Clinical Utility of a CT-based AI Prognostic Model for Segmentectomy in Non-Small Cell Lung Cancer.

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.

Measurement Variability of Same-Day CT Quantification of Interstitial Lung Disease: A Multicenter Prospective Study.

Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes. Deep learning-based texture analysis software was used to segment ILD features. Fibrosis extent was defined as the sum of reticular opacity and honeycombing cysts. Measurement variability between scans was assessed with Bland-Altman analyses for absolute and relative differences with 95% limits of agreement (LOA). The contribution of fibrosis extent to variability was analyzed using a multivariable linear mixed-effects model while adjusting for lung volume. Eight readers assessed ILD fibrosis stability with and without QCT information for 30 randomly selected samples. Results Sixty-five participants were enrolled in this study (mean age, 68.7 years ± 10 [SD]; 47 [72%] men, 18 [28%] women). Between two same-day CT scans, the 95% LOA for the mean absolute and relative differences of quantitative fibrosis extent were -0.9% to 1.0% and -14.8% to 16.1%, respectively. However, these variabilities increased to 95% LOA of -11.3% to 3.9% and -123.1% to 18.4% between CT scans with different reconstruction parameters. Multivariable analysis showed that absolute differences were not associated with the baseline extent of fibrosis (P = .09), but the relative differences were negatively associated (ß = -0.252, P < .001). The QCT results increased readers' specificity in interpreting ILD fibrosis stability (91.7% vs 94.6%, P = .02). Conclusion The absolute QCT measurement variability of fibrosis extent in ILD was 1% in same-day CT scans. Keywords: CT, CT-Quantitative, Thorax, Lung, Lung Diseases, Interstitial, Pulmonary Fibrosis, Diagnosis, Computer Assisted, Diagnostic Imaging Supplemental material is available for this article. © RSNA, 2024.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). METHODS: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. RESULTS: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). CONCLUSION: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Association between body fat decrease during the first year after diagnosis and the prognosis of idiopathic pulmonary fibrosis: CT-based body composition analysis.

BACKGROUND: The prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF. METHODS: This single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020. The fat area (cm2, sum of subcutaneous and visceral fat) and muscle area (cm2) at the T12-L1 level were obtained from chest CT images using a fully automatic deep learning-based software. Changes in the body composition were dichotomized using thresholds dividing the lowest quartile and others, respectively (fat area: -52.3 cm2, muscle area: -7.4 cm2). Multivariable Cox regression analyses adjusted for PFT result and IPF extent on CT images and the log-rank test were performed to assess the association between the fat area change during the first year post-diagnosis and the composite outcome of death or lung transplantation. RESULTS: In total, 307 IPF patients (69.3 ± 8.1 years; 238 men) were included. During the first year post-diagnosis, fat area, muscle area, and body mass index (BMI) changed by -15.4 cm2, -1 cm2, and - 0.4 kg/m2, respectively. During a median follow-up of 47 months, 146 patients had the composite outcome (47.6%). In Cox regression analyses, a change in the fat area < -52.3 cm2 was associated with composite outcome incidence in models adjusted with baseline clinical variables (hazard ratio [HR], 1.566, P = .022; HR, 1.503, P = .036 in a model including gender, age, and physiology [GAP] index). This prognostic value was consistent when adjusted with one-year changes in clinical variables (HR, 1.495; P = .030). However, the change in BMI during the first year was not a significant prognostic factor (P = .941). Patients with a change in fat area exceeding this threshold experienced the composite outcome more frequently than their counterparts (58.4% vs. 43.9%; P = .007). CONCLUSION: A ≥ 52.3 cm2 decrease in fat area, automatically measured using deep learning technique, at T12-L1 in one year post-diagnosis was an independent poor prognostic factor in IPF patients.

Fleischner Society: Glossary of Terms for Thoracic Imaging.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

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.

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

BACKGROUND. Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. OBJECTIVE. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. METHODS. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). The reference standard for proper ETT position was TCD between greater than 3 cm and less than 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD of 1 cm or less. ROC analysis was performed. RESULTS. AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At a threshold y-axis TCD of 2 cm or less, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). CONCLUSION. AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion as well as on chest radiographs obtained of patients in the ICU at two institutions. CLINICAL IMPACT. Automated AI identification of improper ETT position on chest radiographs may allow earlier repositioning and thereby reduce complications.

Diagnostic performance and prognostic value of CT-defined visceral pleural invasion in early-stage lung adenocarcinomas.

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.

Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality.

OBJECTIVES: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality. MATERIALS AND METHODS: This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models. RESULTS: The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status. CONCLUSIONS: The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.

Strategies to Improve Smoking Cessation for Participants in Lung Cancer Screening Program: Analysis of Factors Associated with Smoking Cessation in Korean Lung Cancer Screening Project (K-LUCAS).

