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Background and objective: Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI). Methods: A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen’s kappa (κ), and the system’s efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated. Key findings and limitations: Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied. Conclusions and clinical implications: The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance. Patient summary: Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.

PurposeWhile there are reports of treatment-related endocrine disruptions and catecholamine surges in pheochromocytoma/paraganglioma (PPGL) patients treated with [177Lu]Lu-DOTA-TATE therapy, the spectrum of these abnormalities in the immediate post-treatment period (within 48 hours) has not been previously evaluated and is likely underestimated.MethodsThe study population included patients (≥18 years) enrolled in a phase 2 trial for treatment of somatostatin receptor (SSTR)-2+ inoperable/metastatic pheochromocytoma/paraganglioma with [177Lu]Lu-DOTA-TATE (7.4 GBq per cycle for 1 – 4 cycles). Hormonal measurements [adrenocorticotropic hormone (ACTH), cortisol, thyroid stimulating hormone (TSH), free thyroxine (FT4), follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone, estradiol, growth hormone, prolactin], catecholamines, and metanephrines were obtained on days-1, 2, 3, 30, and 60 per cycle as per trial protocol, and were retrospectively analyzed.ResultsAmong the 27 patients (age: 54 ± 12.7 years, 48.1% females) who underwent hormonal evaluation, hypoprolactinemia (14.1%), elevated FSH (13.1%), and elevated LH (12.5%) were the most frequent hormonal abnormalities across all 4 cycles combined. On longitudinal follow-up, significant reductions were noted in i. ACTH without corresponding changes in cortisol, ii. TSH, and FT4, and iii. prolactin at or before day-30 of [177Lu]Lu-DOTA-TATE. No significant changes were observed in the gonadotropic axis and GH levels. Levels of all hormones on day-60 were not significantly different from day-1 values, suggesting the transient nature of these changes. However, two patients developed clinical, persistent endocrinopathies (primary hypothyroidism: n=1 male; early menopause: n=1 female). Compared to day-1, a significant % increase in norepinephrine, dopamine, and normetanephrine levels were noted at 24 hours following [177Lu]Lu-DOTA-TATE dose and peaked within 48 hours.Conclusions[177Lu]Lu-DOTA-TATE therapy is associated with alterations in endocrine function likely from radiation exposure to SSTR2+ endocrine tissues. However, these changes may sometimes manifest as clinically significant endocrinopathies. It is therefore important to periodically assess endocrine function during [177Lu]Lu-DOTA-TATE therapy, especially among symptomatic patients.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT03206060?term=NCT03206060&draw=2&rank=1, identifier NCT03206060.

Abstract The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.

The risks of development of colorectal and endometrial cancers in individuals with Lynch syndrome (LS) are well known and have been widely studied. In recent years, the potential association of other malignancies, including prostate cancer, with LS has been considered. Decision-making regarding screening for prostate cancer in the generalized population can be complicated; accounting for the possibility of a higher risk of cancer conferred by a potential genetic predisposition confounds the creation of salient guidelines even further. Although tissue-agnostic treatment approvals have been granted to several immune checkpoint inhibitors (ICIs) for their use in the treatment of subsets of patients whose tumors exhibit high levels of microsatellite instability or high tumor mutational burden, a paucity of data exists regarding the use of ICIs in the first line treatment of patients with locally advanced prostate cancer harboring these features. A significant reduction in tumor volume in response to the combination of immune checkpoint inhibition and androgen deprivation therapy is described in this report of a male with Muir-Torre syndrome who was found to have locally advanced adenocarcinoma of the prostate. While anecdotal, the anti-tumor activity of this combination of therapy is notable and calls attention to the importance of considering further investigation of the use of immune checkpoint blockade as a primary therapeutic option in patients with localized prostate cancer.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

BackgroundCurrent prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor’s eccentricity (shape), signal-to-clutter ratio (SCR), and volume.PurposeConduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer.MethodsThis study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor’s eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (0.70) for ROC curves and p-values (

BackgroundEvaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.MethodsMP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.ResultsThe patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.ConclusionsThis first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization.

Purpose: This phase 1 trial aimed to identify the maximally tolerated hypofractionated dose schedule for postoperative radiation therapy (PORT) after radical prostatectomy. Secondary objectives included biochemical control and quality of life (QoL) measures. Methods and Materials: Patients were treated on 1 of 3 dose levels (DLs): 56.4 Gy in 20 fractions (DL1), 51.2 Gy in 15 fractions (DL2), and 44.2 Gy in 10 fractions (DL3). Treatment was delivered to the prostate bed without pelvic nodal irradiation. Dose escalation followed a standard 3 + 3 design with an expansion for 6 additional patients at the maximally tolerated hypofractionated dose schedule. Acute dose-limiting toxicity (DLT) was defined as grade 3 toxicity lasting >4 days within 21 days of PORT completion; late DLT was defined as grade 4 gastrointestinal (GI) or genitourinary (GU) toxicity. Results: Between January 2018 and August 2019, 15 patients underwent radiation treatment: 3 on DL1, 3 on DL2, and 9 on DL3. The median follow-up was 24 months. There were no DLTs, and the maximally tolerated hypofractionated dose schedule was identified as DL3. Two of the 15 patients (13.3%) experienced biochemical failure (prostate-specific antigen >0.1). Ten of 15 patients (67%) had grade 2+ acute toxicities, consisting of transient GI toxicities. Three patients experienced late grade 2+ GI toxicity, and 5 patients experienced late grade 2+ GU toxicity. Late grade 3 GU toxicity occurred in 2 patients. There were no grade 4+ acute or late toxicities. There were no significant differences in GI measures of QoL, however, there was an increase in GU symptoms and corresponding decrease in GU QoL between 12 and 24 months. Conclusions: The maximum tolerated hypofractionated dose schedule for hypofractionated PORT to the prostate bed was determined to be 44.2 Gy in 10 daily fractions. The most frequent clinically significant toxicities were late grade 2+ GU toxicities, which corresponded to a worsening of late GU QoL.

