Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Removing grass clippings reduces bermudagrass mite (Acari: Eriophyidae) infestation during turfgrass regrowth.

Bermudagrass mite (Aceria cynodoniensis Sayed) infestation stunts bermudagrass (Cynodon spp. [Poales: Poaceae]) growth, leading to thinned turf and lower aesthetic and recreational value. Bermudagrass mites cause characteristic symptoms called witch's brooms, including shortened internodes and leaves and the proliferation of tillers. Grass clippings produced by mowing or scalping bermudagrass harbor mites, which abandon the desiccating grass clippings and spread to surrounding turfgrass. Dropped grass clippings can lead to infestation of new turfgrass. Nursery experiments were conducted with potted bermudagrass to determine the effect of removing witch's brooms or grass clippings after scalping on witch's broom densities on the recovering bermudagrass. Additionally, laboratory experiments were conducted to assess the potential for mites to abandon detached witch's brooms and to evaluate mite survival after leaving their hosts. The number of initial witch's brooms and individually removing witch's brooms did not affect subsequent witch's broom densities, suggesting that infested but asymptomatic terminals later developed into witch's brooms. Removing grass clippings after scalping reduced witch's broom densities by over 65% in two trials. Most mites (96%) abandoned witch's brooms within 48 h after detaching witch's brooms, and adult mites survived an average of 5.6 h after removal from the host plant. Removing clippings after scalping may improve bermudagrass mite management and limit damage on the recovering turfgrass. Additionally, clippings resulting from regular mowing or scalping should be disposed of properly because this study demonstrates that mites abandon desiccating host plants and survive sufficiently long to infest surrounding turfgrass.

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Bermudagrass mite (Acari: Eriophyidae) infestation worsens in response to increasing nitrogen fertility and decreasing irrigation volume but not mowing height.

Severe bermudagrass mite (Aceria cynodoniensis Sayed) infestation stunts turfgrass growth and reduces the aesthetic and recreational value of managed bermudagrass. Management practices, such as fertilization, mowing, and irrigation, may impact bermudagrass mite infestation and damage, but empirical evidence is lacking. Two 20 wk experiments were conducted with potted bermudagrass in a greenhouse or nursery to evaluate the effect of varying nitrogen rates (0, 24.5, or 49 kg N/ha), mowing heights (1.3, 2.5, 3.8, or 5 cm), and irrigation rates (60%, 100%, or 140% evapotranspiration [ET] rate) on the densities of witch's brooms (i.e., stunted and deformed terminals symptomatic of infestation) and bermudagrass mites. Increasing nitrogen fertility from 0 to 49 kg N/ha increased witch's broom and bermudagrass mite densities by 292% and 339%, respectively. Bermudagrass fertilized with nitrogen maintained higher turf quality than unfertilized grass despite greater mite damage. Decreasing irrigation from 140% to 60% of the ET rate also increased witch's broom densities by 124%. Mowing height did not consistently affect witch's broom or mite densities. Witch's broom and mite densities were positively correlated and followed a general trend with greater densities in April-August and a decline in densities in August-October. These findings suggest that nitrogen fertilization and water stress influence bermudagrass mite damage. Thus, limiting nitrogen fertilization to a level necessary to maintain turfgrass health and quality (0.5 kg N/ha) and minimizing turfgrass water stress can complement current chemical control strategies as part of an integrated pest management program.

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.

SimpleMind: An open-source software environment that adds thinking to deep neural networks.

Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software environment for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The knowledge base can then be applied to an input image to recognize and understand its content. SimpleMind brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are dynamically chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross-checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Proof-of-principle example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI environment.

Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability. PURPOSE: The purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability. METHODS: Our dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics. RESULTS: During the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attention maps are not satisfactory; when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set. CONCLUSIONS: Our results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool. MATERIALS AND METHODS: A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks. To check the ETT tip placement, a "safe zone" was computed as the region inside the trachea and 3-7 cm above the carina. Two AI outputs were evaluated: (1) ETT overlay, (2) ETT misplacement alert messages. Clinically relevant performance metrics were compared against prespecified thresholds of >85% overlay accuracy and positive predictive value (PPV) > 30% and negative predictive value NPV > 95% for alerts to move into clinical validation. RESULTS: An ETT was present in 285 of 512 test cases. The AI detected 95% (271/285) of ETTs, 233 (86%) of these with accurate tip localization. The system (correctly) did not generate an ETT overlay in 221/227 CXRs where the tube was absent for an overall overlay accuracy of 89% (454/512). The alert messages indicating that either the ETT was misplaced or not detected had a PPV of 83% (265/320) and NPV of 98% (188/192). CONCLUSION: The chest X-ray AI met prespecified performance thresholds to move into clinical validation.

Adhesive-Free, Stretchable, and Permeable Multiplex Wound Care Platform.

The wound healing process remains a poorly understood biological mechanism. The high morbidity and mortality rates associated with chronic wounds are a critical concern to the health care industry. Although assessments and treatment options exist, these strategies have primarily relied on static wound dressings that do not consider the dynamic physicochemical microenvironment and can often create additional complications through the frequent dressing changing procedure. Inspired by the need for engineering "smart" bandages, this study resulted in a multifaceted approach to developing an adhesive-free, permeable, and multiplex sensor system. The electronic-extracellular matrix (e-ECM) platform is capable of noninvasively monitoring chemical and physical changes in real-time on a flexible, stretchable, and permeable biointegrated platform. The multiplex sensors are constructed atop a soft, thin, and microfibrous substrate of silicone to yield a conformal, adhesive-free, convective, or diffusive wound exudate flow, and passive gas transfer for increased cellular epithelization and unobstructed physical and chemical sensor monitoring at the wound site. This platform emulates the native epidermal mechanics and physical extracellular matrix architecture for intimate bio-integration. The multiple biosensor array can continuously examine inflammatory biomarker such as lactate, glucose, pH, oxygen, and wound temperature that correlates to the wound healing status. Additionally, a heating element was incorporated to maintain the optimal thermal conditions at the wound bed. The e-ECM electrochemical biosensors were tested in vitro, within phosphate-buffered saline, and ex vivo, within wound exudate. The "smart" wound bandage combines biocompatible materials, treatments, and monitoring modalities on a microfibrous platform for complex wound dynamic control and analysis.

Upcycling Compact Discs for Flexible and Stretchable Bioelectronic Applications.

Electronic waste is a global issue brought about by the short lifespan of electronics. Viable methods to relieve the inundated disposal system by repurposing the enormous amount of electronic waste remain elusive. Inspired by the need for sustainable solutions, this study resulted in a multifaceted approach to upcycling compact discs. The once-ubiquitous plates can be transformed into stretchable and flexible biosensors. Our experiments and advanced prototypes show that effective, innovative biosensors can be developed at a low-cost. An affordable craft-based mechanical cutter allows pre-determined patterns to be scored on the recycled metal, an essential first step for producing stretchable, wearable electronics. The active metal harvested from the compact discs was inert, cytocompatible, and capable of vital biopotential measurements. Additional studies examined the material's resistive emittance, temperature sensing, real-time metabolite monitoring performance, and moisture-triggered transience. This sustainable approach for upcycling electronic waste provides an advantageous research-based waste stream that does not require cutting-edge microfabrication facilities, expensive materials, and high-caliber engineering skills.

Effect of indacaterol/glycopyrronium on ventilation and perfusion in COPD: a randomized trial.

