Application progress of CT radiomics in gastrointestinal stromal tumor

doi:10.3969/j.issn.1674-8115.2023.07.015

Abstract

Abstract:

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor in the gastrointestinal tract, with complex biological characteristics and varying risks, and the treatment methods and prognosis of patients with different risks are quite different; therefore, early diagnosis and risk assessment are crucial for its precision treatment. In recent years, CT radiomics, as an emerging imaging technology, can transform traditional CT image features into a large number of data, thereby reflecting the inherent heterogeneity of GIST and even correlating with its gene expression features. This paper reviews the research progress of CT radiomics in the diagnosis and prediction of GIST with the help of machine learning. The current CT radiomics can not only be used for the differential diagnosis of GIST and other gastric diseases, but also for the risk evaluation of GIST. Furthermore, pathological analysis and gene diagnosis can be performed based on CT images, and then the first-line treatment effect and long-term prognosis can be predicted. At present, various prediction models constructed by combination of CT radiomics and clinical information have been well verified in the specific practice of different clinical problems, showing broad application prospects. However, in the specific clinical application process, different methods of sample data collection and processing, differences in the selection of machine learning algorithms, and the selection of 2D or 3D images all affect the specific effectiveness of CT radiomics. Hence, unified and standardized application rules for radiomics has to be established.

Key words: gastrointestinal stromal tumor (GIST), CT radiomics, artificial intelligence (AI), machine learning

CLC Number:

R735.2

MA Ben, ZHAO Cheng, SHU Yijun, DONG Ping. Application progress of CT radiomics in gastrointestinal stromal tumor[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(7): 923-930.

