Integration and evaluation of chest X-ray artificial intelligence in clinical practice.

Purpose: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice. Approach: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice. Results: The alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful. Conclusions: The AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Angular needle tracker and stabilizer for image-guided interventions.

INTRODUCTION: Minimally invasive image-guided interventions have changed the face of procedural medicine. For these procedures, safety and efficacy depend on precise needle placement. Needle targeting devices help improve the accuracy of needle placement, but their use has not seen broad penetration. Some of these devices are costly and require major modifications to the clinical workflow. In this article, we developed a low-cost, disposable, and easy-to-use angulation tracking device, which was based on a redesigned commercial passive needle holder. MATERIAL AND METHODS: The new design provided real-time angulation information for needle tracking. In this design, two potentiometers were used as angulation sensors, and they were connected to two axes of the passive needle holder's arch structure through a 3 D-printed bridge structure. A control unit included an Arduino Pro Mini, a Bluetooth module, and two rechargeable batteries. The angulation was calculated and communicated in real time to a novel developed smartphone app, where real-time angulation information was displayed for guiding the operator to position the needle to the planned angles. RESULTS: The open-air test results showed that the average errors are 1.03° and 1.08° for left-right angulation and head-foot angulation, respectively. The animal cadaver tests revealed that the novel system had an average angular error of 3.2° and a radial distance error of 3.1 mm. CONCLUSIONS: The accuracy was comparable with some commercially available solutions. The novel and low-cost needle tracking device may find a role as part of a real-time precision approach to both planning and implementation of image-guided therapies.

A new mouse mutant with cleavage-resistant versican and isoform-specific versican mutants demonstrate that proteolysis at the Glu441-Ala442 peptide bond in the V1 isoform is essential for interdigital web regression.

Two inherent challenges in the mechanistic interpretation of protease-deficient phenotypes are defining the specific substrate cleavages whose reduction generates the phenotypes and determining whether the phenotypes result from loss of substrate function, substrate accumulation, or loss of a function(s) embodied in the substrate fragments. Hence, recapitulation of a protease-deficient phenotype by a cleavage-resistant substrate would stringently validate the importance of a proteolytic event and clarify the underlying mechanisms. Versican is a large proteoglycan required for development of the circulatory system and proper limb development, and is cleaved by ADAMTS proteases at the Glu441-Ala442 peptide bond located in its alternatively spliced GAGß domain. Specific ADAMTS protease mutants have impaired interdigit web regression leading to soft tissue syndactyly that is associated with reduced versican proteolysis. Versikine, the N-terminal proteolytic fragment generated by this cleavage, restores interdigit apoptosis in ADAMTS mutant webs. Here, we report a new mouse transgene, Vcan AA, with validated mutations in the GAGß domain that specifically abolish this proteolytic event. Vcan AA/AA mice have partially penetrant hindlimb soft tissue syndactyly. However, Adamts20 inactivation in Vcan AA/AA mice leads to fully penetrant, more severe syndactyly affecting all limbs, suggesting that ADAMTS20 cleavage of versican at other sites or of other substrates is an additional requirement for web regression. Indeed, immunostaining with a neoepitope antibody against a cleavage site in the versican GAGα domain demonstrated reduced staining in the absence of ADAMTS20. Significantly, mice with deletion of Vcan exon 8, encoding the GAGß domain, consistently developed soft tissue syndactyly, whereas mice unable to include exon 7, encoding the GAGα domain in Vcan transcripts, consistently had fully separated digits. These findings suggest that versican is cleaved within each GAG-bearing domain during web regression, and affirms that proteolysis in the GAGß domain, via generation of versikine, has an essential role in interdigital web regression.

Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it. Continuous learning AI presents the next substantial step in this direction and brings a new set of opportunities and challenges. Herein, the authors discuss the main concepts and requirements for implementing continuous AI in radiology and illustrate them with examples from emerging applications.

Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology.

Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific three-dimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (fA, fB, fC) were compared with epithelial, stromal, and luminal area fractions (fepithelium, fstroma, flumen) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (fA, fB, fC) and (fepithelium, fstroma, flumen), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests. Results Prostate specimens from nine men (mean age, 65 years ± 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The fA, fB, and fC were correlated with fepithelium, fstroma, and flumen (all P < .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased fA (PCa vs benign, 0.37 ± 0.05 vs 0.27 ± 0.06; P < .001), decreased fC (PCa vs benign, 0.18 ± 0.06 vs 0.31 ± 0.13; P = .01), increased fepithelium (PCa vs benign, 0.44 ± 0.13 vs 0.26 ± 0.16; P < .001), and decreased flumen (PCa vs benign, 0.14 ± 0.08 vs 0.27 ± 0.18; P = .004). Conclusion Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lee and Hectors in this issue.

Radiology's Information Architecture Could Migrate to One Emulating That of Smartphones.

Diagnostic radiology (DxR), having had successful serial co-evolutions with imaging equipment and PACS, is faced with another. With a backdrop termed "globotics transition," it should create an IT and informatics infrastructure capable of integrating artificial intelligence (AI) into current critical communication functions of PACS and incorporating functions currently residing in balkanized products. DxR will face the challenge of adopting sustaining and disruptive AI innovations simultaneously. In this co-evolution, a major selection force for AI will be increasing the flow of information and patients; "increasing" means faster flow over larger areas defined by geography and content. Larger content includes a broader spectrum of imaging and nonimaging information streams that facilitate medical decision making. Evolution to faster flow will gravitate toward a hierarchical IT architecture consisting of many small channels feeding into fewer larger channels, something potentially difficult for current PACS. Smartphone-like architecture optimized for communication and integration could provide a large-channel backbone and many smaller feeding channels for basic functions, as well as those needing to innovate rapidly. New, more flexible architectures stimulate market competition in which DxR could act as an artificial selection force to influence development of faster increased flow in current PACS companies, in disruptors such as consolidated AI companies, or in entirely new entrants like Apple or Google. In this co-evolution, DxR should be able to stimulate design of a modern communication medium that increases the flow of information and decreases the time and energy necessary to absorb it, thereby creating even more indispensable clinical value for itself.

Insights on UNC-104-dynein/dynactin interactions and their implications on axonal transport in Caenorhabditis elegans.

Bidirectional cargo transport in neurons can be explained by two models: the "tug-of-war model" for short-range transport, in which several kinesin and dynein motors are bound to the same cargo but travel in opposing directions, and by the "motor coordination model" for long-range transport, in which small adaptors or the cargo itself activates or deactivates opposing motors. Direct interactions between the major axonal transporter kinesin-3 UNC-104(KIF1A) and the dynein/dynactin complex remains unknown. In this study, we dissected and evaluated the interaction sites between UNC-104 and dynein as well as between UNC-104 and dynactin using yeast two-hybrid assays. We found that the DYLT-1(Tctex) subunit of dynein binds near the coiled coil 3 (CC3) of UNC-104, and that the DYRB-1(Roadblock) subunit binds near the CC2 region of UNC-104. Regarding dynactin, we specifically revealed strong interactions between DNC-6(p27) and the FHA-CC3 stretch of UNC-104, as well as between the DNC-5(p25) and the CC2-CC3 region of UNC-104. Motility analysis of motors and cargo in the nervous system of Caenorhabditis elegans revealed impaired transport of UNC-104 and synaptic vesicles in dynein and dynactin mutants (or in RNAi knockdown animals). Further, in these mutants UNC-104 clustering along axons was diminished. Interestingly, when dynamic UNC-104 motors enter a stationary UNC-104 cluster their dwelling times are increased in dynein mutants (suggesting that dynein may act as an UNC-104 activator). In summary, we provide novel insights on how UNC-104 interacts with the dynein/dynactin complex and how UNC-104 driven axonal transport depends on dynein/dynactin in C. elegans neurons.

Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI.

PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. METHODS: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. RESULTS: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set. CONCLUSION: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).

Selective middle cerebral artery occlusion in the rabbit: Technique and characterization with pathologic findings and multimodal MRI.

