Imaging diagnosis and research progress of gastric cancer in peritoneal metastasis_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WANG Bo , LIU Dan , LIU Xijiao ,  WU Bing

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R.China;

Corresponding author：

WU Bing, Email: bingwu69@163.com

Keywords：

gastric cancer; peritoneal metastasis; radiomics; artificial intelligence

DOI：

10.7507/1007-9424.202311001

Video：

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Abstract Full text Figures/Tables Video References Cited by

Gastric cancer remains one of the most prevalent and fatal malignancies in China. Peritoneal metastasis represents a frequent mode of dissemination or recurrence in patients with advanced disease and confers an extremely poor prognosis. In recent years, considerable progress has been made in imaging techniques, with modalities including CT, ultrasound, MRI and PET-CT being implemented to evaluate peritoneal metastasis. However, adequate detection remains challenging, particularly for occult peritoneal metastasis. With the advent of precision medicine, radiomics and artificial intelligence have undergone rapid development and show considerable promise for the early prediction of peritoneal metastasis in gastric cancer, providing a new means of diagnosis and treatment for patients with peritoneal metastasis.

Citation： WANG Bo, LIU Dan, LIU Xijiao, WU Bing. Imaging diagnosis and research progress of gastric cancer in peritoneal metastasis. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2024, 31(3): 265-271. doi: 10.7507/1007-9424.202311001 Copy