Figures/Tables 3

TrendMD

References 50

1	JEMAL A, BRAY F, CENTER M M, et al. Global cancer statistics[J]. CA Cancer J Clin, 2011, 61(2): 69-90.
2	LEE I S, PARK Y S, LEE J H, et al. Pathologic discordance of differentiation between endoscopic biopsy and postoperative specimen in mucosal gastric adenocarcinomas[J]. Ann Surg Oncol, 2013, 20(13): 4231-4237.
3	JOENSUU H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor[J]. Hum Pathol, 2008, 39(10): 1411-1419.
4	JOENSUU H, HOHENBERGER P, CORLESS C L. Gastrointestinal stromal tumour[J]. Lancet, 2013, 382(9896): 973-983.
5	DEMETRI G D, VON MEHREN M, BLANKE C D, et al. Efficacy and safety of imatinib mesylate in advanced gastrointestinal stromal tumors[J]. N Engl J Med, 2002, 347(7): 472-480.
6	VERWEIJ J, CASALI P G, ZALCBERG J, et al. Progression-free survival in gastrointestinal stromal tumours with high-dose imatinib: randomised trial[J]. Lancet, 2004, 364(9440): 1127-1134.
7	MIETTINEN M, SOBIN L H, LASOTA J. Gastrointestinal stromal tumors of the stomach: a clinicopathologic, immunohistochemical, and molecular genetic study of 1 765 cases with long-term follow-up[J]. Am J Surg Pathol, 2005, 29(1): 52-68.
8	GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577.
9	YIP S S F, AERTS H J W L. Applications and limitations of radiomics[J]. Phys Med Biol, 2016, 61(13): R150-R166.
10	ZHANG L J, KANG L Q, LI G C, et al. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors[J]. Radiol Med, 2020, 125(5): 465-473.
11	LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446.
12	CHEN T, LIU S Q, LI Y, et al. Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning[J]. EBioMedicine, 2019, 39: 272-279.
13	SUN Z Q, HU S D, LI J, et al. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images[J]. J Xray Sci Technol, 2019, 27(6): 1021-1031.
14	ZHENG J, XIA Y, XU A Q, et al. Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors[J]. Abdom Radiol (NY), 2022, 47(1): 85-93.
15	STARMANS M P A, TIMBERGEN M J M, VOS M, et al. Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach[J]. J Digit Imaging, 2022, 35(2): 127-136.
16	李定杰, 吴慧, 刘如, 等. 基于诊断CT影像组学对食管癌放疗疗效早期评估[J]. 中华放射肿瘤学杂志, 2019, 28(10): 731-734.
	LI D J, WU H, LIU R, et al. Early evaluation of radiotherapy effect of esophageal cancer based on diagnostic CT imaging histology[J]. Chinese Journal of Radiation Oncology, 2019, 28(10): 731-734.
17	李华秀, 李振辉, 李鹍, 等. CT影像组学预测局部进展期直肠癌新辅助治疗的效果[J]. 中国医学影像学杂志, 2020, 28(1): 44-50.
	LI H X, LI Z H, LI K, et al. Efficacy of CT radiomics in predicting neoadjuvant therapy of locally advanced rectal cancer[J]. Chinese Journal of Medical Imaging, 2020, 28(1): 44-50.
18	ZHOU Y, HE L, HUANG Y Q, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma[J]. Abdom Radiol, 2017, 42(6): 1695-1704.
19	GAO X J, MA T T, CUI J L, et al. A radiomics-based model for prediction of lymph node metastasis in gastric cancer[J]. Eur J Radiol, 2020, 129: 109069.
20	BA-SSALAMAH A, MUIN D, SCHERNTHANER R, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT[J]. Eur J Radiol, 2013, 82(10): e537-e543.
21	ZEYDANLI T, KILIC H K. Performance of quantitative CT texture analysis in differentiation of gastric tumors[J]. Jpn J Radiol, 2022, 40(1): 56-65.
22	CASTELLANO G, BONILHA L, LI L M, et al. Texture analysis of medical images[J]. Clin Radiol, 2004, 59(12): 1061-1069.
23	BASHIR U, SIDDIQUE M M, MCLEAN E, et al. Imaging heterogeneity in lung cancer: techniques, applications, and challenges[J]. AJR Am J Roentgenol, 2016, 207(3): 534-543.
24	YE H, XIN H, ZHENG Q, et al. Prognostic role of the primary tumour site in patients with operable small intestine and gastrointestinal stromal tumours: a large population-based analysis[J]. Oncotarget, 2017, 9(8): 8147-8154.
25	MIETTINEN M, LASOTA J. Gastrointestinal stromal tumors: review on morphology, molecular pathology, prognosis, and differential diagnosis[J]. Arch Pathol Lab Med, 2006, 130(10): 1466-1478.
26	MIETTINEN M, LASOTA J. Gastrointestinal stromal tumors: pathology and prognosis at different sites[J]. Semin Diagn Pathol, 2006, 23(2): 70-83.
27	FLETCHER C D M, BRIDGE J A, HOGENDOORN P C W, et al. WHO classification of tumours of soft tissue and bone[M]. 4th ed. Lyon: IARC Press, 2013.
28	中国临床肿瘤学会胃肠间质瘤专家委员会. 中国胃肠间质瘤诊断治疗共识(2017年版)[J]. 肿瘤综合治疗电子杂志, 2018, 4(1): 31-43.
	Expert Committee on Gastrointestinal Stromal Tumor, Chinese Society of Clinical Oncology. Chinese consensus on diagnosis and treatment of gastrointestinal stromal tumor (2017 edition)[J]. Journal of Multidisciplinary Cancer Management (Electronic Version), 2018, 4(1): 31-43.
29	CHOI I Y, YEOM S K, CHA J, et al. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection[J]. Abdom Radiol (NY), 2019, 44(7): 2346-2356.
30	SONG Y C, LI J, WANG H X, et al. Radiomics nomogram based on contrast-enhanced CT to predict the malignant potential of gastrointestinal stromal tumor: a two-center study[J]. Acad Radiol, 2022, 29(6): 806-816.
31	DEMETRI G D, VON MEHREN M, ANTONESCU C R, et al. NCCN Task Force report: update on the management of patients with gastrointestinal stromal tumors[J]. J Natl Compr Canc Netw, 2010, 8(Suppl 2): S1-S44.
32	RUTKOWSKI P, PRZYBYŁ J, ZDZIENICKI M. Extended adjuvant therapy with imatinib in patients with gastrointestinal stromal tumors[J]. Mol Diagn Ther, 2013, 17(1): 9-19.
33	ECKARDT A J, ADLER A, GOMES E M, et al. Endosonographic large-bore biopsy of gastric subepithelial tumors: a prospective multicenter study[J]. Eur J Gastroenterol Hepatol, 2012, 24(10): 1135-1144.
34	ZHAO Y L, FENG M B, WANG M H, et al. CT radiomics for the preoperative prediction of Ki67 index in gastrointestinal stromal tumors: a multi-center study[J]. Front Oncol, 2021, 11: 689136.
35	WANG C, LI H L, JIAERKEN Y, et al. Building CT radiomics-based models for preoperatively predicting malignant potential and mitotic count of gastrointestinal stromal tumors[J]. Transl Oncol, 2019, 12(9): 1229-1236.
36	BASILIO-DE-OLIVEIRA R P, PANNAIN V L N. Prognostic angiogenic markers (endoglin, VEGF, CD31) and tumor cell proliferation (Ki67) for gastrointestinal stromal tumors[J]. World J Gastroenterol, 2015, 21(22): 6924-6930.
37	ZHANG Q W, GAO Y J, ZHANG R Y, et al. Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort[J]. Clin Transl Med, 2020, 9(1): 12.
38	KURATA Y, HAYANO K, OHIRA G, et al. Fractal analysis of contrast-enhanced CT images for preoperative prediction of malignant potential of gastrointestinal stromal tumor[J]. Abdom Radiol, 2018, 43(10): 2659-2664.
39	朱从波, 廖国庆, 赵丁民. Ki-67对胃肠道间质瘤预后的评估价值[J]. 临床与病理杂志, 2018, 38(8): 1632-1639.
	ZHU C B, LIAO G Q, ZHAO D M. Prognostic value of Ki-67 index in gastrointestinal stromal tumor[J]. Journal of Clinical and Pathological Research, 2018, 38(8): 1632-1639.
40	XU F, MA X H, WANG Y C, et al. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation[J]. Eur J Radiol, 2018, 107: 90-97.
41	LIU X J, YIN Y, WANG X Z, et al. Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation[J]. Ann Transl Med, 2021, 9(19): 1496.
42	PALATRESI D, FEDELI F, DANTI G, et al. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience[J]. Radiol Med, 2022, 127(2): 117-128.
43	CHEN T, NING Z Y, XU L L, et al. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively[J]. Eur Radiol, 2019, 29(3): 1074-1082.
44	CHU H R, PANG P P, HE J, et al. Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors[J]. Sci Rep, 2021, 11(1): 12009.
45	WANG M H, FENG Z, ZHOU L X, et al. Computed-tomography-based radiomics model for predicting the malignant potential of gastrointestinal stromal tumors preoperatively: a multi-classifier and multicenter study[J]. Front Oncol, 2021, 11: 582847.
46	REN C Y, WANG S P, ZHANG S J. Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors[J]. Cancer Imaging, 2020, 20(1): 5.
47	ZHANG Q W, ZHOU X X, ZHANG R Y, et al. Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: a multicenter study[J]. Clin Transl Med, 2020, 10(3): e291.
48	COLLEWET G, STRZELECKI M, MARIETTE F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification[J]. Magn Reson Imaging, 2004, 22(1): 81-91.
49	LUBNER M G, STABO N, LUBNER S J, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes[J]. Abdom Imaging, 2015, 40(7): 2331-2337.
50	HUANG Y Q, LIANG C H, HE L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. J Clin Oncol, 2016, 34(18): 2157-2164.

