Longitudinal Image Data for Outcome Modeling.

IF 3.2 3区 医学 Q2 ONCOLOGY Clinical oncology Pub Date : 2024-06-27 DOI:10.1016/j.clon.2024.06.053
J E van Timmeren, J Bussink, P Koopmans, R J Smeenk, R Monshouwer
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Abstract

In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.

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用于结果建模的纵向图像数据。
在肿瘤学中,医学成像对诊断、治疗计划和治疗执行至关重要。众所周知,治疗反应复杂多样,涉及治疗、患者特征和肿瘤微环境等因素。纵向图像分析能够跟踪时间变化,有助于疾病监测、治疗评估和结果预测。这有助于加强个性化医疗。然而,纵向二维和三维图像分析面临着独特的挑战,包括图像配准、可靠的分割、处理不同的成像间隔和稀疏数据。本综述概述了纵向图像分析的技术和方法,主要侧重于放射肿瘤学的结果建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinical oncology
Clinical oncology 医学-肿瘤学
CiteScore
5.20
自引率
8.80%
发文量
332
审稿时长
40 days
期刊介绍: Clinical Oncology is an International cancer journal covering all aspects of the clinical management of cancer patients, reflecting a multidisciplinary approach to therapy. Papers, editorials and reviews are published on all types of malignant disease embracing, pathology, diagnosis and treatment, including radiotherapy, chemotherapy, surgery, combined modality treatment and palliative care. Research and review papers covering epidemiology, radiobiology, radiation physics, tumour biology, and immunology are also published, together with letters to the editor, case reports and book reviews.
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