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Background: Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and methods: Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results: For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions: The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change. [ABSTRACT FROM AUTHOR]

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T1-weighted MRI from a pre-existing repository of clinical planning CTs. Methods: MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T1 weighted MRI scans. Results: In our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases Conclusion: Pelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models. [ABSTRACT FROM AUTHOR]

We use Raman spectroscopy to investigate the thin-film morphology of conjugated polymers [poly(9,9-di-n-octylfluorene-alt-benzothiadiazole (F8BT)] in terms of the polymer chain conformation at interfaces with quartz, a crosslinked benzocyclobutene derivative, polyvinylphenol, and poly(3,4-ethylenedioxythiophene):poly(styrenesulphonate). The polymer chains near the substrate interface adopt a more planar conformation (lower torsion angle between fluorene and benzothiadiazole units) than chains in the bulk of the film for all substrates studied. On annealing, polymer chains both near the interface and in the bulk of the film adopt more planar conformations than in their pristine states but to a different degree. The influence of F8BT molecular weight on polymer chain conformation near the substrate interface is also examined. These results are confirmed by additional absorption and photoluminescence measurements. [ABSTRACT FROM AUTHOR]

Quantum-chemical calculations performed on poly[2,7-(9,9-di-n-octylfluorene)(1-x)-co-4,7-(2,1,3-benzothiadiazole)(x)] copolymers (0≤x≤0.5) show that the lowest unoccupied molecular orbital is always highly localized on the benzothiadiazole (BT) units while the highest occupied molecular orbital is delocalized over the whole chain. Chains with a low BT content are characterized by a reduced oscillator strength of the lowest optical transition and by an increased charge-transfer character of the exciton. These results are supported experimentally by a blueshift of the lowest energy absorption band upon reduction in the BT ratio, lower photoluminescence efficiency, longer excited state lifetimes, and greater solvent dependence of the emission properties. [ABSTRACT FROM AUTHOR]

Quantum-chemical calculations performed on poly[2,7-(9,9-di-n-octylfluorene)(1-x)-co-4,7-(2,1,3-benzothiadiazole)(x)] copolymers (0≤x≤0.5) show that the lowest unoccupied molecular orbital is always highly localized on the benzothiadiazole (BT) units while the highest occupied molecular orbital is delocalized over the whole chain. Chains with a low BT content are characterized by a reduced oscillator strength of the lowest optical transition and by an increased charge-transfer character of the exciton. These results are supported experimentally by a blueshift of the lowest energy absorption band upon reduction in the BT ratio, lower photoluminescence efficiency, longer excited state lifetimes, and greater solvent dependence of the emission properties. [ABSTRACT FROM AUTHOR]

This article reports on in-plane and out-of-plane refractive index (n) and extinction coefficient (k) values measured using variable-angle spectroscopic ellipsometry for poly(9,9-di-n-octylfluorene-alt-benzothiadiazole) (F8BT) thin films of different molecular weights (Mn=9-255 kg/mol), both in the pristine and annealed states. The in-plane n and k values are generally larger than the out-of-plane values for all pristine films leading to a measurable optical anisotropy which becomes much stronger as F8BT molecular weight increases. This indicates that polymer chains lie preferentially in the plane of the substrate and this configuration is more energetically favorable for longer polymer chains. Upon annealing, a larger reduction in kout-of-plane than in kin-plane is measured leading to a further increase in optical anisotropy. A redistribution of oscillator strengths and a broadening toward lower energies in absorption spectra are also observed indicating significant restructuring of F8BT chains upon annealing. [ABSTRACT FROM AUTHOR]

