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Application progress of MRI based artificial intelligence in rectal cancer
ZHU Yu  OUYANG Zhiqiang  SHAN Haiyan  YANG Lu  CHU Jixiang  LIAO Chengde  KE Tengfei  YANG Jun 

ZHU Y, OUYANG Z Q, SHAN H Y, et al. Application progress of MRI based artificial intelligence in rectal cancer[J]. Chin J Magn Reson Imaging, 2023, 14(9): 176-180. DOI:10.12015/issn.1674-8034.2023.09.032.


[Abstract] High resolution MRI of rectum is the preferred imaging method for evaluating rectal cancer (RC) because of its high soft tissue resolution and its ability to clearly display the rectal wall, mesocenteric fascia, peritoneal reflow and invasion of adjacent organs. However, the semantic features of conventional MRI are still insufficient to assist clinicians in making diagnosis and treatment decisions. Therefore, in the treatment and follow-up process of RC patients, new non-invasive imaging markers are needed to quantitatively describe tumor characteristics, guide clinical development of treatment strategies, and realize individualized diagnosis and treatment. With the development and wide application of artificial intelligence in medicine, it provides an objective reference basis for colorectal cancer evaluation based on high-resolution MRI, which can better assist clinicians to make accurate diagnosis and treatment decisions. This paper summarizes the application of AI in RC lesion segmentation, T stage evaluation, lymph node metastasis prediction, efficacy evaluation after neoadjuvant therapy, and prognosis prediction in recent years, and makes a summary and prospect, aiming to help readers better understand the application progress of MRI-based AI in RC, and provide some reference direction for future research.
[Keywords] rectal cancer；artificial intelligence；magnetic resonance imaging；machine learning

ZHU Yu1   OUYANG Zhiqiang2   SHAN Haiyan1   YANG Lu1   CHU Jixiang1   LIAO Chengde2   KE Tengfei1   YANG Jun1*  

1 Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming 650118, China

2 Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming 650051, China

Corresponding author: Yang J, E-mail: imdyang@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82060313); Medical Leader Project of Health Commission of Yunnan Province (No. D-2018009).
Received  2023-05-01
Accepted  2023-07-21
DOI: 10.12015/issn.1674-8034.2023.09.032
ZHU Y, OUYANG Z Q, SHAN H Y, et al. Application progress of MRI based artificial intelligence in rectal cancer[J]. Chin J Magn Reson Imaging, 2023, 14(9): 176-180. DOI:10.12015/issn.1674-8034.2023.09.032.

