Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma.

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nuclear Medicine Communications Pub Date : 2024-05-01 Epub Date: 2024-02-19 DOI:10.1097/MNM.0000000000001826
Yasuhiro Fukushima, Keisuke Suzuki, Mai Kim, Wenchao Gu, Satoshi Yokoo, Yoshito Tsushima
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引用次数: 0

Abstract

Objectives: Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning.

Methods: A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed.

Results: From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83.

Conclusion: Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.

下牙龈鳞状细胞癌中骨髓侵犯对 18F-FDG PET 纹理分析机器学习的评估
目的:下牙龈鳞状细胞癌(LGSCC)有可能侵犯牙槽骨。传统上,LGSCC 的诊断依赖于形态学成像,但这些评估与手术结果之间存在不一致。本研究旨在评估LGSCC骨髓侵犯与PET纹理特征之间的相关性,并通过机器学习提高诊断准确性:我们对2009年至2017年期间接受18F-氟脱氧葡萄糖(FDG)PET/计算机断层扫描(CT)检查的159例LGSCC患者进行了回顾性分析。我们从 PET 图像中提取了放射学特征,重点是病理骨髓侵犯检测。提取的特征经过基于最小绝对收缩和选择算子算法的选择,然后通过 XGBoost 软件包用于机器学习,以区分骨髓侵犯的存在。结果:在 159 名患者中,有 88 名符合进一步分析的条件(59 名男性;平均年龄 69.2 岁),其中 69 名患者(78%)被确定为病理骨髓侵犯。确定了三个重要的放射学特征:灰度共现矩阵_相关性(Gray level co-occurrence matrix_Correlation)、基于强度的密度四分位数范围(INTENSITY-BASED_IntensityInterquartileRange)和表面体积比(MORPHOLOGICAL_SurfaceToVolumeRatio)。利用 PET 放射特征检测骨髓侵犯的 XGBoost 机器学习模型的曲线下面积值为 0.83:我们的研究结果凸显了18F-FDG PET放射学特征与机器学习相结合的潜力,是改善LGSCC诊断和治疗的一个很有前途的途径。使用18F-FDG PET纹理特征可为确定LGSCC患者是否存在骨髓侵犯提供一种稳健而准确的方法。
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来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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