New progression of radiomics in diagnosis of gastric cancer

doi:10.16139/j.1007-9610.2023.01.07

Abstract

Abstract:

Gastric cancer is a common entity of malignant tumor in China with the third cause of mortality and morbidity among all malignancies. In recent years, radiomics has emerged as a quantitative tool for imaging analysis with the rise of artificial intelligence. Currently, radiomics has been applied in many aspects of gastric cancer. In this review, we will focus on the progression of radiomics in diagnosis of gastric cancer, and describe the role of radiomics in differential diagnosis, staging and detection of histopathological biomarkers in detail, to reveal the value of radiomics in the precision medicine of gastric cancer.

Key words: Gastric cancer, Radiomics, Precision medicine

CLC Number:

R735.2

ZHANG Huan, CHEN Yong. New progression of radiomics in diagnosis of gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 42-48.

References 69

[1]	BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6):394-424. doi: 10.3322/caac.v68.6 URL
[2]	国家卫生健康委员会. 胃癌诊治指南(2022年版)[EB/OL]. 2022.http://www.nhc.gov.cn/yzygj/s7659/202204/a0e67177df1f439898683e1333957c74.shtml.
	National Health Commission. Guidelines for the diagnosis and treatment of gastric cancer (2022 edition)[EB/OL]. 2022.http://www.nhc.gov.cn/yzygj/s7659/202204/a0e67177df1f439898683e1333957c74.shtml.
[3]	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. doi: 10.1016/j.ejca.2011.11.036 pmid: 22257792
[4]	AMIN M B, GREENE F L, EDGE S B, et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging[J]. CA Cancer J Clin, 2017, 67(2):93-99. doi: 10.3322/caac.21388 URL
[5]	LORDICK F, CARNEIRO F, CASCINU S, et al. Gastric cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2022, 33(10):1005-1020. doi: 10.1016/j.annonc.2022.07.004 pmid: 35914639
[6]	AJANI J A, D’AMICO T A, BENTREM D J, et al. Gastric Cancer, Version 2.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2022, 20(2):167-192. doi: 10.6004/jnccn.2022.0008 URL
[7]	WANG F H, ZHANG X T, LI Y F, et al. The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer, 2021[J]. Cancer Commun(London), 2021, 41(8):747-795.
[8]	CHANG X, GUO X, LI X, et al. Potential value of radiomics in the identification of stage T3 and T4a esophagogastric junction adenocarcinoma based on contrast-enhanced CT images[J]. Front Oncol, 2021, 11:627947. doi: 10.3389/fonc.2021.627947 URL
[9]	SUN R J, FANG M J, TANG L, et al. CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer[J]. Eur J Radiol, 2020, 132:109277. doi: 10.1016/j.ejrad.2020.109277 URL
[10]	PAN B, ZHANG W, CHEN W, et al. Establishment of the radiologic tumor invasion index based on radiomics splenic features and clinical factors to predict serous invasion of gastric cancer[J]. Front Oncol, 2021, 11:682456. doi: 10.3389/fonc.2021.682456 URL
[11]	IMAOKA W, IDA K, KATOH T, et al. Is curative endoscopic treatment of early gastric cancer possible?[J]. Endoscopy, 1987, 19(Suppl 1):7-11.
[12]	HARTGRINK H H, VAN DE VELDE C J H, PUTTER H, et al. Extended lymph node dissection for gastric cancer: who may benefit? Final results of the randomized Dutch gastric cancer group trial[J]. J Clin Oncol, 2004, 22(11):2069-2077. doi: 10.1200/JCO.2004.08.026 pmid: 15082726
[13]	ZENG Q, LI H, ZHU Y, et al. Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer[J]. Front Med(Lausanne), 2022, 9:986437.
[14]	CHEN W, WANG S, DONG D, et al. Evaluation of lymph node metastasis in advanced gastric cancer using magnetic resonance imaging-based radiomics[J]. Front Oncol, 2019, 9:1265. doi: 10.3389/fonc.2019.01265 pmid: 31824847