PURPOSE: Smoking cessation intervention is one of the key components of successful lung cancer screening program. We investigated the effectiveness and related factors of smoking cessation services provided to the participants in a population-based lung cancer screening trial. MATERIALS AND METHODS: The Korean Lung Cancer Screening Project (K-LUCAS) is a nationwide, multi-center lung cancer screening trial that evaluates the feasibility of implementing population-based lung cancer screening. All 5,144 current smokers who participated in the K-LUCAS received a mandatory smoking cessation counseling. Changes in smoking status were followed up using a telephone survey in 6 months after lung cancer screening participation. The lung cancer screening's impact on smoking cessation is analyzed by variations in the smoking cessation interventions provided in screening units. RESULTS: Among 4,136 survey responders, participant's motivation to quit smoking increased by 9.4% on average after lung cancer screening. After 6 months from the initial screening, 24.3% of participants stopped smoking, and 10.6% of participants had not smoked continuously for at least 6 months after screening. Over 80% of quitters stated that participation in lung cancer screening motivated them to quit smoking. Low-cost public smoking cessation program combined with lung cancer screening increased the abstinence rates. The smokers were three times more likely to quit smoking when the smoking cessation counseling was provided simultaneously with low-dose computed tomography screening results than when provided separately. CONCLUSION: A mandatory smoking cessation intervention integrated with screening result counselling by a physician after participation in lung cancer screening could be effective for increasing smoking cessation attempts.

Prognostic value of CT-defined ground-glass opacity in early-stage lung adenocarcinomas: a single-center study and meta-analysis.

OBJECTIVES: The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis. METHODS: In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression. Commercial artificial intelligence software was adopted as the second reader for the presence of ground-glass opacity. The primary end points were freedom from recurrence (FFR) and lung cancer-specific survival (LCSS). In a meta-analysis, we systematically searched Embase and OVID-MEDLINE up to December 30, 2021, for the studies based on the eighth-edition staging system. The pooled hazard ratios (HRs) of solid nodules (i.e., absence of ground-glass opacity) for various end points were calculated with a multi-level random effects model. RESULTS: In a cohort of 612 patients, solid nodules were associated with worse outcomes for FFR (adjusted HR, 1.98; 95% CI: 1.17-3.51; p = 0.01) and LCSS (adjusted HR, 1.937; 95% CI: 1.002-4.065; p = 0.049). The artificial intelligence assessment and multiple sensitivity analyses revealed consistent results. The meta-analysis included 13 studies with 12,080 patients. The pooled HR of solid nodules was 2.13 (95% CI: 1.69-2.67; I2 = 30.4%) for overall survival, 2.45 (95% CI: 1.52-3.95; I2 = 0.0%) for FFR, and 2.50 (95% CI: 1.28-4.91; I2 = 30.6%) for recurrence-free survival. CONCLUSIONS: The absence of ground-glass opacity in early-stage lung adenocarcinomas is associated with worse postoperative survival. CLINICAL RELEVANCE STATEMENT: Early-stage lung adenocarcinomas manifesting as solid nodules at preoperative chest CT, which indicates the absence of ground-glass opacity, were associated with poor postoperative survival. There is room for improvement of the clinical T categorization in the next edition staging system. KEY POINTS: • In a retrospective study of 612 patients with stage I lung adenocarcinoma, solid nodules were associated with shorter freedom from recurrence (adjusted hazard ratio [HR], 1.98; p = 0.01) and lung cancer-specific survival (adjusted HR, 1.937; p = 0.049). • Artificial intelligence-assessed solid nodules also showed worse prognosis (adjusted HR for freedom from recurrence, 1.94 [p = 0.01]; adjusted HR for lung cancer-specific survival, 1.93 [p = 0.04]). • In meta-analyses, the solid nodules were associated with shorter freedom from recurrence (HR, 2.45) and shorter overall survival (HR, 2.13).

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

OBJECTIVES: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications. METHODS: This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning-based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis). Patients were grouped according to the absolute decline of predicted FVC (< 5%, 5-10%, and ≥ 10%) and ILD progression assessed by thoracic radiologists, and their quantification results were compared among these groups. The associations between quantification results and survival were evaluated using multivariable Cox regression analysis. RESULTS: In total, 468 patients (239 men; 64 ± 9.5 years) were included. Fibrosis and total ILD extents more increased in patients with larger FVC decline (p < .001 in both). Patients with ILD progression had higher fibrosis and total ILD extent increases than those without ILD progression (p < .001 in both). Increases in fibrosis and total ILD extent were significant prognostic factors when adjusted for absolute FVC declines of ≥ 5% (hazard ratio [HR] 1.844, p = .01 for fibrosis; HR 2.484, p < .001 for total ILD extent) and ≥ 10% (HR 2.918, p < .001 for fibrosis; HR 3.125, p < .001 for total ILD extent). CONCLUSION: Changes in ILD CT quantification correlated with changes in FVC and visual assessment of ILD progression, and they were independent prognostic factors in ILD patients. CLINICAL RELEVANCE STATEMENT: Quantifying the CT features of interstitial lung disease using deep learning techniques could play a key role in defining and predicting the prognosis of progressive fibrosing interstitial lung disease. KEY POINTS: • Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease. • Deep learning-based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors. • Deep learning-based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

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.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

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.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.

OBJECTIVE: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT). MATERIALS AND METHODS: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit. RESULTS: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; P = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; P = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; P = 0.647). CONCLUSION: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.