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Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.

PDF file - 43K, Chloroquine and 6-AN treatment stimulate IL-6 production in Tsc2-null MEFs.

PDF file - 98K, Autophagy and pentose phosphate pathway inhibition suppresses proliferation of Tsc2-null cystadenoma 105K cells.

The mammalian target of rapamycin complex 1 (mTORC1) is hyperactive in many human cancers and in tuberous sclerosis complex (TSC). Autophagy, a key mTORC1-targeted process, is a critical determinant of metabolic homeostasis. Metabolomic profiling was performed to elucidate the cellular consequences of autophagy dysregulation under conditions of hyperactive mTORC1. It was discovered that TSC2-null cells have distinctive autophagy-dependent pentose phosphate pathway (PPP) alterations. This was accompanied by enhanced glucose uptake and utilization, decreased mitochondrial oxygen consumption, and increased mitochondrial reactive oxygen species (ROS) production. Importantly, these findings revealed that the PPP is a key autophagy-dependent compensatory metabolic mechanism. Furthermore, PPP inhibition with 6-aminonicotinamide (6-AN) in combination with autophagy inhibition suppressed proliferation and prompted the activation of NF-κB and CASP1 in TSC2-deficient, but not TSC2-proficient cells. These data demonstrate that TSC2-deficient cells can be therapeutically targeted, without mTORC1 inhibitors, by focusing on their metabolic vulnerabilities.Implications: This study provides proof-of-concept that therapeutic targeting of diseases with hyperactive mTORC1 can be achieved without the application of mTORC1 inhibitors. Mol Cancer Res; 12(1); 48–57. ©2013 AACR.

PDF file - 42K, Parthenolide rescues proliferation in Tsc2-/- MEFs.

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PDF file - 35K, Chloroquine treatment induces ROS production.

PDF file - 55K, Effect of chloroquine on the metabolome of Tsc2+/+ MEFs.

PDF file - 204K, mTORC1 signaling upon chloroquine/6-AN treatment and effect of mTORC1 inhibition on the proliferation of Tsc2-/- cells treated with the dual drug combination.

PDF file - 34K, Effect of chloroquine on the metabolome of Tsc2+/+ MEFs.

PDF file - 61K, Effect of chloroquine in Tsc2-deficient models.

XLSX file - 144K, Metabolomics data (raw and processed).

Background Despite the increasing number of older adults as the population ages, there is a lack of frailty prevention guidelines for community-dwelling older adults. The Korean Frailty and Aging Cohort Study conducted this systematic review of literature on contributors to frailty and developed guidelines on the primary prevention of frailty in community-dwelling older adults. Methods This study updated a previous systematic review of contributors to frailty by adding the most recent articles. Based on this updated systematic review, experts in geriatrics and gerontology developed guidelines for preventing frailty using the Delphi method. Results These guidelines categorized the recommendations into physical activity, resilience, oral health, management of non-communicable diseases, involvement in society, smoking cessation, and eating various kinds of food. Conclusion Unlike previous frailty-related guidelines, this study developed evidence-based frailty prevention guidelines based on a systematic review. The guidelines are expected to contribute to the healthy aging of community-dwelling older adults by the primary prevention of frailty.

Objective. To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector. Approach. Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network. Main results. Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs. Quantitative results and clinical evaluation from radiologists also show that our method can provide accurate translation results. Significance. Quantitative evaluation of CT images from multi-site or longitudinal studies has been a difficult problem due to the image variation depending on CT scan parameters and manufacturers. The proposed method can be utilized to address this for the quantitative analysis of multi-domain CT images.

