RESUMO
OBJECTIVES: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS: Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION: Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT: The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. KEY POINTS: ⢠Addressed accurate bone tumour classification using radiographic features. ⢠Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. ⢠Enhanced diagnosis by integrating prior expert patient assessments.
RESUMO
BACKGROUND: Glenoid loosening, a common complication of shoulder arthroplasty, could relate to implant design and bone quality. However, the role of bone density has not been tested experimentally yet. In this study, tests on cadaveric specimens of varying bone density were performed to evaluate the effects of bone quality on loosening of typical anatomic glenoid implants. METHODS: Cadaveric scapulae scanned with a quantitative computed tomography scanner to determine bone mineral density (BMD) were implanted with either pegged or keeled cemented glenoid components and tested under constant glenohumeral load while a humeral head component was moved cyclically in the inferior and superior directions. Implant superior and inferior edge lifting, defined as displacement from the underlying bone, was measured with linear variable differential transducers until we reached 23,000 test cycles, and statistical testing was performed for differences in edge lifting due to implant design and related to periprosthetic BMD. RESULTS: Edge lifting was statistically significant at all time points, but on average, implant design had no effect. Lifting was highest in specimens in which BMD below the lifting edge was lower, with trends of increased displacement with decreased BMD. CONCLUSIONS: Implant lifting was greater in glenoids of lower bone density for both implant designs. This finding suggests that fixation failure will most likely occur in bone of lower density and that the fixation design itself may play a secondary role.
Assuntos
Artroplastia do Ombro/métodos , Densidade Óssea , Cabeça do Úmero/cirurgia , Articulação do Ombro/cirurgia , Tomografia Computadorizada por Raios X/métodos , Idoso , Cadáver , Feminino , Humanos , Cabeça do Úmero/diagnóstico por imagem , Masculino , Desenho de PróteseRESUMO
BACKGROUND: An aberrant cellular microenvironment characterized by pathological cells or inflammation represents an added risk factor across various cancer types. While the significance of chronic inflammation in the development of most diffuse tumors has been extensively studied, an exception to this analysis exists in the context of chondrosarcomas. Chondrosarcomas account for 20-30% of all bone sarcomas, with an estimated global incidence of 1 in 100,000. The average age at diagnosis is 50, and over 70% of patients are over 40. This retrospective study aimed to examine the role of C-reactive protein (CRP) as a prognostic factor in relation to the histopathological findings in chondrosarcoma. METHODS: In this retrospective study, 70 patients diagnosed with chondrosarcoma and treated between 2004 and 2019 were included. Preoperative CRP levels were measured in mg/dL, with non-pathological values defined as below 0.5 mg/dL. Disease-free survival time was calculated from the initial diagnosis to events such as local recurrence or metastasis. Follow-up status was categorized as death from disease, no evidence of disease, or alive with disease. Patients were excluded if they had insufficient laboratory values, missing follow-up information, or incomplete histopathological reports. RESULTS: The calculated risk estimation of a reduced follow-up time was 2.25 timed higher in the patients with a CRP level >0.5 mg/dL (HR 2.25 and 95% CI 1.13-4.45) and 3 times higher in patients with a tumor size > pT2 (HR 3 and 95% CI 1.59-5.92). We can easily confirm that risk factors for reduced prognosis lie in chondrosarcoma high grading, preoperative pathological CRP- level, and a size > 8 cm. CONCLUSIONS: A pretreatment CRP value greater than 0.5 mg/dL can be considered a sensitive prognostic and risk factor for distant metastasis for chondrosarcoma patients.
RESUMO
Background: The diagnostic work-up of musculoskeletal tumors is a multifactorial process. During the early phase, differential diagnoses are made using basic radiological imaging. In this phase, part of the decision making is based on the patient's age, as well as the incidence and predilection sites of different entities. Unfortunately, this information is based on older and fragmented data. In this study, we retrospectively evaluated all soft-tissue and bone tumors around the knee in children treated at our tertiary center in the last 20 years, with the aim of verifying the data used today. Methods: In this retrospective study, the databank of our tertiary center was used to give an overview of treated tumors around the knee in children. Results: We were able to include 224 children with bone and soft-tissue tumors around the knee. The cohort consisted of 184 bone tumors, of which 144 were benign and 40 malignant. The 40 soft-tissue tumors comprised 30 benign and 10 malignant masses. The most common lesions were osteochondromas (88) in the bone and tenosynovial giant-cell tumors (12) in the soft tissue. Conclusions: With this original work, we were able to verify and supplement earlier studies, as well as deepen our insight into these very rare diseases.
RESUMO
Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.
RESUMO
Overweight patients have higher complication rates during and after surgical procedures. In total hip arthroplasty (THA), postoperative infection is a major complication. In this study, we show that the patient's body mass index (BMI) can be approximated by a newly developed grading system using preoperative X-rays. Furthermore, we show that a higher score and BMI result in a higher risk of infection. For this retrospective study, 635 patients undergoing THA or revision surgeries in 2018 and 2019 were included. The preoperatively acquired X-rays of the pelvis were analyzed using a four-stage grading system. The infection rate was compared to our score and the patients' BMI. The mean BMI (95% confidence) of all patients graded as grade 0 was 25.16 (24.83; 25.50) kg/m2, for grade 1, it was 30.31 (29.52; 31.09) kg/m2, for grade 2, it was 35.06 (33.59; 36.54) kg/m2, and it was 45.03 (39.65; 50.41) kg/m2 for grade 3. The risk of infection was 4% in patients with normal radiographs, rising from 7% in patients graded as 1 up to 18% in each of the highest categories. This study shows that we were able to create a semi-quantitative grading tool for the abdominal contour displayed on X-rays of the pelvis in order to estimate the patients' BMI and therefore the infection rate. A higher abdominal contour grade showed higher infection rates at follow-up.
RESUMO
The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially.