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基于对比增强MRI的Delta放射组学对肌层浸润性膀胱癌放化疗反应的预测潜力

Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer.

作者信息

Isemoto Kohei, Waseda Yuma, Fujiwara Motohiro, Kimura Koichiro, Hirahara Daisuke, Saho Tatsunori, Takaya Eichi, Arita Yuki, Kwee Thomas C, Fukuda Shohei, Tanaka Hajime, Yoshida Soichiro, Fujii Yasuhisa

机构信息

Department of Urology, Institute of Science Tokyo, Tokyo 113-8519, Japan.

Department of Urology, Insured Medical Care Management, Tokyo Medical and Dental University, Tokyo 113-8519, Japan.

出版信息

Diagnostics (Basel). 2025 Mar 21;15(7):801. doi: 10.3390/diagnostics15070801.

Abstract

: Delta-radiomics involves analyzing feature variations at different acquisition time-points. This study aimed to assess the utility of delta-radiomics feature analysis applied to contrast-enhanced (CE) and non-contrast-enhanced (NE) T1-weighted images (WI) in predicting the therapeutic response to chemoradiotherapy (CRT) in patients diagnosed with muscle-invasive bladder cancer (MIBC). : Forty-three patients with non-metastatic MIBC (cT2-4N0M0) who underwent partial or radical cystectomy after induction CRT were, retrospectively, reviewed. Pathological complete response (pCR) to CRT was defined as the absence of residual viable tumor cells in the cystectomy specimen. Identical volumes of interest corresponding to the index bladder cancer lesions on CE- and NE-T1WI on pre-therapeutic 1.5-T MRI were collaboratively delineated by one radiologist and one urologist. Texture analysis was performed using "LIFEx" software. The subtraction of radiological features between CE- and NE-T1WI yielded 112 delta-radiomics features, which were utilized in multiple machine-learning algorithms to construct optimal predictive models for CRT responsiveness. Additionally, the predictive performance of the radiomics model constructed using CE-T1WI alone was assessed. : Twenty-one patients (49%) achieved pCR. The best-performing delta-radiomics model, employing the "Extreme Gradient Boosting" algorithm, yielded an area under the receiver operating characteristic curve (AUC) of 0.85 (95% confidence interval [CI]: 0.75-0.95), utilizing four signal intensity-based delta-radiomics features. This outperformed the best model derived from CE-T1WI alone (AUC: 0.63, 95% CI: 0.50-0.75), which incorporated two morphological features and one signal intensity-based radiomics feature. : Delta-radiomics analysis applied to pre-therapeutic CE- and NE-MRI demonstrated promising predictive ability for CRT responsiveness prior to treatment initiation.

摘要

Δ放射组学涉及分析不同采集时间点的特征变化。本研究旨在评估将Δ放射组学特征分析应用于对比增强(CE)和非对比增强(NE)T1加权图像(WI),以预测诊断为肌肉浸润性膀胱癌(MIBC)的患者对放化疗(CRT)的治疗反应的效用。回顾性分析了43例非转移性MIBC(cT2 - 4N0M0)患者,这些患者在诱导CRT后接受了部分或根治性膀胱切除术。CRT的病理完全缓解(pCR)定义为膀胱切除标本中无残留存活肿瘤细胞。由一名放射科医生和一名泌尿科医生共同勾勒出治疗前1.5-T MRI上CE-T1WI和NE-T1WI上与索引膀胱癌病变对应的相同感兴趣体积。使用“LIFEx”软件进行纹理分析。CE-T1WI和NE-T1WI之间的放射学特征相减产生了112个Δ放射组学特征,这些特征被用于多种机器学习算法中,以构建CRT反应性的最佳预测模型。此外,还评估了仅使用CE-T1WI构建的放射组学模型的预测性能。21例患者(49%)实现了pCR。表现最佳的Δ放射组学模型采用“极端梯度提升”算法,利用四个基于信号强度的Δ放射组学特征,在受试者操作特征曲线(AUC)下的面积为0.85(95%置信区间[CI]:0.75 - 0.95)。这优于仅从CE-T1WI得出的最佳模型(AUC:0.63,95% CI:0.50 - 0.75),后者纳入了两个形态学特征和一个基于信号强度的放射组学特征。应用于治疗前CE和NE MRI的Δ放射组学分析在治疗开始前对CRT反应性显示出有前景的预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae0e/11988543/1ea0ee932847/diagnostics-15-00801-g001.jpg

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