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Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy

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Abdominal Radiology Aims and scope

Abstract

Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.

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References

  1. 1 Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. 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.

    Article  PubMed  Google Scholar 

  2. The global, regional, and national burden of stomach cancer in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease study 2017. Lancet Gastroenterol Hepatol. 2020. 5(1): 42-54.

  3. 3 Sasako M, Sakuramoto S, Katai H, et al. Five-year outcomes of a randomized phase III trial comparing adjuvant chemotherapy with S-1 versus surgery alone in stage II or III gastric cancer. J Clin Oncol. 2011. 29(33): 4387-93.

    Article  CAS  PubMed  Google Scholar 

  4. 4 Cunningham D, Allum WH, Stenning SP, et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med. 2006. 355(1): 11-20.

    Article  CAS  PubMed  Google Scholar 

  5. 5 Russell MC. Comparison of neoadjuvant versus a surgery first approach for gastric and esophagogastric cancer. J Surg Oncol. 2016. 114(3): 296-303.

    Article  PubMed  Google Scholar 

  6. Ajani JA, D'Amico TA, Bentrem DJ, et al. Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022. 20(2): 167-192.

  7. 7 Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet. 2020. 396(10251): 635-648.

    Article  CAS  PubMed  Google Scholar 

  8. 8 D'Ugo D, Persiani R, Rausei S, et al. Response to neoadjuvant chemotherapy and effects of tumor regression in gastric cancer. Eur J Surg Oncol. 2006. 32(10): 1105-9.

    Article  CAS  PubMed  Google Scholar 

  9. 9 Lowy AM, Mansfield PF, Leach SD, Pazdur R, Dumas P, Ajani JA. Response to neoadjuvant chemotherapy best predicts survival after curative resection of gastric cancer. Ann Surg. 1999. 229(3): 303-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10 Sylvie L, Susanne B, Katja O. Prediction of response and prognosis by a score including only pretherapeutic parameters in 410 neoadjuvant treated gastric cancer patients. Recent Results Cancer Res. 2012. 196: 269-89.

    Article  PubMed  Google Scholar 

  11. 11 Zurlo IV, Schino M, Strippoli A, et al. Predictive value of NLR, TILs (CD4+/CD8+) and PD-L1 expression for prognosis and response to preoperative chemotherapy in gastric cancer. Cancer Immunol Immunother. 2022. 71(1): 45-55.

    Article  CAS  PubMed  Google Scholar 

  12. 12 Jia Y, Ye L, Ji K, et al. Death-associated protein-3, DAP-3, correlates with preoperative chemotherapy effectiveness and prognosis of gastric cancer patients following perioperative chemotherapy and radical gastrectomy. Br J Cancer. 2014. 110(2): 421-9.

    Article  CAS  PubMed  Google Scholar 

  13. 13 Kwee RM, Kwee TC. Imaging in local staging of gastric cancer: a systematic review. J Clin Oncol. 2007. 25(15): 2107-16.

    Article  PubMed  Google Scholar 

  14. 14 Kwee RM, Kwee TC. Imaging in assessing lymph node status in gastric cancer. Gastric Cancer. 2009. 12(1): 6-22.

    Article  CAS  PubMed  Google Scholar 

  15. 15 Cardoso R, Coburn N, Seevaratnam R, et al. A systematic review and meta-analysis of the utility of EUS for preoperative staging for gastric cancer. Gastric Cancer. 2012. 15 Suppl 1: S19-26.

    Article  PubMed  Google Scholar 

  16. 16 Borggreve AS, Goense L, Brenkman H, et al. Imaging strategies in the management of gastric cancer: current role and future potential of MRI. Br J Radiol. 2019. 92(1097): 20181044.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17 Caivano R, Rabasco P, Lotumolo A, et al. Gastric cancer: The role of diffusion weighted imaging in the preoperative staging. Cancer Invest. 2014. 32(5): 184-90.

    Article  CAS  PubMed  Google Scholar 

  18. 18 Yoshikawa T, Tanabe K, Nishikawa K, et al. Accuracy of CT staging of locally advanced gastric cancer after neoadjuvant chemotherapy: cohort evaluation within a randomized phase II study. Ann Surg Oncol. 2014. 21 Suppl 3: S385-9.

    Article  PubMed  Google Scholar 

  19. Sandø AD, Fougner R, Grønbech JE, Bringeland EA. The value of restaging CT following neoadjuvant chemotherapy for resectable gastric cancer. A population-based study. World J Surg Oncol. 2021. 19(1): 212.

  20. 20 Park SR, Lee JS, Kim CG, et al. Endoscopic ultrasound and computed tomography in restaging and predicting prognosis after neoadjuvant chemotherapy in patients with locally advanced gastric cancer. Cancer. 2008. 112(11): 2368-76.

