Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT.

Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD).To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns.We retrospectively extracted between 15-25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results.The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73-1.06; p = 0.187). Furthermore, the consultants' odds of correct pattern recognition was 78 % higher than the residents' odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62-5.06; p = 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ±â€Š0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ±â€Š0.17/κ = 0.38 ±â€Š0.17.There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification. · There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in interstitial lung disease.. · There are even non-significant tendencies that the use of soft kernels lead to a higher probability of correct pattern identification.. · These results challenge the current recommendations and the routinely performed separate lung kernel reconstructions for lung parenchyma analysis.. CITATION FORMAT: · Klaus JB, Christodoulidis S, Peters AA et al. Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT. Fortschr Röntgenstr 2023; 195: 47 - 54.

Avoiding the Intercostal Arteries in Percutaneous Thoracic Interventions.

The purpose of this study was to define relevant intercostal artery (ICA) anatomy potentially impacting the safety of thoracic percutaneous interventional procedures. An ICA abutting the upper rib and running in the subcostal groove was defined as the lowest risk zone for interventions requiring a supracostal needle puncture. A theoretical high-risk zone was defined by the ICA coursing in the lower half of the intercostal space (ICS), and a theoretical moderate-risk zone was defined by the ICA coursing below the subcostal groove but in the upper half of the ICS. Arterial phase computed tomography data from 250 patients were analyzed, revealing demographic variability, with high-risk zones extending more laterally with advancing age and with more cranial ribs. Overall, within the 97.5th percentile, an ICS puncture >7-cm lateral to the spinous process incurs moderate risk and >10-cm lateral incurs the lowest risk.

Knee Diameter and Cross-Sectional Area as Biomarkers for Cartilage Knee Degeneration on Magnetic Resonance Images.

Background and Objectives: Osteoarthritis (OA) of the knee is a degenerative disorder characterized by damage to the joint cartilage, pain, swelling, and walking disability. The purpose of this study was to assess whether demographic and radiologic parameters (knee diameters and knee cross-sectional area from magnetic resonance (MR) images) could be used as surrogate biomarkers for the prediction of OA. Materials and Methods: The knee diameters and cross-sectional areas of 481 patients were measured on knee MR images, and the corresponding demographic parameters were extracted from the patients' clinical records. The images were graded based on the modified Outerbridge arthroscopic classification that was used as ground truth. Receiver-operating characteristic (ROC) analysis was performed on the collected data. Results: ROC analysis established that age was the most accurate predictor of severe knee cartilage degeneration (corresponding to Outerbridge grades 3 and 4) with an area under the curve (AUC) of the specificity-sensitivity plot of 0.865 ± 0.02. An age over 41 years was associated with a sensitivity and specificity for severe degeneration of 82.8% (CI: 77.5-87.3%), and 76.4% (CI: 70.4-81.6%), respectively. The second-best degeneration predictor was the normalized knee cross-sectional area, with an AUC of 0.767 ± 0.04), followed by BMI (AUC = 0.739 ± 0.02), and normalized knee maximal diameter (AUC = 0.724 ± 0.05), meaning that knee degeneration increases with increasing knee diameter. Conclusions: Age is the best predictor of knee damage progression in OA and can be used as surrogate marker for knee degeneration. Knee diameters and cross-sectional area also correlate with the extent of cartilage lesions. Though less-accurate predictors of damage progression than age, they have predictive value and are therefore easily available surrogate markers of OA that can be used also by general practitioners and orthopedic surgeons.

Correlation of gastrointestinal perforation location and amount of free air and ascites on CT imaging.