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS). This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. The five features remaining after the LASSO analysis were %LAA−950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index. A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality. • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950 , AWT_Pi10_6 th , AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Tumor necrosis factor receptor-associated protein 1 (TRAP1) is a mitochondrial homolog of HSP90 chaperones. It plays an important role in protection against oxidative stress and apoptosis by regulating reactive oxidative species (ROS). Recent studies have suggested TRAP1 as a regulator of metabolism and mitochondrial homeostasis in diverse pathological states, including cancer. To further elucidate the mechanistic role of TRAP1 in regulating tumor cell survival, we used gamitrinib-triphenylphosphonium (G-TPP) to inhibit TRAP1 signaling pathways in colon cancer. Inhibition of TRAP1 disrupted redox homeostasis and increased ER stress in colon cancer. To confirm the G-TPP effects of TRAP1 inhibition in colon cancer, we performed TRAP1 CRISPR/Cas9 to deplete TRAP1 expression in colon cancer cell line CT26. TRAP1-depleted cells were found to acquire several distinct phenotypes: induction of reactive oxidative species (ROS) level, reduction of cell growth, and lower pH value of culture medium. In addition, proteomic data showed that depletion of TRAP1 induced significant metabolism alteration in CT26 cells. Using seahorse assay to access mitochondrial respiration and glycolysis, we found that TRAP1-depleted cells were insensitive to Oxidative Phosphorylation (OXPHOS) inhibitor treatment and significantly increased extracellular acidification rate (ECAR) compared to parental cells. Furthermore, higher glucose consumption rate and higher lactic acid level of culture medium were observed in TRAP1-depleted cells. On the other hand, Glycolytic inhibitor (2DG) led to excessive ROS production, reduction of glucose uptake and exacerbated cell death in TRAP1-depleted cell compared to parental cells. Taken together, depletion of TRAP1 induces mitochondrial dysfunction and modulates metabolic switch toward aerobic glycolysis in TRAP1-depleted colon cancer cells. Dual inhibition of TRAP1 and glycolytic signaling may increase therapeutic efficiency through induction of excessive ROS production in colon cancer. Citation Format: Hong-Yuan Tsai, Miao-Hsueh Chen, Herath Lakmini Senavirathna, Jihye Yun, Sheng Pan, Ru Chen. Synergistically suppressive effects by combination of glycolytic inhibitor with TRAP1-depletion in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 273.

To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets.Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67],The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis.This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines. We developed a fully automated quantitative tool for detecting ILA by modifying deep learning-based quantification of interstitial lung disease and evaluated its performance using the radiologists' consensus for ILA as a reference standard.A total of 336 participants (mean age, 70.5 ± 6.1 years; M:F = 282:54) were included. Inter-reader agreements were substantial for the presence of ILA (weighted κ, 0.74) and fair for its subtypes (weighted κ, 0.38). The quantification system for identifying ILA using a threshold of 5 % in at least one zone showed 67.6 % sensitivity, 93.3 % specificity, and 90.5 % accuracy. Eight of 20 (40 %) false positives identified by the system were underestimated by readers for ILA extent. Contrast-enhancement in a certain vendor and suboptimal inspiration caused a true false-positive on the system (all P 0.05). The best cut-off value of abnormality extent detecting ILA on the system was 3.6 % (sensitivity, 84.8 %; specificity 92.4 %).Inter-reader agreement was substantial for ILA but only fair for its subtypes. Applying an automated quantification system in routine clinical practice may aid the objective identification of ILA.

There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.

The main objective of this study was to find the relationship between oral health status and grip strength in older adults living in the community. Low handgrip strength was associated with using full dentures and remaining 0–9 teeth in older men. Low grip strength may serve as an important indicator of poor oral health, especially among men in late life. Poor oral conditions in older adults are not limited to oral problems, which lead to physical problems. Prior research insufficiently has probed the relationship between oral health and physical function. This study investigated the relationship between oral health status and grip strength in older adults living in the community. This study used data from the 2014–2018 Korean National Health and Nutrition Survey. Oral health status was assessed by the type of dental prosthesis, the number of teeth. Grip strength was determined as the highest value among the three consecutive measurements of the dominant hand. The covariates included age, income, education, alcohol drinking, smoking, body mass index, sedentary time, comorbidity and number of caries teeth. Multiple logistic regression analysis was performed to examine the association between oral health status and grip strength. Data on 6,437 older adults (men 2766; women 3671) were analyzed. The mean age was 72.9 ± 0.1 years. In the crude model of logistic regression analysis, both men and women had an association between full denture use and low grip strength compared to high grip strength. After controlling for covariates, the remaining 0–9 teeth was associated with low grip strength compared to high grip strength in men [odds ratio (OR) = 1.39, 95% confidence interval (CI) = 1.03–1.88]. The use of full dentures was also associated with low grip strength compared to high grip strength in men (OR = 1.47, 95% CI = 1.09–1.98). No significant associations were found in women. Low handgrip strength was associated with using full dentures and remaining 0–9 teeth in older men. Low grip strength, one of the key characteristics of sarcopenia and frailty, may serve as an important indicator of poor oral health, especially among men in late life.

