Delta放射组学和肿瘤大小：乳腺癌和结直肠癌肝转移化疗反应的一种新的预测放射组学模型。

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Tomography Pub Date : 2025-02-20 DOI:10.3390/tomography11030020

Nicolò Gennaro, Moataz Soliman, Amir A Borhani, Linda Kelahan, Hatice Savas, Ryan Avery, Kamal Subedi, Tugce A Trabzonlu, Chase Krumpelman, Vahid Yaghmai, Young Chae, Jochen Lorch, Devalingam Mahalingam, Mary Mulcahy, Al Benson, Ulas Bagci, Yuri S Velichko

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引用次数: 0

摘要

背景/目的：放射学特征在预处理图像上显示与肿瘤大小相关。然而，在治疗后图像上，这种关联受到治疗效果的影响，并且在应答者和无应答者之间有所不同。本研究引入了一种新的模型，称为基线参考Delta放射组学，该模型将放射学特征与肿瘤大小之间的关联整合到Delta放射组学中，以预测乳腺癌（BC）和结直肠癌（CRC）肝转移的化疗反应。材料和方法：一项回顾性研究分析了83例BC患者和84例CRC患者的对比增强CT扫描。其中57例BC患者有106个肝脏病变，37例CRC患者有109个病变，在全身化疗后接受了治疗后影像学检查。放射学特征是根据人工分割从每个患者最多三个病灶中提取出来的。通过测量最长直径来评估肿瘤反应，并根据RECIST 1.1标准将其分为进展性疾病（PD）、部分缓解（PR）或稳定性疾病（SD）。建立了分类模型，仅使用预处理数据、Delta放射组学和基线参考Delta放射组学来预测化疗反应。使用混淆矩阵度量来评估模型的性能。结果：基线参照的Delta放射组学在预测肝转移化疗患者的肿瘤反应方面与现有放射组学模型相当或更好。预测反应的敏感性、特异性和平衡准确度分别为0.66 ~ 0.97、0.81 ~ 0.97和80% ~ 90%。结论：通过将放射学特征与肿瘤大小之间的关系整合到Delta放射组学中，基线参考Delta放射组学为预测乳腺癌和结直肠癌肝转移的化疗反应提供了一种有希望的方法。

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Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer.

Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer.

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来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.