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Introduction: Mortality differences in chronic obstructive pulmonary disease (COPD) between nonsmokers and smokers remain unclear. We compared the risk of death associated with smoking and COPD on mortality. Methods: The study included participants aged ≥40 years who visited pulmonary clinics and were categorised into COPD or non-COPD and smoker or nonsmoker on the basis of spirometry results and cigarette consumption. Mortality rates were compared between groups using statistical analysis for all-cause mortality, respiratory disease-related mortality, and cardiocerebrovascular disease-related mortality. Results: Among 5811 participants, smokers with COPD had a higher risk of all-cause (adjusted hazard ratio (aHR), 1.69; 95% confidence interval (CI), 1.23-2.33) and respiratory disease-related mortality (aHR, 2.14; 95% CI, 1.20-3.79) than nonsmokers with COPD. Non-smokers with and without COPD had comparable risks of all-cause mortality (aHR, 1.39; 95% CI, 0.98-1.97) and respiratory disease-related mortality (aHR, 1.77; 95% CI, 0.85-3.68). However, nonsmokers with COPD had a higher risk of cardiocerebrovascular disease-related mortality than nonsmokers without COPD (aHR, 2.25; 95% CI, 1.15-4.40). Conclusion: The study found that smokers with COPD had higher risks of all-cause mortality and respiratory disease-related mortality compared to nonsmokers with and without COPD. Meanwhile, nonsmokers with COPD showed comparable risks of all-cause and respiratory mortality but had a higher risk of cardiocerebrovascular disease-related mortality compared to nonsmokers without COPD.
Subject(s)
Cause of Death , Pulmonary Disease, Chronic Obstructive , Smoking , Humans , Pulmonary Disease, Chronic Obstructive/mortality , Pulmonary Disease, Chronic Obstructive/physiopathology , Pulmonary Disease, Chronic Obstructive/diagnosis , Male , Female , Middle Aged , Aged , Risk Factors , Smoking/adverse effects , Smoking/mortality , Smoking/epidemiology , Risk Assessment , Non-Smokers/statistics & numerical data , Cerebrovascular Disorders/mortality , Cerebrovascular Disorders/etiology , Adult , Smokers/statistics & numerical data , Time Factors , Prognosis , Cardiovascular Diseases/mortality , Lung/physiopathologyABSTRACT
OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.
Subject(s)
Lung Diseases , Pneumothorax , Humans , Pneumothorax/diagnostic imaging , Neural Networks, Computer , Radiography , AlgorithmsABSTRACT
BACKGROUND: To identify some clinical and laboratory independent risk factors for postoperative recompression among elderly osteoporotic vertebral compression fractures (OVCF) patients. METHODS: A retrospective analysis was conducted on 287 elderly OVCF patients after percutaneous vertebroplasty (PVP). Relevant risk factors for recompression were screened and further analyzed through multivariate logistic regression. RESULTS: Within postoperative 1 year, recompression had occurred in 72 patients, with an incidence of 25.1% (72/287). Multivariate logistic analysis indicated that mean spinal BMD < - 2.85 (OR: 4.55, 95%CI 2.22-9.31, P < 0.001), ODI ≥ 68.05% (OR: 6.78, 95%CI 3.16-14.55, P < 0.001), PNI score < 43.1 (OR: 2.81, 95%CI 1.34-5.82, P = 0.005), and mFI score ≥ 0.225 (OR: 8.30, 95%CI 3.14-21.95, P < 0.001) were four distinct risk factors that independently contributed to postoperative recompression. CONCLUSIONS: Spinal BMD, ODI, PNI and mFI independently predict recompression in OVCF patients after PVP treatment.