PURPOSE: To analyze the amount of free abdominal gas and ascites on computed tomography (CT) images relative to the location of a perforation. METHODS: We retrospectively included 172 consecutive patients (93:79 = m:f) with GIT perforation, who underwent abdominal surgery (ground truth for perforation location). The volume of free air and ascites were quantified on CT images by 4 radiologists and a semiautomated software. The relation of the perforation location (upper/lower GIT) and amount of free air and ascites was analyzed by the Mann-Whitney test. Furthermore, best volume cutoff for upper and lower GIT perforation, areas under the curve (AUC), and interreader volume agreement were assessed. RESULTS: There was significantly more abdominal ascites with upper GIT perforation (333 ml, range 5 to 2000 ml) than with lower GIT perforation (100 ml, range 5 to 2000 ml, p = 0.022). The highest volume of free air was found with perforations of the stomach, descending colon and sigmoid colon. Significantly less free air was found with perforations of the small bowel and ascending colon compared to the aforementioned. An ascites volume > 333 ml was associated with an upper GIT perforation demonstrating an AUC of 0.63 ± 0.04. CONCLUSION: Using a two-step process based on the volumes of free air and free fluid can help localizing the site of perforation to the upper, middle or lower GI tract.

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.

MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system. RESULTS: The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001). CONCLUSIONS: The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.

Correlation between fat signal ratio on T1-weighted MRI in the lower vertebral bodies and age, comparing 1.5-T and 3-T scanners.

BACKGROUND: The hypothesis was that the fat-dependent T1 signal intensity in vertebral bodies increases with age due to red-yellow marrow conversion. PURPOSE: To analyze the increasing fatty conversion of red bone marrow with age. MATERIAL AND METHODS: A continuous sample of 524 patients (age range 2-96 years) with normal lumbar spine MRIs (T11-L5) was retrospectively selected in order to get a representative sample from our 1.5-T and 3-T MRI units (Siemens, Erlangen, Germany). Four radiologists read the images independently. Absolute T1 signal intensities were measured in the lower vertebral bodies and standardized by dividing their value by the signal of the subcutaneous fat on lumbar and sacral level. RESULTS: The standardized T1 signal correlated significantly with patients' age at the 1.5-T unit, with the best correlation demonstrated by thoracic vertebra T11, followed by lumbar vertebra L1, with correlation coefficients (R) of 0.64 (95% CI 0.53-0.72, P < 0.0001) and 0.49 (95% CI 0.38-0.59, P < 0.0001), respectively. For women and men, the R values were similar in thoracic vertebra T11 at 0.62 (95% CI 0.49-0.72) and 0.64 (95% CI 0.44-0.77), respectively. The vertebral signal correlated significantly better with age in the 1.5-T compared to the 3-T unit on all vertebral levels: the best R value of the 3-T unit was only 0.20 (95% CI 0.09-0.30, P < 0.0001). Our study showed an average increase of the relative T1 signal in T11 of 10% per decade. CONCLUSION: T1 fat signal ratio increases with age in the vertebral bodies, which could help estimating the age of a person. Best age correlation was found when measuring T1 signal in T11, standardized by the sacral subcutaneous fat signal and using a 1.5-T MRI.

Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images.

OBJECTIVES: The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories and to compare this with the performance of radiologists on the same task. MATERIALS AND METHODS: For the comparison, a total of 105 cases of pulmonary fibrosis were studied (54 cases of nonspecific interstitial pneumonia and 51 cases of usual interstitial pneumonia). All diagnoses were interstitial lung disease board consensus diagnoses (radiologically or histologically proven cases) and were retrospectively selected from our database. Two subspecialized chest radiologists made a consensual ground truth radiological diagnosis, according to the Fleischner Society recommendations. A comparison analysis was performed between the INTACT system and 2 other radiologists with different years of experience (readers 1 and 2). The INTACT system consists of a sequential pipeline in which first the anatomical structures of the lung are segmented, then the various types of pathological lung tissue are identified and characterized, and this information is then fed to a random forest classifier able to recommend a radiological diagnosis. RESULTS: Reader 1, reader 2, and INTACT achieved similar accuracy for classifying pulmonary fibrosis into the original 4 categories: 0.6, 0.54, and 0.56, respectively, with P > 0.45. The INTACT system achieved an F-score (harmonic mean for precision and recall) of 0.56, whereas the 2 readers, on average, achieved 0.57 (P = 0.991). For the pooled classification (2 groups, with and without the need for biopsy), reader 1, reader 2, and CAD had similar accuracies of 0.81, 0.70, and 0.81, respectively. The F-score was again similar for the CAD system and the radiologists. The CAD system and the average reader reached F-scores of 0.80 and 0.79 (P = 0.898). CONCLUSIONS: We found that a computer-aided detection algorithm based on machine learning was able to classify idiopathic pulmonary fibrosis with similar accuracy to a human reader.