Background: Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments. In this pilot study, we investigate the use of machine-learning approaches to automatically delineate tissue compartments in STS, and use this approach to monitor post-radiotherapy changes. Methods: Eighteen patients with retroperitoneal sarcoma were imaged using multi-parametric MRI; 8/18 received a follow-up imaging study 2–4 weeks after pre-operative radiotherapy. Eight commonly-used supervised machine-learning techniques were optimized for classifying pixels into one of five tissue sub-types using an exhaustive cross-validation approach and expert-defined regions of interest as a gold standard. Final pixel classification was smoothed using a Markov Random Field (MRF) prior distribution on the final machine-learning models. Findings: 5/8 machine-learning techniques demonstrated high median cross-validation accuracies (82.2%, range 80.5–82.5%) with no significant difference between these five methods. One technique was selected (Naïve-Bayes) due to its relatively short training and class-prediction times (median 0.73 and 0.69 ms, respectively on a 3.5 GHz personal machine). When combined with the MRF-prior, this approach was successfully applied in all eight post-radiotherapy imaging studies and provided visualization and quantification of changes to independent STS sub-regions following radiotherapy for heterogeneous response assessment. Interpretation: Supervised machine-learning approaches to tissue classification in multi-parametric MRI of soft-tissue sarcomas provide quantitative evaluation of heterogeneous tissue changes following radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate repeatability of quantitative multi-parametric MRI in retroperitoneal sarcomas, assess parameter changes with radiotherapy, and correlate pre-operative values with histopathological findings in the surgical specimens. Materials and Methods: Thirty patients with retroperitoneal sarcoma were imaged at baseline, of whom 27 also underwent a second baseline examination for repeatability assessment. 14/30 patients were treated with pre-operative radiotherapy and were imaged again after completing radiotherapy (50.4 Gy in 28 daily fractions, over 5.5 weeks). The following parameter estimates were assessed in the whole tumor volume at baseline and following radiotherapy: apparent diffusion coefficient (ADC), parameters of the intra-voxel incoherent motion model of diffusion-weighted MRI (D, f , D*), transverse relaxation rate, fat fraction, and enhancing fraction after gadolinium-based contrast injection. Correlation was evaluated between pre-operative quantitative parameters and histopathological assessments of cellularity and fat fraction in post-surgical specimens (ClinicalTrials.gov, registration number NCT01902667). Results: Upper and lower 95% limits of agreement were 7.1 and −6.6%, respectively for median ADC at baseline. Median ADC increased significantly post-radiotherapy. Pre-operative ADC and D were negatively correlated with cellularity (r = −0.42, p = 0.01, 95% confidence interval (CI) −0.22 to −0.59 for ADC; r = −0.45, p = 0.005, 95% CI −0.25 to −0.62 for D), and fat fraction from Dixon MRI showed strong correlation with histopathological assessment of fat fraction (r = 0.79, p = 10−7, 95% CI 0.69–0.86). Conclusion: Fat fraction on MRI corresponded to fat content on histology and therefore contributes to lesion characterization. Measurement repeatability was excellent for ADC; this parameter increased significantly post-radiotherapy even in disease categorized as stable by size criteria, and corresponded to cellularity on histology. ADC can be utilized for characterizing and assessing response in heterogeneous retroperitoneal sarcomas. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion-weighted (DW) and dynamic contrast-enhanced magnetic resonance imaging (MRI) are increasingly applied for the assessment of functional tissue biomarkers for diagnosis, lesion characterization, or for monitoring of treatment response. However, these techniques are vulnerable to the influence of various factors, so there is a necessity for a standardized MR quality assurance procedure utilizing a phantom to facilitate the reliable estimation of repeatability of these quantitative biomarkers arising from technical factors (e.g., B1 variation) affecting acquisition on scanners of different vendors and field strengths. The purpose of this study is to present a novel phantom designed for use in quality assurance for multicenter trials, and the associated repeatability measurements of functional and quantitative imaging protocols across different MR vendors and field strengths. Methods: A cylindrical acrylic phantom was manufactured containing 7 vials of polyvinylpyrrolidone (PVP) solutions of different concentrations, ranging from 0% (distilled water) to 25% w/w, to create a range of differentMR contrast parameters. Temperature control was achieved by equilibration with ice-water. Repeated MR imaging measurements of the phantom were performed on four clinical scanners (two at 1.5 T, two at 3.0 T; two vendors) using the same scanning protocol to assess the long-term and short-term repeatability. The scanning protocol consisted of DW measurements, inversion recovery (IR) T1 measurements, multiecho T2 measurement, and dynamic T1-weighted sequence allowing multiple variable flip angle (VFA) estimation of T1 values over time. For each measurement, the corresponding calculated parameter maps were produced. On each calculated map, regions of interest (ROIs) were drawn within each vial and the median value of these voxels was assessed. For the dynamic data, the autocorrelation function and their variance were calculated; for the assessment of the repeatability, the coefficients of variation (CoV) were calculated. Results: For both field strengths across the available vendors, the apparent diffusion coefficient (ADC) at 0 ◦C ranged from (1.12±0.01)×10−3 mm2/s for pure water to (0.48±0.02)×10−3 mm2/s for the 25% w/w PVP concentration, presenting a minor variability between the vendors and the field strengths. T2 and IR-T1 relaxation time results demonstrated variability between the field strengths and the vendors across the different acquisitions. Moreover, the T1 values derived from the VFA method exhibited a large variation compared with the IR-T1 values across all the scanners for all repeated measurements, although the calculation of the standard deviation of the VFA-T1 estimate across each ROI and the autocorrelation showed a stability of the signal for three scanners, with autocorrelation of the signal over the dynamic series revealing a periodic variation in one scanner. Finally, the ADC, the T2, and the IR-T1 values exhibited an excellent repeatability across the scanners, whereas for the dynamic data, the CoVs were higher. Conclusions: The combination of a novel PVP phantom, with multiple compartments to give a physiologically relevant range of ADC and T1 values, together with ice-water as a temperaturecontrolled medium, allows reliable quality assurance measurements that can be used to measure agreement between MRI scanners, critical in multicenter functional and quantitative imaging studies. [ABSTRACT FROM AUTHOR]