Subject(s)
Fractures, Compression , Frailty , Kyphoplasty , Osteoporotic Fractures , Spinal Fractures , Vertebroplasty , Humans , Aged , Fractures, Compression/surgery , Vertebroplasty/adverse effects , Spinal Fractures/epidemiology , Spinal Fractures/surgery , Spinal Fractures/etiology , Bone Cements/therapeutic use , Retrospective Studies , Nutrition Assessment , Frailty/complications , Frailty/epidemiology , Prognosis , Osteoporotic Fractures/epidemiology , Osteoporotic Fractures/surgery , Risk Factors , Treatment Outcome , Kyphoplasty/adverse effectsABSTRACT
OBJECTIVES: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. METHODS: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. RESULTS: In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03). CONCLUSION: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. CLINICAL RELEVANCE STATEMENT: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. KEY POINTS: ⢠Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. ⢠Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. ⢠Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors.
Subject(s)
Adenocarcinoma of Lung , Lung Neoplasms , Aged , Female , Humans , Adenocarcinoma of Lung/diagnostic imaging , Adenocarcinoma of Lung/pathology , Lung Neoplasms/pathology , Neoplasm Staging , Pleura/diagnostic imaging , Pleura/pathology , Prognosis , Tomography, X-Ray Computed , Male , Middle AgedABSTRACT
OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: ⢠A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. ⢠The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. ⢠A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.
Subject(s)
Deep Learning , Idiopathic Pulmonary Fibrosis , Radiography, Thoracic , Humans , Idiopathic Pulmonary Fibrosis/diagnostic imaging , Idiopathic Pulmonary Fibrosis/mortality , Male , Female , Prognosis , Retrospective Studies , Aged , Radiography, Thoracic/methods , Middle Aged , Vital CapacityABSTRACT
OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS: We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS: The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.
Subject(s)
Deep Learning , Lung Neoplasms , Humans , Middle Aged , Drug Tapering , Lung Neoplasms/diagnostic imaging , Retrospective Studies , Tomography, X-Ray ComputedABSTRACT
BACKGROUND: This study aims to evaluate the risk factors of refracture in elderly patients with osteoporotic vertebral compression fracture (OVCF) patients after percutaneous vertebroplasty (PVP) and construct a predictive nomogram model. METHODS: Elderly symptomatic OVCF patients undergoing PVP were enrolled and grouped based on the development of refracture within 1 year postoperatively. Univariate and multivariate logistic regression analyses were performed to identify risk factors. Subsequently, a nomogram prediction model was constructed and evaluated based on these risk factors. RESULTS: A total of 264 elderly OVCF patients were enrolled in the final cohort. Among these, 48 (18.2%) patients had suffered refracture within 1 year after surgery. Older age, lower mean spinal BMD, multiple vertebral fracture, lower albumin/fibrinogen ratio (AFR), no postoperative regular anti-osteoporosis, and exercise were six independent risk factors identified for postoperative refracture. The AUC of the constructed nomogram model based on these six factors was 0.812 with a specificity and sensitivity of 0.787 and 0.750, respectively. CONCLUSIONS: In summary, the nomogram model based on the six risk factors had clinical efficacy for refracture prediction.
Subject(s)
Fractures, Compression , Osteoporotic Fractures , Spinal Fractures , Vertebroplasty , Humans , Aged , Fractures, Compression/surgery , Spinal Fractures/surgery , Spinal Fractures/etiology , Vertebroplasty/adverse effects , Nomograms , Osteoporotic Fractures/surgery , Treatment Outcome , Risk Factors , Bone Cements , Retrospective StudiesABSTRACT
Introduction: Pedicle screw fixation (PSF) has been the standard therapy for the treatment of various spinal diseases. Although complications are identified regularly, iatrogenic vascular injury is one of the rare but life-threatening complications. In this literature, we describe the first case of inferior vena cava (IVC) injury during pedicle screw removal. Case description: A 31-year-old man was treated by percutaneous pedicle screw fixation for an L1 compression fracture. After a year, the fracture healed well and hardware removal surgery was performed. During the procedure, the hardware on the right was removed unremarkably except for the L2 pedicle screw which slipped into the retroperitoneum because of the improper technique. The CT angiogram revealed the screw had breached the anterior cortex of the L2 vertebral body and penetrated the IVC. After multidisciplinary cooperation, the defect of IVC was reconstructed and the L2 screw was removed from the posterior approach in the end. Result: The patient recovered well and was discharged after 3 weeks without further events. The removal of the contralateral implants was unremarkable at 7 months postoperatively. At the 3-year follow-up, the patient returned to his normal daily activity without any complaints. Conclusion: Although pedicle screw removal is a rather simple procedure, severe complications may have occurred from this procedure. Surgeons should keep vigilant to avoid the complication noted in this case.
