影像组学在抗肿瘤药物临床试验疗效评估中的应用和挑战

doi:10.24920/003985

摘要/Abstract

摘要：

抗肿瘤药物的快速发展改善了恶性肿瘤患者预后。以CT、MRI和PET为代表的影像学手段作为重要的终点替代指标,在新药临床试验中发挥着越来越重要的作用。但因抗肿瘤药物作用靶点、应用线数等不同,治疗后影像学征象变化的个体差异较大,单用RECIST标准已无法满足个体化精准评效的需求。影像组学作为一种依托于新兴计算机技术的特征提取和模型构建手段,有望协助临床进行抗肿瘤药物治疗疗效的精准评估。本文介绍了影像组学的基本概念,回顾了影像组学在抗肿瘤药物疗效评价方面的最新进展,深入分析影像组学在预测肿瘤分子标志物、评价治疗疗效和预后预测方面的应用潜力,探讨了目前影像组学在抗肿瘤药物临床研究中存在的主要问题和未来发展方向。

关键词: 影像组学, 深度学习, 机器学习, 抗肿瘤药物, 疗效评价

Abstract:

The recent spring up of the antineoplastic agents and the prolonged survival bring both challenge and chance to radiological practice. Radiological methods including CT, MRI and PET play an increasingly important role in evaluating the efficacy of these antineoplastic drugs. However, different antineoplastic agents potentially induce different radiological signs, making it a challenge for radiological response evaluation, which depends mainly on one-sided morphological response evaluation criteria in solid tumors (RECIST) in the status quo of clinical practice. This brings opportunities for the development of radiomics, which is promising to serve as a surrogate for response evaluations of anti-tumor treatments. In this article, we introduce the basic concepts of radiomics, review the state-of-art radiomics researches with highlights of radiomics application in predictions of molecular biomarkers, treatment response, and prognosis. We also provide in-depth analyses on major obstacles and future direction of this new technique in clinical investigations on new antineoplastic agents.

Key words: radiomics, deep learning, machine learning, antineoplastic agents, response evaluation

基金资助: 北京自然科学基金(Z180001);北京自然科学基金(Z200015);北大百度基金资助项目(2020BD027);国家自然科学基金重大研究计划(91959205)

Jiazheng Li, Lei Tang. Radiomics in Antineoplastic Agents Development: Application and Challenge in Response Evaluation[J].Chinese Medical Sciences Journal, 2021, 36(3): 187-195.

