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The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site., Deep learning models have been trained on The Cancer Genome Atlas to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. Here, the authors demonstrate that site-specific histologic signatures can lead to biased estimates of accuracy for such models, and propose a method to minimize such bias.

We are developing a one-sided tomographic method for imaging prompt gamma rays, without moving the source, sample, or detector, in a standard prompt gamma activation analysis setting. Dual-plane pixelated CdZnTe detectors developed from a commercial system with external timing synchronization electronics are used to measure prompt gamma rays that are scattered in the first plane and subsequently absorbed in the second plane. Tomographic images have been reconstructed from the 2.2 MeV gamma rays from H in 3 mm plastic balls using inter-plane events for the first time, demonstrating the feasibility of spatially resolving mm scale H features.

Objectives Numerous studies on malignant mesothelioma (MM) highlight the prognostic importance of histologic subtype, nuclear grade, and necrosis. This study compares these parameters in paired biopsy and resection specimens of pleural MM. Methods Histologic subtype, percentage of epithelioid morphology, nuclear grade, and the presence or absence of necrosis were compared in 429 paired biopsies and resection specimens of pleural MM from 19 institutions. Results Histologic subtype was concordant in 81% of cases (κ = 0.58). When compared with resection specimens, epithelioid morphology at biopsy had a positive predictive value (PPV) of 78.9% and a negative predictive value (NPV) of 93.5%; sarcomatoid morphology showed high PPV (92.9%) and NPV (99.3%), and biphasic morphology PPV was 89.7% and NPV was 79.7%. Agreement of the percentage of epithelioid morphology was fair (κ = 0.27). Nuclear grade and necrosis were concordant in 75% (κ = 0.59) and 81% (κ = 0.53) of cases, respectively. Nuclear grade showed moderate (κ = 0.53) and substantial (κ = 0.67) agreement from patients with and without neoadjuvant therapy, respectively, and necrosis showed moderate (κ = 0.47 and κ = 0.60) agreement, respectively, in the same subsets of paired specimens. Conclusions Paired biopsy-resection specimens from pleural MM show overall moderate agreement in pathologic parameters. These findings may help guide postbiopsy management and triage of patients with MM.

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. This site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the digital image characteristics constituting this histologic batch effect. As an example, we show that patient ethnicity within the TCGA breast cancer cohort can be inferred from histology due to site-level batch effect, which must be accounted for to ensure equitable application of DL. Batch effect also leads to overoptimistic estimates of model performance, and we propose a quadratic programming method to guide validation that abrogates this bias.

Background: Fine needle aspiration (FNA) of renal masses can distinguish between benign and malignant neoplasms in 73–94% of cases. Previous studies suggested the correct subclassification of renal cell carcinomas (RCCs) by cytomorphology can be achieved in up to 80% of cases. However, as RCCs become increasingly subclassified by molecular signatures, correct subclassification based on cytology alone is increasingly difficult. Design: Two FNA passes (2 stained with Diff-Quik® and 2 with the Papanicolaou method) were performed on all fresh nephrectomy specimens for a 1-year period. There were 30 cases in this study, with 29 primary renal tumors and 1 case of metastatic lung adenocarcinoma. Each case was assigned a random number and came with 2 slides (1 from each staining method). Eight cytopathologists were asked to provide a diagnosis and the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading if applicable. Fleiss’ Kappa and Cohen’s Kappa equations were used to look at inter-rater variability. Results: When compared to the surgical pathology diagnosis, the average percent correct diagnosis for all cytopathologist was 35%. Chromophobe RCCs had the best average percent accuracy at 72% followed by clearcell RCC at 48%. Average accuracy for grading RCCs was 40%. Inter-rater variability among the cytopathologists for all RCC diagnoses was fair with a Fleiss’ Kappa coefficient of 0.28. For the WHO/ISUP grade, the weighted coefficient for each pathologist ranged from 0.11 to 0.45, ranging from fair to moderate, respectively. Conclusions: Renal tumors are difficult to classify on cytopathology alone. Core needle biopsy and ancillary studies are necessary if diagnosis will change management.

This presentation was first delivered at Photonics West 2020 on 4 February 2020 and has been included as part of this Digital Forum to enable scholarly dialogue. Please use the original citation when citing: Proceedings Volume 11284, Smart Photonic and Optoelectronic Integrated Circuits XXII; 112840W (2020) https://doi.org/10.1117/12.2545127 Searching for natural materials exhibiting larger electron-electron interactions constitutes a traditional approach to high temperature superconductivity research. Very recently we pointed out that the newly developed field of electromagnetic metamaterials deals with the somewhat related task of dielectric response engineering on a sub-100 nm scale. Considerable enhancement of the electron-electron interaction may be expected in such metamaterial scenarios as in epsilon near zero (ENZ) and hyperbolic metamaterials. In both cases dielectric function may become small and negative in substantial portions of the relevant four-momentum space, leading to enhancement of the electron pairing interaction. This approach has been verified in experiments with aluminium-based metamaterials. Metamaterial superconductor with Tc = 3.9 K have been fabricated, that is three times that of pure aluminium (Tc = 1.2 K), which opens up new possibilities to considerably improve Tc of other simple superconductors. A theoretical model based on the Maxwell-Garnett approximation provides a microscopic explanation of this effect in terms of electron-electron pairing mediated by a hybrid plasmon-phonon excitation. We report the observations of this excitation in Al-Al2O3 core-shell metamaterials using inelastic neutron scattering. This result provides support for this novel mechanism of superconductivity in metamaterials.

Objective To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients. Methods This prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR. Results Time spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes, p = 0.005) but returned to the paper baseline by 6 months (2.8 minutes, p = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use. Conclusion Informatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician–patient interactions, these strategies may help alleviate risk of physician burnout.

e23529 Background: Soft tissue sarcomas (STS) are histologically and molecularly heterogenous with variable biologic behavior and differential therapeutic responses. Molecular analysis of STS is commonly utilized to identify targetable mutations or identify STS subtypes that have predictive and prognostic implications. To address the challenge of rapid, accurate classification of STS subtypes, we sought to develop an approach using a form of artificial intelligence called deep learning to identify meaningful hidden information directly from digital H+E slide images. We trained a deep learning model using genotype and phenotype data from The Cancer Genome Atlas (TCGA) and companion diagnostic digital H+E slide images, in order to identify therapeutic biomarkers and prognostic molecular subtypes. Methods: Digital whole slide images (WSIs) from TCGA were downloaded and regions of tumor were annotated by pathologists. WSIs were carved into 604 um by 604 um square tiles within the tumor region and saved at an image size of 598 px by 598 px for a resolution of ~1um/px. These tiles were used as input to a deep convolution neural network (Xception model pretrained on imagenet). Performance results are reported at the as the average of three-fold cross-validation, averaged at the patient level. Results: 206 sarcomas available through the TCGA were used as training set. Sarcoma histologies include 80 leiomyosarcoma (LMS) (27 uterine LMS and 53 non-uterine LMS), 50 dedifferentiated liposarcoma (DDLPS), 44 undifferentiated pleomorphic sarcoma (UPS), 72 myxofibrosarcoma (MFS), and 10 synovial sarcoma (SS). FNCLCC grade 1 (n = 14), grade 2 (n = 112), and grade 3 (n = 80). Histology could be predicted automatically with very high accuracy for LMS (Area under Receiver-operator curve (AUROC) 0.93 and 0.95 for uterine and non-uterine LMS respectively), DDLPS (AUROC 0.70), UPS (AUROC 0.76), and MFS (AUROC 0.76). FNCLCC grade could be predicted for grade 3, 2, 1 with AUROC of 0.79, 0.73, and 0.81 respectively. High vs. low mitotic rate (threshold 15) could be predicted with AUROC of 0.70. Within non-uterine LMS, prognostic molecular cluster could be predicted with AUC of 0.62 for cluster with worse recurrence-free and disease specific survival. Within DDLPS, DNA methylation cluster 2 associated with worse disease specific survival was predicted with AUC of 0.80. Conclusions: Deep learning algorithm analysis of histologic slides was able to predict clinically meaningful characteristics and molecular clusters with reasonable accuracy. Retrospective and prospective validation is needed.