[1]
GLYNNE-JONES R, WYRWICZ L, TIRET E, et al. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J/OL]. Ann Oncol, 2017, 28(Suppl 4): iv22-iv40. DOI: 10.1093/annonc/mdx224.
[2]
HORVAT N, CARLOS TAVARES ROCHA C, CLEMENTE OLIVEIRA B, et al. MRI of rectal cancer: tumor staging, imaging techniques, and management[J]. Radiographics, 2019, 39(2): 367-387. DOI: 10.1148/rg.2019180114.
[3]
GÜRSES B, BÖGE M, ALTıNMAKAS E, et al. Multiparametric MRI in rectal cancer[J]. Diagn Interv Radiol, 2019, 25(3): 175-182. DOI: 10.5152/dir.2019.18189.
[4]
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.
[5]
BI W L, HOSNY A, SCHABATH M B, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. DOI: 10.3322/caac.21552.
[6]
CHALLEN R, DENNY J, PITT M, et al. Artificial intelligence, bias and clinical safety[J]. BMJ Qual Saf, 2019, 28(3): 231-237. DOI: 10.1136/bmjqs-2018-008370.
[7]
HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510. DOI: 10.1038/s41568-018-0016-5.
[8]
WANG P P, DENG C L, WU B. Magnetic resonance imaging-based artificial intelligence model in rectal cancer[J]. World J Gastroenterol, 2021, 27(18): 2122-2130. DOI: 10.3748/wjg.v27.i18.2122.
[9]
TREBESCHI S, VAN GRIETHUYSEN J J M, LAMBREGTS D M J, et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR[J/OL]. Sci Rep, 2017, 7(1): 5301 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/28706185/. DOI: 10.1038/s41598-017-05728-9.
[10]
IBTEHAZ N, RAHMAN M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Netw, 2020, 121: 74-87. DOI: 10.1016/j.neunet.2019.08.025.
[11]
WANG J Z, LU J Y, QIN G, et al. Technical Note: a deep learning-based autosegmentation of rectal tumors in MR images[J]. Med Phys, 2018, 45(6): 2560-2564. DOI: 10.1002/mp.12918.
[12]
KNUTH F, ADDE I A, HUYNH B N, et al. MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts[J]. Acta Oncol, 2022, 61(2): 255-263. DOI: 10.1080/0284186X.2021.2013530.
[13]
LI D A, CHU X H, CUI Y F, et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer[J/OL]. Comput Methods Programs Biomed, 2022, 213: 106493 [2023-01-01]. https://pubmed.ncbi.nlm.nih.gov/34749245/. DOI: 10.1016/j.cmpb.2021.106493.
[14]
ZABIHOLLAHY F, VISWANATHAN A N, SCHMIDT E J, et al. Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse-to-fine convolutional neural network[J]. Med Phys, 2021, 48(11): 7028-7042. DOI: 10.1002/mp.15268.
[15]
SUI D, ZHANG K, LIU W F, et al. CST: a multitask learning framework for colorectal cancer region mining based on transformer[J/OL]. Biomed Res Int, 2021, 2021: 6207964 [2022-10-07]. https://pubmed.ncbi.nlm.nih.gov/34671677/. DOI: 10.1155/2021/6207964.
[16]
ORONSKY B, REID T, LARSON C, et al. Locally advanced rectal cancer: the past, present, and future[J]. Semin Oncol, 2020, 47(1): 85-92. DOI: 10.1053/j.seminoncol.2020.02.001.
[17]
BENSON A B, VENOOK A P, AL-HAWARY M M, et al. Rectal cancer, version 2.2018, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2018, 16(7): 874-901. DOI: 10.6004/jnccn.2018.0061.
[18]
LU H D, YUAN Y, ZHOU Z, et al. Assessment of MRI-based radiomics in preoperative T staging of rectal cancer: comparison between minimum and maximum delineation methods[J/OL]. Biomed Res Int, 2021, 2021: 5566885 [2023-06-28]. https://pubmed.ncbi.nlm.nih.gov/34337027/. DOI: 10.1155/2021/5566885.
[19]
YOU J, YIN J D. Performances of whole tumor texture analysis based on MRI: predicting preoperative T stage of rectal carcinomas[J/OL]. Front Oncol, 2021, 11: 678441 [2023-06-28]. https://pubmed.ncbi.nlm.nih.gov/34414105/. DOI: 10.3389/fonc.2021.678441.
[20]
WU Q Y, LIU S L, SUN P, et al. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network[J]. Chin Med J, 2021, 134(7): 821-828. DOI: 10.1097/CM9.0000000000001401.
[21]
HOU M, ZHOU L, SUN J H. Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer[J]. Eur Radiol, 2023, 33(1): 1-10. DOI: 10.1007/s00330-022-08952-8.
[22]
MORENO C C, SULLIVAN P S, MITTAL P K. MRI evaluation of rectal cancer: staging and restaging[J]. Curr Probl Diagn Radiol, 2017, 46(3): 234-241. DOI: 10.1067/j.cpradiol.2016.11.011.
[23]
GRÖNE J, LOCH F N, TAUPITZ M, et al. Accuracy of various lymph node staging criteria in rectal cancer with magnetic resonance imaging[J]. J Gastrointest Surg, 2018, 22(1): 146-153. DOI: 10.1007/s11605-017-3568-x.
[24]
LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
[25]
ZHAO X Y, XIE P Y, WANG M M, et al. Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-MRI for rectal cancer: a multicentre study[J/OL]. EBioMedicine, 2020, 56: 102780 [2022-10-10]. https://pubmed.ncbi.nlm.nih.gov/32512507/. DOI: 10.1016/j.ebiom.2020.102780.
