i-n Vapor-Deposited Perovskite Solar Cells.

As perovskite-based photovoltaics near commercialization, it is imperative to develop industrial-scale defect-passivation techniques. Vapor deposition is a solvent-free fabrication technique that is widely implemented in industry and can be used to fabricate metal-halide perovskite thin films. We demonstrate markably improved growth and optoelectronic properties for vapor-deposited [CH(NH2)2]0.83Cs0.17PbI3 perovskite solar cells by partially substituting PbI2 for PbCl2 as the inorganic precursor. We find the partial substitution of PbI2 for PbCl2 enhances photoluminescence lifetimes from 5.6 ns to over 100 ns, photoluminescence quantum yields by more than an order of magnitude, and charge-carrier mobility from 46 cm2/(V s) to 56 cm2/(V s). This results in improved solar-cell power conversion efficiency, from 16.4% to 19.3% for the devices employing perovskite films deposited with 20% substitution of PbI2 for PbCl2. Our method presents a scalable, dry, and solvent-free route to reducing nonradiative recombination centers and hence improving the performance of vapor-deposited metal-halide perovskite solar cells.

Heterologous Synthesis and Secretion of Ricinoleic Acid in Starmerella bombicola with Sophorolipid as an Intermediate.

Ricinoleic acid (RA) is a long-chain hydroxy fatty acid produced from castor bean that is used in the manufacturing of a variety of industrial products. The demand for RA keeps increasing due to its broad applications. However, due to the presence of a potent toxin ricin, the native oilseed plant is not an ideal source for hydroxy fatty acid production. Although there have been significant efforts on engineering different microorganisms for heterologous production of RA, all had very limited success. The main reason for this is the exhibited toxicity of the intracellularly accumulated RA. To avoid this issue, we genetically modified a Starmerella bombicola strain by engineering its native sophorolipid production pathway to direct the synthesized RA bound with sophorolipid to be secreted out of the cell, followed by acid hydrolysis to recover RA. The engineered S. bombicola strain expressing the heterologous codon-optimized oleate hydroxylase-encoding gene from ergot fungus Claviceps purpurea resulted in a record production titer of RA at about 2.96 g/L. Thus, this work highlights a new strategy to produce a high level of hydroxy fatty acids in engineered yeast through a sophorolipid intermediate, enabling a new biocatalysis platform for the future.

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors. METHODS: The preoperative and postoperative cohorts were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO (Response Assessment in Pediatric Neuro-Oncology) criteria (AutoRAPNO) was determined. Performance was compared to that of 2 expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared. RESULTS: A total of 794 preoperative MRIs from 794 patients and 1003 postoperative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with intraclass correlation coefficients (ICCs) of 0.912 and 0.960 for the 2 preoperative and 0.947 and 0.896 for the 2 postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC = 0.909; Rater 3 ICC = 0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions. CONCLUSIONS: Our automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.

Phase segregation in mixed-halide perovskites affects charge-carrier dynamics while preserving mobility.

Mixed halide perovskites can provide optimal bandgaps for tandem solar cells which are key to improved cost-efficiencies, but can still suffer from detrimental illumination-induced phase segregation. Here we employ optical-pump terahertz-probe spectroscopy to investigate the impact of halide segregation on the charge-carrier dynamics and transport properties of mixed halide perovskite films. We reveal that, surprisingly, halide segregation results in negligible impact to the THz charge-carrier mobilities, and that charge carriers within the I-rich phase are not strongly localised. We further demonstrate enhanced lattice anharmonicity in the segregated I-rich domains, which is likely to support ionic migration. These phonon anharmonicity effects also serve as evidence of a remarkably fast, picosecond charge funnelling into the narrow-bandgap I-rich domains. Our analysis demonstrates how minimal structural transformations during phase segregation have a dramatic effect on the charge-carrier dynamics as a result of charge funnelling. We suggest that because such enhanced recombination is radiative, performance losses may be mitigated by deployment of careful light management strategies in solar cells.

Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma.

