RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

Transcriptomic basis of sex loss in the pea aphid.

BACKGROUND: Transitions from sexual to asexual reproduction are common in eukaryotes, but the underlying mechanisms remain poorly known. The pea aphid-Acyrthosiphon pisum-exhibits reproductive polymorphism, with cyclical parthenogenetic and obligate parthenogenetic lineages, offering an opportunity to decipher the genetic basis of sex loss. Previous work on this species identified a single 840 kb region controlling reproductive polymorphism and carrying 32 genes. With the aim of identifying the gene(s) responsible for sex loss and the resulting consequences on the genetic programs controlling sexual or asexual embryogenesis, we compared the transcriptomic response to photoperiod shortening-the main sex-inducing cue-of a sexual and an obligate asexual lineage of the pea aphid, focusing on heads (where the photoperiodic cue is detected) and embryos (the final target of the cue). RESULTS: Our analyses revealed that four genes (one expressed in the head, and three in the embryos) of the region responded differently to photoperiod in the two lineages. We also found that the downstream genetic programs expressed during embryonic development of a future sexual female encompass ∼1600 genes, among which miRNAs, piRNAs and histone modification pathways are overrepresented. These genes mainly co-localize in two genomic regions enriched in transposable elements (TEs). CONCLUSIONS: Our results suggest that the causal polymorphism(s) in the 840 kb region somehow impair downstream epigenetic and post-transcriptional regulations in obligate asexual lineages, thereby sustaining asexual reproduction.

The definition of treatment resistance in anxiety disorders: a Delphi method-based consensus guideline.

Anxiety disorders are very prevalent and often persistent mental disorders, with a considerable rate of treatment resistance which requires regulatory clinical trials of innovative therapeutic interventions. However, an explicit definition of treatment-resistant anxiety disorders (TR-AD) informing such trials is currently lacking. We used a Delphi method-based consensus approach to provide internationally agreed, consistent and clinically useful operational criteria for TR-AD in adults. Following a summary of the current state of knowledge based on international guidelines and an available systematic review, a survey of free-text responses to a 29-item questionnaire on relevant aspects of TR-AD, and an online consensus meeting, a panel of 36 multidisciplinary international experts and stakeholders voted anonymously on written statements in three survey rounds. Consensus was defined as ≥75% of the panel agreeing with a statement. The panel agreed on a set of 14 recommendations for the definition of TR-AD, providing detailed operational criteria for resistance to pharmacological and/or psychotherapeutic treatment, as well as a potential staging model. The panel also evaluated further aspects regarding epidemiological subgroups, comorbidities and biographical factors, the terminology of TR-AD vs. "difficult-to-treat" anxiety disorders, preferences and attitudes of persons with these disorders, and future research directions. This Delphi method-based consensus on operational criteria for TR-AD is expected to serve as a systematic, consistent and practical clinical guideline to aid in designing future mechanistic studies and facilitate clinical trials for regulatory purposes. This effort could ultimately lead to the development of more effective evidence-based stepped-care treatment algorithms for patients with anxiety disorders.

Antipsychotic Medication and Risk of Metabolic Disorders in People With Schizophrenia: A Longitudinal Study Using the UK Clinical Practice Research Datalink.

BACKGROUND AND HYPOTHESIS: Antipsychotics are first-line drug treatments for schizophrenia. When antipsychotic monotherapy is ineffective, combining two antipsychotic drugs is common although treatment guidelines warn of possible increases in side effects. Risks of metabolic side effects with antipsychotic polypharmacy have not been fully investigated. This study examined associations between antipsychotic polypharmacy and risk of developing diabetes, hypertension, or hyperlipidemia in adults with schizophrenia, and impact of co-prescription of first- and second-generation antipsychotics. STUDY DESIGN: A population-based prospective cohort study was conducted in the United Kingdom using linked primary care, secondary care, mental health, and social deprivation datasets. Cox proportional hazards models with stabilizing weights were used to estimate risk of metabolic disorders among adults with schizophrenia, comparing patients on antipsychotic monotherapy vs polypharmacy, adjusting for demographic and clinical characteristics, and antipsychotic dose. STUDY RESULTS: Median follow-up time across the three cohorts was approximately 14 months. 6.6% developed hypertension in the cohort assembled for this outcome, with polypharmacy conferring an increased risk compared to monotherapy, (adjusted Hazard Ratio = 3.16; P = .021). Patients exposed to exclusive first-generation antipsychotic polypharmacy had greater risk of hypertension compared to those exposed to combined first- and second-generation polypharmacy (adjusted HR 0.29, P = .039). No associations between polypharmacy and risk of diabetes or hyperlipidemia were found. CONCLUSIONS: Antipsychotic polypharmacy, particularly polypharmacy solely comprised of first-generation antipsychotics, increased the risk of hypertension. Future research employing larger samples, follow-up longer than the current median of 14 months, and more complex methodologies may further elucidate the association reported in this study.