1.	Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	Zheng R, Zhang S, Zeng H, et al. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center, 2022, 2(1): 1-9.
3.	Thomassen I, van Gestel YR, van Ramshorst B, et al. Peritoneal carcinomatosis of gastric origin: a population-based study on incidence, survival and risk factors. Int J Cancer, 2014, 134(3): 622-628.
4.	Bonnot PE, Piessen G, Kepenekian V, et al. Cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastases (CYTO-CHIP study): A propensity score analysis. J Clin Oncol, 2019, 37(23): 2028-2040.
5.	Macrì A, Morabito F. The use of intraperitoneal chemotherapy for gastric malignancies. Expert Rev Anticancer Ther, 2019, 19(10): 879-888.
6.	Wang FH, Zhang XT, Li YF, et al. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021. Cancer Commun (Lond), 2021, 41(8): 747-795.
7.	Lordick F, Carneiro F, Cascinu S, et al. Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol, 2022, 33(10): 1005-1020.
8.	Li Z, Li Z, Jia S, et al. Depth of tumor invasion and tumor-occupied portions of stomach are predictive factors of intra-abdominal metastasis. Chin J Cancer Res, 2017, 29(2): 109-117.
9.	Li K, Cannon JGD, Jiang SY, et al. Diagnostic staging laparoscopy in gastric cancer treatment: A cost-effectiveness analysis. J Surg Oncol, 2018, 117(6): 1288-1296.
10.	Paget S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev, 1989, 8(2): 98-101.
11.	Kanda M, Kodera Y. Molecular mechanisms of peritoneal dissemination in gastric cancer. World J Gastroenterol, 2016, 22(30): 6829-6840.
12.	马茹, 姬忠贺, 张颖, 等. 胃肠道癌腹膜转移的核心病理机制. 中华胃肠外科杂志, 2021, 24(3): 198-203.
13.	Bootsma S, Bijlsma MF, Vermeulen L. The molecular biology of peritoneal metastatic disease. EMBO Mol Med, 2023, 15(3): e15914.
14.	van’t Sant I, Engbersen MP, Bhairosing PA, et al. Diagnostic performance of imaging for the detection of peritoneal metastases: a meta-analysis. Eur Radiol, 2020, 30(6): 3101-3112.
15.	Koh JL, Yan TD, Glenn D, et al. Evaluation of preoperative computed tomography in estimating peritoneal cancer index in colorectal peritoneal carcinomatosis. Ann Surg Oncol, 2009, 16(2): 327-333.
16.	Kim SJ, Kim HH, Kim YH, et al. Peritoneal metastasis: detection with 16- or 64-detector row CT in patients undergoing surgery for gastric cancer. Radiology, 2009, 253(2): 407-415.
17.	中国抗癌协会胃癌专业委员会. 胃癌腹膜转移诊治中国专家共识(2023版). 中华胃肠外科杂志, 2023, 26(8): 717-728.
18.	Li ZY, Tang L, Li ZM, et al. Four-point computed tomography scores for evaluation of occult peritoneal metastasis in patients with gastric cancer: A region-to-region comparison with staging laparoscopy. Ann Surg Oncol, 2020, 27(4): 1103-1109.
19.	Kim SH, Choi YH, Kim JW, et al. Clinical significance of computed tomography-detected ascites in gastric cancer patients with peritoneal metastases. Medicine (Baltimore), 2018, 97(8): e9343.
20.	Gu X, Li Y, Shi G, et al. Construction of a nomogram model for predicting peritoneal metastasis in gastric cancer: focused on cardiophrenic angle lymph node features. Abdom Radiol (NY), 2023, 48(4): 1227-1236.
21.	Nehra AK, Dane B, Yeh BM, et al. Dual-energy, spectral and photon counting computed tomography for evaluation of the gastrointestinal tract. Radiol Clin North Am, 2023, 61(6): 1031-1049.
22.	Zeng Y, Geng D, Zhang J. Noise-optimized virtual monoenergetic imaging technology of the third-generation dual-source computed tomography and its clinical applications. Quant Imaging Med Surg, 2021, 11(11): 4627-4643.
23.	Darras KE, Clark SJ, Kang H, et al. Virtual monoenergetic reconstruction of contrast-enhanced CT scans of the abdomen and pelvis at 40 keV improves the detection of peritoneal metastatic deposits. Abdom Radiol (NY), 2019, 44(2): 422-428.
24.	Giandola T, Maino C, Marrapodi G, et al. Imaging in gastric cancer: current practice and future perspectives. Diagnostics (Basel), 2023, 13(7): 1276.
25.	Power DG, Schattner MA, Gerdes H, et al. Endoscopic ultrasound can improve the selection for laparoscopy in patients with localized gastric cancer. J Am Coll Surg, 2009, 208(2): 173-178.
26.	Ayoub F, Chapman CG, Chen H, et al. Endoscopic ultrasound predicts risk of occult intra-abdominal metastases in localized gastric cancer: A validation study. Gastroenterology Res, 2023, 16(1): 9-16.
27.	Bozkurt M, Doganay S, Kantarci M, et al. Comparison of peritoneal tumor imaging using conventional MR imaging and diffusion-weighted MR imaging with different b values. Eur J Radiol, 2011, 80(2): 224-228.
