［1］HAN B,ZHENG R,ZENG H,et al.Cancer incidence and mortality in China,2022［J］.J Natl Cancer Cent,2024,4(1):47-53.
[2]GULLO I,CARNEIRO F,OLIVEIRA C,et al.Heterogeneity in gastric cancer:From pure morphology to molecular classifications［J］.Pathobiology,2017,85(1-2):50-63.
[3]WANG X,FAN J.Spatiotemporal molecular medicine:A new era of clinical and translational medicine［J］.Clin Transl Med,2021,11(1):294.
[4]KWEE RM,KWEE TC.Modern imaging techniques for preoperative detection of distant metastases in gastric cancer［J］.World J Gastroenterol,2015,21(37):10502-10509.
[5]HE X,LIU X,ZUO F,et al.Artificial intelligence-based multi-omics analysis fuels cancer precision medicine［J］.Semin Cancer Biol,2023,88:187-200.
[6]USHIJIMA T,CLARK SJ,TAN P.Mapping genomic and epigenomic evolution in cancer ecosystems［J］.Science,2021,373(6562):1474-1479.
[7]MARUSYK A,POLYAK K.Tumor heterogeneity:Causes and consequences［J］.Biochim Biophys Acta,2010,1805(1):105-117.
[8]MEACHAM CE,MORRISON SJ.Tumour heterogeneity and cancer cell plasticity［J］.Nature,2013,501(7467):328-337.
[9]TAN IB,IVANOVA T,LIM KH,et al.Intrinsic subtypes of gastric cancer,based on gene expression pattern,predict survival and respond differently to chemotherapy［J］.Gastroenterology,2011,141(2):476-485.e1-11.
[10]BEDARD PL,HANSEN AR,RATAIN MJ,et al.Tumour heterogeneity in the clinic［J］.Nature,2013,501(7467):355-364.
[11]HANAHAN D,WEINBERG ROBERT A.Hallmarks of cancer:The next generation［J］.Cell,2011,144(5):646-674.
[12]ZHOU Z,WU S,LAI J,et al.Identification of trunk mutations in gastric carcinoma:a case study［J］.BMC Med Genomics,2017,10(1):49.
[13]CAMARGO MC,ANDERSON WF,KING JB,et al.Divergent trends for gastric cancer incidence by anatomical subsite in US adults［J］.Gut,2011,60(12):1644-1649.
[14]HUANG KL,MASHL RJ,WU Y,et al.Pathogenic germline variants in 10,389 adult cancers［J］.Cell,2018,173(2):355-370.
[15]ZHANG P,YANG M,ZHANG Y,et al.Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer［J］.Cell Rep,2019,27(6):1934-1947.
[16]KANEDA A,FEINBERG AP.Loss of imprinting of IGF2:A common epigenetic modifier of intestinal tumor risk［J］.Cancer Res,2005,65(24):11236-11240.
[17]MATSUSAKA K,KANEDA A,NAGAE G,et al.Classification of Epstein-Barr virus-positive gastric cancers by definition of DNA methylation epigenotypes［J］.Cancer Res,2011,71(23):7187-7197.
[18]VECCHI M,NUCIFORO P,ROMAGNOLI S,et al.Gene expression analysis of early and advanced gastric cancers［J］.Oncogene,2007,26(29):4284-4294.
[19]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.
[20]ZHANG Q,YU T,ZHAO Z,et al.Temporal heterogeneity of HER2 expression in metastatic gastric cancer:A case report［J］.World Journal of Surgical Oncology,2022,20(1):157.
[21]JIN MZ,JIN WL.The updated landscape of tumor microenvironment and drug repurposing［J］.Signal Transduct Target Ther,2020,5(1):166.
[22]BHAT AV,HORA S,PAL A,et al.Stressing the(Epi) genome:Dealing with reactive oxygen species in cancer［J］.Antioxid Redox Signal,2018,29(13):1273-1292.
[23]NOWELL PC.The clonal evolution of tumor cell populations［J］.Science,1976,194(4260):23-28.
[24]ROCKEN C,AMALLRAJA A,HALSKE C,et al.Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine［J］.Genome Med,2021,13(1):177.
[25]BOGER C,KRUGER S,BEHRENS HM,et al.Epstein-Barr virus-associated gastric cancer reveals intratumoralheterogeneity of PIK3CA mutations［J］.Ann Oncol,2017,28(5):1005-1014.
[26]SUNDAR R,LIU DH,HUTCHINS GG,et al.Spatial profiling of gastric cancer patient-matched primary and locoregional metastases reveals principles of tumour dissemination［J］.Gut,2021,70(10):1823-1832.
