Wallner Christoph, Alam Mansoor, Drysch Marius, Wagner Johannes Maximilian, Sogorski Alexander, Dadras Mehran, von Glinski Maxi, Reinkemeier Felix, Becerikli Mustafa, Heute Christoph, Nicolas Volkmar, Lehnhardt Marcus, Behr Björn
Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bürkle-de-la-Camp Platz 1, 44789 Bochum, Germany.
National Centre of Robotics and Automation, University of Engineering & Technology, Peshawar 25000, Pakistan.
Cancers (Basel). 2021 Oct 1;13(19):4961. doi: 10.3390/cancers13194961.
soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients.
a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control.
in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined.
the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.
软组织肉瘤是恶性肿瘤的一个子集，相对罕见，占成年期所有恶性肿瘤的1%。由于这些肿瘤的罕见性，其诊断和治疗的质量存在显著差异。一个至关重要的方面是肺部血行转移的诊断。指南建议通过X射线进行常规肺部成像。随着基于人工智能的诊断支持不断发展，到目前为止，尚未在肉瘤中得到应用。本研究的目的是以最敏感和特异的方式利用人工智能对肉瘤患者的肺部X射线进行转移分析。
使用一组来自德语区大容量肉瘤中心的伴有肉瘤转移的肺部X射线创建并训练了一个Python脚本。纳入了26例肺转移患者。所有患者均有胸部X射线及相应的肺部CT扫描，且有组织活检结果。可训练图像数量扩充至600张。为了评估生物学敏感性和特异性，该脚本在以肺部CT作为对照的肺部X射线上进行了测试。
在本研究中，我们展示了一种新型的基于卷积神经网络的系统，其精度为71.2%，特异性为90.5%，敏感性为94%，召回率为94%，准确率为91.2%。确定了对即使是小病灶也有良好的检测效果。
所创建的脚本建立了在安全水平上检查肺部X射线有无转移的选项，特别是对于这种罕见的肿瘤实体。