Purpose of review Femtosecond laser is a promising new technology for the field of cataract surgery. Early studies have investigated many factors including visual outcomes, complication rates, and financial overhead costs. This review analyzes the most recent clinical studies of visual and refractive outcomes in laser cataract surgery, including those that make comparisons to outcomes found in conventional phacoemulsification cataract surgery. Recent findings As femtosecond laser cataract surgery has only emerged recently, there is limited literature available regarding visual outcomes. Most but not all existing studies showed no statistically significant difference in visual acuity and mean absolute refractive error between laser and conventional cataract surgery cases. Summary The majority of studies examined found visual acuity or refractive outcomes of femtosecond laser to be statistically equivalent to those of conventional phacoemulsification cataract surgery. However, the learning curve involved with laser use may account for these early results, which could potentially improve as better technology and surgical techniques are developed. Further long-term outcomes studies are necessary to more accurately evaluate the benefits and drawbacks of femtosecond laser cataract surgery.

A major initiative of the Quantitative Imaging Biomarker Alliance (QIBA) is to develop standards-based documents called “Profiles”, which describe one or more technical performance claims for a given imaging modality. The term “actor” denotes any entity (device, software, person) whose performance must meet certain specifications in order for the claim to be met. The objective of this paper is to present the statistical issues in testing actors’ conformance with the specifications. In particular, we present the general rationale and interpretation of the claims, the minimum requirements for testing whether an actor achieves the performance requirements, the study designs used for testing conformity, and the statistical analysis plan. We use three examples to illustrate the process: apparent diffusion coefficient (ADC) in solid tumors measured by MRI, change in Perc 15 as a biomarker for the progression of emphysema, and percent change in solid tumor volume by CT as a biomarker for lung cancer progression.

This paper presents a practical ultrasonic system for near real-time imaging of spatial temperature distributions in water caused by absorption of radiation. Initial testing with radiation from a highly attenuated infrared lamp demonstrates that the system is able to map sub-millikelvin temperature changes, thus making it suitable for characterizing dose profiles of therapy-level ionizing radiation beams. The system uses a fan-beam tomographic reconstruction algorithm to invert time-of-flight data derived from ultrasonic pulses produced and detected by a circular array of transducers immersed in water. Temperature dependence of the speed of sound in water permits the conversion of these measured two-dimensional velocity distributions into temperature distributions that indicate the absorbed radiation dose. The laboratory prototype, based on a 128-element transducer array, is used to acquire temperature maps of a 230 mm × 230 mm area every 4 s with sub-millikelvin resolution in temperature and about 5 mm resolution in space. Earlier measurements with a single-channel version of this prototype suggest refinements in signal-conditioning electronics and signal-processing algorithms that would allow the present instrument to resolve temperature changes as low as a few microkelvin. Possible applications include real-time intensity profiling of radiation beams and three-dimensional characterization of the absorbed dose.

This paper describes recent developments in the area of high-precision ultrasonic thermometry with the potential to provide on-site direct determination of radiation doses administered for cancer treatment. Conventional calorimeters used for this purpose measure radiation-induced heating in a water phantom at one point in space by means of immersed thermistors and are subject to various thermal disturbances due to Ohmic heating and interactions of the radiation with the sensor probes. By contrast, the method described here is based on a high-resolution ultrasonic system that determines the change of the speed of sound due to small temperature changes in an acoustic propagation path in the radiation-heated water, thereby avoiding such undesired thermal effects. The thermometer is able to measure tens of microkelvin changes in the water temperature averaged over the acoustic path of about 60 cm at room temperature, with root-mean-squared noise of about 5 microK. Both incandescent and ionizing radiation heating data are presented for analog and digital implementations of a laboratory prototype. This application of the ultrasonic technique opens up possibilities for a new approach to performing therapy-level radiation dosimetry for medical clinics and standards laboratories.