RATIONALE: The long-acting ß2-agonist/long-acting muscarinic antagonist combination indacaterol/glycopyrronium (IND/GLY) elicits bronchodilation, improves symptoms, and reduces exacerbations in COPD. Magnetic resonance imaging (MRI) of the lung with hyperpolarized gas and gadolinium contrast enhancement enables assessment of whole lung functional responses to IND/GLY. OBJECTIVES: The primary objective was assessment of effect of IND/GLY on global ventilated lung volume (%VV) versus placebo in COPD. Lung function, regional ventilation and perfusion in response to IND/GLY were also measured. METHODS: This double-blind, randomized, placebo-controlled, crossover study assessed %VV and pulmonary perfusion in patients with moderate-to-severe COPD after 8 days of once-daily IND/GLY treatment (110/50 µg) followed by 8 days of placebo, or vice versa, using inhaled hyperpolarized 3He gas and gadolinium contrast-enhanced MRI, respectively. Lung function measures including spirometry were performed for each treatment after 8 days. MEASUREMENTS AND MAIN RESULTS: Of 31 patients randomized, 29 completed both treatment periods. IND/GLY increased global %VV versus placebo (61.73% vs. 56.73%, respectively, least squares means treatment difference: 5.00% [90% CI 1.40 to 8.60]; P = 0.025). IND/GLY improved whole lung index of ventilation volume to perfusion volume (V/Q) ratio versus placebo; 94% (90% CI 83 to 105) versus 86% (90% CI 75 to 97; P = 0.047), respectively. IND/GLY showed a trend to improve diffusing capacity for carbon monoxide (DLCO) (+ 0.66 mL/min/mmHg; P = 0.082). By Day 8, forced expiratory volume in 1 s (FEV1) was increased by 0.32 L versus placebo (90% CI 0.26 to 0.38; P < 0.0001), substantiating earlier findings and providing evidence of assay sensitivity for this trial. CONCLUSIONS: IND/GLY improved lung ventilation assessed by 3He MRI after 1 week of treatment. This observation may provide mechanistic support for the symptomatic clinical benefit shown with IND/GLY in COPD. Clinical trial registered with www.clinicaltrials.gov (NCT02634983).

Radiographic read paradigms and the roles of the central imaging laboratory in neuro-oncology clinical trials.

Determination of therapeutic benefit in intracranial tumors is intimately dependent on serial assessment of radiographic images. The Response Assessment in Neuro-Oncology (RANO) criteria were established in 2010 to provide an updated framework to better characterize tumor response to contemporary treatments. Since this initial update a number of RANO criteria have provided some basic principles for the interpretation of changes on MR images; however, the details of how to operationalize RANO and other criteria for use in clinical trials are ambiguous and not standardized. In this review article designed for the neuro-oncologist or treating clinician, we outline essential steps for performing radiographic assessments by highlighting primary features of the Imaging Charter (referred to as the Charter for the remainder of this article), a document that describes the clinical trial imaging methodology and methods to ensure operationalization of the Charter into the workings of a clinical trial. Lastly, we provide recommendations for specific changes to optimize this methodology for neuro-oncology, including image registration, requirement of growing tumor for eligibility in trials of recurrent tumor, standardized image acquisition guidelines, and hybrid reader paradigms that allow for both unbiased measurements and more comprehensive interpretation.

Permethrin Residual Activity Against Ambrosia Beetle (Coleoptera: Curculionidae: Scolytinae) Attacks Following Field Aging and Simulated Rainfall Weathering.