    Article  PubMed  Google Scholar 

  21. Redondo-Cerezo E, Martínez-Cara JG, Jiménez-Rosales R, et al. Endoscopic ultrasound in gastric cancer staging before and after neoadjuvant chemotherapy. A comparison with PET-CT in a clinical series. United European Gastroenterol J. 2017. 5(5): 641-647.

  22. 22 Peng T, Lou Z, Wang X, et al. Clinical Comparison of Endoscopic Ultrasonography and CT in Preoperative TN Staging of Esophagogastric Junction Cancer. Contrast Media Mol Imaging. 2022. 2022: 5810405.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23 Guo T, Yao F, Yang AM, et al. Endoscopic ultrasound in restaging and predicting pathological response for advanced gastric cancer patients after neoadjuvant chemotherapy. Asia Pac J Clin Oncol. 2014. 10(2): e28-32.

    Article  PubMed  Google Scholar 

  24. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009. 45(2): 228-47.

  25. 25 Kurokawa Y, Shibata T, Sasako M, et al. Validity of response assessment criteria in neoadjuvant chemotherapy for gastric cancer (JCOG0507-A). Gastric Cancer. 2014. 17(3): 514-21.

    Article  CAS  PubMed  Google Scholar 

  26. 26 Mazzei MA, Bagnacci G, Gentili F, et al. Gastric Cancer Maximum Tumour Diameter Reduction Rate at CT Examination as a Radiological Index for Predicting Histopathological Regression after Neoadjuvant Treatment: A Multicentre GIRCG Study. Gastroenterol Res Pract. 2018. 2018: 1794524.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27 Wang ZL, Li YL, Li XT, Tang L, Li ZY, Sun YS. Role of CT in the prediction of pathological complete response in gastric cancer after neoadjuvant chemotherapy. Abdom Radiol (NY). 2021. 46(7): 3011-3018.

    Article  PubMed  Google Scholar 

  28. 28 Chen C, Dong H, Shou C, et al. The Correlation Between Computed Tomography Volumetry and Prognosis of Advanced Gastric Cancer Treated with Neoadjuvant Chemotherapy. Cancer Manag Res. 2020. 12: 759-768.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29 Lee SM, Kim SH, Lee JM, et al. Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer. Abdom Imaging. 2009. 34(4): 430-40.

    Article  PubMed  Google Scholar 

  30. 30 Beer AJ, Wieder HA, Lordick F, et al. Adenocarcinomas of esophagogastric junction: multi-detector row CT to evaluate early response to neoadjuvant chemotherapy. Radiology. 2006. 239(2): 472-80.

    Article  PubMed  Google Scholar 

  31. 31 Achilli P, De Martini P, Ceresoli M, et al. Tumor response evaluation after neoadjuvant chemotherapy in locally advanced gastric adenocarcinoma: a prospective, multi-center cohort study. J Gastrointest Oncol. 2017. 8(6): 1018-1025.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32 Ang J, Hu L, Huang PT, et al. Contrast-enhanced ultrasonography assessment of gastric cancer response to neoadjuvant chemotherapy. World J Gastroenterol. 2012. 18(47): 7026-32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33 Satoh A, Shuto K, Okazumi S, et al. Role of perfusion CT in assessing tumor blood flow and malignancy level of gastric cancer. Dig Surg. 2010. 27(4): 253-60.

    Article  PubMed  Google Scholar 

  34. 34 Lundsgaard Hansen M, Fallentin E, Lauridsen C, Law I, et al. 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. 2014. 9(5): e97605.

    Article  PubMed  Google Scholar 

  35. 36 Sun ZQ, Yan G, Ge YX, et al. Can low-dose CT perfusion imaging accurately assess response of advanced gastric cancer with neoadjuvant chemotherapy. J Xray Sci Technol. 2017. 25(6): 981-991.

    CAS  PubMed  Google Scholar 

  36. 36 Bellomi M, Petralia G, Sonzogni A, Zampino MG, Rocca A. CT perfusion for the monitoring of neoadjuvant chemotherapy and radiation therapy in rectal carcinoma: initial experience. Radiology. 2007. 244(2): 486-93.

    Article  PubMed  Google Scholar 

  37. 37 Djuric-Stefanovic A, Micev M, Stojanovic-Rundic S, Pesko P, Dj S. Absolute CT perfusion parameter values after the neoadjuvant chemoradiotherapy of the squamous cell esophageal carcinoma correlate with the histopathologic tumor regression grade. Eur J Radiol. 2015. 84(12): 2477-84.

    Article  CAS  PubMed  Google Scholar 

  38. 38 Becker K, Mueller JD, Schulmacher C, et al. Histomorphology and grading of regression in gastric carcinoma treated with neoadjuvant chemotherapy. Cancer. 2003. 98(7): 1521-30.