28.	Low RN. MR imaging of the peritoneal spread of malignancy. Abdom Imaging, 2007, 32(3): 267-283.
29.	Takahara T, Imai Y, Yamashita T, et al. Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display. Radiat Med, 2004, 22(4): 275-282.
30.	Kwee TC, Takahara T, Ochiai R, et al. Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): features and potential applications in oncology. Eur Radiol, 2008, 18(9): 1937-1952.
31.	Michielsen K, Vergote I, Op de Beeck K, et al. Whole-body MRI with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to CT and FDG-PET/CT. Eur Radiol, 2014, 24(4): 889-901.
32.	De Vuysere S, Vandecaveye V, De Bruecker Y, et al. Accuracy of whole-body diffusion-weighted MRI (WB-DWI/MRI) in diagnosis, staging and follow-up of gastric cancer, in comparison to CT: a pilot study. BMC Med Imaging, 2021, 21(1): 18.
33.	党娜, 杜敏, 张谷青, 等. ¹⁸F-FDG PET/CT显像在胃癌伴同时性腹膜转移中的预测价值. 医学影像学杂志, 2023, 33(1): 43-47.
34.	Lin R, Lin Z, Chen Z, et al. [⁶⁸Ga]Ga-DOTA-FAPI-04 PET/CT in the evaluation of gastric cancer: comparison with [¹⁸F]FDG PET/CT. Eur J Nucl Med Mol Imaging, 2022, 49(8): 2960-2971.
35.	Miao Y, Feng R, Guo R, et al. Utility of [⁶⁸Ga]FAPI-04 and [¹⁸F]FDG dual-tracer PET/CT in the initial evaluation of gastric cancer. Eur Radiol, 2023, 33(6): 4355-4366.
36.	Perlaza P, Ortín J, Pagès M, et al. Should ¹⁸F-FDG PET/CT be routinely performed in the clinical staging of locally advanced gastric adenocarcinoma? Clin Nucl Med, 2018, 43(6): 402-410.
37.	Kim JH, Heo SH, Kim JW, et al. Evaluation of recurrence in gastric carcinoma: Comparison of contrast-enhanced computed tomography and positron emission tomography/computed tomography. World J Gastroenterol, 2017, 23(35): 6448-6456.
38.	Soussan M, Des Guetz G, Barrau V, et al. Comparison of FDG-PET/CT and MR with diffusion-weighted imaging for assessing peritoneal carcinomatosis from gastrointestinal malignancy. Eur Radiol, 2012, 22(7): 1479-1487.
39.	Wang Y, Luo W, Li Y. [⁶⁸Ga]Ga-FAPI-04 PET MRI/CT in the evaluation of gastric carcinomas compared with [¹⁸F]-FDG PET MRI/CT: a meta-analysis. Eur J Med Res, 2023, 28(1): 34.
40.	Ott K, Herrmann K, Schuster T, et al. Molecular imaging of proliferation and glucose utilization: utility for monitoring response and prognosis after neoadjuvant therapy in locally advanced gastric cancer. Ann Surg Oncol, 2011, 18(12): 3316-3323.
41.	Honma Y, Terauchi T, Tateishi U, et al. Imaging peritoneal metastasis of gastric cancer with ¹⁸F-fluorothymidine positron emission tomography/computed tomography: a proof-of-concept study. Br J Radiol, 2018, 91(1089): 20180259.
42.	Lindner T, Loktev A, Altmann A, et al. Development of quinoline-based theranostic ligands for the targeting of fibroblast activation protein. J Nucl Med, 2018, 59(9): 1415-1422.
43.	Ruan D, Zhao L, Cai J, et al. Evaluation of FAPI PET imaging in gastric cancer: a systematic review and meta-analysis. Theranostics, 2023, 13(13): 4694-4710.
44.	Kim HY, Kim YH, Yun G, et al. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One, 2018, 13(3): e0194755.
45.	Li LM, Feng LY, Liu CC, et al. Can visceral fat parameters based on computed tomography be used to predict occult peritoneal metastasis in gastric cancer? World J Gastroenterol, 2023, 29(15): 2310-2321.
46.	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. Ann Oncol, 2019, 30(3): 431-438.
47.	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. Eur Radiol, 2020, 30(1): 239-246.
48.	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. Front Oncol, 2021 Oct 26: 11: 740111.
49.	Mirniaharikandehei S, Heidari M, Danala G, et al. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. Comput Methods Programs Biomed, 2021, 200: 105937.
50.	Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med, 2019, 25(1): 24-29.
51.	Huang Z, Liu D, Chen X, et al. Deep convolutional neural network based on computed tomography images for the preoperative diagnosis of occult peritoneal metastasis in advanced gastric cancer. Front Oncol, 2020, 10: 601869.
52.	Jiang Y, Liang X, Wang W, et al. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw Open, 2021, 4(1): e2032269.
53.	Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med, 2019, 179(3): 293-294.
54.	Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA, 2018, 320(11): 1192-1193.