BACKGROUND: A reliable animal model of ischemic stroke is vital for pre-clinical evaluation of stroke therapies. We describe a reproducible middle cerebral artery (MCA) embolic occlusion in the French Lop rabbit characterized with multimodal MRI and histopathologic tissue analysis. NEW METHOD: Fluoroscopic-guided microcatheter placement was performed in five consecutive subjects with angiographic confirmation of MCA occlusion with autologous clot. Multimodal MRI was obtained prior to occlusion and up to six hours post after which repeat angiography confirmed sustained occlusion. The brain was harvested for histopathologic examination. RESULTS: Angiography confirmed successful MCA catheterization and durable (>6 h) MCA occlusion in all animals. There was increase of ADC volume over time and variable final core volume presumably related to individual variation in collateral flow. FLAIR hyperintensity indicative of cytotoxic edema and parenchymal contrast enhancement reflective of blood brain barrier disruption was observed over time. Tissue staining of the ischemic brain showed edema and structural alterations consistent with infarction. COMPARISON WITH EXISTING METHODS: This study describes a technique of selective catheterization and embolic occlusion of the MCA in the rabbit with MRI characterization of evolution of ischemia in the model. CONCLUSIONS: We demonstrate the feasibility of a rabbit model of embolic MCA occlusion with angiographic documentation. Serial MR imaging demonstrated changes comparable to those observed in human ischemic stroke, confirmed histopathologically.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

PURPOSE: We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. MATERIALS AND METHODS: In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. RESULTS: Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. CONCLUSION: We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

p16INK4a : A surrogate marker of high-risk human papillomavirus infection in squamous cell carcinoma of the nasal vestibule.

BACKGROUND: The purpose of this study was to analyze the role of p16INK4a and the prevalence of human papillomavirus (HPV) in squamous cell carcinoma (SCC) of the nasal vestibule. METHODS: Patients diagnosed from 1995 to 2014 were included in this study. Assessment of p16INK4a and HPV-DNA polymerase chain reaction (PCR) was performed and analyzed with respect to baseline, clinicopathological, and outcome parameters. The p16INK4a positivity was defined as unequivocal nuclear and cytoplasmic staining of ≥70% of the cells, whereas 50%-69% was considered to be a "borderline" result. RESULTS: There were 46 patients with SCCs of the nasal vestibule, of whom 31 (67.4%) were available for p16INK4a and 30 (65.2%) for analysis of HPV. Expression of p16INK4a was present in 19.4% and showed coincidence with high-risk HPV (P < .001). Neither p16INK4a nor HPV-DNA had significant impact on outcome. CONCLUSION: Significant immunoreactivity for p16INK4a was present in about one-fifth of the samples and figured as a surrogate marker of high-risk HPV infection. There was no influence on outcome.

Regioselective 1,4-Conjugate Addition of Grignard Reagents to α,ß-γ,δ-Dienones and α,ß-γ,δ-Dienyl Thiol Esters.

Alkyl Grignard reagents (Et, nBu, iPr, cyclohexyl), with the exception of tBuMgCl, undergo exclusive or exceptionally highly regioselective 1,4-addition reactions to α,ß-γ,δ-unsaturated ketones, while aryl and heteroaryl Grignard reagents give mixed results ranging from exclusive 1,4-addition (1-naphthyl, 2-N-methylpyrrolyl) to regioselective 1,2-addition (2-furyl, 2:1). All alkyl, aryl, and heteroaryl Grignard reagents examined gave exclusive 1,4-addition reactions with α,ß-γ,δ-unsaturated thiol esters, with the exception of tBuMgCl, which gave an 80:20 mixture of 1,4:1,6-addition products. The high chemo- and regioselectivity observed for these reactions is attributed to a radical or radical-like pathway for the alkyl Grignard reagents and possibly a carbanion pathway for aryl Grignard reagents. The α,γ-dienyl thiol esters provide for a one-pot tandem 1,4-addition-nucleophilic acyl substitution reaction sequence to afford 3-substituted 4-enone moieties.

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study.

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. ©RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.