[15]	FENG Q X, LIU C, QI L, et al. An intelligent clinical decision support system for preoperative prediction of lymph node metastasis in gastric cancer[J]. J Am Coll Radiol, 2019, 16(7):952-960. doi: 10.1016/j.jacr.2018.12.017 URL
[16]	JIANG Y, WANG W, CHEN C, et al. Radiomics signature on computed tomography imaging: association with lymph node metastasis in patients with gastric cancer[J]. Front Oncol, 2019, 9:340. doi: 10.3389/fonc.2019.00340 pmid: 31106158
[17]	LI J, DONG D, FANG M, et al. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer[J]. Eur Radiol, 2020, 30(4):2324-2333. doi: 10.1007/s00330-019-06621-x pmid: 31953668
[18]	WANG Y, LIU W, YU Y, et al. CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer[J]. Eur Radiol, 2020, 30(2):976-986. doi: 10.1007/s00330-019-06398-z pmid: 31468157
[19]	YANG J, WU Q, XU L, et al. Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer[J]. Radiother Oncol, 2020, 150:89-96. doi: S0167-8140(20)30322-4 pmid: 32531334
[20]	LIU Q, LI J, XIN B, et al. ¹⁸F-FDG PET/CT radiomics for preoperative prediction of lymph node metastases and nodal staging in gastric cancer[J]. Front Oncol, 2021, 11:723345. doi: 10.3389/fonc.2021.723345 URL
[21]	MENG L, DONG D, CHEN X, et al. 2D and 3D CT radiomic features performance comparison in characterization of gastric cancer: a multi-center study[J]. IEEE J Biomed Health Inform, 2021, 25(3):755-763. doi: 10.1109/JBHI.6221020 URL
[22]	SUN Z, JIANG Y, CHEN C, et al. Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: a multicenter study[J]. Radiother Oncol, 2021, 165:179-190. doi: 10.1016/j.radonc.2021.11.003 pmid: 34774652
[23]	WANG L, GONG J, HUANG X, et al. CT-based radiomics nomogram for preoperative prediction of No.10 lymph nodes metastasis in advanced proximal gastric cancer[J]. Eur J Surg Oncol, 2021, 47(6):1458-1465. doi: 10.1016/j.ejso.2020.11.132 pmid: 33261951
[24]	WANG X, LI C, FANG M, et al.Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer[J]. BMC Med Imaging, 2021, 21(1):58. doi: 10.1186/s12880-021-00587-3 pmid: 33757460
[25]	GUAN X, LU N, ZHANG J. Computed tomography-based deep learning nomogram can accurately predict lymph node metastasis in gastric cancer[J]. Dig Dis and Sci, 2022.
[26]	LI Y, XIE F, XIONG Q, et al. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: a systematic review and meta-analysis[J]. Front Oncol, 2022, 12:946038. doi: 10.3389/fonc.2022.946038 URL
[27]	XUE X Q, YU W J, SHAO X L, et al. Radiomics model based on preoperative ¹⁸F-fluorodeoxyglucose PET predicts N2-3b lymph node metastasis in gastric cancer patients[J]. Nucl Med Commun, 2022, 43(3):340-349. doi: 10.1097/MNM.0000000000001523 URL
[28]	SHIOZAKI H, ELIMOVA E, SLACK R S, et al. Prognosis of gastric adenocarcinoma patients with various burdens of peritoneal metastases[J]. J Surg Oncol, 2016, 113(1):29-35. doi: 10.1002/jso.24087 pmid: 26603684
[29]	KURAMOTO M, SHIMADA S, IKESHIMA S, et al. Extensive intraoperative peritoneal lavage as a standard prophylactic strategy for peritoneal recurrence in patients with gastric carcinoma[J]. Ann Surg, 2009, 250(2):242-246. doi: 10.1097/SLA.0b013e3181b0c80e pmid: 19638909
[30]	HUANG J, CHEN Y, ZHANG Y, et al. Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer[J]. Abdom Radiol(NY), 2022, 47(1):66-75.
[31]	DONG D, TANG L, LI Z Y, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer[J]. Ann Oncol, 2019, 30(3):431-438. doi: S0923-7534(19)31081-6 pmid: 30689702
[32]	WANG L, LV P, XUE Z, et al. Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer[J]. Eur J Surg Oncol, 2022, 48(10):2166-2173. doi: 10.1016/j.ejso.2022.06.034 pmid: 35817631