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Zheng R, Zhang S, Zeng H, et al. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center, 2022, 2(1): 1-9.
3. Thomassen I, van Gestel YR, van Ramshorst B, et al. Peritoneal carcinomatosis of gastric origin: a population-based study on incidence, survival and risk factors. Int J Cancer, 2014, 134(3): 622-628.
4. Bonnot PE, Piessen G, Kepenekian V, et al. Cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastases (CYTO-CHIP study): A propensity score analysis. J Clin Oncol, 2019, 37(23): 2028-2040.
5. Macrì A, Morabito F. The use of intraperitoneal chemotherapy for gastric malignancies. Expert Rev Anticancer Ther, 2019, 19(10): 879-888.
6. Wang FH, Zhang XT, Li YF, et al. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021. Cancer Commun (Lond), 2021, 41(8): 747-795.
7. Lordick F, Carneiro F, Cascinu S, et al. Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol, 2022, 33(10): 1005-1020.
8. Li Z, Li Z, Jia S, et al. Depth of tumor invasion and tumor-occupied portions of stomach are predictive factors of intra-abdominal metastasis. Chin J Cancer Res, 2017, 29(2): 109-117.
9. Li K, Cannon JGD, Jiang SY, et al. Diagnostic staging laparoscopy in gastric cancer treatment: A cost-effectiveness analysis. J Surg Oncol, 2018, 117(6): 1288-1296.
10. Paget S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev, 1989, 8(2): 98-101.
11. Kanda M, Kodera Y. Molecular mechanisms of peritoneal dissemination in gastric cancer. World J Gastroenterol, 2016, 22(30): 6829-6840.
12. 马茹, 姬忠贺, 张颖, 等. 胃肠道癌腹膜转移的核心病理机制. 中华胃肠外科杂志, 2021, 24(3): 198-203.
13. Bootsma S, Bijlsma MF, Vermeulen L. The molecular biology of peritoneal metastatic disease. EMBO Mol Med, 2023, 15(3): e15914.
14. van’t Sant I, Engbersen MP, Bhairosing PA, et al. Diagnostic performance of imaging for the detection of peritoneal metastases: a meta-analysis. Eur Radiol, 2020, 30(6): 3101-3112.
15. Koh JL, Yan TD, Glenn D, et al. Evaluation of preoperative computed tomography in estimating peritoneal cancer index in colorectal peritoneal carcinomatosis. Ann Surg Oncol, 2009, 16(2): 327-333.
16. Kim SJ, Kim HH, Kim YH, et al. Peritoneal metastasis: detection with 16- or 64-detector row CT in patients undergoing surgery for gastric cancer. Radiology, 2009, 253(2): 407-415.
17. 中国抗癌协会胃癌专业委员会. 胃癌腹膜转移诊治中国专家共识(2023版). 中华胃肠外科杂志, 2023, 26(8): 717-728.
18. Li ZY, Tang L, Li ZM, et al. Four-point computed tomography scores for evaluation of occult peritoneal metastasis in patients with gastric cancer: A region-to-region comparison with staging laparoscopy. Ann Surg Oncol, 2020, 27(4): 1103-1109.
19. Kim SH, Choi YH, Kim JW, et al. Clinical significance of computed tomography-detected ascites in gastric cancer patients with peritoneal metastases. Medicine (Baltimore), 2018, 97(8): e9343.
20. Gu X, Li Y, Shi G, et al. Construction of a nomogram model for predicting peritoneal metastasis in gastric cancer: focused on cardiophrenic angle lymph node features. Abdom Radiol (NY), 2023, 48(4): 1227-1236.
21. Nehra AK, Dane B, Yeh BM, et al. Dual-energy, spectral and photon counting computed tomography for evaluation of the gastrointestinal tract. Radiol Clin North Am, 2023, 61(6): 1031-1049.
22. Zeng Y, Geng D, Zhang J. Noise-optimized virtual monoenergetic imaging technology of the third-generation dual-source computed tomography and its clinical applications. Quant Imaging Med Surg, 2021, 11(11): 4627-4643.
23. Darras KE, Clark SJ, Kang H, et al. Virtual monoenergetic reconstruction of contrast-enhanced CT scans of the abdomen and pelvis at 40 keV improves the detection of peritoneal metastatic deposits. Abdom Radiol (NY), 2019, 44(2): 422-428.
24. Giandola T, Maino C, Marrapodi G, et al. Imaging in gastric cancer: current practice and future perspectives. Diagnostics (Basel), 2023, 13(7): 1276.
25. Power DG, Schattner MA, Gerdes H, et al. Endoscopic ultrasound can improve the selection for laparoscopy in patients with localized gastric cancer. J Am Coll Surg, 2009, 208(2): 173-178.
26. Ayoub F, Chapman CG, Chen H, et al. Endoscopic ultrasound predicts risk of occult intra-abdominal metastases in localized gastric cancer: A validation study. Gastroenterology Res, 2023, 16(1): 9-16.
27. Bozkurt M, Doganay S, Kantarci M, et al. Comparison of peritoneal tumor imaging using conventional MR imaging and diffusion-weighted MR imaging with different b values. Eur J Radiol, 2011, 80(2): 224-228.
