Helcio Mendonça Pereira1,2,3, Maria Eugenia Leite Duarte4, Igor Ribeiro Damasceno2, Luiz Afonso de Oliveira Moura Santos3, Marcello Henrique Nogueira-Barbosa3,5. 1. Department of Medical Imaging, National Institute of Orthopedics and Traumatology (INTO), Rio de Janeiro, Rio de Janeiro, Brazil. 2. Department of Medical Imaging, National Institute of Cancer (INCA), Rio de Janeiro, Rio de Janeiro, Brazil. 3. Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil. 4. Research Division of National Institute of Orthopedics and Traumatology (INTO), Rio de Janeiro, Brazil. 5. Department of Orthopedic Surgery, University of Missouri health Care, Columbia, Missouri, United States.
Abstract
OBJECTIVE: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. METHODS AND MATERIALS: This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model. RESULTS: The best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters. CONCLUSIONS: Machine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. ADVANCES IN KNOWLEDGE: Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.
OBJECTIVE: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. METHODS AND MATERIALS: This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model. RESULTS: The best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters. CONCLUSIONS: Machine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. ADVANCES IN KNOWLEDGE: Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.
Authors: M P Link; A M Goorin; M Horowitz; W H Meyer; J Belasco; A Baker; A Ayala; J Shuster Journal: Clin Orthop Relat Res Date: 1991-09 Impact factor: 4.176
Authors: Sigbjørn Smeland; Stefan S Bielack; Jeremy Whelan; Mark Bernstein; Pancras Hogendoorn; Mark D Krailo; Richard Gorlick; Katherine A Janeway; Fiona C Ingleby; Jakob Anninga; Imre Antal; Carola Arndt; Ken L B Brown; Trude Butterfass-Bahloul; Gabriele Calaminus; Michael Capra; Catharina Dhooge; Mikael Eriksson; Adrienne M Flanagan; Godehard Friedel; Mark C Gebhardt; Hans Gelderblom; Robert Goldsby; Holcombe E Grier; Robert Grimer; Douglas S Hawkins; Stefanie Hecker-Nolting; Kirsten Sundby Hall; Michael S Isakoff; Gordana Jovic; Thomas Kühne; Leo Kager; Thekla von Kalle; Edita Kabickova; Susanna Lang; Ching C Lau; Patrick J Leavey; Stephen L Lessnick; Leo Mascarenhas; Regine Mayer-Steinacker; Paul A Meyers; Raj Nagarajan; R Lor Randall; Peter Reichardt; Marleen Renard; Catherine Rechnitzer; Cindy L Schwartz; Sandra Strauss; Lisa Teot; Beate Timmermann; Matthew R Sydes; Neyssa Marina Journal: Eur J Cancer Date: 2019-01-25 Impact factor: 9.162