Large-Population based Deep Learning Models in Classifying Primary Bone Tumors and Bone Infections based on Radiographs: a Retrospective and Multi-reader Multi-center Study

<p>This retrospective multicenter study collected patients via consecutive sampling between 2013 and 2022 from two cohorts: training cohort (from the Second Xiangya Hospital of Central South University) and testing cohort (from Xiangya Hospital of Central South University and Hunan Children's Hospital of Central South University). These lesions were identified to have bone involvement through pre-operative radiographs and were histologically diagnosed following biopsy or surgery.&nbsp;<br>(i) For the inclusion criteria, lesions were confirmed and diagnosed as PBTs according to the 2020 World Health Organization (WHO) system for the classification for tumors of bone 1 while bone infections were confirmed and proven by histology and (or) bacterial culture. The other vital inclusion criteria are evident as well as available clinical information and pre-operative radiographs.&nbsp;<br>(ii) The screening criteria: (a) radiographs were from patients diagnosed between 2013 and 2022 (b) in selected three hospitals; (c) radiographs with robust quality for reliable assessments of the bone lesions and (d) all of these radiographs were pre-operative.&nbsp;<br>Clinical characteristics like age, gender, and the location of the lesion of interest and so on were obtained from the patients' electronic medical records after data desensitization and standardization.<br>Radiographs were kept and downloaded as Digital Imaging and Communications in Medicine (DICOM) files from the picture archiving and communication system (PACS) at their original sizes and resolutions. All of these radiograph images have undergone desensitization processing of disengaging patient protected health information from DICOM data to meet the relevant legal criteria and requirements of US (HIPAA) as well as European (GDPR). Delineating the region of interest (ROI) was performed by two proficient radiologists. ROIs were meticulously outlined via Click 2 Crop (version 5.2.2) (https://click-2-crop.en.softonic.com/) to closely segment pertinent entities present in each PBT or bone infection. The smallest rectangular box that can completely cover the ROI was manually annotated as the boundary box by senior seniority radiologist to ensure accuracy. Afterwards, the annotated ROIs were used as ground truth for the model development process.</p>