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OBJECTIVE: To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF). METHODS: Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (DLCO) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for DLCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years. RESULTS: In total, 161 patients (median age [IQR], 68 [62-73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson's r, 0.40 for CT-Norm% and - 0.37 for CT-Fib%; both p < 0.001) and DLCO (0.52 for CT-Norm% and - 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and DLCO (HRs, 0.98 [95% CI 0.97-0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01-1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for DLCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78-0.85] vs. 0.82 [0.79-0.86]; p = 0.75). CONCLUSION: CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software were independent prognostic factors for overall survival in IPF. KEY POINTS: ⢠Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis. ⢠CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson's r, 0.40 for normal and - 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson's r, 0.52 and - 0.46, respectively). ⢠Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables.
Subject(s)
Deep Learning , Idiopathic Pulmonary Fibrosis , Male , Humans , Aged , Prognosis , Carbon Monoxide , Retrospective Studies , Idiopathic Pulmonary Fibrosis/pathology , Tomography, X-Ray Computed , Vital Capacity , Fibrosis , Lung/pathologyABSTRACT
PURPOSE: To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest radiographs (CRs) of patients with positive interferon-gamma release assay (IGRA) results in different scenarios of clinical implementation. MATERIALS AND METHODS: We collected the CRs of consecutive patients with positive IGRA results. Findings of active pulmonary tuberculosis on CRs were independently evaluated by the CAD and a thoracic radiologist, followed by interpretation using the CAD. Sensitivity and specificity were evaluated in different scenarios: (a) radiologists' interpretation, (b) radiologists' CAD-assisted interpretation, and (c) CAD-based prescreening (radiologists' interpretation for positive CAD results only). We conducted a reader test to compare the accuracy of the CAD with those of 5 radiologists. RESULTS: Among 1780 patients (men, 53.8%; median age, 56 y), 44 (2.5%) were diagnosed with active pulmonary tuberculosis. The CAD-assisted interpretation exhibited a higher sensitivity (81.8% vs. 72.7%; P =0.046) but lower specificity than the radiologists' interpretation (84.1% vs. 85.7%; P <0.001). The CAD-based prescreening exhibited a higher specificity than the radiologists' interpretation (88.8% vs. 85.7%; P <0.001) at the same sensitivity, with a workload reduction of 85.2% (1780 to 263). In the reader test, the CAD exhibited a higher sensitivity than radiologists (72.7% vs. 59.5%; P =0.005) at the same specificity (88.0%), and CAD-assisted interpretation significantly improved the sensitivity of radiologists' interpretation (72.3%; P <0.001). CONCLUSIONS: For identifying active pulmonary tuberculosis among patients with positive IGRA results, deep learning-based CAD can enhance the sensitivity of interpretation. CAD-based prescreening may reduce the radiologists' workload at an improved specificity.
Subject(s)
Deep Learning , Tuberculosis, Pulmonary , Tuberculosis , Male , Humans , Middle Aged , Interferon-gamma Release Tests , Radiographic Image Interpretation, Computer-Assisted/methods , Sensitivity and Specificity , Tuberculosis, Pulmonary/diagnostic imaging , Computers , Retrospective StudiesABSTRACT
OBJECTIVE: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. MATERIALS AND METHODS: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. RESULTS: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. CONCLUSION: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.