PURPOSE: To determine the influence of preprocessing on the repeatability and redundancy of radiomics features extracted using a popular open-source radiomics software package in a scan-rescan glioblastoma MRI study. MATERIALS AND METHODS: In this study, a secondary analysis of T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1-weighted postcontrast images from 48 patients (mean age, 56 years [range, 22-77 years]) diagnosed with glioblastoma were included from two prospective studies (ClinicalTrials.gov NCT00662506 [2009-2011] and NCT00756106 [2008-2011]). All patients underwent two baseline scans 2-6 days apart using identical imaging protocols on 3-T MRI systems. No treatment occurred between scan and rescan, and tumors were essentially unchanged visually. Radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) under varying conditions, including normalization strategies and intensity quantization. Subsequently, intraclass correlation coefficients were determined between feature values of the scan and rescan. RESULTS: Shape features showed a higher repeatability than intensity (adjusted P < .001) and texture features (adjusted P < .001) for both T2-weighted FLAIR and T1-weighted postcontrast images. Normalization improved the overlap between the region of interest intensity histograms of scan and rescan (adjusted P < .001 for both T2-weighted FLAIR and T1-weighted postcontrast images), except in scans where brain extraction fails. As such, normalization significantly improves the repeatability of intensity features from T2-weighted FLAIR scans (adjusted P = .003 [z score normalization] and adjusted P = .002 [histogram matching]). The use of a relative intensity binning strategy as opposed to default absolute intensity binning reduces correlation between gray-level co-occurrence matrix features after normalization. CONCLUSION: Both normalization and intensity quantization have an effect on the level of repeatability and redundancy of features, emphasizing the importance of both accurate reporting of methodology in radiomics articles and understanding the limitations of choices made in pipeline design. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Tiwari and Verma in this issue.

Limits to Electrical Mobility in Lead-Halide Perovskite Semiconductors.

Semiconducting polycrystalline thin films are cheap to produce and can be deposited on flexible substrates, yet high-performance electronic devices usually utilize single-crystal semiconductors, owing to their superior charge-carrier mobilities and longer diffusion lengths. Here we show that the electrical performance of polycrystalline films of metal-halide perovskites (MHPs) approaches that of single crystals at room temperature. Combining temperature-dependent terahertz conductivity measurements and ab initio calculations we uncover a complete picture of the origins of charge-carrier scattering in single crystals and polycrystalline films of CH3NH3PbI3. We show that Fröhlich scattering of charge carriers with multiple phonon modes is the dominant mechanism limiting mobility, with grain-boundary scattering further reducing mobility in polycrystalline films. We reconcile the large discrepancy in charge-carrier diffusion lengths between single crystals and films by considering photon reabsorption. Thus, polycrystalline films of MHPs offer great promise for devices beyond solar cells, including light-emitting diodes and modulators.

Halide Segregation in Mixed-Halide Perovskites: Influence of A-Site Cations.

Mixed-halide perovskites offer bandgap tunability essential for multijunction solar cells; however, a detrimental halide segregation under light is often observed. Here we combine simultaneous in situ photoluminescence and X-ray diffraction measurements to demonstrate clear differences in compositional and optoelectronic changes associated with halide segregation in MAPb(Br0.5I0.5)3 and FA0.83Cs0.17Pb(Br0.4I0.6)3 films. We report evidence for low-barrier ionic pathways in MAPb(Br0.5I0.5)3, which allow for the rearrangement of halide ions in localized volumes of perovskite without significant compositional changes to the bulk material. In contrast, FA0.83Cs0.17Pb(Br0.4I0.6)3 lacks such low-barrier ionic pathways and is, consequently, more stable against halide segregation. However, under prolonged illumination, it exhibits a considerable ionic rearrangement throughout the bulk material, which may be triggered by an initial demixing of A-site cations, altering the composition of the bulk perovskite and reducing its stability against halide segregation. Our work elucidates links between composition, ionic pathways, and halide segregation, and it facilitates the future engineering of phase-stable mixed-halide perovskites.

Temperature Coefficients of Perovskite Photovoltaics for Energy Yield Calculations.

Temperature coefficients for maximum power (T PCE), open circuit voltage (V OC), and short circuit current (J SC) are standard specifications included in data sheets for any commercially available photovoltaic module. To date, there has been little work on determining the T PCE for perovskite photovoltaics (PV). We fabricate perovskite solar cells with a T PCE of -0.08 rel %/°C and then disentangle the temperature-dependent effects of the perovskite absorber, contact layers, and interfaces by comparing different device architectures and using drift-diffusion modeling. A main factor contributing to the small T PCE of perovskites is their low intrinsic carrier concentrations with respect to Si and GaAs, which can be explained by its wider band gap. We demonstrate that the unique increase in E g with increasing temperatures seen for perovskites results in a reduction in J SC but positively influences V OC. The current limiting factors for the T PCE in perovskite PV are identified to originate from interfacial effects.