[27]HOFMANN M,STOSS O,SHI D,et al.Assessment of a HER2 scoring system for gastric cancer:results from a validation study［J］.Histopathology,2008,52(7):797-805.
[28]YANG J,LUO H,LI Y,et al.Intratumoral heterogeneity determines discordant results of diagnostic tests for human epidermal growth factor receptor(HER) 2 in gastric cancer specimens［J］.Cell Biochem Biophys,2012,62(1):221-228.
[29]KANAYAMA K,IMAI H,YONEDA M,et al.Significant intratumoral heterogeneity of human epidermal growth factor receptor 2 status in gastric cancer:A comparative study of immunohistochemistry,FISH,and dual-color in situ hybridization［J］.Cancer Sci,2016,107(4):536-542.
[30]Cancer Genome Atlas Research Network.Comprehensive molecular characterization of gastric adenocarcinoma［J］.Nature,2014,513(7517):202-209.
[31]TAN P,YEOH KG.Genetics and molecular pathogenesis of gastric adenocarcinoma［J］.Gastroenterology,2015,149(5):1153-1162.
[32]MATHIAK M,WARNEKE VS,BEHRENS HM,et al.Clinicopathologic caracteristics of microsatellite instable gastric carcinomas revisited:Urgent need for standardization［J］.Appl Immunohistochem Mol Morphol,2017,25(1):12-24.
[33]PECTASIDES E,STACHLER MD,DERKS S,et al.Genomic heterogeneity as a barrier to precision medicine in gastroesophageal adenocarcinoma［J］.Cancer Discov,2018,8(1):37-48.
[34]VON LOGA K,WOOLSTON A,PUNTA M,et al.Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer［J］.Nat Commun,2020,11(1):139.
[35]CARNEIRO F,SEIXAS M,SOBRINHO-SIMOES M.New elements for an updated classification of the carcinomas of the stomach［J］.Pathology-Research and Practice,1995,191(6):571-584.
[36]STELZNER S,EMMRICH P.The mixed type in Laurén's classification of gastric carcinoma.Histologic description and biologic behavior［J］.General & Diagnostic Pathology,1997,143(1):39-48.
[37]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.
[38]ELIZABETH G,DAVIDE C.Physics nobel scooped by machine-learning pioneers［J/OL］.Nature,2024,634(10):523-524［2024-10-21］.https://www.nobelprize.org/prizes/physics/2024/advanced-information.DOI:10.1038/d41586-024-03213-8.
[39]GILLIES RJ,KINAHAN PE,HRICAK H.Radiomics:Images are more than pictures,they are data［J］.Radiology,2016,278(2):563-577.
[40]HUANG Y,LIU Z,HE L,et al.Radiomics signature:A potential biomarker for the prediction of disease-free survival in early-stage(I or II) non-small cell lung cancer［J］.Radiology,2016,281(3):947-957.
[41]AERTS HJWL,VELAZQUEZ ER,LEIJENAAR RTH,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach［J］.Nature Communications,2014,5(1):4006.
[42]ESTEVA A,KUPREL B,NOVOA RA,et al.Dermatologist-level classification of skin cancer with deep neural networks［J］.Nature,2017,542(7639):115-118.
[43]POLAN DF,BRADY SL,KAUFMAN RA.Tissue segmentation of computed tomography images using a Random Forest algorithm:a feasibility study［J］.Phys Med Biol,2016,61(17):6553-6569.
[44]SHINOHARA T,OHYAMA S,YAMAGUCHI T,et al.Clinical value of multidetector row computed tomography in detecting lymph node metastasis of early gastric cancer［J］.Eur J Surg Oncol,2005,31(7):743-748.
[45]MACHLOWSKA J,BAJ J,SITARZ M,et al.Gastric cancer:Epidemiology,risk factors,classification,genomic characteristics and treatment strategies［J］.Int J Mol Sci,2020,21(11):4012.
[46]KUMAR V,GU Y,BASU S,et al.Radiomics:the process and the challenges［J］.Magn Reson Imaging,2012,30(9):1234-1248.
[47]LAMBIN P,LEIJENAAR RTH,DEIST TM,et al.Radiomics:the bridge between medical imaging and personalized medicine［J］.Nature Reviews Clinical Oncology,2017,14(12):749-762.
[48]AFSHAR P,MOHAMMADI A,PLATANIOTIS KN,et al.From handcrafted to deep-learning-based cancer radiomics:Challenges and opportunities［J］.IEEE Signal Processing Magazine,2019,36(4):132-160.
[49]MUTLAG W,ALI S,MOSAD Z,et al.Feature extraction methods:A review［J］.Journal of Physics:Conference Series,2020,1591(1):012028.
