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

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Traditional response evaluation criteria for anti-tumor treatment are difficult to meet the requirement for precise and individual assessment. This review introduces advances, obstacles, and future directions of radiomics in investigations of response evaluation on antineoplastic agents, aiming to provide new ideas for researchers.

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

Funding: This work is supported by the Beijing Natural Science Foundation(Z180001);This work is supported by the Beijing Natural Science Foundation(Z200015);PKU-Baidu Fund(2020BD027);the National Natural Science Foundation of China(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.

Figures/Tables 2

Table 1

Representative radiomics studies regarding evaluation or prediction of tumor response to anti-neoplastic therapies"

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

Table 1

Table 2

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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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