[33]	LIU S, HE J, LIU S, et al. Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer[J]. Eur Radiol, 2020, 30(1):239-246. doi: 10.1007/s00330-019-06368-5 pmid: 31385045
[34]	CHEN Y, XI W, YAO W, et al. Dual-energy computed tomography-based radiomics to predict peritoneal metastasis in gastric cancer[J]. Front Oncol, 2021, 11:659981. doi: 10.3389/fonc.2021.659981 URL
[35]	XUE B, JIANG J, CHEN L, et al. Development and validation of a radiomics model based on ¹⁸F-FDG PET of primary gastric cancer for predicting peritoneal metastasis[J]. Front Oncol, 2021, 11:740111. doi: 10.3389/fonc.2021.740111 URL
[36]	CUNNINGHAM D, ALLUM W H, STENNING S P, et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer[J]. N Engl J Med, 2006, 355(1):11-20. doi: 10.1056/NEJMoa055531 URL
[37]	AL-BATRAN S E, HOMANN N, PAULIGK C, et al. Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel versus fluorouracil or capecita-bine plus cisplatin and epirubicin for locally advanced, resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4): a randomised, phase 2/3 trial[J]. Lancet, 2019, 393(10184):1948-1957. doi: 10.1016/S0140-6736(18)32557-1 URL
[38]	XU Q, SUN Z, LI X, et al. Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy[J]. Eur Radiol, 2021, 31(11):8765-8774. doi: 10.1007/s00330-021-07962-2 pmid: 33909133
[39]	WANG L, ZHANG Y, CHEN Y, et al. The performance of a dual-energy CT derived radiomics model in differentiating serosal invasion for advanced gastric cancer patients after neoadjuvant chemotherapy: iodine map combined with 120-kV equivalent mixed images[J]. Front Oncol, 2020, 10:562945. doi: 10.3389/fonc.2020.562945 URL
[40]	MA Z, FANG M, HUANG Y, et al. CT-based radiomics signature for differentiating Borrmann type Ⅳ gastric cancer from primary gastric lymphoma[J]. Eur J Radiol, 2017, 91:142-147. doi: 10.1016/j.ejrad.2017.04.007 URL
[41]	FENG B, HUANG L, LIU Y, et al. A transfer learning radiomics nomogram for preoperative prediction of borrmann type Ⅳ gastric cancer from primary gastric lymphoma[J]. Front Oncol, 2021, 11:802205. doi: 10.3389/fonc.2021.802205 URL
[42]	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.
[43]	Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma[J]. Nature, 2014, 513(7517):202-209. doi: 10.1038/nature13480
[44]	ZHAO H, LI W, LYU P, et al. TCGA-TCIA-based CT radiomics study for noninvasively predicting epstein-barr virus status in gastric cancer[J]. Am J Roentgenol, 2021, 217(1):124-134. doi: 10.2214/AJR.20.23534 URL
[45]	ZHANG C, WEN H L, ZHANG R, et al. Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study[J]. Acta Radiol, 2022, 63(8):1005-1013. doi: 10.1177/02841851211029083 URL
[46]	BANG Y J, VAN CUTSEM E, FEYEREISLOVA A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial[J]. Lancet, 2010, 376(9742):687-697. doi: 10.1016/S0140-6736(10)61121-X URL
[47]	LI Y, CHENG Z, GEVAERT O, et al. A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer[J]. Chin J Cancer Res, 2020, 32(1):62-71. doi: 10.21147/j.issn.1000-9604.2020.01.08 URL
[48]	MA T, CUI J, WANG L, et al. A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer[J]. Front Genet, 2022, 13:968027. doi: 10.3389/fgene.2022.968027 URL
[49]	GUAN X, LU N, ZHANG J. Evaluation of epidermal growth factor receptor 2 status in gastric cancer by CT-based deep learning radiomics nomogram[J]. Front Oncol, 2022, 12:905203. doi: 10.3389/fonc.2022.905203 URL
