Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study.

BACKGROUND: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. METHODS: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans. RESULTS: ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure. CONCLUSION: AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.

A ciliated adenocarcinoma of the lung mimicking lung abscess and pneumonia.

This case report describes a 78-year-old man initially treated for pneumonia and lung abscess who was resistant to antimicrobial treatment and was eventually diagnosed with ciliated adenocarcinoma. Ciliated adenocarcinoma, a rare non-terminal respiratory unit (TRU)-type lung adenocarcinoma, presents a unique diagnostic challenge because of its similarity to pneumonia and lung abscesses. Morphologically, the ciliated adenocarcinoma in this case appeared to be a non-TRU type adenocarcinoma, with partial mucous epithelium, no visible extracellular mucus, thyroid transcription factor (TTF)-1 negativity, and mucin (MUC) 5AC positivity on immunostaining. The patient was considered to have ciliated adenocarcinoma based on the fact that the mucous epithelium was partial and extracellular mucus was not prominent. This case emphasizes the importance of considering malignancy in patients with non-resolving pulmonary infections.

Single-centre observational study of the safety and efficacy of thoracoscopy under local anaesthesia for the management of thoracic infections.

OBJECTIVES: Thoracoscopy under local anaesthesia is widely performed to diagnose malignancies and infectious diseases. However, few reports have described the use of this procedure for diagnosing and treating intrathoracic infections. This study aimed to evaluate the safety and efficacy of thoracoscopy under local anaesthesia for the management of intrathoracic infections. RESULTS: Data from patients who underwent thoracoscopy procedures performed by chest physicians under local anaesthesia at our hospital between January 2018 and December 2023 were retrospectively reviewed. We analysed their demographic factors, reasons for the examinations, diseases targeted, examination lengths, anaesthetic methods used, diagnostic and treatment success rates, as well as any adverse events. Thirty patients were included. Of these, 12 (40%) had thoracoscopies to diagnose infections, and 18 (60%) had them to treat pyothorax. In terms of diagnosing pleurisy, the causative microorganism of origin was identified via thoracoscopy in only three of 12 (25.0%) patients. For diagnosing pyothorax, the causative microorganism was identified in 7 of 18 (38.9%) patients. Methicillin-resistant Staphylococcus aureus was the most common causative microorganism identified. The treatment success rates were very high, ranging between 94.4 and 100%, whereas the identification rate of the causative microorganisms behind infections was low, ranging between 25.0 and 38.9%. The most frequent adverse events included perioperative hypoxaemia and pain. There were two (6.7%) serious adverse events of grade ≥ 3, but none resulted in death. CONCLUSIONS: The efficacy of managing intrathoracic infections through thoracoscopy under local anaesthesia is commendable. Nonetheless, the diagnostic accuracy of the procedure, regarding the precise identification of the causative microorganisms responsible for intrathoracic infections, persists at a notably low level, presenting a substantial clinical hurdle.

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy.

BACKGROUND AND PURPOSE: Symptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP. MATERIALS AND METHODS: AIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT's quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort. RESULTS: Overall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively. CONCLUSION: AIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

Anti-aminoacyl tRNA synthetase antibodies showing the discrepancy between enzyme-linked immunosorbent assay and RNA-immunoprecipitation.

Anti-aminoacyl-tRNA synthetase (ARS) antibodies are myositis-specific antibodies associated with anti-synthetase syndrome (ASSD). Some patients are positive for anti-ARS antibodies on enzyme-linked immunosorbent assay (ELISA) but negative on RNA-immunoprecipitation (RNA-IP) (the gold standard method). Whether these patients should be considered truly positive for anti-ARS antibodies remains unclear. Therefore, we investigated the clinical characteristics of these patients and verified the authenticity of their anti-ARS positivity. Patients who were positive for anti-ARS antibodies on ELISA were divided into the non-discrepant (positive on RNA-IP, n = 52) and discrepant (negative on RNA-IP, n = 8) groups. Patient clinical characteristics were compared between the groups. For each positive individual, the authenticity of anti-ARS antibody positivity on ELISA was cross-examined using protein-IP and western blotting. All patients in the discrepant group had lung involvement, including five (63%) with interstitial lung disease. The overall survival time was significantly lower in the discrepant group than in the non-discrepant group (p < 0.05). Validation tests confirmed the presence of anti-ARS antibodies in the sera of the discrepant group but indicated different reactivity from typical anti-ARS antibodies. In conclusion, some anti-ARS antibodies are detected by ELISA but not RNA-IP. Such anti-ARS antibody discrepancies need further elucidation to attain validation of the diagnostic process in ASSD.

Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

BACKGROUND: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). METHODS: We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. RESULTS: In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. CONCLUSIONS: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.