新诊断多发性骨髓瘤患者的全身低剂量计算机断层扫描可预测细胞遗传风险:一项深度学习放射基因组学研究。

IF 1.9 3区 医学 Q2 ORTHOPEDICS Skeletal Radiology Pub Date : 2025-02-01 Epub Date: 2024-06-27 DOI:10.1007/s00256-024-04733-0
Shahriar Faghani, Mana Moassefi, Udit Yadav, Francis K Buadi, Shaji K Kumar, Bradley J Erickson, Wilson I Gonsalves, Francis I Baffour
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引用次数: 0

摘要

目的开发全身低剂量 CT(WBLDCT)深度学习模型,并确定其预测多发性骨髓瘤(MM)细胞遗传学异常的准确性:纳入了MM患者在确诊后一年内进行的WBLDCT检查。通过荧光原位杂交(FISH)对克隆浆细胞进行细胞遗传学评估,将患者分为高危(HR)和标准危(SR)。FISH检测中出现del(17p)、t(14;16)、t(4;14)和t(14;20)的患者被定义为HR。在患者个体水平上,数据集被平均分为五组(折叠),用于模型训练。记录各组接收者操作曲线下面积(AUROC)的平均值和标准偏差(SD):研究共纳入 151 名 MM 患者。t(4;14)模型表现最佳,平均(标清)AUROC为0.874(0.073)。三染色体的 AUROC 最低:AUROC为0.717(0.058)。HR细胞遗传学的2年和5年生存率分别为87%和71%,而SR细胞遗传学的2年和5年生存率分别为91%和79%。WBLDCT深度学习模型的生存预测显示,HR细胞遗传学患者的2年和5年生存率分别为87%和71%,而SR细胞遗传学患者的2年和5年生存率分别为92%和81%:在WBLDCT扫描基础上训练的深度学习模型可预测用于MM风险分层的细胞遗传学异常的存在。对模型性能的评估显示,该模型对各种细胞遗传学异常进行了良好到卓越的分类。
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Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.

Objective: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).

Materials and methods: WBLDCTs of MM patients performed within a year of diagnosis were included. Cytogenetic assessments of clonal plasma cells via fluorescent in situ hybridization (FISH) were used to risk-stratify patients as high-risk (HR) or standard-risk (SR). Presence of any of del(17p), t(14;16), t(4;14), and t(14;20) on FISH was defined as HR. The dataset was evenly divided into five groups (folds) at the individual patient level for model training. Mean and standard deviation (SD) of the area under the receiver operating curve (AUROC) across the folds were recorded.

Results: One hundred fifty-one patients with MM were included in the study. The model performed best for t(4;14), mean (SD) AUROC of 0.874 (0.073). The lowest AUROC was observed for trisomies: AUROC of 0.717 (0.058). Two- and 5-year survival rates for HR cytogenetics were 87% and 71%, respectively, compared to 91% and 79% for SR cytogenetics. Survival predictions by the WBLDCT deep learning model revealed 2- and 5-year survival rates for patients with HR cytogenetics as 87% and 71%, respectively, compared to 92% and 81% for SR cytogenetics.

Conclusion: A deep learning model trained on WBLDCT scans predicted the presence of cytogenetic abnormalities used for risk stratification in MM. Assessment of the model's performance revealed good to excellent classification of the various cytogenetic abnormalities.

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来源期刊
Skeletal Radiology
Skeletal Radiology 医学-核医学
CiteScore
4.40
自引率
9.50%
发文量
253
审稿时长
3-8 weeks
期刊介绍: Skeletal Radiology provides a forum for the dissemination of current knowledge and information dealing with disorders of the musculoskeletal system including the spine. While emphasizing the radiological aspects of the many varied skeletal abnormalities, the journal also adopts an interdisciplinary approach, reflecting the membership of the International Skeletal Society. Thus, the anatomical, pathological, physiological, clinical, metabolic and epidemiological aspects of the many entities affecting the skeleton receive appropriate consideration. This is the Journal of the International Skeletal Society and the Official Journal of the Society of Skeletal Radiology and the Australasian Musculoskelelal Imaging Group.
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Introduction to the special issue on soccer injuries. Imaging-detected sports injuries and imaging-guided interventions in athletes during the 2022 FIFA football (soccer) World Cup. Management of radiology services during the 2022 FIFA football (soccer) World Cup. Imaging of muscle injuries in soccer. Maturation-dependent patterns of knee injuries among symptomatic pediatric soccer players on MRI.
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