28. Low RN. MR imaging of the peritoneal spread of malignancy. Abdom Imaging, 2007, 32(3): 267-283.
29. Takahara T, Imai Y, Yamashita T, et al. Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display. Radiat Med, 2004, 22(4): 275-282.
30. Kwee TC, Takahara T, Ochiai R, et al. Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): features and potential applications in oncology. Eur Radiol, 2008, 18(9): 1937-1952.
31. Michielsen K, Vergote I, Op de Beeck K, et al. Whole-body MRI with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to CT and FDG-PET/CT. Eur Radiol, 2014, 24(4): 889-901.
32. De Vuysere S, Vandecaveye V, De Bruecker Y, et al. Accuracy of whole-body diffusion-weighted MRI (WB-DWI/MRI) in diagnosis, staging and follow-up of gastric cancer, in comparison to CT: a pilot study. BMC Med Imaging, 2021, 21(1): 18.
33. 党娜, 杜敏, 张谷青, 等. ¹⁸F-FDG PET/CT显像在胃癌伴同时性腹膜转移中的预测价值. 医学影像学杂志, 2023, 33(1): 43-47.
34. Lin R, Lin Z, Chen Z, et al. [⁶⁸Ga]Ga-DOTA-FAPI-04 PET/CT in the evaluation of gastric cancer: comparison with [¹⁸F]FDG PET/CT. Eur J Nucl Med Mol Imaging, 2022, 49(8): 2960-2971.
35. Miao Y, Feng R, Guo R, et al. Utility of [⁶⁸Ga]FAPI-04 and [¹⁸F]FDG dual-tracer PET/CT in the initial evaluation of gastric cancer. Eur Radiol, 2023, 33(6): 4355-4366.
36. Perlaza P, Ortín J, Pagès M, et al. Should ¹⁸F-FDG PET/CT be routinely performed in the clinical staging of locally advanced gastric adenocarcinoma? Clin Nucl Med, 2018, 43(6): 402-410.
37. Kim JH, Heo SH, Kim JW, et al. Evaluation of recurrence in gastric carcinoma: Comparison of contrast-enhanced computed tomography and positron emission tomography/computed tomography. World J Gastroenterol, 2017, 23(35): 6448-6456.
38. Soussan M, Des Guetz G, Barrau V, et al. Comparison of FDG-PET/CT and MR with diffusion-weighted imaging for assessing peritoneal carcinomatosis from gastrointestinal malignancy. Eur Radiol, 2012, 22(7): 1479-1487.
39. Wang Y, Luo W, Li Y. [⁶⁸Ga]Ga-FAPI-04 PET MRI/CT in the evaluation of gastric carcinomas compared with [¹⁸F]-FDG PET MRI/CT: a meta-analysis. Eur J Med Res, 2023, 28(1): 34.
40. Ott K, Herrmann K, Schuster T, et al. Molecular imaging of proliferation and glucose utilization: utility for monitoring response and prognosis after neoadjuvant therapy in locally advanced gastric cancer. Ann Surg Oncol, 2011, 18(12): 3316-3323.
41. Honma Y, Terauchi T, Tateishi U, et al. Imaging peritoneal metastasis of gastric cancer with ¹⁸F-fluorothymidine positron emission tomography/computed tomography: a proof-of-concept study. Br J Radiol, 2018, 91(1089): 20180259.
42. Lindner T, Loktev A, Altmann A, et al. Development of quinoline-based theranostic ligands for the targeting of fibroblast activation protein. J Nucl Med, 2018, 59(9): 1415-1422.
43. Ruan D, Zhao L, Cai J, et al. Evaluation of FAPI PET imaging in gastric cancer: a systematic review and meta-analysis. Theranostics, 2023, 13(13): 4694-4710.
44. Kim HY, Kim YH, Yun G, et al. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One, 2018, 13(3): e0194755.
45. Li LM, Feng LY, Liu CC, et al. Can visceral fat parameters based on computed tomography be used to predict occult peritoneal metastasis in gastric cancer? World J Gastroenterol, 2023, 29(15): 2310-2321.
46. 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. Ann Oncol, 2019, 30(3): 431-438.
47. 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. Eur Radiol, 2020, 30(1): 239-246.
48. 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. Front Oncol, 2021 Oct 26: 11: 740111.
49. Mirniaharikandehei S, Heidari M, Danala G, et al. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. Comput Methods Programs Biomed, 2021, 200: 105937.
50. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med, 2019, 25(1): 24-29.
51. Huang Z, Liu D, Chen X, et al. Deep convolutional neural network based on computed tomography images for the preoperative diagnosis of occult peritoneal metastasis in advanced gastric cancer. Front Oncol, 2020, 10: 601869.
52. Jiang Y, Liang X, Wang W, et al. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw Open, 2021, 4(1): e2032269.
53. Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med, 2019, 179(3): 293-294.
54. Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA, 2018, 320(11): 1192-1193.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Imaging diagnosis and research progress of gastric cancer in peritoneal metastasis

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