Pharmacokinetics of intranasal amiloride in healthy volunteers.

Anxiety and panic disorders are the most common mental illnesses in the United States and lack effective treatment options. Acid-sending ion channels (ASICs) in the brain were shown to be associated with fear conditioning and anxiety responses and therefore are potential targets for treating panic disorder. Amiloride is an inhibitor of the ASICs in the brain and was shown to reduce panic symptoms in preclinical animal models. An intranasal formulation of amiloride will be highly beneficial to treat acute panic attacks due to advantages such as the rapid onset of action and patient compliance. The aim of this single-center, open-label trial was to evaluate the basic pharmacokinetics (PKs) and safety of amiloride after intranasal administration in healthy human volunteers at three doses (0.2, 0.4, and 0.6 mg). Amiloride was detected in plasma within 10 min of intranasal administration and showed a biphasic PK profile with an initial peak within 10 min of administration followed by a second peak between 4 and 8 h of administration. The biphasic PKs indicate an initial rapid absorption via the nasal pathway and later slower absorption by non-nasal pathways. Intranasal amiloride exhibited a dose-proportional increase in the area under the curve and did not exhibit any systemic toxicity. These data indicate that intranasal amiloride is rapidly absorbed and safe at the doses evaluated and can be further considered for clinical development as a portable, rapid, noninvasive, and nonaddictive anxiolytic agent to treat acute panic attacks.

A review of artificial intelligence in prostate cancer detection on imaging.

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

Impact of standardizing care for agitation in dementia using an integrated care pathway on an inpatient geriatric psychiatry unit.

OBJECTIVES: This study examined the effectiveness of an integrated care pathway (ICP), including a medication algorithm, to treat agitation associated with dementia. DESIGN: Analyses of data (both prospective and retrospective) collected during routine clinical care. SETTING: Geriatric Psychiatry Inpatient Unit. PARTICIPANTS: Patients with agitation associated with dementia (n = 28) who were treated as part of the implementation of the ICP and those who received treatment-as-usual (TAU) (n = 28) on the same inpatient unit before the implementation of the ICP. Two control groups of patients without dementia treated on the same unit contemporaneously to the TAU (n = 17) and ICP groups (n = 36) were included to account for any secular trends. INTERVENTION: ICP. MEASUREMENTS: Cohen Mansfield Agitation Inventory (CMAI), Neuropsychiatric Inventory Questionnaire (NPIQ), and assessment of motor symptoms were completed during the ICP implementation. Chart review was used to obtain length of inpatient stay and rates of psychotropic polypharmacy. RESULTS: Patients in the ICP group experienced a reduction in their scores on the CMAI and NPIQ and no changes in motor symptoms. Compared to the TAU group, the ICP group had a higher chance of an earlier discharge from hospital, a lower rate of psychotropic polypharmacy, and a lower chance of having a fall during hospital stay. In contrast, these outcomes did not differ between the two control groups. CONCLUSIONS: These preliminary results suggest that an ICP can be used effectively to treat agitation associated with dementia in inpatients. A larger randomized study is needed to confirm these results.

Bridging the gap between prostate radiology and pathology through machine learning.

BACKGROUND: Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. Currently, magnetic resonance imaging (MRI) is considered the most sensitive non-invasive imaging modality that enables visualization, detection, and localization of prostate cancer, and is increasingly used to guide targeted biopsies for prostate cancer diagnosis. However, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. PURPOSE: Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. We compare different labeling strategies and the effects they have on the performance of different machine learning models for prostate cancer detection on MRI. METHODS: Four different deep learning models (SPCNet, U-Net, branched U-Net, and DeepLabv3+) were trained to detect prostate cancer on MRI using 75 patients with radical prostatectomy, and evaluated using 40 patients with radical prostatectomy and 275 patients with targeted biopsy. Each deep learning model was trained with four different label types: pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel-level Gleason patterns) on whole-mount histopathology images. The pathologist and digital pathologist labels (collectively referred to as pathology labels) were mapped onto pre-operative MRI using an automated MRI-histopathology registration platform. RESULTS: Radiologist labels missed cancers (ROC-AUC: 0.75-0.84), had lower lesion volumes (~68% of pathology lesions), and lower Dice overlaps (0.24-0.28) when compared with pathology labels. Consequently, machine learning models trained with radiologist labels also showed inferior performance compared to models trained with pathology labels. Digital pathologist labels showed high concordance with pathologist labels of cancer (lesion ROC-AUC: 0.97-1, lesion Dice: 0.75-0.93). Machine learning models trained with digital pathologist labels had the highest lesion detection rates in the radical prostatectomy cohort (aggressive lesion ROC-AUC: 0.91-0.94), and had generalizable and comparable performance to pathologist label-trained-models in the targeted biopsy cohort (aggressive lesion ROC-AUC: 0.87-0.88), irrespective of the deep learning architecture. Moreover, machine learning models trained with pixel-level digital pathologist labels were able to selectively identify aggressive and indolent cancer components in mixed lesions on MRI, which is not possible with any human-annotated label type. CONCLUSIONS: Machine learning models for prostate MRI interpretation that are trained with digital pathologist labels showed higher or comparable performance with pathologist label-trained models in both radical prostatectomy and targeted biopsy cohort. Digital pathologist labels can reduce challenges associated with human annotations, including labor, time, inter- and intra-reader variability, and can help bridge the gap between prostate radiology and pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI.