Subject(s)
Artificial Intelligence , Image Interpretation, Computer-Assisted , Radiography, Thoracic , Humans , Male , Radiography , Radiography, Thoracic/methods , Retrospective Studies , Female , Middle Aged , Aged , Aged, 80 and overABSTRACT
Background The impact of artificial intelligence (AI)-based computer-aided detection (CAD) software has not been prospectively explored in real-world populations. Purpose To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups. Materials and Methods In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between July 2020 and December 2021 in a health screening center were enrolled and randomized into intervention (AI group) and control (non-AI group) arms. One of three designated radiologists with 13-36 years of experience interpreted each radiograph, referring to the AI-based CAD results for the AI group. The primary outcome was the detection rate, that is, the number of true-positive radiographs divided by the total number of radiographs, of actionable lung nodules confirmed on CT scans obtained within 3 months. Actionable nodules were defined as solid nodules larger than 8 mm or subsolid nodules with a solid portion larger than 6 mm (Lung Imaging Reporting and Data System, or Lung-RADS, category 4). Secondary outcomes included the positive-report rate, sensitivity, false-referral rate, and malignant lung nodule detection rate. Clinical outcomes were compared between the two groups using univariable logistic regression analyses. Results A total of 10 476 participants (median age, 59 years [IQR, 50-66 years]; 5121 men) were randomized to an AI group (n = 5238) or non-AI group (n = 5238). The trial met the predefined primary outcome, demonstrating an improved detection rate of actionable nodules in the AI group compared with the non-AI group (0.59% [31 of 5238 participants] vs 0.25% [13 of 5238 participants], respectively; odds ratio, 2.4; 95% CI: 1.3, 4.7; P = .008). The detection rate for malignant lung nodules was higher in the AI group compared with the non-AI group (0.15% [eight of 5238 participants] vs 0.0% [0 of 5238 participants], respectively; P = .008). The AI and non-AI groups showed similar false-referral rates (45.9% [56 of 122 participants] vs 56.0% [56 of 100 participants], respectively; P = .14) and positive-report rates (2.3% [122 of 5238 participants] vs 1.9% [100 of 5238 participants]; P = .14). Conclusion In health checkup participants, artificial intelligence-based software improved the detection of actionable lung nodules on chest radiographs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Auffermann in this isssue.
Subject(s)
Lung Neoplasms , Precancerous Conditions , Male , Humans , Middle Aged , Artificial Intelligence , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/pathology , Tomography, X-Ray Computed , Radiography , Lung/pathology , Sensitivity and Specificity , Radiography, Thoracic/methods , Radiographic Image Interpretation, Computer-Assisted/methodsABSTRACT
Objective@#It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. @*Materials and Methods@#Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient’s medical record at least 30 days after the ED visit. @*Results@#We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70–1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79–1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. @*Conclusion@#AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.
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O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.
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Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.
Subject(s)
Adenocarcinoma of Lung , Adenocarcinoma , Deep Learning , Lung Neoplasms , Female , Humans , Middle Aged , Adenocarcinoma/pathology , Adenocarcinoma of Lung/diagnostic imaging , Adenocarcinoma of Lung/pathology , ErbB Receptors/genetics , Retrospective Studies , Tomography, X-Ray Computed/methods , Male , AgedABSTRACT
Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop and to validate a deep learning-based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods In this retrospective study, data from patients with COPD who underwent postbronchodilator spirometry and chest radiography from 2011-2015 were collected and split into training (n = 3475), validation (n = 435), and internal test (n = 315) data sets. The algorithm for predicting survival from chest radiographs was trained (hereafter, DLSPCXR), and then age, body mass index, and forced expiratory volume in 1 second (FEV1) were integrated within the model (hereafter, DLSPinteg). For external test, three independent cohorts were collected (n = 394, 416, and 337). The discrimination performance of DLSPCXR was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) at 5-year survival. Goodness of fit was assessed by using the Hosmer-Lemeshow test. Using one external test data set, DLSPinteg was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George's Respiratory Questionnaire (SGRQ). Results DLSPCXR had a higher performance at predicting 5-year survival than FEV1 in two of the three external test cohorts (TD AUC: 0.73 vs 0.63 [P = .004]; 0.67 vs 0.60 [P = .01]; 0.76 vs 0.77 [P = .91]). DLSPCXR demonstrated good calibration in all cohorts. The DLSPinteg model showed no differences in TD AUC compared with BODE (0.87 vs 0.80; P = .34), ADO (0.86 vs 0.89; P = .51), and SGRQ (0.86 vs 0.70; P = .09), and showed higher TD AUC than CAT (0.93 vs 0.55; P < .001). Conclusion A deep learning model using chest radiographs was capable of predicting survival in patients with chronic obstructive pulmonary disease. © RSNA, 2022 Online supplemental material is available for this article.
Subject(s)
Deep Learning , Pulmonary Disease, Chronic Obstructive , Forced Expiratory Volume , Humans , Pulmonary Disease, Chronic Obstructive/diagnostic imaging , Radiography , Respiratory Function Tests , Retrospective StudiesABSTRACT
Background Although various guidelines discourage performing brain MRI for staging purposes in asymptomatic patients with clinical stage IA non-small cell lung cancer (NSCLC), evidence regarding their postoperative survival is lacking. Purpose To investigate the survival benefit of performing brain MRI in asymptomatic patients with early-stage NSCLC. Materials and Methods Patients who underwent curative resection between February 2009 and March 2016 for clinical TNM stage T1N0M0 NSCLC were retrospectively included. Patient survival and development of brain metastasis during postoperative surveillance were documented. The cumulative survival rate and incidence of brain metastasis were compared between patients who underwent surgery with or without staging brain MRI by using Cox regression and a Fine-Gray subdistribution hazard model, respectively, for multivariable adjustment. Propensity score matching and inverse probability of treatment weighting were applied for confounder adjustment. Results A total of 628 patients (mean age, 64 years ± 10 [SD]; 319 men) were included, of whom 53% (331 of 628) underwent staging brain MRI. In the multivariable analyses, brain MRI did not show prognostic benefits for brain metastasis-free survival (hazard ratio [HR], 1.06; 95% CI: 0.69, 1.63; P = .79), time to brain metastasis (HR, 1.60; 95% CI: 0.70, 3.94; P = .29), and overall survival (HR, 0.86; 95% CI, 0.54, 1.37; P = .54). Consistent results were obtained after propensity score matching (brain metastasis-free survival [HR, 0.97; 95% CI: 0.60, 1.57; P = .91], time to brain metastasis [HR, 1.29; 95% CI: 0.50, 3.33; P = .60], and overall survival [HR, 0.89; 95% CI: 0.53, 1.51; P = .67]) and inverse probability of treatment weighting. Conclusion No difference was observed between asymptomatic patients with clinical stage IA non-small cell lung cancer who underwent staging brain MRI and those who did not in terms of brain metastasis-free survival, time to brain metastasis, and overall survival. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Bizzi and Pascuzzo in this issue.
