Siegel R, Desantis C, Jemal A. Cancer statistics, 2014 (2014) CA Cancer J Clin 64:9–29.
 Degiuli M, Sasako M, Ponti A, Calvo F (2004) Survival results of a multicentre phase II study to evaluate D2 gastrectomy for gastric cancer. Br J Cancer 90:1727–1732.
 Sano T, Sasako M, Yamamoto S, et al (2004) Cancer surgery: morbidity and mortality results from a prospective randomized controlled trial comparing d2 and extended para-aortic lymphadenectomy—Japan Clinical Oncology Group study 9501. J Clin Oncol 22:2767–2773.
 Takahashi T, Saikawa Y, Kitagawa Y. Gastric cancer: current status of diagnosis and treatment (2013) Cancers 5:48–63.
 Cunningham D, Allum WH, Stenning SP, et al (2006) Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med 355:11–20.
 Ychou M, Boige V, Pignon JP, et al (2011) Perioperative chemotherapy compared with surgery alone for resectable gastroesophageal adenocarcinoma: an FNCLCC and FFCD multicenter phase III trial. J Clin Oncol 29:1715–1721.
 Glimelius B, Ekstrom K, Hoffman K, et al (1997) Randomized comparison between chemotherapy plus best supportive care with best supportive care in advanced gastric cancer. Ann Oncol 8:163–168.
 Aoyama T, Nishikawa K, Fujitani K, et al (2017) Early results of a randomized two-by-two factorial phase II trial comparing neoadjuvant chemotherapy with two and four courses of cisplatin/S–1and docetaxel/cisplatom/S–1 as neoadjuvant chemotherapy for locally advanced gastric cancer. Ann Oncol 28: 1876–1881.
 Xue K, Ying XJ, Bu ZD, et al (2018) Oxaliplatin plus S–1 or capecitabine as neoadjuvant or adjuvant chemotherapy for locally advanced gastric cancer with D2 lymphadenectomy: 5-year follow-up results of a phase II-III randomized trial. Chin J Cancer Res 30: 516–525.
 AI-Batran SE, Homann N, Pauligk C, et al (2017) Effect of neoadjuvant chemotherapy followed by surgical resection on survival in patients with limited metastatic gastric or gastroesophageal junction cancer: the AIO-FLOT3 trial. JAMA Oncol 3: 1237–1244.
 Xiong BH, Cheng Y, Ma L, Shang CQ (2014) An updated meta-analysis of randomized controlled trial assessing the effect of preoperative chemotherapy in advanced gastric Cancer. Cancer Invest 32:272–284.
 Weber WA, Ott K, Becker K, et al (2001) Prediction of response to preoperative chemotherapy in adenocarcinomas of the esophagogastric junction by metabolic imaging. J Clin Oncol 19: 3058–3065.
 Wieder HA, Ott K, Lordick F, et al (2007) Prediction of tumor response by FDG-PET: comparison of the accuracy of single and sequential studies in patients with adenocarcinomas of the esophagogastric junction. Eur J Nucl Med Mol Imaging 34: 1925–1932.
 Hansen ML, Fallentin E, Lauridsen C, et al (2014) Computed tomography (CT) perfusion as an early predictive marker for treatment response to neoadjuvant chemotherapy in gastroesophageal junction cancer and gastric cancer-a prospective study. PloS One 9: e97605.
 Lee SM, Kim SH, Lee JM, et al (2009) Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer. Abdom Imaging 34:430–440.
 Ang J, Hu L, Huang PT, et al (2012) Contrast-enhanced ultrasonography assessment of gastric cancer response to neoadjuvant chemotherapy. World J Gastroenterol 18:7026–7032.
 Giganti F, De Cobelli F, Canevari C, et al (2014) Response to chemotherapy in gastric adenocarcinoma with diffusion-weighted MRI and (18) F-FDG-PET/CT: correlation of apparent diffusion coefficient and partial volume corrected standardized uptake value with histological tumor regression grade. J Magn Reson Imaging 40:1147–57.
 Schneider PM, Eshmuminov D, Rordorf T, et al (2018) 18FDG-PET-CT identifies histopathological non-responders after neoadjuvant chemotherapy in locally advanced gastric and cardia cancer: cohort study. BMC Cancer 18: 548.
 Kumar V, Gu Y, Basu S, et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248.
 Li Y, Liu X, Xu K, et al (2018) MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol 28:356–362.
 Esteva A, Kuprel B, Novoa RA, et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118.
 Braman NM, Etesami M, Prasanna P, et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57.
 Yang L, Dong D, Fang MJ, et al (2018) Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol 28:2058–2067.
 Mandard AM, Dalibard F, Mandard JC, et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 73: 2680–2686.
 Noble F, Lloyd MA, Turkington R, et al (2017) Multicentre cohort study to define and validate pathological assessment of response to neoadjuvant therapy in oesophagogastric adenocarcinoma. Br J Surg 104: 1816–1828.
 Qiao X, Jiao H (2018) Data mining techniques in analyzing process data: a didactic. Front Psychol 9: 2231.
 Laster L (1967) Statistical background of methods of principle component analysis. J Periodontol 38: Suppl 649–666.
 Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Machine Learn 63: 3–42.
 Maree R, Geurts P, Wehenkel L (2007) Random subwindows and extremely randomized trees for image classification in cell biology. BMC Cell Biol 8 Suppl 1: S2.
 Jiang YM, Chen CL, Xie JJ, et al (2018) Radiomics signature of computed tomography imaging for prediction of survival and chemotheapeutic benefits in gastric cancer. EbioMedicine 36: 171–182.
 Yoon SH, Kim YH, Lee YJ, et al (2016) Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment. Plos One 11: e0161278.
 Aerts HJ, Velazquez ER, Leijenaar RT, et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006.
 Tan P, Yeoh KG (2015) Genetics and Molecular Pathogenesis of Gastric Adenocarcinoma. Gastroenterology 149: e3.
 O’Connor JP, Aboagye EO, Adams JE, et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186.
 Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am College Radiol 12: 862–866.
 Grossmann P, Stringfield O, El-Hachem N, et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6: e23421.
 Fox MJ, Gibbs P, Pickles MD (2016) Minkowski functionals: An MRI texture analysis tool for determination of the aggressiveness of breast cancer. J Magn Reson Imaging 43:903–910.
 Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336.
 Segal E, Sirlin CB, Ooi C, et al (2007) Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25:675–680.
 Wang WT, Yang L, Yang ZX, et al (2018) Assessment of microvascular invasion of hepatocellular carcinoma with diffusion kurtosis imaging. Radiology 286: 571–580