图/表 2

Author	Journal	Publication Year	Study Type	Cancer	Sample Size		Image Type	Orientation	Segmentation	Radiomics Features	Algorithms
Author	Journal	Publication Year	Study Type	Cancer	Training	Validation	Image Type	Orientation	Segmentation	Radiomics Features	Algorithms
Sun R^[15]	Lancet Oncol	2018	Retrospective; Multicenter	Pancancer	135	356	CE-CT	Estimate tumor CD8 cell count	Primary lesion, biggest lesion, target lesions; a peripheral ring	First-, second-order features, and volumes features	linear elastic-net model
Jiang Y^[16]	Ann Oncol	2020	Retrospective; Multicenter	Gastric cancer	262	1516	Abdomen portal venous phase CE-CT	Predict tumor immune status	Primary tumor; a peripheral ring	First-, second-, and higher order features and shape features	LASSO method
Jiang Y^[17]	Lancet Digit Health	2021	Retrospective; Multicenter	Gastric cancer	321	1888	Abdomen portal venous phase CE-CT	Assess tumor stroma microenvironment	primary tumor	Deep learning features	Attention U-net for deformable image augmentation and deep ResNet
Mu W^[18]	Nat Commun	2020	Retrospective; Multicenter	NSCLC	429	468	¹⁸F-FDG PET/CT	Define EGFR mutation status	Primary tumor; a peripheral ring	Deep learning features	2D small-residual-convolutional-network
Rossi G^[19]	Cancer Res	2021	Retrospective; Multicenter	NSCLC	109	61	Chest CT without contrast	Define EGFR mutation status	Tumor corresponding to the biological sample	Shape and textural features	Support Vector Machine
Tian P^[20]	Theranostics	2021	Retrospective	NSCLC	750	283	CT	Predict PD-L1 expression	Primary tumor	Deep learning features	Deep learning network
Shi R^[21]	Am J Cancer Res	2020	Retrospective; Multicenter	Colo- rectal cancer	124	35	Abdomen portal venous phase CE-CT	Distinguish patients with RAS or BRAF mutation	Metastatic liver lesions	First-order features, shape-based features, and textural features	ANN method
Trebeschi S^[22]	Ann Oncol	2019	Retrospective; Multicenter	NSCLC; Melanoma	203	301	Chest CT or chest-abdomen CE-CT	Predict lesion’s progression and patient’s OS before immunotherapy	Lesions with diameter > 5 mm	Laplacian of Gaussian filters, wavelets decompositions, and non-linearities features	Random forest
Vaidya P^[23]	Lancet Digit Health	2020	Retrospective; Multicenter	NSCLC	329	196	Chest CT with or without contrast	Predict survival after adjuvant chemotherapy	Target nodule; a peripheral ring	Gabor, Haralick, Laws, Laplace, and Collage feature	LASSO method
Vaidya P^[24]	J Immunother Cancer	2020	Retrospective	NSCLC	30	79	CT with or without contrast	Distinguish hyperprogression from response patterns to immunotherapy	All target lesions; a peripheral ring	Gabor, Haralick, Laws, Laplace, and Collage feature	Random forest
Dercle L^[25]	J Natl Cancer Inst	2020	Retrospective; Multicenter	Colo- rectal cancer	439	229	Abdomen portal venous phase CE-CT	Predict tumor sensitivity to anti- EGFR treatment	Metastatic liver lesions	Radiomics features*, deep learning features	Random forest
Dercle L^[26]	Clin Cancer Res	2020	Retrospective; Multicenter	NSCLC	72	20	Chest CT	Predict sensitivity to targeted- treatment	Largest measurable lung lesion	Radiomics features*	Random forest
Basler L^[27]	Clin Cancer Res	2020	Retrospective	Melanoma	112	112^#	¹⁸F-FDG PET/CT	Distinguish hyperprogression in patients treated with immunotherapy	All lesions	Shape, intensity, and texture features	Logistic regression and regularized with L₂ penalty
Xu Y^[28]	Clin Cancer Res	2019	Retrospective; Multicenter	NSCLC	107	161	Chest CT	Predict OS for patients with definitive RT and chemotherapy	Manually defined the seed points of tumor as input	Deep learning features	CNN and RNN
Song J^[29]	Clin Cancer Res	2018	Retrospective; Multicenter	NSCLC	1032	253	Chest CE-CT	Discriminate patients with rapid and slow progression to EGFR-TKI therapy	Primary tumors	Intensity, shape, and texture features	Cox regression model with LASSO penalty
Song J^[30]	JAMA Netw Open	2020	Retrospective; Multicenter	NSCLC	145	320	Chest CT with or without contrast	Predict survival for patients treated with TKI therapy	Whole CT images used as the input	120-dimensional semantic features	LASSO, Cox proportional hazards regression
Zhang N^[31]	Theranostics	2020	Retrospective	NSCLC	41	41	¹⁸F-FDG PET/CT	Predict treatment response to RT with concurrent chemotherapy	Primary tumors; involved lymph nodes	Morphology, boundary sharpness, intensity, and gray-level co-occurrence matrix features	LASSO method
Jiang Y^[32]	Ebiomedicine	2018	Retrospective; Multicenter	Gastric cancer	228	1363	Abdomen portal venous phase CE-CT	Predict survival when patients receive adjuvant chemotherapy	Primary tumors	Gray-level histogram, co-occurrence matrix, run-length matrix, absolute gradient, autoregressive model, wavelet transform features	LASSO method

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Section	Item No.	CONSORT-AI item extensions
Title and Abstract
Title	1a,b(i)	Indicate that the intervention involves artificial intelligence/machine learning in the title and/or abstract and specify the type of model.
Title	1a,b(ii)	State the intended use of the AI intervention within the trial in the title and/ or abstract.
Introduction
Background and objectives	2a(i)	Explain the intended use for the AI intervention in the context of the clinical pathway, including its purpose and its intended users (e.g., healthcare professionals, patients, public).
Methods
Participates	4a(i)	State the inclusion and exclusion criteria at the level of participants.
	4a(ii)	State the inclusion and exclusion criteria at the level of the input data.
	4b	Describe how the AI intervention was integrated into the trial setting, including any onsite or offsite requirements.
Interventions	5(i)	State which version of the AI algorithm was used.
	5(ii)	Describe how the input data were acquired and selected for the AI intervention.
	5(iii)	Describe how poor quality or unavailable input data were assessed and handled.
	5(iv)	Specify whether there was human-AI interaction in the handling of the input data, and what level of expertise was required for users.
	5(v)	Specify the output of the AI intervention.
	5(vi)	Explain how the AI intervention’s outputs contributed to decision-making or other elements of clinical practice.
Results
Harms	19	Describe results of any analysis of performance errors and how errors were identified, where applicable. If no such analysis was planned or done, justify why not.
Other information
Funding	25	State whether and how the AI intervention and/or its code can be accessed, including any restrictions to access or re-use.

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