    Article  PubMed  Google Scholar 

  39. 39 McCollough CH, Leng S, Yu L, Fletcher JG. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. Radiology. 2015. 276(3): 637-53.

    Article  PubMed  Google Scholar 

  40. 40 Du W, Yu M, Luo X, Chen M. Application Value of Spectral CT Imaging in Quantitative Analysis of Early Lung Adenocarcinoma. J Oncol. 2022. 2022: 2944473.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41 Zhang Y, Chen J, Yuan F, Zhang B, Ding B, Zhang H. Prognostic role of iodine values for gastric cancer after neoadjuvant chemotherapy: a strong independent prognostic factor. Diagn Interv Radiol. 2022. 28(5): 388-395.

    Article  PubMed  Google Scholar 

  42. 42 Gao X, Zhang Y, Yuan F, et al. Locally advanced gastric cancer: total iodine uptake to predict the response of primary lesion to neoadjuvant chemotherapy. J Cancer Res Clin Oncol. 2018. 144(11): 2207-2218.

    Article  CAS  PubMed  Google Scholar 

  43. 43 Tang L, Li ZY, Li ZW, et al. Evaluating the response of gastric carcinomas to neoadjuvant chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol. 2015. 70(11): 1198-204.

    Article  CAS  PubMed  Google Scholar 

  44. 44 Yang L, Li Y, Shi GF, Zhou T, Tan BB. The Concentration of Iodine in Perigastric Adipose Tissue: A Novel Index for the Assessment of Serosal Invasion in Patients with Gastric Cancer after Neoadjuvant Chemotherapy. Digestion. 2018. 98(2): 87-94.

    Article  CAS  PubMed  Google Scholar 

  45. 45 Apfaltrer P, Meyer M, Meier C, et al. Contrast-enhanced dual-energy CT of gastrointestinal stromal tumors: is iodine-related attenuation a potential indicator of tumor response. Invest Radiol. 2012. 47(1): 65-70.

    Article  CAS  PubMed  Google Scholar 

  46. 46 Uhrig M, Simons D, Ganten MK, Hassel JC, Schlemmer HP. Histogram analysis of iodine maps from dual energy computed tomography for monitoring targeted therapy of melanoma patients. Future Oncol. 2015. 11(4): 591-606.

    Article  CAS  PubMed  Google Scholar 

  47. 47 Heijmen L, Verstappen MC, Ter Voert EE, et al. Tumour response prediction by diffusion-weighted MR imaging: ready for clinical use. Crit Rev Oncol Hematol. 2012. 83(2): 194-207.

    Article  PubMed  Google Scholar 

  48. 48 Feng Y, Liu H, Ding Y, et al. Combined dynamic DCE-MRI and diffusion-weighted imaging to evaluate the effect of neoadjuvant chemotherapy in cervical cancer. Tumori. 2020. 106(2): 155-164.

    Article  CAS  PubMed  Google Scholar 

  49. 49 Tong T, Sun Y, Gollub MJ, et al. Dynamic contrast-enhanced MRI: Use in predicting pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. J Magn Reson Imaging. 2015. 42(3): 673-80.

    Article  PubMed  Google Scholar 

  50. 50 Zheng D, Lai G, Chen Y, et al. Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma. J Magn Reson Imaging. 2018. 48(5): 1208-1216.

    Article  PubMed  Google Scholar 

  51. 51 De Cobelli F, Giganti F, Orsenigo E, et al. Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology. Eur Radiol. 2013. 23(8): 2165-74.

    Article  PubMed  Google Scholar 

  52. 52 Giganti F, De Cobelli F, Canevari C, et al. 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. 2014. 40(5): 1147-57.

    Article  PubMed  Google Scholar 

  53. 53 Li J, Yan LL, Zhang HK, et al. Dynamic contrast-enhanced and diffusion-weighted MR imaging in early prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. Abdom Radiol (NY). 2022. 47(10): 3394-3405.

    Article  PubMed  Google Scholar 

  54. 54 Zhu Y, Jiang Z, Wang B, et al. Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer. Front Oncol. 2022. 12: 841460.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55 Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988. 168(2): 497-505.

    Article  PubMed  Google Scholar 

  56. 56 Federau C. Measuring Perfusion: Intravoxel Incoherent Motion MR Imaging. Magn Reson Imaging Clin N Am. 2021. 29(2): 233-242.

    Article  PubMed  Google Scholar 

  57. 57 Fu J, Tang L, Li ZY, et al. Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy. Eur J Radiol. 2020. 128: 108974.

    Article  PubMed  Google Scholar 

  58. 58 Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005. 53(6): 1432-40.

    Article  PubMed  Google Scholar 

  59. 59 Sun K, Chen X, Chai W, et al. Breast Cancer: Diffusion Kurtosis MR Imaging-Diagnostic Accuracy and Correlation with Clinical-Pathologic Factors. Radiology. 2015. 277(1): 46-55.