Adult ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) bore into ornamental nursery trees resulting in trunk vascular tissue damage, which can potentially kill trees. Ambrosia beetle exposure to surface-applied insecticides is minimal after internal trunk galleries are formed, so effective management requires insecticide treatments to be applied near the time of infestation or to have residual activity on the bark. Tree trunk sections (bolts) were used to determine the effect of field aging or irrigation (i.e., simulated rainfall weathering) on permethrin residual activity against ambrosia beetles. In all experiments, 30-cm-long bolts from Liriodendron tulipifera L. (Magnoliales: Magnoliaceae) were hollowed and filled with 70% ethanol at field deployment to induce ambrosia beetle attacks over a 2-wk period. To evaluate insecticide residual activity, permethrin was sprayed onto tree bolts at 0, 8, 17, or 24 d before ethanol addition, and then bolts were deployed along a wooded border in fall 2017 and spring 2018. Tree bolts with permethrin residues ≤17 d old had significantly fewer ambrosia beetle attacks than bolts with 24-d-old residues or the non-permethrin-treated control bolts. To evaluate simulated rainfall weathering, permethrin was applied to tree bolts 8 or 22 d before ethanol (spring 2018) or 10 or 24 d before ethanol (fall 2018) with half of the bolts receiving regular irrigation events. Irrigation had no significant effect on permethrin residual activity against ambrosia beetles during either test. This study determined ambrosia beetle control was affected by permethrin residue age more than simulated rainfall weathering, and a reapplication interval of ≤17 d maximized beetle control.

High throughput image labeling on chest computed tomography by deep learning.

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.

Using Transitional Changes on High-Resolution Computed Tomography to Monitor the Impact of Cyclophosphamide or Mycophenolate Mofetil on Systemic Sclerosis-Related Interstitial Lung Disease.

OBJECTIVE: To examine changes in the extent of specific patterns of interstitial lung disease (ILD) as they transition from one pattern to another in response to immunosuppressive therapy in systemic sclerosis-related ILD (SSc-ILD). METHODS: We evaluated changes in the quantitative extent of specific lung patterns of ILD using volumetric high-resolution computed tomography (HRCT) scans obtained at baseline and after 2 years of therapy in patients treated with either cyclophosphamide (CYC) for 1 year or mycophenolate mofetil (MMF) for 2 years in Scleroderma Lung Study II. ILD patterns included lung fibrosis, ground glass, honeycombing, and normal lung. Net change was calculated as the difference in the probability of change from one ILD pattern to another. Wilcoxon's signed rank test was used to compare the changes. RESULTS: Forty-seven and 50 patients had baseline and follow-up scans in the CYC and MMF groups, respectively. Mean net improvements reflecting favorable changes from one ILD pattern to another in the whole lung in the CYC and MMF groups, respectively, were as follows: from lung fibrosis to a normal lung pattern, 21% and 19%; from a ground-glass pattern to a normal lung pattern, 30% and 28%; and from lung fibrosis to a ground-glass pattern, 5% and 0.5%. The mean overall improvement in transitioning from a ground-glass pattern or lung fibrosis to a normal lung pattern was significant for both treatments (all P < 0.001). CONCLUSION: Significantly favorable transitions from both ground-glass and lung fibrosis ILD patterns to a normal lung pattern were observed in patients undergoing immunosuppressive treatment for SSc-ILD, suggesting the usefulness of examining these transitions for insights into the underlying pathobiology of treatment response.

Prediction of idiopathic pulmonary fibrosis progression using early quantitative changes on CT imaging for a short term of clinical 18-24-month follow-ups.

