Effect of smoking on the diagnostic results and complication rates of percutaneous transthoracic needle biopsy.

OBJECTIVE: To investigate the association of smoking with the outcomes of percutaneous transthoracic needle biopsy (PTNB). METHODS: In total, 4668 PTNBs for pulmonary lesions were retrospectively identified. The associations of smoking status (never, former, current smokers) and smoking intensity (≤ 20, 21-40, > 40 pack-years) with diagnostic results (malignancy, non-diagnostic pathologies, and false-negative results in non-diagnostic pathologies) and complications (pneumothorax and hemoptysis) were assessed using multivariable logistic regression analysis. RESULTS: Among the 4668 PTNBs (median age of the patients, 66 years [interquartile range, 58-74]; 2715 men), malignancies, non-diagnostic pathologies, and specific benign pathologies were identified in 3054 (65.4%), 1282 (27.5%), and 332 PTNBs (7.1%), respectively. False-negative results for malignancy occurred in 20.5% (236/1153) of non-diagnostic pathologies with decidable reference standards. Current smoking was associated with malignancy (adjusted odds ratio [OR], 1.31; 95% confidence interval [CI]: 1.02-1.69; p = 0.03) and false-negative results (OR, 2.64; 95% CI: 1.32-5.28; p = 0.006), while heavy smoking (> 40 pack-years) was associated with non-diagnostic pathologies (OR, 1.69; 95% CI: 1.19-2.40; p = 0.003) and false-negative results (OR, 2.12; 95% CI: 1.17-3.92; p = 0.02). Pneumothorax and hemoptysis occurred in 21.8% (1018/4668) and 10.6% (495/4668) of PTNBs, respectively. Heavy smoking was associated with pneumothorax (OR, 1.33; 95% CI: 1.01-1.74; p = 0.04), while heavy smoking (OR, 0.64; 95% CI: 0.40-0.99; p = 0.048) and current smoking (OR, 0.64; 95% CI: 0.42-0.96; p = 0.04) were inversely associated with hemoptysis. CONCLUSION: Smoking history was associated with the outcomes of PTNBs. Current and heavy smoking increased false-negative results and changed the complication rates of PTNBs. CLINICAL RELEVANCE STATEMENT: Smoking status and intensity were independently associated with the outcomes of PTNBs. Non-diagnostic pathologies should be interpreted cautiously in current or heavy smokers. A patient's smoking history should be ascertained before PTNB to predict and manage complications. KEY POINTS: • Smoking status and intensity might independently contribute to the diagnostic results and complications of PTNBs. • Current and heavy smoking (> 40 pack-years) were independently associated with the outcomes of PTNBs. • Operators need to recognize the association between smoking history and the outcomes of PTNBs.

Deep learning segmentation and registration-driven lung parenchymal volume and movement CT analysis in prone positioning.

PURPOSE: To conduct a volumetric and movement analysis of lung parenchyma in prone positioning using deep neural networks (DNNs). METHOD: We included patients with suspected interstitial lung abnormalities or disease who underwent full-inspiratory supine and prone chest CT at a single institution between June 2021 and March 2022. A thoracic radiologist visually assessed the fibrosis extent in the total lung (using units of 10%) on supine CT. After preprocessing the images into 192×192×192 resolution, a DNN automatically segmented the whole lung and pulmonary lobes in prone and supine CT images. Affine registration matched the patient's center and location, and the DNN deformably registered prone and supine CT images to calculate the x-, y-, z-axis, and 3D pixel movements. RESULTS: In total, 108 CT pairs had successful registration. Prone positioning significantly increased the left lower (90.2±69.5 mL, P = 0.000) and right lower lobar volumes (52.5±74.2 mL, P = 0.000). During deformable registration, the average maximum whole-lung pixel movements between the two positions were 1.5, 1.9, 1.6, and 2.8 cm in each axis and 3D plane. Compared to patients with <30% fibrosis, those with ≥30% fibrosis had smaller volume changes (P<0.001) and smaller pixel movements in all axes between the positions (P = 0.000-0.007). Forced vital capacity (FVC) correlated with the left lower lobar volume increase (Spearman correlation coefficient, 0.238) and the maximum whole-lung pixel movements in all axes (coefficients, 0.311 to 0.357). CONCLUSIONS: Prone positioning led to the preferential expansion of the lower lobes, correlated with FVC, and lung fibrosis limited lung expansion during prone positioning.

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.

Post-treatment Radiographic Severity and Mortality in Mycobacterium avium Complex Pulmonary Disease.

Rationale: Imaging studies are widely performed when treating Mycobacterium avium complex pulmonary disease (MAC-PD); however, the clinical significance of post-treatment radiographic change is unknown. Objectives: To determine whether a deep neural network trained with pulmonary tuberculosis could adequately score the radiographic severity of MAC-PD and then to examine relationships between post-treatment radiographic severity and its change from baseline and long-term prognosis. Methods: We retrospectively collected chest radiographs of adult patients with MAC-PD treated for ⩾6 months at baseline and at 3, 6, 9, and 12 months of treatment. We correlated the radiographic severity score generated by a deep neural network with visual and clinical severity as determined by radiologists and mycobacterial culture status, respectively. The associations between the score, improvement from baseline, and mortality were analyzed using Cox proportional hazards regression. Results: In total, 342 and 120 patients were included in the derivation and validation cohorts, respectively. The network's severity score correlated with radiologists' grading (Spearman coefficient, 0.40) and mycobacterial culture results (odds ratio, 1.02; 95% confidence interval [CI], 1.0-1.05). A significant decreasing trend in the severity score was observed over time (P < 0.001). A higher score at 12 months of treatment was independently associated with higher mortality (adjusted hazard ratio, 1.07; 95% CI, 1.03-1.10). Improvements in radiographic scores from baseline were associated with reduced mortality, regardless of culture conversion (adjusted hazard ratio, 0.42; 95% CI, 0.22-0.80). These findings were replicated in the validation cohort. Conclusions: Post-treatment radiographic severity and improvement from baseline in patients with MAC-PD were associated with long-term survival.

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.

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.

Comparison of the Efficacy of Diluted Polyethylene Glycol and Low-Density (0.1% w/v) Barium Sulfate Suspension for CT Enterography.

Purpose: To compare small bowel distension and side effects between a diluted polyethylene glycol (PEG) solution and a low-density (0.1% w/v) barium sulfate suspension (LDBSS) for CT enterography (CTE) preparation. Materials and Methods: Total 173 consecutive patients who underwent CTE were enrolled in this study. The LDBSS (1 L) was used in 50 patients, and the diluted iso-osmotic PEG solution (1 L) was used in 123 patients. Two blinded radiologists independently scored jejunal and ileal distensions on a 5-point scale. To compare side effects between the two groups, the patients reported whether they had immediate complications after the administration of the oral contrast media. Results: For ileal and jejunal distension, the diluted PEG solution showed no difference from the LDBSS for either reader (ileum: reader 1, median, 4; 4, interquartile range, 3-4; 3-4, p = 0.997; reader 2, median, 4; 4, interquartile range, 3.3-4.0; 3-4, p = 0.064; jejunum: reader 1, median, 2; 2, interquartile range, 2-3; 2-3, p = 0.560; reader 2, median, 3; 2, interquartile range, 2-3; 2-3, p = 0.192). None of the patients complained of immediate complications following administration of either of the oral contrast media. Conclusion: The diluted PEG solution showed comparable bowel distension compared to LDBSS and no immediate side effects; thus, it can be a useful alternative.

Comparison of Computed Diffusion-Weighted Imaging b2000 and Acquired Diffusion-Weighted Imaging b2000 for Detection of Prostate Cancer.

Purpose: To compare the sensitivity of tumor detection and inter-observer agreement between acquired diffusion-weighted imaging (aDWI) b2000 and computed DWI (cDWI) b2000 in patients with prostate cancer (PCa). Materials and Methods: Eighty-eight patients diagnosed with PCa by radical prostatectomy and having undergone pre-operative 3 Tesla-MRI, including DWI (b, 0, 100, 1000, 2000 s/mm2), were included in the study. cDWI b2000 was obtained from aDWI b0, b100, and b1000. Two independent reviewers performed a review of the aDWI b2000 and cDWI b2000 images in random order at 4-week intervals. A region of interest was drawn for the largest tumor on each dataset, and a Prostate Imaging-Reporting and Data System (PI-RADS) score based on PI-RADS v2.1 was recorded. Histologic topographic maps served as the reference standard. Results: The study population's Gleason scores were 6 (n = 16), 7 (n = 53), 8 (n = 9), and 9 (n = 10). According to the reviewers, the sensitivities of cDWI b2000 and aDWI b2000 showed no significant differences (for reviewer 1, both 94% [83/88]; for reviewer 2, both 90% [79/88]; p = 1.000, respectively). The kappa values of cDWI b2000 and aDWI b2000 for the PI-RADS score were 0.422 (95% confidence interval [CI], 0.240-0.603) and 0.495 (95% CI, 0.308-0.683), respectively. Conclusion: cDWI b2000 showed comparable sensitivity with aDWI b2000, in addition to sustained moderate inter-observer agreement, in the detection of PCa.

Dynamic contrast-enhanced magnetic resonance imaging for risk stratification in patients with prostate cancer.