Purpose: To develop methods for optimization of diffusion-weighted MRI (DW-MRI) in the abdomen and pelvis on 1.5 T MR scanners from three manufacturers and assess repeatability of apparent diffusion coefficient (ADC) estimates in a temperature-controlled phantom and abdominal and pelvic organs in healthy volunteers. Methods: Geometric distortion, ghosting, fat suppression, and repeatability and homogeneity of ADC estimates were assessed using phantoms and volunteers. Healthy volunteers (ten per scanner) were each scanned twice on the same scanner. One volunteer traveled to all three institutions in order to provide images for qualitative comparison. The common volunteer was excluded from quantitative analysis of the data from scanners 2 and 3 in order to ensure statistical independence, giving n = 10 on scanner 1 and n = 9 on scanners 2 and 3 for quantitative analysis. Repeatability and interscanner variation of ADC estimates in kidneys, liver, spleen, and uterus were assessed using within-patient coefficient of variation (wCV) and Kruskal-Wallis tests, respectively. Results: The coefficient of variation of ADC estimates in the temperature-controlled phantom was 1%-4% for all scanners. Images of healthy volunteers from all scanners showed homogeneous fat suppression and no marked ghosting or geometric distortion. The wCV of ADC estimates was 2%-4% for kidneys, 3%-7% for liver, 6%-9% for spleen, and 7%-10% for uterus. ADC estimates in kidneys, spleen, and uterus showed no significant difference between scanners but a significant difference was observed in liver (p < 0.05). Conclusions: DW-MRI protocols can be optimized using simple phantom measurements to produce good quality images in the abdomen and pelvis at 1.5 T with repeatable quantitative measurements in a multicenter study. [ABSTRACT FROM AUTHOR]

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Intratumoral heterogeneity means single site tumor biopsy might not be representative. Here we develop and optimize an MRI-informed robotic multiregional 'smart' biopsy technique in retroperitoneal and pelvic sarcomas (RPS). We also explore the relationship between imaging and histological biomarkers., Materials and Methods: Participants with suspected RPS underwent multiparametric (mp)MRI within a week prior to biopsy. Three target regions with differing MRI characteristics were contoured. Robotic or freehand multiregional biopsy was performed, collecting samples from each target region in separate specimen pots. CT/MRI fusion extracted quantitative imaging biomarkers for correlation with histological biomarkers at precise biopsy sites. The primary endpoint was feasibility and safety. Spearman's correlation explored the relationship between imaging and histological biomarkers., Results: Twelve participants (7 women), median age 58.6 years interquartile range [IQR]: 52 - 75 years underwent biopsy. All procedures were technically successful with same-day discharge. The within-tumor range of Apparent Diffusion Coefficeint correlated very strongly with the within-tumor range of Ki-67 proliferation index; Spearman's ρ = 0.91 (95% CI 0.68 to 0.98). Ranges represent intratumoral heterogeneity uniquely obtained by multiregional biopsy., Conclusion: Multiregional robotic MRI-informed, CT-guided biopsy is feasible and safe in RPS. Sampling three distinct regions within tumors provides a more comprehensive and accurate representation of tumor biology than standard biopsy. The close relationship between imaging and histological heterogeneity biomarkers has broader implications for pancancer biopsy techniques, imaging characterization, and personalized treatment selection., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Edward W Johnston reports financial support was provided by Royal College of Radiologists. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate lesions, and 15% for breast lesions are claimed to represent true changes with 95% confidence. This report also discusses the development of the ADC Profile, highlighting its various stages, and describes the workflow essential to achieving a standardized implementation of advanced quantitative diffusion-weighted MRI in the clinic. The presented QIBA ADC Profile guidelines should enable successful clinical application of ADC as a quantitative imaging biomarker and ensure reproducible ADC measurements that can be used to confidently evaluate longitudinal changes and treatment response for individual patients., (© RSNA, 2024 See also the editorial by Haider in this issue.)

