The role of radiomics and artificial intelligence in predicting and evaluating the efficacy of neoadjuvant chemoradiotherapy for rectal cancer
Ouyang Ganlu1,2, Wang Xin1,2
1Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China; 2Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
Abstract：Predicting and evaluating the efficacy of neoadjuvant therapy for rectal cancer are of clinical significance and health economic value. At present, exploring the methods of predicting and evaluating the efficacy of neoadjuvant therapy have become research hotspot, focus and difficulty at home and abroad. Radiomics and artificial intelligence (AI) are two rapidly developing technologies. It is worthy of integrating radiomics with AI to build a model for predicting and evaluating the efficacy of neoadjuvant therapy and support individualized clinical decision-making and treatment options. In this article, literature review related to neoadjuvant chemoradiotherapy for rectal cancer based on radiomics and AI was conducted, aiming to explore the prospect and advantages of radiomics and AI in the prediction and evaluation of neoadjuvant therapy.
Ouyang Ganlu,Wang Xin. The role of radiomics and artificial intelligence in predicting and evaluating the efficacy of neoadjuvant chemoradiotherapy for rectal cancer[J]. Chinese Journal of Radiation Oncology, 2023, 32(4): 360-364.
[1] Benson AB, Venook AP, Al-Hawary MM, et al. NCCN guidelines insights: rectal cancer, version 6.2020[J]. J Natl Compr Canc Netw, 2020, 18(7): 806-815. DOI: 10.6004/jnccn.2020.0032. [2] Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2018,29(Suppl 4):iv263. DOI: 10.1093/annonc/mdy161. [3] Collette L, Bosset JF, den Dulk M, et al. Patients with curative resection of cT3-4 rectal cancer after preoperative radiotherapy or radiochemotherapy: does anybody benefit from adjuvant fluorouracil-based chemotherapy? A trial of the European organisation for research and treatment of cancer radiation oncology group[J]. J Clin Oncol, 2007,25(28):4379-4386. DOI: 10.1200/JCO.2007.11.9685. [4] Park IJ, You YN, Agarwal A, et al.Neoadjuvant treatment response as an early response indicator for patients with rectal cancer[J]. J Clin Oncol, 2012,30(15):1770-1776. DOI: 10.1200/JCO.2011.39.7901. [5] Ryan JE, Warrier SK, Lynch AC, et al.Assessing pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review[J]. Colorectal Dis, 2015,17(10):849-861. DOI: 10.1111/codi.13081. [6] Wen BX, Zhang LN, Wang CT, et al.Prognostic significance of clinical and pathological stages on locally advanced rectal carcinoma after neoadjuvant chemoradiotherapy[J]. Radiat Oncol, 2015,10:124. DOI: 10.1186/s13014- 015-0425-5. [7] Sung S, Son SH, Kay CS, et al.Prognosis can be predicted more accurately using pre- and postchemoradiotherapy carcinoembryonic antigen levels compared to only prechemoradiotherapy carcinoembryonic antigen level in locally advanced rectal cancer patients who received neoadjuvant chemoradiotherapy[J]. Medicine (Baltimore), 2016,95(10):e2965. DOI: 10.1097/MD.0000 000000002965. [8] Zhang LN, OuYang PY, Xiao WW, et al. Elevated CA19-9 as the most significant prognostic factor in locally advanced rectal cancer following neoadjuvant chemoradiotherapy[J]. Medicine (Baltimore), 2015,94(45):e1793. DOI: 10.1097/MD.0000000000001793. [9] Del Gobbo A, Ferrero S.Immunohistochemical markers as predictors of histopathologic response and prognosis in rectal cancer treated with preoperative adjuvant therapy: state of the art[J]. Gastroenterol Res Pract, 2017,2017:2808235. DOI: 10.1155/2017/2808235. [10] Liu H, Wang H, Wu JH, et al.Lymphocyte nadir predicts tumor response and survival in locally advanced rectal cancer after neoadjuvant chemoradiotherapy: immunologic relevance[J]. Radiother Oncol, 2019,131:52-59. DOI: 10.1016/j.radonc.2018.12.001. [11] Huang MY, Huang JJ, Huang CM, et al.Relationship between expression of proteins ERCC1, ERCC2, and XRCC1 and clinical outcomes in patients with rectal cancer treated with FOLFOX-based preoperative chemoradiotherapy[J]. World J Surg, 2017,41(11):2884-2897. DOI: 10.1007/s00268-017-4070-z. [12] Hecht M, Büttner-Herold M, Erlenbach-Wünsch K, et al.PD-L1 is upregulated by radiochemotherapy in rectal adenocarcinoma patients and associated with a favourable prognosis[J]. Eur J Cancer, 2016,65:52-60. DOI: 10.1016/j.ejca.2016.06.015. [13] Yokoi K, Yamashita K, Ishii S, et al.Comprehensive molecular exploration identified promoter DNA methylation of the CRBP1 gene as a determinant of radiation sensitivity in rectal cancer[J]. Br J Cancer, 2017,116(8):1046-1056. DOI: 10.1038/bjc.2017.65. [14] Gollub MJ, Blazic I, Bates D, et al.Pelvic MRI after induction chemotherapy and before long-course chemoradiation therapy for rectal cancer: what are the imaging findings?