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.

Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study.

BACKGROUND: Professional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions. OBJECTIVE: The aim of this study was to develop an EHR-based pragmatic cardio-oncology registry and, as proof of principle, to investigate care gaps in the cardiovascular care of cancer patients. METHODS: We generated a programmatically deidentified, real-time EHR-based cardio-oncology registry from all patients in our institutional Cancer Population Registry (N=8275, 2011-2017). We investigated: (1) left ventricular ejection fraction (LVEF) assessment before and after treatment with potentially cardiotoxic agents; and (2) guideline-directed medical therapy (GDMT) for left ventricular dysfunction (LVD), defined as LVEF<50%, and symptomatic heart failure with reduced LVEF (HFrEF), defined as LVEF<50% and Problem List documentation of systolic congestive heart failure or dilated cardiomyopathy. RESULTS: Rapid development of an EHR-based cardio-oncology registry was feasible. Identification of tests and outcomes was similar using the EHR-based cardio-oncology registry and manual chart abstraction (100% sensitivity and 83% specificity for LVD). LVEF was documented prior to initiation of cancer therapy in 19.8% of patients. Prevalence of postchemotherapy LVD and HFrEF was relatively low (9.4% and 2.5%, respectively). Among patients with postchemotherapy LVD or HFrEF, those referred to cardiology had a significantly higher prescription rate of a GDMT. CONCLUSIONS: EHR data can efficiently populate a real-time, pragmatic cardio-oncology registry as a byproduct of clinical care for health care delivery investigations.

Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.

PURPOSE: While multi-parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. METHODS: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole-mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per-pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. RESULTS: Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. CONCLUSIONS: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction.

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.

The impact of psychiatric and medical comorbidity on the risk of mortality: a population-based analysis.

BACKGROUND: As life expectancy increases, more people have chronic psychiatric and medical health disorders. Comorbidity may increase the risk of premature mortality, an important challenge for health service delivery. METHODS: Population-based cohort study in Ontario, Canada of all 11 246 910 residents aged ⩾16 and <105 on 1 April 2012 and alive on 31 March 2014. Secondary analyses included subjects having common medical disorders in 10 separate cohorts. Exposures were psychiatric morbidity categories identified using aggregated diagnosis groups (ADGs) from Johns Hopkins Adjusted Clinical Groups software® (v10.0); ADG 25: Persistent/Recurrent unstable conditions; e.g. acute schizophrenic episode, major depressive disorder (recurrent episode), ADG 24: Persistent/Recurrent stable conditions; e.g. depressive disorder, paranoid personality disorder, ADG 23: Time-limited/minor conditions; e.g. adjustment reaction with brief depressive reaction. The outcome was all-cause mortality (April 2014-March 2016). RESULTS: Over 2 years' follow-up, there were 188 014 deaths (1.7%). ADG 25 conferred an almost threefold excess mortality after adjustment compared to having no psychiatric morbidity [adjusted hazard ratio 2.94 (95% CI 2.91-2.98, p < 0.0001)]. Adjusted hazard ratios for ADG 24 and ADG 23 were 1.12 (95% CI 1.11-1.14, p < 0.0001) and 1.31 (95% CI 1.26-1.36, p < 0.0001). In all 10 medical disorder cohorts, ADG 25 carried significantly greater mortality risk compared to no psychiatric comorbidity. CONCLUSIONS: Psychiatric disorders, particularly those graded persistent/recurrent and unstable, were associated with excess mortality in the whole population, and in the medical disorder cohorts examined. Future research should examine whether service design accounting for psychiatric disorder comorbidity improves outcomes across the spectrum of medical disorders.