[50]TUCERYAN M,JAIN AK.Handbook of pattern recognition and computer vision［M］.Singapore:World Scientific,1993:235-276.
[51]GHALATI MK,NUNES A,FERREIRA H,et al.Texture analysis and its applications in biomedical imaging:A survey［J］.IEEE Reviews in Biomedical Engineering,2022,15(3):222-246.
[52]SARATKAR S,RAUT R,THUTE T,et al.Review of machine learning and deep learning techniques for medical image analysis［J］.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things(ICoICI),2024,1(1):1437-1443.
[53]WANG XX,DING Y,WANG SW,et al.Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer［J］.Cancer Imaging,2020,20(1):83.
[54]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.
[55]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.
[56]WANG Y,YU Y,HAN W,et al.CT radiomics for distinction of human epidermal growth factor receptor 2 negative gastric cancer［J］.Acad Radiol,2021,28(3):86-92.
[57]YANG J,WANG L,QIN J,et al.Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer［J］.Phys Med Biol,2022,67(5):7.
[58]YANG L,SUN J,YU X,et al.Diagnosis of serosal invasion in gastric adenocarcinoma by dual-energy CT radiomics:Focusing on localized gastric wall and peritumoral radiomics features［J］.Front Oncol,2022,12(1):848425.
[59]DONG D,FANG MJ,TANG L,et al.Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer:an international multicenter study［J］.Ann Oncol,2020,31(7):912-920.
[60]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.
[61]TAN X,YANG X,HU S,et al.Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer:A radiomics nomogram analysis based on CT images and clinicopathological features［J］.J Xray Sci Technol,2023,31(1):49-61.
[62]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(1):110393.
[63]甄思雨,梁长华,王笑天,等.基于临床、能谱CT及影像组学构建胃癌神经侵犯的预测模型［J］.中国医学影像学杂志,2024,32(4):339-345,347.
ZHEN SY,LIANG CH,WANG XT,et al.Prediction model of perineural invasion of gastric cancer based on clinical,spectral CT and radiomics［J］.Chinese Journal of Medical Imaging,2018,32(4):339-345,347.
[64]CHEN X,ZHUANG Z,PEN L,et al.Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer［J］.Abdominal Radiology,2024,49(5):1363-1375.
[65]ONOYAMA T,ISHIKAWA S,ISOMOTO H.Gastric cancer and genomics:review of literature［J］.Journal of Gastroenterology,2022,57(8):505-516.
[66]WANG ZN,XU HM,JIANG L,et al.Expression of survivin in primary and metastatic gastric cancer cells obtained by laser capture microdissection［J］.World J Gastroenterol,2004,10(21):3094-3098.
[67]LIU S,SHI H,JI C,et al.Preoperative CT texture analysis of gastric cancer:correlations with postoperative TNM staging［J］.Clin Radiol,2018,73(8):756.
[68]LIU S,LIANG W,HUANG P,et al.Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer［J］.Radiol Med,2023,128(5):509-519.
[69]DONG D,TANG L,LI ZY,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.
[70]SHI S,MIAO Z,ZHOU Y,et al.Radiomics signature for predicting postoperative disease-free survival of patients with gastric cancer:development and validation of a predictive nomogram［J］.Diagn Interv Radiol,2022,28(5):441-449.
[71]ZHENG H,ZHENG Q,JIANG M,et al.Evaluation the benefits of additional radiotherapy for gastric cancer patients after D2 resection using CT based radiomics［J］.Radiol Med,2023,128(6):679-688.
[72]ZWANENBURG A,VALLIERES M,ABDALAH MA,et al.The image biomarker standardization initiative:Standardized quantitative radiomics for high-throughput image-based phenotyping［J］.Radiology,2020,295(2):328-338.
[73]LI J,QIU Z,ZHANG C,et al.ITHscore:comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features［J］.European Radiology,2023,33(2):893-903.
[74]SHI Z,HUANG X,CHENG Z,et al.MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer［J］.Radiology,2023,308(1):222830.
[75]LIU Z,DUAN T,ZHANG Y,et al.Radiogenomics:a key component of precision cancer medicine［J］.British Journal of Cancer,2023,129(5):741-753.
[76]JIN Y,XU Y,LI Y,et al.Integrative radiogenomics approach for risk assessment of postoperative and adjuvant chemotherapy benefits for gastric cancer patients［J］.Front Oncol,2021,11(1):755271.
[77]LIU H,WANG Y,LIU Y,et al.Contrast-enhanced computed tomography-based radiogenomics analysis for predicting prognosis in gastric cancer［J］.Front Oncol,2022,12(1):882786.