[1]	Xiaofeng WANG, Lu ZHOU, Leyu YAO, Fan HE, Haixia PENG, Daming YANG, Xiaolin HUANG. Effect of DCNN model-assisted colorectal polyp detection system on the detection of colorectal polyps by junior physicians [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(2): 205-210.
[2]	Xin LI, Qing FAN. Application progress of machine learning in the study of facial features of patients with depression [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(1): 124-129.
[3]	RONG Wen-wen1, WANG Gang1, ZHU Qi-li2. Discussion on value of medical records-structured specialized disease database based on artificial intelligence in clinical research [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2020, 40(7): 995-1000.
[4]	LU Yan-qiao, SHEN Lan, HE Ben. Application of artificial intelligence in assisted diagnosis and treatment of cardiovascular disease [J]. , 2020, 40(2): 259-.
[5]	JIA Zhi-ying1, 2, DONG Min-ye1, 2, SHI Zhen-su2, 3, JIN Chun-lin4, LI Guo-hong1, 2. Study of a screening system for mild cognitive impairment based on machine learning model [J]. , 2019, 39(8): 908-.
[6]	XU Yu-peng1, DU Yu-chen1, 2, CHEN Feng-e1. Automatic layer segmentation of optical coherence tomography images in retinal vascular diseases [J]. , 2019, 39(6): 613-.
[7]	SANG Chao1, XIE Guo-xiang2, LIANG Dan-dan1, ZHAO Ai-hua1, JIA Wei1, 2, CHEN Tian-lu1. Improvement of liver fibrosis diagnostic models based on Youden index [J]. , 2019, 39(10): 1156-.
[8]	ZHANG Xin-yu1, ZHANG Jing2, ZHU Xiao-qiang1, CAO Ying-ying1, CHEN Hao-yan1. Bacterial signatures for diagnosis of colorectal cancerfecal metagenomics analysis [J]. , 2018, 38(9): 1019-.