    Article  PubMed  Google Scholar 

  60. 60 Stahl A, Ott K, Schwaiger M, Weber WA. Comparison of different SUV-based methods for monitoring cytotoxic therapy with FDG PET. Eur J Nucl Med Mol Imaging. 2004. 31(11): 1471-8.

    Article  CAS  PubMed  Google Scholar 

  61. 61 Wieder HA, Ott K, Lordick F, et al. 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. 2007. 34(12): 1925-32.

    Article  PubMed  Google Scholar 

  62. 62 Mi L, Zhao Y, Zhao X, et al. (18)F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography Metabolic Parameters Before and After Neoadjuvant Chemotherapy Can Predict the Postoperative Prognosis of Locally Advanced Gastric Cancer. Cancer Biother Radiopharm. 2021. 36(8): 662-671.

    CAS  PubMed  Google Scholar 

  63. 63 Güç ZG, Turgut B, Avci A, Cengiz F, Eren Kalender M, Alacacioğlu A. Predicting pathological response and overall survival in locally advanced gastric cancer patients undergoing neoadjuvant chemotherapy: the role of PET/computed tomography. Nucl Med Commun. 2022. 43(5): 560-567.

    Article  PubMed  Google Scholar 

  64. 64 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): 3316-23.

    Article  PubMed  Google Scholar 

  65. 65 Vallböhmer D, Hölscher AH, Schneider PM, et al. [18F]-fluorodeoxyglucose-positron emission tomography for the assessment of histopathologic response and prognosis after completion of neoadjuvant chemotherapy in gastric cancer. J Surg Oncol. 2010. 102(2): 135-40.

    Article  PubMed  Google Scholar 

  66. 66 Schneider PM, Eshmuminov D, Rordorf T, et al. 18FDG-PET-CT identifies histopathological non-responders after neoadjuvant chemotherapy in locally advanced gastric and cardia cancer: cohort study. BMC Cancer. 2018. 18(1): 548.

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67 Chen Q, Zhang L, Liu S, et al. Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol. 2022. 32(9): 5852-5868.

    Article  CAS  PubMed  Google Scholar 

  68. 68 Li Z, Zhang D, Dai Y, et al. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study. Chin J Cancer Res. 2018. 30(4): 406-414.

    Article  PubMed  PubMed Central  Google Scholar 

  69. 69 Sun KY, Hu HT, Chen SL, et al. CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer. BMC Cancer. 2020. 20(1): 468.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70 Wang W, Peng Y, Feng X, et al. Development and Validation of a Computed Tomography-Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer. JAMA Netw Open. 2021. 4(8): e2121143.

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71 Xie K, Cui Y, Zhang D, et al. Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study. Front Oncol. 2021. 11: 770758.

    Article  CAS  PubMed  Google Scholar 

  72. 72 Chen Y, Xu W, Li YL, et al. CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy. Front Oncol. 2022. 12: 758863.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73 Song R, Cui Y, Ren J, et al. CT-based radiomics analysis in the prediction of response to neoadjuvant chemotherapy in locally advanced gastric cancer: A dual-center study. Radiother Oncol. 2022. 171: 155-163.

    Article  CAS  PubMed  Google Scholar 

  74. 74 Xu Q, Sun Z, Li X, et al. Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy. Eur Radiol. 2021. 31(11): 8765-8774.

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75 Chen Y, Wei K, Liu D, et al. A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer. Front Oncol. 2021. 11: 675458.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Li J, Yin H, Wang Y, et al. Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer. Eur Radiol. 2022 .

  77. Li J, Zhang HL, Yin HK, et al. Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. J Magn Reson Imaging. 2022 .

  78. 78 Mazzei MA, Di Giacomo L, Bagnacci G, et al. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer). Quant Imaging Med Surg. 2021. 11(6): 2376-2387.

    Article  PubMed  PubMed Central  Google Scholar 

  79. 79 Chartrand G, Cheng PM, Vorontsov E, et al. Deep Learning: A Primer for Radiologists. Radiographics. 2017. 37(7): 2113-2131.

    Article  PubMed  Google Scholar 

  80. 80 Zhang J, Cui Y, Wei K, et al. Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study. Gastric Cancer. 2022. 25(6): 1050-1059.

    Article  CAS  PubMed  Google Scholar 

  81. 81 Cui Y, Zhang J, Li Z, et al. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study. EClinicalMedicine. 2022. 46: 101348.

    Article  PubMed  PubMed Central  Google Scholar 

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This work was supported by National Natural Science Foundation of China (Grant Nos. 82102151, 82071872).

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Deng, J., Zhang, W., Xu, M. et al. Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy. Abdom Radiol 48, 3661–3676 (2023). https://doi.org/10.1007/s00261-023-04046-1

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