OBJECTIVE: High-resolution computed tomography (HRCT) plays an indispensable role in the diagnosis of idiopathic pulmonary fibrosis (IPF). Due to unpredictability in progression and the short median survival of 2-5 years, it is critical to delineate the patients with rapid progression. The aim is to evaluate the predictability of IPF progression using the early quantitative changes. METHODS: Automated texture-based quantitative lung fibrosis (QLF) was calculated from the anonymized HRCT. Two datasets were collected retrospectively: (1) a pilot study of 35 subjects with three sequential scans (baseline and 6 and 12 months) to obtain a threshold, where visual assessments were stable at 6 months but worsened at 12 months; (2) 157 independent subjects to test the threshold. Landmark Cox regressions were used to compare the progression-free survival (PFS) defined by pulmonary function using the threshold from the early changes in QLF. C-indexes were reported as estimations of the concordance of prediction. RESULTS: A threshold of 4% QLF change at 6 months corresponded to the mean change that worsened on HRCT visually at 12 months from the pilot study. Using the threshold, significant differences were found in the independent dataset (hazard ratio (HZ) = 5.92, p = 0.001 by Cox model, C-index = 0.71 at the most severe lobe; and HZ = 3.22, p = 0.012, C-index = 0.68 in the whole lung). Median PFS was 11.9 months for subjects with ≥ 4% changes, whereas median PFS was greater than 18 months for subjects with < 4% changes at the most severe lobe. CONCLUSION: Early structural changes on HRCT using a quantitative score can predict progression in lung function. KEY POINTS: • Changes on HRCT using quantitative texture-based scores can play a pivotal role for providing information and an aid tool for timely management decision for patients with IPF. • Quantitative changes on HRCT of 4% or more, which matched 6-month prior changes with visual assessment of worsening, can play a pivotal role for providing prediction of clinical progression by 3-5 folds higher in the next incidence, compared with those of subjects with less than 4% changes. • Early structural changes of 4% or more in a paired HRCT scans derived by quantitative scores can predict the progression in lung function in 1-2 years in subjects with IPF, which is critical information for timely management decision for subjects with IPF where the median survival is 2 to 5 years.

Correction to: Toward clinically usable CAD for lung cancer screening with computed tomography.

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

Association of tumor grade, enhancement on multiphasic CT and microvessel density in patients with clear cell renal cell carcinoma.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) comprises nearly 90% of all diagnosed RCC subtypes and has the worst prognosis and highest metastatic potential. The strongest prognostic factors for patients with ccRCC include histological subtype and Fuhrman grade, which are incorporated into prognostic models. Since ccRCC is a highly vascularized tumor, there may be differences in enhancement patterns on multidetector CT (MDCT) due to the hemodynamics and microvessel density (MVD) of the lesions. This may provide a noninvasive method to characterize incidentally detected low- and high-grade ccRCCs on MDCT. The purpose of our study was to determine the correlation between MDCT enhancement parameters, ccRCC MVD, and Fuhrman grade to determine its utility and value in assessing tumor vascularity and grade in vivo. METHODS: In this retrospective, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low-grade (LG), and 43 high-grade (HG) ccRCCs underwent preoperative four-phase MDCT. A 3D volume of interest (VOI) was obtained for every tumor and absolute enhancement and the wash-in/wash-out of enhancement for each phase was assessed. Immunohistochemistry on resected specimens was used to quantify MVD. Linear regression and Pearson correlation were used to investigate the strength of the association between 3D VOI enhancement and MVD. Stepwise logistic regression analysis determined independent predictors of HG ccRCC. Cut-off values and odds Ratio (OR) with 95% CIs were reported. The clinical, radiomic, and pathologic features with the highest performance in the stepwise logistic regression analysis were evaluated using receiver operator characteristics (ROC) and area under the curve (AUC). RESULTS: Absolute enhancement in the nephrographic phase < 52.1 Hounsfield Units (HU) (HR 0.979, 95% CI 0.964-0.994, p value = 0.006), lesion size > 4.3 cm (HR 1.450, 95% CI 1.211-1.738, p value < 0.001), and an intratumoral MVD < 15% (HR 0.932, 95% CI 0.867-1.002, p value = 0.058) were independent predictors of HG ccRCC with an AUC of 0.818 (95% CI 0.725-0.911). HG ccRCCs had a significant association between 3D VOI enhancement and MVD in each post-contrast phase (r2 = 0.238 to 0.455, p < 0.05). CONCLUSIONS: Absolute enhancement of the entire lesion obtained from a 3D VOI in the nephrographic phase on preoperative MDCT can provide quantitative data that are a significant, independent predictor of a high-grade clear cell RCC and can be used to assess tumor vascularity and grade in vivo.

Prediction of progression in idiopathic pulmonary fibrosis using CT scans at baseline: A quantum particle swarm optimization - Random forest approach.

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.