[50]	LING Z Q, TANAKA A, LI P, et al. Microsatellite instabi-lity with promoter methylation and silencing of hMLH1 can regionally occur during progression of gastric carcinoma[J]. Cancer Lett, 2010, 297(2):244-251. doi: 10.1016/j.canlet.2010.05.017 URL
[51]	CRISTESCU R, LEE J, NEBOZHYN M, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes[J]. Nat Med, 2015, 21(5):449-456. doi: 10.1038/nm.3850 pmid: 25894828
[52]	VAN VELZEN M J M, DERKS S, VAN GRIEKEN N C T, et al. MSI as a predictive factor for treatment outcome of gastroesophageal adenocarcinoma[J]. Cancer Treat Rev, 2020, 86:102024. doi: 10.1016/j.ctrv.2020.102024 URL
[53]	ZENG Q, ZHU Y, LI L, et al. CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer[J]. Front Oncol, 2022, 12:883109. doi: 10.3389/fonc.2022.883109 URL
[54]	LIANG X, WU Y, LIU Y, et al. A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics[J]. Abdom Radiol(NY), 2022, 47(6):2036-2045.
[55]	TONG Y, LI J, CHEN J, et al. A radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency in gastric adenocarcinoma[J]. Front Oncol, 2022, 12:865548. doi: 10.3389/fonc.2022.865548 URL
[56]	ADACHI Y, YASUDA K, INOMATA M, et al. Pathology and prognosis of gastric carcinoma: well versus poorly differentiated type[J]. Cancer, 2000, 89(7):1418-1424. doi: 10.1002/(ISSN)1097-0142 URL
[57]	HUANG H, XU F, CHEN Q, et al. The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer[J]. Phys Eng Sci Med, 2022, 45(4):1063-1071. doi: 10.1007/s13246-022-01170-y pmid: 36063347
[58]	SHI C, YU Y, YAN J, et al. The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer[J]. BMC Med imaging, 2022, 22(1):173. doi: 10.1186/s12880-022-00899-y pmid: 36192686
[59]	QIU M Z, CAI M Y, ZHANG D S, et al. Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China[J]. J Transl Med, 2013, 11:58. doi: 10.1186/1479-5876-11-58
[60]	HU S B, LIU C H, WANG X, et al. Pathological evaluation of neoadjuvant chemotherapy in advanced gastric cancer[J]. World J Surg Oncol, 2019, 17(1):3. doi: 10.1186/s12957-018-1534-z
[61]	CHEN T, WU J, CUI C, et al. CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort[J]. J Transl Med, 2022, 20(1):38. doi: 10.1186/s12967-022-03232-x pmid: 35073917
[62]	WANG X X, DING Y, WANG S W, et al. Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer[J]. Cancer Imaging, 2020, 20(1):83. doi: 10.1186/s40644-020-00358-3
[63]	WANG Y, LIU W, YU Y, et al. Potential value of CT radiomics in the distinction of intestinal-type gastric adenocarcinomas[J]. Eur Radiol, 2020, 30(5):2934-2944. doi: 10.1007/s00330-019-06629-3 pmid: 32020404
[64]	SUN Z, JIN L, ZHANG S, et al. Preoperative prediction for lauren type of gastric cancer: a radiomics nomogram analysis based on CT images and clinical features[J]. J Xray Sci Technol, 2021, 29(4):675-686.
[65]	ZHENG H, ZHENG Q, JIANG M, et al. Contrast-enhanced CT based radiomics in the preoperative prediction of perineural invasion for patients with gastric cancer[J]. Eur J Radiol, 2022, 154:110393. doi: 10.1016/j.ejrad.2022.110393 URL
[66]	CHEN X, YANG Z, YANG J, et al. Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study[J]. Cancer Imaging, 2020, 20(1):24. doi: 10.1186/s40644-020-00302-5 pmid: 32248822
[67]	LI Q, FENG Q X, QI L, et al. Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging[J]. Abdom Radiol, 2022, 47(2):496-507. doi: 10.1007/s00261-021-03309-z
[68]	FAN L, LI J, ZHANG H, et al. Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables[J]. Abdom Radiol(NY), 2022, 47(4):1209-1222.
[69]	CHEN Q, ZHANG L, LIU S, et al. Radiomics in precision medicine for gastric cancer: opportunities and challenges[J]. Eur Radiol, 2022, 32(9):5852-5868. doi: 10.1007/s00330-022-08704-8 pmid: 35316364