Correction to: Reconstruction of AAOS type III and IV acetabular defects with the Ganz reinforcement ring: high failure in pelvic discontinuity.

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Learning from the living to diagnose the dead - parallels between CT findings after survived drowning and fatal drowning.

A case of survived drowning is presented where CT findings of the lungs were in keeping with several findings seen on post-mortem CT (PMCT) examination after fatal drowning. These findings include interlobular septal thickening, peribronichal cuffing, mosaic pattern ground glass opacities, and perivascular nodular ground glass opacities. The absence of confounding normal post-mortem changes allows for a discussion on subtle differences between findings related to aspiration and pulmonary edema after drowning. This case represents a learning opportunity for radiologists and pathologists challenged by the complex lung findings after drowning on PMCT.

Reconstruction of AAOS type III and IV acetabular defects with the Ganz reinforcement ring: high failure in pelvic discontinuity.

BACKGROUND: Large acetabular defects and pelvic discontinuity represent complex problems in revision total hip arthroplasty. This study aimed to investigate whether reconstruction with the Ganz reinforcement ring would provide durable function in large acetabular defects. PATIENTS AND METHODS: 46 hips (45 patients, 19 male, 26 female, mean age 68 years) with AAOS type III and IV defects undergoing acetabular revision with the Ganz reinforcement ring were evaluated at a mean follow-up of 74 months (24-161 months). Fourteen patients died during follow-up. All surviving patients were available for clinical assessment and radiographic studies. Radiographs were evaluated for bone healing and component loosening. A Cox-regression model was performed to identify factors influencing survival of the Ganz-ring. RESULTS: In the group of AAOS III defects, 3 of 26 acetabular reconstructions failed, all due to aseptic loosening. In pelvic discontinuity (AAOS IV), 9 of 20 hips failed due to aseptic loosening (n = 4), deep infection (n = 3), and non-union of the pelvic ring (n = 2). With acetabular revision for any reason as an endpoint, the estimated Kaplan-Meier 5-year survival was 86% in type III defects and 57% in type IV defects, respectively. The presence of pelvic discontinuity was identified as the only independent predictive factor for failure of the Ganz ring acetabular reconstruction (AAOS III vs. IV, Hazard ratio: 0.217, 95%, Confidence interval: 0.054-0.880, p = 0.032). CONCLUSION: The Ganz reinforcement ring remains a favorable implant for combined segmental and cavitary defects. However, defects with pelvic discontinuity demonstrate high failure rates. The indications should therefore be narrowed to acetabular defects not associated with pelvic discontinuity.

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.

Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists.

BACKGROUND: Despite the decreasing relevance of chest radiography in lung cancer screening, chest radiography is still frequently applied to assess for lung nodules. The aim of the current study was to determine the accuracy of a commercial AI based CAD system for the detection of artificial lung nodules on chest radiograph phantoms and compare the performance to radiologists in training. METHODS: Sixty-one anthropomorphic lung phantoms were equipped with 140 randomly deployed artificial lung nodules (5, 8, 10, 12 mm). A random generator chose nodule size and distribution before a two-plane chest X-ray (CXR) of each phantom was performed. Seven blinded radiologists in training (2 fellows, 5 residents) with 2 to 5 years of experience in chest imaging read the CXRs on a PACS-workstation independently. Results of the software were recorded separately. McNemar test was used to compare each radiologist's results to the AI-computer-aided-diagnostic (CAD) software in a per-nodule and a per-phantom approach and Fleiss-Kappa was applied for inter-rater and intra-observer agreements. RESULTS: Five out of seven readers showed a significantly higher accuracy than the AI algorithm. The pooled accuracies of the radiologists in a nodule-based and a phantom-based approach were 0.59 and 0.82 respectively, whereas the AI-CAD showed accuracies of 0.47 and 0.67, respectively. Radiologists' average sensitivity for 10 and 12 mm nodules was 0.80 and dropped to 0.66 for 8 mm (P=0.04) and 0.14 for 5 mm nodules (P<0.001). The radiologists and the algorithm both demonstrated a significant higher sensitivity for peripheral compared to central nodules (0.66 vs. 0.48; P=0.004 and 0.64 vs. 0.094; P=0.025, respectively). Inter-rater agreements were moderate among the radiologists and between radiologists and AI-CAD software (K'=0.58±0.13 and 0.51±0.1). Intra-observer agreement was calculated for two readers and was almost perfect for the phantom-based (K'=0.85±0.05; K'=0.80±0.02); and substantial to almost perfect for the nodule-based approach (K'=0.83±0.02; K'=0.78±0.02). CONCLUSIONS: The AI based CAD system as a primary reader acts inferior to radiologists regarding lung nodule detection in chest phantoms. Chest radiography has reasonable accuracy in lung nodule detection if read by a radiologist alone and may be further optimized by an AI based CAD system as a second reader.

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.

LARS® band and tube for extensor mechanism reconstructions in proximal tibial modular endoprostheses after bone tumors.

UNLABELLED: Wide tumor resections around the proximal tibia (pT) are related to compromised function and high complication rates. This retrospective study aims to present the technique employed as well as functional and surgical outcomes of patients undergoing a Ligament Advanced Reinforcement System (LARS®) reconstruction of the knee extensor apparatus after tumor resection and modular endoprosthetic reconstruction of the proximal tibia. Twenty-five patients who received an artificial ligament after pT resection (11 men and 14 women; mean age, 29years; range 11 to 75years, with a minimum follow-up of 24months) were analyzed regarding the ISOLS failure mode classification. Twenty patients received LARS® during primary surgery, five patients during a revision of a pT modular endoprosthesis. LARS® was available as a band or a tube. The mean extension lag was nine degrees (range, 0 to 30°), the mean flexion was 103° (range, 60 to 130°). The mean extension lag and active flexion in primary implanted LARS were 7.8° and 101° versus secondarily implanted 45° and 115° (p<0.0001; p=0.15). Eleven out of 14 primary implanted LARS® band/tubes (71%) did well with extension lag (0 to 10°). LARS® usage as a band or as a tube showed similar results. The estimated five-year survival of LARS® was 92%. The median survival of LARS® implanted primarily was better than in the case of secondary implantation (p=0.006). Extensor mechanism reconstruction by LARS® band or tube shows excellent function and satisfactory implant survival after primary reconstruction of the extensor mechanism after proximal tibia resection. We experienced no LARS® rupture for only mechanical reasons. LEVEL OF EVIDENCE: Level IV retrospective study.