Ultrasonography Provides a Diagnosis Similar to That of Nerve Conduction Studies for Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is the most common peripheral entrapment neuropathy. Its diagnosis is based on clinical symptoms and neurophysiological evaluation. Recently, ultrasonography has been introduced as a promising noninvasive diagnostic alternative. In this study, the authors compared ultrasonography with neurophysiological findings for the diagnosis of CTS in 96 patients/hands with clinical symptoms of CTS. The latency, amplitude, distance, and velocity of the median and ulnar nerves were measured. Needle electromyography was performed in the abductor pollicis brevis, in addition to muscles of the arm and forearm, to exclude proximal median nerve, brachial plexus, or radicular abnormalities. Ultrasonography was based on the morphologic/anatomic changes of the median nerve cross-sectional area in the sagittal plane of the wrist at the level of the pisiform bone, the changes of its regional echogenicity, and the identification of coexisting pathologies, such as tenosynovitis, space-occupying lesions, supplementary muscles, and vessels, that may provoke indirectly an increase of the pressure in the carpal tunnel. Eighty-seven (90%) of the 96 patients/hands with clinical symptoms of CTS showed positive findings in both ultrasonography and nerve conduction studies. Six (6%) patients showed positive findings only in nerve conduction studies, and 3 (3%) patients showed positive findings only in ultrasonography; the difference was not statistically significant. The sensitivity and the specificity of nerve conduction studies compared with ultrasonography was 97% and 89% compared with 94% and 55%, respectively. A positive correlation and proportional increase of the ultrasonography measurements compared with the increase of the nerve conduction studies severity was observed. [Orthopedics. 2019; 42(5):e460-e464.].

Assessment of 3-T MRI using susceptibility-weighted imaging to detect and evaluate intra- or periarticular blood metabolites and meniscal tears of the knee.

PURPOSE: The purpose of this study was to assess the suitability of susceptibility-weighted imaging (SWI) sequences using the 3T MRI-unit for assessment of potential intraarticular pathologies in patients with acute and chronic torsion trauma of the knee joint. MATERIAL AND METHODS: Sixty-three patients with subacute and chronic rotary knee joint trauma of either the left or right knee were studied using an Achieva MRI 3T device (Philips, Amsterdam, Netherlands). Ground truth was set by two expert radiologists with seven and 10 years of experience in musculoskeletal imaging. Readings were performed separately for meniscus and joint space including synovia, ligaments, and periarticular soft tissue. Haemorrhage was defined as any lesion that was either T1 or SWI positive, without proton density (PD)-hypointensity (calcification). A lesion was defined as any pathology/variant with any signal positivity of either T1, PD, or SWI. RESULTS: A total of 63 patients were included (F : M = 22 : 41). The median age of the cohort was 29 years (range 13 to 71 years). Thirty-nine patients showed a meniscal tear, and only three of them (7.7%) demonstrated a meniscal haemorrhage. A total of 18 patients suffered from a periarticular injury, and 16 patients (88.9%) demonstrated a concomitant periarticular haemorrhage. CONCLUSIONS: These data suggest that SWI can be used for the diagnosis of intra- or periarticular blood metabolites because their potential have an impact on mechanical conflict with the surface of the knee joints, in particular the cartilage and their effect on malacic lesions, but it performs poorly in the detection of meniscal pathologies.