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  • Springer Science and Business Media LLC  (2)
  • Gersing, Alexandra S.  (2)
  • 1
    Online Resource
    Online Resource
    Diagnosis of joint invasion in patients with malignant bone tumors: value and reproducibility of direct and indirect signs on MR imaging
    Springer Science and Business Media LLC ; 2022
    In:  European Radiology Vol. 32, No. 7 ( 2022-07), p. 4738-4748
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 32, No. 7 ( 2022-07), p. 4738-4748
    Abstract: To evaluate the performance and reproducibility of MR imaging features in the diagnosis of joint invasion (JI) by malignant bone tumors. Methods MR images of patients with and without JI ( n = 24 each), who underwent surgical resection at our institution, were read by three radiologists. Direct (intrasynovial tumor tissue (ITT), intraarticular destruction of cartilage/bone, invasion of capsular/ligamentous insertions) and indirect (tumor size, signal alterations of epiphyseal/transarticular bone (bone marrow replacement/edema-like), synovial contrast enhancement, joint effusion) signs of JI were assessed. Odds ratios, sensitivity, specificity, PPV, NPV, and reproducibilities (Cohen’s and Fleiss’ κ ) were calculated for each feature. Moreover, the diagnostic performance of combinations of direct features was assessed. Results Forty-eight patients (28.7 ± 21.4 years, 26 men) were evaluated. All readers reliably assessed the presence of JI (sensitivity = 92–100 %; specificity = 88–100%, respectively). Best predictors for JI were direct visualization of ITT (OR = 186–229, p 〈 0.001) and destruction of intraarticular bone (69–324, p 〈 0.001). Direct visualization of ITT was also highly reliable in assessing JI (sensitivity, specificity, PPV, NPV = 92–100 %), with excellent reproducibility ( κ = 0.83). Epiphyseal bone marrow replacement and synovial contrast enhancement were the most sensitive indirect signs, but lacked specificity (29–54%). By combining direct signs with high specificity, sensitivity was increased (96 %) and specificity (100 %) was maintained. Conclusion JI by malignant bone tumors can reliably be assessed on preoperative MR images with high sensitivity, specificity, and reproducibility. Particularly direct visualization of ITT, destruction of intraarticular bone, and a combination of highly specific direct signs were valuable, while indirect signs were less predictive and specific. Key Points • Direct visualization of intrasynovial tumor was the single most sensitive and specific (92–100%) MR imaging sign of joint invasion. • Indirect signs of joint invasion, such as joint effusion or synovial enhancement, were less sensitive and specific compared to direct signs. • A combination of the most specific direct signs of joint invasion showed best results with perfect specificity and PPV (both 100%) and excellent sensitivity and NPV (both 96 %).
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-022-08586-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1472718-3
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  • 2
    Online Resource
    Online Resource
    Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors
    Springer Science and Business Media LLC ; 2022
    In:  European Radiology Vol. 32, No. 9 ( 2022-04-09), p. 6247-6257
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 32, No. 9 ( 2022-04-09), p. 6247-6257
    Abstract: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. Methods In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant ( n = 213, 24.2%) or benign ( n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. Results The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. Conclusions An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. Key Points • The developed machine learning model could differentiate benign from malignant bone tumors  using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-022-08764-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1472718-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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