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 reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

High-resolution MR imaging with gadoxetate disodium for the comprehensive evaluation of potential living liver donors.

Several major transplantation centers have used composite multimodality evaluation for the preoperative evaluation of potential living liver donors. This approach can be time-consuming and, although rare, can cause complications. We aimed to demonstrate the clinical feasibility of our comprehensive preoperative MR protocol for the preoperative assessment of living liver donor candidates instead of composite multimodality evaluation. Thirty-five consecutive living liver donor candidates underwent multiphasic liver CT and comprehensive donor protocol MR examinations for preoperative evaluation in a single large-volume liver transplantation (LT) center. Three blinded abdominal radiologists reviewed the CT and MR images for vascular and biliary variations. The strength of agreement between CT and MR angiography was assessed using the kappa index. The detection rate of biliary anatomical variations was calculated. The sensitivity and specificity for detecting significant steatosis (>5%) were calculated. The estimated total volume and right lobe volumes measured by MR volumetry were compared with the corresponding CT volumetry measurements using the intraclass correlation coefficient (ICC). Among the 35 patients, 26 underwent LT. The measurement of agreement showed a moderate to substantial agreement between CT and MR angiography interpretations (kappa values, 0.47-0.79; p < 0.001). Combining T2-weighted and T1-weighted MR cholangiography techniques detected all biliary anatomical variations in 9 of the 26 patients. MR-proton density fat fraction showed a sensitivity of 100% (3/3) and a specificity of 91.3% (21/23) for detecting pathologically determined steatosis (>5%). MR volumetry reached an excellent agreement with CT volumetry (reviewers 1 and 2: ICC, 0.92; 95% CI, 0.84-0.96). Our one-stop comprehensive liver donor MR imaging protocol can provide complete information regarding hepatic vascular and biliary anatomies, hepatic parenchymal quality, and liver volume for living liver donor candidates and can replace composite multimodality evaluation.

Improved diagnostic performance of plain radiography for cervical ossification of the posterior longitudinal ligament using deep learning.

BACKGROUND: A high false-negative rate has been reported for the diagnosis of ossification of the posterior longitudinal ligament (OPLL) using plain radiography. We investigated whether deep learning (DL) can improve the diagnostic performance of radiologists for cervical OPLL using plain radiographs. MATERIALS AND METHODS: The training set consisted of 915 radiographs from 207 patients diagnosed with OPLL. For the test set, we used 200 lateral cervical radiographs from 100 patients with cervical OPLL and 100 patients without OPLL. An observer performance study was conducted over two reading sessions. In the first session, we compared the diagnostic performance of the DL-model and the six observers. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) at the vertebra and patient level. The sensitivity and specificity of the DL model and average observers were calculated in per-patient analysis. Subgroup analysis was performed according to the morphologic classification of OPLL. In the second session, observers evaluated the radiographs by referring to the results of the DL-model. RESULTS: In the vertebra-level analysis, the DL-model showed an AUC of 0.854, which was higher than the average AUC of observers (0.826), but the difference was not significant (p = 0.292). In the patient-level analysis, the performance of the DL-model had an AUC of 0.851, and the average AUC of observers was 0.841 (p = 0.739). The patient-level sensitivity and specificity were 91% and 69% in the DL model, and 83% and 68% for the average observers, respectively. Both the DL-model and observers showed decreases in overall performance in the segmental and circumscribed types. With knowledge of the results of the DL-model, the average AUC of observers increased to 0.893 (p = 0.001) at the vertebra level and 0.911 (p < 0.001) at the patient level. In the subgroup analysis, the improvement was largest in segmental-type (AUC difference 0.087; p = 0.002). CONCLUSIONS: The DL-based OPLL detection model can significantly improve the diagnostic performance of radiologists on cervical radiographs.

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. MATERIALS AND METHODS: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). RESULTS: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). CONCLUSION: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

Shear-wave elastography for the assessment of testicular involvement of hematologic malignancies in children and young adults: a feasibility study.

PURPOSE: This study aimed to assess the feasibility of shear-wave elastography (SWE) for assessing the testicular involvement of hematologic malignancies in children and young adults. METHODS: Eight patients (mean age, 11.0 years; range, 0.8 to 20 years) with biopsy-confirmed testicular involvement of hematologic malignancy between January 2018 and December 2020 were retrospectively evaluated. Multiparametric ultrasound examinations, including grayscale, color Doppler ultrasonography (CDUS), and SWE, were performed. Stiffness was measured in the involved testicular area and contralateral normal parenchyma. If there was bilateral testicular involvement, the stiffness of the involved area and the adjacent normal echoic parenchyma was measured on one testis. The Mann-Whitney U test was used to compare stiffness values. RESULTS: On grayscale, the testicular lesions were noted as a solitary mass in one patient, multiple lesions in four patients, and diffuse involvement in three patients. On CDUS and SWE, all patients demonstrated increased vascularity, and the stiffness of the involved area was higher than the values of normal parenchyma (the involved area vs. normal parenchyma, 11.6 kPa [3.9-20.2 kPa] vs. 2.9 kPa [1.1-3.7 kPa], P=0.003). The ratio of stiffness between the involved area and normal parenchyma was 3.4, ranging from 1.9 to 5.1. One patient showed decreased stiffness on follow-up SWE after treatment (affected testis vs. normal testis: initial, 13.8 vs. 3.2 kPa; 1 year later, 2.2 vs. 2.4 kPa). CONCLUSION: Increased testicular stiffness on SWE in children and young adults with hematologic malignancies suggests the possibility of testicular involvement.