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/−0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively.

PURPOSEWe aimed to assess post-interventional and 36-month follow-up results of a single-center, single-arm, in-bore phase I trial of focal laser ablation (FLA) guided by multiparametric magnetic resonance imaging (mpMRI).METHODSFLA procedures were done in-bore MRI using a transperineal approach. Primary endpoints were feasibility and safety expressed as lack of grade 3 complications. Secondary endpoints were changes in international prostate symptom score (IPSS), sexual health inventory for men (SHIM), quality of life (QoL) scores, and serum prostate specific antigen (PSA) levels. Treatment outcomes were assessed by combined mpMRI-ultrasound fusion-guided and extended sextant systematic biopsy after 12, 24, and optionally after 36 months.RESULTSFifteen participants were included. Seven patients (46.67%) had Gleason 3+3 and 8 patients (53.33%) had Gleason 3+4 cancer. All patients tolerated the procedure well, and no grade 3/4 complications occurred. All grade 1 and 2 complications were transient and resolved completely. There was no significant change in mean IPSS from baseline (-1, p = 0.460) and QoL (0, p = 0.441) scores following FLA but there was a significant drop in mean SHIM scores (-2, p = 0.010) compared to pretreatment baselines. Mean PSA significantly decreased after FLA (-2.5, p < 0.001). Seven out of 15 patients (46.67%) had residual cancer in, adjacent, or in close proximity to the treatment area (1 × 4+3=7, 1 × 3+4=7, and 5 × 3+3=6). Four out of 15 patients (26.67%) underwent salvage therapy (2 repeat FLA, 2 radical prostatectomy).CONCLUSIONAfter 3 years of follow-up we conclude focal laser ablation is safe and feasible without significant complications.

Abstract OATP1B3 is expressed de novo in primary prostate cancer tissue and to a greater degree in prostate cancer metastases. Gadoxetate disodium is a substrate of OATP1B3, and its uptake has been shown to correlate with OATP1B3 expression in other cancers. We aimed to evaluate use of gadoxetate disodium to image prostate cancer and to track its utility as a biomarker. A single center open-label non-randomized pilot study recruited men with (1) localized, and (2) metastatic castration resistant prostate cancer (mCRPC). Gadoxetate disodium-enhanced MRI was performed at four timepoints post-injection. The Wilcoxon signed rank test was used to compare MRI contrast enhancement ratio (CER) pre-injection and post-injection. OATP1B3 expression was evaluated via immunohistochemistry (IHC) and a pharmacogenomic analysis of OATP1B3, NCTP and OATP1B1 was conducted. The mCRPC subgroup (n = 9) demonstrated significant enhancement compared to pre-contrast images at 20-, 40- and 60-min timepoints (p 0.05). OATP1B3 expression on IHC trended higher contrast enhancement between 20–40 min (p ≤ 0.064) and was associated with contrast enhancement at 60 min (p = 0.0422). OATP1B1 haplotype, with N130D and V174A substitutions, impacted enhancement at 40–60 min (p ≤ 0.038). mCRPC lesions demonstrate enhancement after injection of gadoxetate disodium on MRI and retention over 60 min. As inter-individual variability in OATP1B3 expression and function has both predictive and prognostic significance, gadoxetate disodium has potential as a biomarker in prostate cancer.

Abstract A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Abstract Background Prostate Membrane Specific Antigen (PSMA) positron emission tomography (PET) and multiparametric MRI (mpMRI) have shown high accuracy in identifying recurrent lesions after definitive treatment in prostate cancer (PCa). In this study, we aimed to outline patterns of failure in a group of post-prostatectomy patients who received adjuvant or salvage radiation therapy (PORT) and subsequently experienced biochemical recurrence, using 18F-PSMA PET/CT and mpMRI. Methods PCa patients with biochemical failure post-prostatectomy, and no evident site of recurrence on conventional imaging, were enrolled on two prospective trials of first and second generation 18F-PSMA PET agents (18F-DCFBC and 18F-DCFPyL) in combination with MRI between October 2014 and December 2018. The primary aim of our study is to characterize these lesions with respect to their location relative to previous PORT field and received dose. Results A total of 34 participants underwent 18F-PSMA PET imaging for biochemical recurrence after radical prostatectomy and PORT, with 32/34 found to have 18F-PSMA avid lesions. On 18F-PSMA, 17/32 patients (53.1%) had metastatic disease, 8/32 (25.0%) patients had locoregional recurrences, and 7/32 (21.9%) had local failure in the prostate fossa. On further exploration, we noted 6/7 (86%) of prostate fossa recurrences were in-field and were encompassed by 100% isodose lines, receiving 64.8–72 Gy. One patient had marginal failure encompassed by the 49 Gy isodose. Conclusions 18F-PSMA PET imaging demonstrates promise in identifying occult PCa recurrence after PORT. Although distant recurrence was the predominant pattern of failure, in-field recurrence was noted in approximately 1/5th of patients. This should be considered in tailoring radiotherapy practice after prostatectomy. Trial registration www.clinicaltrials.gov , NCT02190279 and NCT03181867. Registered July 12, 2014, https://clinicaltrials.gov/ct2/show/NCT02190279 and June 8 2017, https://clinicaltrials.gov/ct2/show/NCT03181867 .

PURPOSEChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.METHODSA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student’s t-test or Mann-Whitney U test. Cohen’s kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.RESULTSFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.CONCLUSIONChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.