[J]. Eur Radiol, 2019,29(4):1733-1742. DOI: 10.1007/s00330-018-5726-2. [15] Palmisano A, Esposito A, Di Chiara A, et al.Could early tumour volume changes assessed on morphological MRI predict the response to chemoradiation therapy in locally-advanced rectal cancer?[J]. Clin Radiol, 2018,73(6):555-563. DOI: 10.1016/j.crad.2018.01.007. [16] Peng QL, Lin KS, Shen Y, et al.Identification of potential genes and pathways for response prediction of neoadjuvant chemoradiotherapy in patients with rectal cancer by systemic biological analysis[J]. Oncol Lett, 2019,17(1):492-501. DOI: 10.3892/ol.2018.9598. [17] Jia HX, Shen XT, Guan Y, et al.Predicting the pathological response to neoadjuvant chemoradiation using untargeted metabolomics in locally advanced rectal cancer[J]. Radiother Oncol, 2018,128(3):548-556. DOI: 10.1016/j.radonc.2018.06.022. [18] Greenbaum A, Martin DR, Bocklage T, et al.Tumor heterogeneity as a predictor of response to neoadjuvant chemotherapy in locally advanced rectal cancer[J]. Clin Colorectal Cancer, 2019,18(2):102-109. DOI: 10.1016/j.clcc.2019.02.003. [19] Gonçalves-Ribeiro S, Sanz-Pamplona R, Vidal A, et al.Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling[J]. Ann Oncol, 2017,28(9):2160-2168. DOI: 10.1093/annonc/mdx293. [20] 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.2011.11.036. [21] Parekh V, Jacobs MA.Radiomics: a new application from established techniques[J]. Expert Rev Precis Med Drug Dev, 2016,1(2):207-226. DOI: 10.1080/23808993.2016. 1164013. [22] Liu ZY, Wang S, Dong D, et al.The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019,9(5):1303-1322. DOI: 10.7150/thno.30309. [23] Deo RC.Machine learning in medicine[J]. Circulation, 2015,132(20):1920-1930. DOI: 10.1161/CIRCULATION AHA.115.001593. [24] Shimizu H, Nakayama KI.Artificial intelligence in oncology[J]. Cancer Sci, 2020,111(5):1452-1460. DOI: 10.1111/cas.14377. [25] Hamerla G, Meyer HJ, Hambsch P, et al.Radiomics model based on non-contrast ct shows no predictive power for complete pathological response in locally advanced rectal cancer[J]. Cancers (Basel), 2019,11(11):1680. DOI: 10.3390/cancers11111680. [26] Zhou XZ, Yi YJ, Liu ZY, et al.Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer[J]. Ann Surg Oncol, 2019,26(6):1676-1684. DOI: 10.1245/s10434-019-07300-3. [27] Cui YF, Yang XT, Shi ZQ, et al.Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Eur Radiol, 2019,29(3):1211-1220. DOI: 10.1007/s00330-018-5683-9. [28] Zhang XY, Wang L, Zhu HT, et al.Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI[J]. Radiology, 2020,296(1):56-64. DOI: 10.1148/radiol.2020190936. [29] Nie K, Shi LM, Chen Q, et al.Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI[J]. Clin Cancer Res, 2016,22(21):5256-5264. DOI: 10.1158/1078-0432.CCR-15-2997. [30] Bibault JE, Giraud P, Housset M, et al.Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer[J]. Sci Rep, 2018,8(1):12611. DOI:10.1038/s41598-018-30657-6. [31] Shi LM, Zhang Y, Nie K, et al.Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI[J]. Magn Reson Imaging, 2019,61:33-40. DOI: 10.1016/j.mri.2019.05.003. [32] Liu ZY, Zhang XY, Shi YJ, et al.Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Clin Cancer Res, 2017,23(23):7253-7262. DOI: 10.1158/1078-0432.CCR-17-1038. [33] Yi XP, Pei Q, Zhang YM, et al.MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Front Oncol, 2019,9:552. DOI: 10.3389/fonc.2019.00552. [34] Horvat N, Veeraraghavan H, Khan M, et al.MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy[J]. Radiology, 2018,287(3):833-843. DOI: 10.1148/radiol.2018172300. [35] Ferrari R, Mancini-Terracciano C, Voena C, et al.MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer[J]. Eur J Radiol, 2019,118:1-9. DOI: 10.1016/j.ejrad.2019.06.013. [36] Liang CS, Huang YQ, He L, et al.The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer[J]. Oncotarget, 2016,7(21):31401-31412. DOI: 10.18632/oncotarget.8919. [37] Li YQ, Liu WX, Pei Q, et al.Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer[J]. Cancer Med, 2019,8(17):7244-7252. DOI: 10.1002/cam4.2636. [38] Fu J, Zhong XR, Li N, et al.Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer[J]. Phys Med Biol, 2020,65(7):075001. DOI: 10.1088/1361-6560/ab7970. [39] Bulens P, Couwenberg A, Intven M, et al.Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics[J]. Radiother Oncol, 2020,142:246-252. DOI: 10.1016/j.radonc.2019.07.033.
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