Purpose: Autologous chondrocyte implantation is an established method for the treatment of joint cartilage damage. However, to date it has not been established that autologous chondrocyte implantation is an appropriate procedure for cartilage defects therapy in athletic persons. The aim of this study is to analyze if third‐generation autologous chondrocyte implantation is an appropriate treatment for athletic persons with full cartilage defect of the knee joints.Methods: A total of 84 patients were treated with third‐generation autologous chondrocyte implantation (NOVOCART^® 3D). The mean follow‐up time was 8 years (5–14). Sports activity was measured via UCLA Activity Score and Tegner Activity Scale before the onset of knee pain and postoperatively in an annual clinical evaluation. 41 athletic persons and 43 non‐athletic persons (UCLA‐Cut‐off: 7; Tegner Activity Scale‐Cut‐off: 4) were analyzed. Patient reported outcomes were captured using IKDC subjective, KOOS, Lysholm score and VAS score on movement.Results: Patient reported outcomes (IKDC, VAS at rest, VAS on movement) showed significant improvement (p < 0.001) postoperatively. Athletic persons demonstrated significantly better results than non‐athletic persons in the analyzed outcome scores (IKDC: p < 0.01, KOOS: p < 0.01, Lysholm score: p < 0.01). 96.4% of the patients were able to return to sport and over 50% returned or surpassed their preinjury sports level. The remaining patients were downgraded by a median of two points on the UCLA‐ and 2.5 on the Tegner Activity Scale. A shift from high‐impact sports to active events and moderate or mild activities was found. Furthermore, it was shown that preoperative UCLA score and Tegner Activity Scale correlated significantly with the patient reported outcome postoperatively.Conclusion: Autologous chondrocyte implantation is a suitable treatment option for athletic persons with full‐thickness cartilage defects in the knee. The return to sports activity is possible, but includes a shift from high‐impact sports to less strenuous activities.

Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times. Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting? A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays. Suspicion of four suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, and nodules) was reported on a five-point confidence scale. Confidence scores of the board-certified radiologists were converted into four binary reference standards of different sensitivities. Performance by radiology residents and NRRs without AI support/with AI support were statistically compared by using receiver-operating characteristics (ROCs), Youden statistics, and operating point metrics derived from fitted ROC curves. NRRs could significantly improve performance, sensitivity, and accuracy with AI support in all four pathologies tested. In the most sensitive reference standard (reference standard IV), NRR consensus improved the area under the ROC curve (mean, 95% CI) in the detection of the time-critical pathology pneumothorax from 0.846 (0.785-0.907) without AI support to 0.974 (0.947-1.000) with AI support (P < .001), which represented a gain of 30% in sensitivity and 2% in accuracy (while maintaining an optimized specificity). The most pronounced effect was observed in nodule detection, with NRR with AI support improving sensitivity by 53% and accuracy by 7% (area under the ROC curve without AI support, 0.723 [0.661-0.785]; with AI support, 0.890 [0.848-0.931]; P < .001). Radiology residents had smaller, mostly nonsignificant gains in performance, sensitivity, and accuracy with AI support. We found that in an emergency unit setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to nonradiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0–4; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability/correlation and Kruskal–Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists’ and non-radiologists’ reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1–3). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested.

Background: Soft tissue sarcomas (STSs) are a heterogeneous group of tumors. Wide surgical resection is standard, often combined with neoadjuvant chemotherapy, radiotherapy, or both. Studies have shown the predictive value of tumor necrosis in bone sarcoma (BS); however, the role of necrosis in STS after neoadjuvant therapies is still unclear. This study aimed to investigate the role of chemo- and radiotherapy in the formation of tumor necrosis and to evaluate the influence of tumor necrosis on overall survival and local recurrence-free survival. Data from BS patients and patients who did not receive neoadjuvant therapy were compared. Methods: A total of 779 patients with STS or BS were treated surgically. In all patients, tumor-specific factors such as type, size, or grading and the type of adjuvant therapy were documented. Local recurrence (LR), the diagnosis of metastatic disease, and survival during follow-up were evaluated. Results: A total of 565 patients with STS and 214 with BS were investigated. In STS, 24.1% G1 lesions, 34.1% G2 lesions, and 41.8% G3 lesions were observed. Two hundred twenty-four of the patients with STS and neoadjuvant therapy had either radiotherapy (RTx) (n = 80), chemotherapy (CTx) (n = 93), or both (n = 51). Three hundred forty-one had no neoadjuvant therapy at all. In STS, tumor necrosis after neoadjuvant treatment was significantly higher (53.5%) than in patients without neoadjuvant therapy (15.7%) (p < 0.001). Patients with combined neoadjuvant chemo-/radiotherapy had substantially higher tumor necrosis than those with radiotherapy alone (p = 0.032). There was no difference in tumor necrosis in patients with combined chemo-/radiotherapy and chemotherapy alone (p = 0.4). The mean overall survival for patients with STS was 34.7 months. Tumor necrosis did not influence survival in a subgroup of G2/3 patients. In STS with no neoadjuvant therapy and grading of G2/3, the correlation between necrosis and overall survival was significant (p = 0.0248). There was no significant correlation between local recurrence (LR) and necrosis. Conclusion: STS shows a broad spectrum of necrosis even without neoadjuvant chemo- or radiotherapy. After CTx or/and RTx necrosis is enhanced and is significantly pronounced with a combination of both. There is a trend toward higher necrosis with CTx than with RTx. Grading substantially influences the necrosis rate, but necrosis in soft-tissue sarcoma following neoadjuvant therapy does not correlate with better survival or a lower local recurrence rate, as in bone sarcomas.