Hirozane Toru, Hashimoto Masahiro, Haque Hasnine, Arita Yuki, Mori Tomoaki, Asano Naofumi, Nakayama Robert, Morii Takeshi, Hosogane Naobumi, Matsumoto Morio, Nakamura Masaya, Jinzaki Masahiro
Department of Orthopaedic Surgery, Keio University School of Medicine, Shinjuku, Japan.
Department of Orthopaedic Surgery, Kyorin University Faculty of Medicine, Mitaka, Japan.
Oncology. 2024 Oct 23:1-11. doi: 10.1159/000542228.
The integration of artificial intelligence (AI) into orthopedics has enhanced the diagnosis of various conditions; however, its use in diagnosing soft-tissue tumors remains limited owing to its complexity. This study aimed to develop and assess an AI-driven diagnostic support system for magnetic resonance imaging (MRI)-based soft-tissue tumor diagnosis, potentially improving accuracy and aiding radiologists and orthopedic surgeons.
An experienced orthopedic oncologist and radiologist annotated 720 images from 77 cases (41 benign and 36 malignant soft-tissue tumors). Eleven tumor subtypes were identified and classified into benign and malignant groups based on histological diagnosis. Utilizing the standard machine learning classifier pipeline, we examined and down-selected imaging protocols and their predominant radiomic features within the tumor's three-dimensional region to differentiate between benign and malignant tumors. Among the scan protocols, contrast-enhanced T1-weighted fat-suppressed images showed the most accurate classification based on radiomic features. We focused on the two-dimensional features from the largest tumor boundary surface and its neighboring slices, leveraging texture-based radiomic and deep convolutional neural network features from a pretrained VGG19 model.
The test data comprised 44 contrast-enhanced images (22 benign and 22 malignant soft-tissue tumors) containing six malignant and five benign subtypes distinct from the training data. We compared expert and nonexpert human performances against AI by assessing malignancy detection and the time required for classification. The AI model showed comparable accuracy (AUC 0.91) to that of radiologists (AUC 0.83) and orthopedic surgeons (AUC 0.73). Notably, the AI model processed data approximately 400 times faster than its human counterparts, showcasing its capacity to significantly boost diagnostic efficiency.
We developed an AI-driven diagnostic support system for MRI-based soft-tissue tumor diagnosis. While additional refinement is necessary for clinical applications, our system has exhibited promising potential in differentiating between benign and malignant soft-tissue tumors based on MRI.
人工智能（AI）融入骨科学提高了对各种病症的诊断能力；然而，由于软组织肿瘤的复杂性，其在软组织肿瘤诊断中的应用仍然有限。本研究旨在开发并评估一种基于磁共振成像（MRI）的软组织肿瘤诊断的人工智能驱动诊断支持系统，有望提高诊断准确性并辅助放射科医生和骨科医生。
一位经验丰富的骨肿瘤学家和放射科医生对77例病例（41例良性和36例恶性软组织肿瘤）的720张图像进行了标注。确定了11种肿瘤亚型，并根据组织学诊断分为良性和恶性组。利用标准机器学习分类器流程，我们检查并筛选了成像方案及其在肿瘤三维区域内的主要影像组学特征，以区分良性和恶性肿瘤。在扫描方案中，基于影像组学特征，对比增强T1加权脂肪抑制图像显示出最准确的分类。我们专注于来自最大肿瘤边界表面及其相邻切片的二维特征，利用基于纹理的影像组学和预训练VGG19模型的深度卷积神经网络特征。
测试数据包括44张对比增强图像（22例良性和22例恶性软组织肿瘤），包含与训练数据不同的6种恶性和5种良性亚型。我们通过评估恶性肿瘤检测和分类所需时间，将专家和非专家的人类表现与人工智能进行了比较。人工智能模型显示出与放射科医生（AUC 0.83）和骨科医生（AUC 0.73）相当的准确性（AUC 0.91）。值得注意的是，人工智能模型处理数据的速度比人类快约400倍，展示了其显著提高诊断效率的能力。
我们开发了一种基于MRI的软组织肿瘤诊断的人工智能驱动诊断支持系统。虽然临床应用还需要进一步完善，但我们的系统在基于MRI区分良性和恶性软组织肿瘤方面已展现出有前景的潜力。