BACKGROUND: To investigate the usefulness of perfusion parameters derived from dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of patients diagnosed as prostate cancer (PCa) in differentiating clinically significant cancer [CSC, Gleason score (GS) ≥7] from non-CSC (GS 6). METHODS: A total of 94 patients diagnosed between August 2018 and September 2020 as PCa by radical prostatectomy were included in this retrospective study (mean age: 68.7 years, range, 47-83 years). All of the patients had undergone DCE-MRI on a single 3T-MR scanner. Whole-tumor volume was measured by reviewing a pathologic topographic map as a reference standard. The quantitative DCE perfusion parameters, including volume transfer constant (Ktrans), rate constant (kep), extracellular extravascular space (EES) volume fraction (ve), plasma volume fraction (vp) and area of region of interest (ROI) were calculated under an extended Tofts model. A receiver operating characteristic (ROC) curve analysis by pair-wise comparison was performed to compare the diagnostic performances of the perfusion parameters. RESULTS: The study population comprised GS 6 (n=17), GS 7 (n=57), GS 8 (n=9) and GS 9 (n=11) cases. Among the perfusion parameters, ve differed significantly between CSC (0.238±0.095) and non-CSC (0.300±0.126) (P=0.0308). Area under the curve (AUC) was 0.643 (95% CI, 0.538-0.739), and a maximum accuracy of 64%, a sensitivity of 66%, and a specificity of 53% were estimated. Area of ROI also differed significantly between CSC (201.89±163.87 mm2) and non-CSC (84.99±85.82 mm2) (P=0.0054). AUC was 0.807 (95% CI, 0.713-0.881), and maximum accuracy, sensitivity, and specificity were 81%, 82%, and 76%, respectively. CONCLUSIONS: Size of the tumor and interstitial space volume fraction are significant parameters in differentiating aggressiveness in PCa.

Clinical Assessments and MRI Findings Suggesting Early Surgical Treatment for Patients with Medial Epicondylitis.

Purpose: To evaluate the MRI findings and clinical factors that are characteristic of patients who ultimately undergo surgery for medial epicondylitis. Materials and Methods: Fifty-two consecutive patients who were diagnosed with medial epicondylitis and underwent an elbow MRI between March 2010 and December 2018 were included in this retrospective study. The patients' demographic information, clinical data, and MRI findings were evaluated. All variables were compared between the conservative treatment and surgical treatment groups. Logistic regression analyses were conducted to identify which factors were associated with surgical treatment. Results: Common flexor tear (CFT) tear size showed a statistically significant difference in both the transverse and longitudinal planes (p < 0.001, p = 0.013). The CFT abnormality grade significantly differed in both the transverse and longitudinal planes (p = 0.022, p = 0.003). A significant difference was also found in the medial collateral ligament abnormality (p = 0.025). Logistic regression analyses showed that only the transverse diameter of the CFT tear size (odds ratio: 1.864; 95% confidence interval: 1.264-2.750) was correlated with surgical treatment. Conclusion: Of patients diagnosed with medial epicondylitis, patients with a larger transverse CFT tear size tend to undergo surgical treatment ultimately.

Comparison of diagnostic performance between diffusion kurtosis imaging parameters and mono-exponential ADC for determination of clinically significant cancer in patients with prostate cancer.

PURPOSE: To compare the diagnostic performance between diffusion kurtosis imaging (DKI) parameters and mono-exponential apparent diffusion coefficient (ADC) for determination of clinically significant cancer (CSC, Gleason score (GS) ≥ 7) in patients with histologically proven prostate cancer (PCa). METHODS: A total of 92 patients (mean age: 71.5 years, range: 47-89 years) who had been diagnosed as PCa and undergone 3 T-MRI including DWI (b values, 0, 100, 1000, 2000s/mm2) were included in this study. The DKI parameters, namely apparent diffusion for non-Gaussian distribution (Dapp) and apparent kurtosis coefficient (Kapp), were calculated by dedicated software using mono-exponential and diffusion kurtosis models for quantitation. The measurement was performed for a whole tumor after segmentation, and pathologic topographic maps or systemic biopsy results served as the reference standard for segmentation. To compare the diagnostic performance of each parameter for determination of CSC, pair-wise comparison of receiver operating characteristic (ROC) curves was performed. RESULTS: The study population consisted of GS 6 (n = 18), GS 7 (n = 31), GS 8 (n = 25), GS 9 (n = 15) and GS 10 (n = 3) patients. The area under the ROC curve of Kapp (0.707, 95% CI 0.603-0.798) for discriminating CSC from non-CSC was not significantly different from those of mono-exponential ADC (0.725, 0.622-0.813, P = 0.2175) or Dapp (0.726, 0.623-0.814, P = 0.9628). Diagnostic predictive values of Kapp were estimated to a maximum accuracy of 78%, a sensitivity of 86%, and a specificity of 47%, while those of mono-exponential ADC were 75, 81, and 53%, respectively. CONCLUSION: The DKI parameters showed a diagnostic performance comparable to mono-exponential ADC for determination of CSC in patients with PCa.