Description, Neutralization of CTRP3 opposes degenerative tendon matrix remodeling during tendinopathy., Tendinopathy, the most common disorder affecting tendons, is characterized by chronic disorganization of the tendon matrix, which leads to tendon tear and rupture. The goal was to identify a rational molecular target whose blockade can serve as a potential therapeutic intervention for tendinopathy. We identified C1q/TNF-related protein-3 (CTRP3) as a markedly up-regulated cytokine in human and rodent tendinopathy. Overexpression of CTRP3 enhanced the progression of tendinopathy by accumulating cartilaginous proteoglycans and degenerating collagenous fibers in the mouse tendon, whereas CTRP3 knockdown suppressed the tendinopathy pathogenesis. Functional blockade of CTRP3 using a neutralizing antibody ameliorated overuse-induced tendinopathy of the Achilles and rotator cuff tendons. Mechanistically, CTRP3 elicited a transcriptomic pattern that stimulates abnormal differentiation of tendon stem/progenitor cells and ectopic chondrification as an effect linked to activation of Akt signaling. Collectively, we reveal an essential role for CTRP3 in tendinopathy and propose a potential therapeutic strategy for the treatment of tendinopathy.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

Objectives This study aimed to evaluate the association between dental implants and cognitive function in community-dwelling older adults. Methods Data were collected from the baseline survey (2016-2017) of the Korean Frailty and Aging Cohort Study. The study sample comprised 1115 community-dwelling people aged 70 years to 84 years who had 0-19 natural teeth. Dental implants and natural teeth were identified by panoramic radiography, while the cognitive function was assessed by the Korean version of the Mini-Mental State Examination (MMSE-KC). The association between dental implants and cognitive function was analyzed by multiple linear regression. Sensitivity analysis was performed to test for potential bias. Results The mean number of natural teeth in the study population was 9.50 (standard deviation [SD], 6.42), and the mean MMSE-KC score was 24.93 (SD, 3.55). In the simple univariate analysis, tooth replacement, age, sex, smoking status, alcohol consumption, body mass index, osteoporosis, number of natural teeth, periodontitis, chewing discomfort, tooth-brushing frequency, education level, monthly household income, participation in economic activity, living alone, and marital status had a significant impact on the association. After adjusting for confounders, the association between dental implants and cognitive function remained significant (B, 0.85; standard error, 0.40; pl0.05). Age, body mass index, periodontitis, tooth-brushing frequency, and education level were also significantly associated with cognitive function. The results of the sensitivity analyses were consistent with those of the primary analysis. Conclusions Dental implants were associated with cognitive function in older adults living in the community. Dental implants as tooth replacements may play a role in preserving cognitive function.

Lung lobe segmentation in chest CT has been used for the analysis of lung functions and surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have incomplete and/or fake fissures. Furthermore, lung diseases such as chronic obstructive pulmonary disease (COPD) can increase the difficulty of differentiating the lobar fissures. Lobar fissures have similar intensities to those of the vessels and airway wall, which could lead to segmentation error in automated segmentation. In this study, a fully automated lung lobe segmentation method with 3D U-Net was developed and validated with internal and external datasets. The volumetric chest CT scans of 196 normal and mild-to-moderate COPD patients from three centers were obtained. Each scan was segmented using a conventional image processing method and manually corrected by an expert thoracic radiologist to create gold standards. The lobe regions in the CT images were then segmented using a 3D U-Net architecture with a deep convolutional neural network (CNN) using separate training, validation, and test datasets. In addition, 40 independent external CT images were used to evaluate the model. The segmentation results for both the conventional and deep learning methods were compared quantitatively to the gold standards using four accuracy metrics including the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), mean surface distance (MSD), and Hausdorff surface distance (HSD). In internal validation, the segmentation method achieved high accuracy for the DSC, JSC, MSD, and HSD (0.97 ± 0.02, 0.94 ± 0.03, 0.69 ± 0.36, and 17.12 ± 11.07, respectively). In external validation, high accuracy was also obtained for the DSC, JSC, MSD, and HSD (0.96 ± 0.02, 0.92 ± 0.04, 1.31 ± 0.56, and 27.89 ± 7.50, respectively). This method took 6.49 ± 1.19 s and 8.61 ± 1.08 s for lobe segmentation of the left and right lungs, respectively. Although various automatic lung lobe segmentation methods have been developed, it is difficult to develop a robust segmentation method. However, the deep learning–based 3D U-Net method showed reasonable segmentation accuracy and computational time. In addition, this method could be adapted and applied to severe lung diseases in a clinical workflow.

A sweetener's not-so-sweet effects Obesity increases an individual's risk of developing many types of cancer, including colorectal cancer. One of the factors driving the rise in obesity rates is thought to be the use of high-fructose corn syrup (HFCS) as a sweetener in soft drinks. Goncalves et al. found that ingestion of HFCS promotes the growth of intestinal cancer even in the absence of obesity in mouse tumor models. An enzyme in tumors (ketohexokinase) converts fructose to fructose-1-phosphate, which alters tumor cell metabolism and leads to enhanced cell growth. Whether a similar process occurs in humans remains to be seen. Science , this issue p. 1345