Subject(s)
Brain Neoplasms , Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Brain/pathology , Brain Neoplasms/pathology , Carcinoma, Non-Small-Cell Lung/diagnostic imaging , Carcinoma, Non-Small-Cell Lung/surgery , Humans , Lung Neoplasms/pathology , Magnetic Resonance Imaging , Male , Middle Aged , Neoplasm Staging , Prognosis , Retrospective StudiesABSTRACT
OBJECTIVES: To investigate the efficacy of an artificial intelligence (AI) system for the identification of false negatives in chest radiographs that were interpreted as normal by radiologists. METHODS: We consecutively collected chest radiographs that were read as normal during 1 month (March 2020) in a single institution. A commercialized AI system was retrospectively applied to these radiographs. Radiographs with abnormal AI results were then re-interpreted by the radiologist who initially read the radiograph ("AI as the advisor" scenario). The reference standards for the true presence of relevant abnormalities in radiographs were defined by majority voting of three thoracic radiologists. The efficacy of the AI system was evaluated by detection yield (proportion of true-positive identification among the entire examination) and false-referral rate (FRR, proportion of false-positive identification among all examinations). Decision curve analyses were performed to evaluate the net benefits of applying the AI system. RESULTS: A total of 4208 radiographs from 3778 patients (M:F = 1542:2236; median age, 56 years) were included. The AI system identified initially overlooked relevant abnormalities with a detection yield and an FRR of 2.4% and 14.0%, respectively. In the "AI as the advisor" scenario, radiologists detected initially overlooked relevant abnormalities with a detection yield and FRR of 1.2% and 0.97%, respectively. In a decision curve analysis, AI as an advisor scenario exhibited a positive net benefit when the cost-to-benefit ratio was below 1:0.8. CONCLUSION: An AI system could identify relevant abnormalities overlooked by radiologists and could enable radiologists to correct their false-negative interpretations by providing feedback to radiologists. KEY POINTS: ⢠In consecutive chest radiographs with normal interpretations, an artificial intelligence system could identify relevant abnormalities that were initially overlooked by radiologists. ⢠The artificial intelligence system could enable radiologists to correct their initial false-negative interpretations by providing feedback to radiologists when overlooked abnormalities were present.
Subject(s)
Artificial Intelligence , Radiologists , Humans , Middle Aged , Radiography , Radiography, Thoracic/methods , Retrospective StudiesABSTRACT
BACKGROUND: Osteoarthritis, one of the most common joint diseases, is characterized by the loss of joint function due to articular cartilage destruction. Herein, we review current and previous research involving the clinical applications of arthritis therapy and suggest potential therapeutic options for osteoarthritis in the future. PAST, PRESENT, AND FUTURE TREATMENT: The arthroscopic cartilage regeneration procedure or realignment osteotomy has been performed as a joint-conserving procedure in cases where conservative treatment for damaged articular cartilage and early osteoarthritis failed. If cartilage regeneration is ineffective or if the joint damage progresses, arthroplasty is the main treatment option. The need for biological arthritis treatment has expanded as the healthy lifespan of the global population has increased. Accordingly, minimally invasive surgical treatment has been developed for the treatment of damaged cartilage and early osteoarthritis. However, patients generally prefer to avoid all types of surgery, including minimally invasive surgery. Therefore, in the future, the treatment of osteoarthritis will likely involve injection or medication. CONCLUSION: Currently, arthritis management primarily involves the surgical application of therapeutic agents to the joints. However, nonsurgical or prophylactic methods are expected to become mainstream arthritis therapies in the future.
ABSTRACT
Nanoscale periodic moiré patterns, for example those formed at the interface of a twisted bilayer of two-dimensional materials, provide opportunities for engineering the electronic properties of van der Waals heterostructures1-11. In this work, we synthesized the epitaxial heterostructure of 1T-TiTe2/1T-TiSe2 with various twist angles using molecular beam epitaxy and investigated the moiré pattern induced/enhanced charge density wave (CDW) states with scanning tunnelling microscopy. When the twist angle is near zero degrees, 2 × 2 CDW domains are formed in 1T-TiTe2, separated by 1 × 1 normal state domains, and trapped in the moiré pattern. The formation of the moiré-trapped CDW state is ascribed to the local strain variation due to atomic reconstruction. Furthermore, this CDW state persists at room temperature, suggesting its potential for future CDW-based applications. Such moiré-trapped CDW patterns were not observed at larger twist angles. Our study paves the way for constructing metallic twist van der Waals bilayers and tuning many-body effects via moiré engineering.