A glass polycapillary lens that both bends and focuses a cold neutron beam has been designed and constructed. The bender focuser guides part of the incident beam away from its line of sight and focuses it to a spot of width 0.65 mm at a distance 95 mm from the lens exit and 20 mm below the bottom edge of the beam path, with a gain of 20 in neutron current density. The neutron transmission characteristics of the lens have been determined with two types of position-sensitive detectors, a charge injection device, and an imaging plate. The lens has been tested with prompt gamma measurements on a gadolinium shard and titanium foil.

NIST Standard Reference Materials 2087 and 2088 for medical computed tomography (CT) have become available. The materials allow CT reconstructions to be tied to the International System of Units in length, density, and mass attenuation coefficient.

“Centering in a focus round and neat, Let all your rays of information meet.” (Cowper, Conversation, 1781) Just over two years ago, the guide hall of the newly created Cold Neutron Research Facility (CNRF) at the NIST reactor was inaugurated. This is currently the only cold source at a U.S. research reactor which serves a large number of scientific instruments.1 While most of the instruments installed in the facility are spectrometers for neutron scattering studies covering a wide range of scientific endeavors, two of the new instruments have been designed specifically for chemical quantitative analysis. For a long time, there have been facilities for prompt gamma activation analysis (PGAA) and neutron depth profiling (NDP) at the NIST reactor. These instruments use a white beam from the thermal neutron source. Completely new instruments have been designed which use the long wavelength neutrons available from the cold source. The advantage, of course, is that the absorption cross section increase...

Author(s): Chen, H; Yu, L; Manuel, C; Wong, BJF | Abstract: Electromechanical reshaping (EMR) of cartilage is a promising noninvasive technique with potential for broad application in reconstructive surgery. EMR involves applying direct current electrical fields to localized stress regions and initiating a series of oxidation-reduction reactions, thus effecting a shape change. Previous EMR studies have focused on macroscopic structural measurements of the shape change effect or monitoring of electrical current flow. Only limited investigation of structural changes in the tissue at the histologic level have been performed, and not in real time. This study is the first to use optical coherence tomography (OCT) to examine structural changes in cartilage during EMR. Two platinum needle electrodes were inserted into fixed rectangular rabbit nasal septal cartilage specimens. The spectral domain OCT probe was then positioned above the section of cartilage in which the anode needle was inserted. A constant voltage of 6V was applied for 3 minutes, and images were obtained (8 frames/second). OCT was also performed in specimens undergoing dehydration under ambient conditions and during pH changes produced by the addition of HCl, as both processes accompany EMR. The OCT data identified distinct findings among the three conditions, suggesting that EMR causes a much greater degree of reshaping on a molecular level than dehydration or a change in pH alone. OCT provides a means to gauge structural changes in the tissue matrix during EMR. The application of OCT to image the EMR process will add to our understanding of the mechanisms of action involved and potentially facilitate optimization of this process. © 2010 Copyright SPIE - The International Society for Optical Engineering.

A combination of cold neutron prompt gamma-ray activation analysis (PGAA) and neutron incoherent scattering (NIS) has been used for nondestructive characterization of hydrogen as a function of position in slabs of wet concrete of different compositions. Hydrogen was determined by PGAA by scanning each sample across a 5 mm diameter neutron beam in 10 mm increments, and measuring the 2223 keV prompt gamma ray. NIS measurements were performed by scanning the sample across a 5 mm diameter neutron beam at 5 mm increments and detecting scattered neutrons. The measurements demonstrate the feasibility of the techniques for 2D compositional mapping of hydrogen and other elements in materials, and indicate the potential of these methods for monitoring the uniformity of drying concrete.

Since the recent invention by Kumakhov of polycapillary optics for the control of x-ray beams, a large number of potential applications have been identified. These include materials analysis techniques such as diffraction and microfluorescence, lithography, medical imaging applications such as angiography and mammography, and medical therapy. To develop and further identify these applications, precise knowledge is needed of the performance of a variety of capillary types for different source energies and geometries. Extensive measurements have been performed of transmission and exit divergence as a function of length, bend radius, x-ray source position, and x-ray energy (from 1 to 44 keV). X-ray source divergence was also varied; measurements were performed with point sources and synchrotron beams. The measurements were performed for a variety of polycapillary compositions, diameters, and geometries. In general, data agrees fairly well with Monte Carlo geometrical simulations.