Objective. Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment. Approach. A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre's clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers. Main results . Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal. Significance . The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies., (Creative Commons Attribution license.)

T 2 -weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T 2 W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant ( p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T 2 W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T 1 -weighted MRI from a pre-existing repository of clinical planning CTs., Methods: MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T 1 weighted MRI scans., Results: In our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases., Conclusion: Pelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kalantar, Messiou, Winfield, Renn, Latifoltojar, Downey, Sohaib, Lalondrelle, Koh and Blackledge.)

Background Treatment of advanced epithelial ovarian cancer results in a relapse rate of 75%. Early markers of response would enable optimization of management and improved outcome in both primary and recurrent disease. Purpose To assess the apparent diffusion coefficient (ADC), derived from diffusion-weighted MRI, as an indicator of response, progression-free survival (PFS), and overall survival. Materials and Methods This prospective multicenter trial (from 2012-2016) recruited participants with stage III or IV ovarian, primary peritoneal, or fallopian tube cancer (newly diagnosed, cohort one; relapsed, cohort two) scheduled for platinum-based chemotherapy, with interval debulking surgery in cohort one. Cohort one underwent two baseline MRI examinations separated by 0-7 days to assess ADC repeatability; an additional MRI was performed after three treatment cycles. Cohort two underwent imaging at baseline and after one and three treatment cycles. ADC changes in responders and nonresponders were compared (Wilcoxon rank sum tests). PFS and overall survival were assessed by using a multivariable Cox model. Results A total of 125 participants (median age, 63.3 years [interquartile range, 57.0-70.7 years]; 125 women; cohort one, n = 47; cohort two, n = 78) were included. Baseline ADC (range, 77-258 × 10 -5 mm 2 s -1 ) was repeatable (upper and lower 95% limits of agreement of 12 × 10 -5 mm 2 s -1 [95% confidence interval {CI}: 6 × 10 -5 mm 2 s -1 to 18 × 10 -5 mm 2 s -1 ] and -15 × 10 -5 mm 2 s -1 [95% CI: -21 × 10 -5 mm 2 s -1 to -9 × 10 -5 mm 2 s -1 ]). ADC increased in 47% of cohort two after one treatment cycle, and in 58% and 53% of cohorts one and two, respectively, after three cycles. Percentage change from baseline differed between responders and nonresponders after three cycles (16.6% vs 3.9%; P = .02 [biochemical response definition]; 19.0% vs 6.2%; P = .04 [radiologic definition]). ADC increase after one cycle was associated with longer PFS in cohort two (adjusted hazard ratio, 0.86; 95% CI: 0.75, 0.98; P = .03). ADC change was not indicative of overall survival for either cohort. Conclusion After three cycles of platinum-based chemotherapy, apparent diffusion coefficient (ADC) changes are indicative of response. After one treatment cycle, increased ADC is indicative of improved progression-free survival in relapsed disease. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Quantum-chemical calculations performed on poly[2,7-(9,9-di-n-octylfluorene)(1-x)-co-4,7-(2,1,3-benzothiadiazole)(x)] copolymers (0 < or = x < or = 0.5) show that the lowest unoccupied molecular orbital is always highly localized on the benzothiadiazole (BT) units while the highest occupied molecular orbital is delocalized over the whole chain. Chains with a low BT content are characterized by a reduced oscillator strength of the lowest optical transition and by an increased charge-transfer character of the exciton. These results are supported experimentally by a blueshift of the lowest energy absorption band upon reduction in the BT ratio, lower photoluminescence efficiency, longer excited state lifetimes, and greater solvent dependence of the emission properties.