Cerebrovascular Disease Progression in Patients With ACTA2 Arg179 Pathogenic Variants.

OBJECTIVE: To establish progression of imaging biomarkers of stroke, arterial steno-occlusive disease, and white matter injury in patients with smooth muscle dysfunction syndrome caused by mutations in the ACTA2 gene, we analyzed 113 cerebral MRI scans from a retrospective cohort of 27 patients with ACTA2 Arg179 pathogenic variants. METHODS: Systematic quantifications of arterial ischemic strokes and white matter lesions were performed on baseline and follow-up scans using planimetric methods. Critical stenosis and arterial vessel diameters were quantified applying manual and semiautomated methods to cerebral magnetic resonance angiograms. We then assessed correlations between arterial abnormalities and parenchymal injury. RESULTS: We found characteristic patterns of acute white matter ischemic injury and progressive internal carotid artery stenosis during infancy. Longitudinal analysis of patients older than 1.2 years showed stable white matter hyperintensities but increased number of cystic-like lesions over time. Progressive narrowing of the terminal internal carotid artery occurred in 80% of patients and correlated with the number of critical stenoses in cerebral arteries and arterial ischemic infarctions. Arterial ischemic strokes occurred in same territories affected by critical stenosis. CONCLUSIONS: We found characteristic, early, and progressive cerebrovascular abnormalities in patients with ACTA2 Arg179 pathogenic variants. Our longitudinal data suggest that while steno-occlusive disease progresses over time and is associated with arterial ischemic infarctions and cystic-like white matter lesions, white matter hyperintensities can remain stable over long periods. The evaluated metrics will enable diagnosis in early infancy and be used to monitor disease progression, guide timing of stroke preventive interventions, and assess response to current and future therapies.

Efficient energy transfer mitigates parasitic light absorption in molecular charge-extraction layers for perovskite solar cells.

Organic semiconductors are commonly used as charge-extraction layers in metal-halide perovskite solar cells. However, parasitic light absorption in the sun-facing front molecular layer, through which sun light must propagate before reaching the perovskite layer, may lower the power conversion efficiency of such devices. Here, we show that such losses may be eliminated through efficient excitation energy transfer from a photoexcited polymer layer to the underlying perovskite. Experimentally observed energy transfer between a range of different polymer films and a methylammonium lead iodide perovskite layer was used as basis for modelling the efficacy of the mechanism as a function of layer thickness, photoluminescence quantum efficiency and absorption coefficient of the organic polymer film. Our findings reveal that efficient energy transfer can be achieved for thin (≤10 nm) organic charge-extraction layers exhibiting high photoluminescence quantum efficiency. We further explore how the morphology of such thin polymer layers may be affected by interface formation with the perovskite.

Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density.

OBJECTIVE: We developed deep learning algorithms to automatically assess BI-RADS breast density. METHODS: Using a large multi-institution patient cohort of 108,230 digital screening mammograms from the Digital Mammographic Imaging Screening Trial, we investigated the effect of data, model, and training parameters on overall model performance and provided crowdsourcing evaluation from the attendees of the ACR 2019 Annual Meeting. RESULTS: Our best-performing algorithm achieved good agreement with radiologists who were qualified interpreters of mammograms, with a four-class κ of 0.667. When training was performed with randomly sampled images from the data set versus sampling equal number of images from each density category, the model predictions were biased away from the low-prevalence categories such as extremely dense breasts. The net result was an increase in sensitivity and a decrease in specificity for predicting dense breasts for equal class compared with random sampling. We also found that the performance of the model degrades when we evaluate on digital mammography data formats that differ from the one that we trained on, emphasizing the importance of multi-institutional training sets. Lastly, we showed that crowdsourced annotations, including those from attendees who routinely read mammograms, had higher agreement with our algorithm than with the original interpreting radiologists. CONCLUSION: We demonstrated the possible parameters that can influence the performance of the model and how crowdsourcing can be used for evaluation. This study was performed in tandem with the development of the ACR AI-LAB, a platform for democratizing artificial intelligence.