
Journal of Surgery Concepts & Practice››2023,Vol. 28››Issue (01): 42-48.doi:10.16139/j.1007-9610.2023.01.07
• Experts forum •Previous ArticlesNext Articles
Received:2022-12-01Online:2023-01-25Published:2023-03-25Contact:ZHANG Huan E-mail:huanzhangy@126.comCLC Number:
ZHANG Huan, CHEN Yong. New progression of radiomics in diagnosis of gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 42-48.
| [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.v68.6URL |
| [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.2011.11.036pmid: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.21388URL |
| [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.2022.07.004pmid: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.2022.0008URL |
| [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.2021.627947URL |
| [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.2020.109277URL |
| [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.2021.682456URL |
| [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.2004.08.026pmid: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.2019.01265pmid: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.2018.12.017URL |
| [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.2019.00340pmid: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-xpmid: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-zpmid: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)30322-4pmid: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.2021.723345URL |
| [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.6221020URL |
| [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.2021.11.003pmid: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.2020.11.132pmid: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/s12880-021-00587-3pmid: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.2022.946038URL |
| [27] | XUE X Q, YU W J, SHAO X L, et al. Radiomics model based on preoperative18F-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.0000000000001523URL |
| [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.24087pmid: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.0b013e3181b0c80epmid: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)31081-6pmid: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.2022.06.034pmid: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/s00330-019-06368-5pmid: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.2021.659981URL |
| [35] | XUE B, JIANG J, CHEN L, et al. Development and validation of a radiomics model based on18F-FDG PET of primary gastric cancer for predicting peritoneal metastasis[J]. Front Oncol, 2021, 11:740111. doi:10.3389/fonc.2021.740111URL |
| [36] | CUNNINGHAM D, ALLUM W H, STENNING S P, et al. Perioperative chemotherapyversussurgery alone for resectable gastroesophageal cancer[J]. N Engl J Med, 2006, 355(1):11-20. doi:10.1056/NEJMoa055531URL |
| [37] | AL-BATRAN S E, HOMANN N, PAULIGK C, et al. Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxelversusfluorouracil 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)32557-1URL |
| [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/s00330-021-07962-2pmid: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.2020.562945URL |
| [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.2017.04.007URL |
| [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.2021.802205URL |
| [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.20.23534URL |
| [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:10.1177/02841851211029083URL |
| [46] | BANG Y J, VAN CUTSEM E, FEYEREISLOVA A, et al. Trastuzumab in combination with chemotherapyversuschemotherapy 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-XURL |
| [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.1000-9604.2020.01.08URL |
| [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.2022.968027URL |
| [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.2022.905203URL |
| [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.2010.05.017URL |
| [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.3850pmid: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.2020.102024URL |
| [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.2022.883109URL |
| [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.2022.865548URL |
| [56] | ADACHI Y, YASUDA K, INOMATA M, et al. Pathology and prognosis of gastric carcinoma: wellversuspoorly differentiated type[J]. Cancer, 2000, 89(7):1418-1424. doi:10.1002/(ISSN)1097-0142URL |
| [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-ypmid: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-ypmid: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-xpmid: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/s00330-019-06629-3pmid: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.2022.110393URL |
| [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/s40644-020-00302-5pmid: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/s00330-022-08704-8pmid:35316364 |
| [1] | ZHU Zhenggang.Progress and prospect of surgical comprehensive treatment of gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 1-6. |
| [2] | ZHAO Fazhi, ZHAO Ping.Formation and improvement of surgery-based treatment system for gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 24-30. |
| [3] | LI Guoli, GUO Feilong.Preoperative chemotherapy through intra-arterial combined with intra-venous administration in treatment of advanced gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 31-35. |
| [4] | LIU Wentao, LIU Fukun.Summary and prospect of perioperative comprehensive treatment for gastric cancer in China[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 36-41. |
| [5] | DENG Shijie, YUAN Fei.Advances in diagnosis and molecular detection of EBV-positive gastric cancer and gastric cancer with lymphoid stroma[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 53-57. |
| [6] | LI Jianfang, YU Junxian, YAN Chao, ZHU Zhenggang, LIU Bingya.Hotspots in basic and translational research of gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 7-16. |
| [7] | LU Yiming, XIONG Jianping, TIAN Yantao.Current status and prospect of conversion therapy for far-advanced gastric cancer[J]. Journal of Surgery Concepts & Practice, 2023, 28(01): 17-23. |
| [8] | LIU Hongyuan, GU Hao, YANG Xi, et al. Sequencing analysis on tissue DNA from 55 patients with venous malformations and lymphatic malformations[J]. Journal of Tissue Engineering and Reconstructive Surgery, 2022, 18(3): 209-. |
| [9] | DAI Zhiqiang, ZHENG Jinxin, TANG Zhaoqing, ZHANG Qi, GU Yuan, SHI Zhongyi, HU Guohua, SUN Yihong.Metastatic lymph node ratio to evaluate prognosis of patients with stage Ⅱ-Ⅲ gastric cancer after radical gastrectomy[J]. Journal of Surgery Concepts & Practice, 2022, 27(05): 429-434. |
| [10] | YANG Ruixin, DU Yutong, YAN Ranlin, ZHU Zhenggang, LI Chen, YU Yingyan.Improving exploration of biological sample pretreatment in single-cell transcriptome sequencing of gastrointestinal tumors[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(05): 567-574. |
| [11] | ZHAO Liqin, GUO Weijian, YU Nuoya, WU Junwei, ZHANG Jun.Effects of activin A on migration and aerobic glycolysis of gastric cancer cells[J]. Journal of Internal Medicine Concepts & Practice, 2022, 17(04): 317-323. |
| [12] | LI Nana, QI Tao, ZHU Liming.Clinical value of serum pepsinogen,gastrin 17 andHelicobacter pyloriIgG antibody in primary screening of gastric diseases[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(04): 509-513. |
| [13] | NIE Mingming, ZHU Zhenggang.Clinical significance of diagnostic laparoscopy on precision staging in advanced gastric cancer[J]. Journal of Surgery Concepts & Practice, 2022, 27(04): 365-370. |
| [14] | GUO Liangqi, YAN Zhilong, ZHANG Moucheng.Laparoscopic intragastric surgery in treating of gastric submucosal tumor and early gastric cancer[J]. Journal of Surgery Concepts & Practice, 2022, 27(04): 380-383. |
| [15] | DU Yanran, JIAO Jing, REN Yunyun, ZHOU Jianqiao.Application of ultrasound-based radiomics technology in the evaluation of fetal lung maturity[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(03): 326-330. |
| Viewed | ||||||
| Full text |
|
|||||
| Abstract |
|
|||||