[26]
LI J, ZHOU Y, WANG P, et al. Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer[J]. Quant Imaging Med Surg, 2021, 11(6): 2477-2485. DOI: 10.21037/qims-20-525.
[27]
LIU X C, YANG Q, ZHANG C Y, et al. Multiregional-based magnetic resonance imaging radiomics combined with clinical data improves efficacy in predicting lymph node metastasis of rectal cancer[J/OL]. Front Oncol, 2020, 10: 585767 [2022-10-10]. https://pubmed.ncbi.nlm.nih.gov/33680919/. DOI: 10.3389/fonc.2020.585767.
[28]
KASAI S, SHIOMI A, KAGAWA H, et al. The effectiveness of machine learning in predicting lateral lymph node metastasis from lower rectal cancer: a single center development and validation study[J]. Ann Gastroenterol Surg, 2022, 6(1): 92-100. DOI: 10.1002/ags3.12504.
[29]
LIU Z Y, ZHANG X Y, SHI Y J, 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.
[30]
SHIN J, SEO N, BAEK S E, et al. MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy[J]. Radiology, 2022, 303(2): 351-358. DOI: 10.1148/radiol.211986.
[31]
VAN DER VALK M J M, HILLING D E, BASTIAANNET E, et al. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study[J]. Lancet, 2018, 391(10139): 2537-2545. DOI: 10.1016/S0140-6736(18)31078-X.
[32]
PARK S H, CHO S H, CHOI S H, et al. MRI assessment of complete response to preoperative chemoradiation therapy for rectal cancer: 2020 guide for practice from the Korean society of abdominal radiology[J]. Korean J Radiol, 2020, 21(7): 812-828. DOI: 10.3348/kjr.2020.0483.
[33]
ZHANG X Y, WANG L, ZHU H T, 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.
[34]
JANG B S, LIM Y J, SONG C, et al. Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging[J]. Radiother Oncol, 2021, 161: 183-190. DOI: 10.1016/j.radonc.2021.06.019.
[35]
YARDIMCI A H, KOCAK B, SEL I, et al. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI[J]. Jpn J Radiol, 2023, 41(1): 71-82. DOI: 10.1007/s11604-022-01325-7.
[36]
CHEN X J, WANG W H, CHEN J G, et al. Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost[J]. Int J Colorectal Dis, 2022, 37(7): 1621-1634. DOI: 10.1007/s00384-022-04157-z.
[37]
JAYAPRAKASAM V S, PARODER V, GIBBS P, et al. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer[J]. Eur Radiol, 2022, 32(2): 971-980. DOI: 10.1007/s00330-021-08144-w.
[38]
CERVANTES A, ADAM R, ROSELLÓ S, et al. Metastatic colorectal cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2023, 34(1): 10-32. DOI: 10.1016/j.annonc.2022.10.003.
[39]
LIU Z Y, MENG X C, ZHANG H M, et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer[J/OL]. Nat Commun, 2020, 11(1): 4308 [2023-04-01]. https://pubmed.ncbi.nlm.nih.gov/32855399/. DOI: 10.1038/s41467-020-18162-9.
[40]
CUI Y F, YANG W H, REN J L, et al. Prognostic value of multiparametric MRI-based radiomics model: potential role for chemotherapeutic benefits in locally advanced rectal cancer[J]. Radiother Oncol, 2021, 154: 161-169. DOI: 10.1016/j.radonc.2020.09.039.
[41]
TIBERMACINE H, ROUANET P, SBARRA M, et al. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches[J]. Br J Surg, 2021, 108(10): 1243-1250. DOI: 10.1093/bjs/znab191.
[42]
LIU X Y, ZHANG D F, LIU Z Y, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a multicentre study[J/OL]. EBioMedicine, 2021, 69: 103442 [2023-04-01]. https://pubmed.ncbi.nlm.nih.gov/34157487/. DOI: 10.1016/j.ebiom.2021.103442.
[43]
LIANG M, CAI Z T, ZHANG H M, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis[J]. Acad Radiol, 2019, 26(11): 1495-1504. DOI: 10.1016/j.acra.2018.12.019.
[44]
SIMON H L, DE PAULA T R, PROFETA DA LUZ M M, et al. Predictors of positive circumferential resection margin in rectal cancer: a current audit of the national cancer database[J]. Dis Colon Rectum, 2021, 64(9): 1096-1105. DOI: 10.1097/DCR.0000000000002115.
[45]
WANG D S, XU J H, ZHANG Z D, et al. Evaluation of rectal cancer circumferential resection margin using faster region-based convolutional neural network in high-resolution magnetic resonance images[J]. Dis Colon Rectum, 2020, 63(2): 143-151. DOI: 10.1097/DCR.0000000000001519.
[46]
JHAVERI K S, HOSSEINI-NIK H, THIPPHAVONG S, et al. MRI detection of extramural venous invasion in rectal cancer: correlation with histopathology using elastin stain[J]. AJR Am J Roentgenol, 2016, 206(4): 747-755. DOI: 10.2214/AJR.15.15568.
[47]
ABE T, YASUI M, IMAMURA H, et al. Combination of extramural venous invasion and lateral lymph node size detected with magnetic resonance imaging is a reliable biomarker for lateral lymph node metastasis in patients with rectal cancer[J/OL]. World J Surg Oncol, 2022, 20(1): 5 [2023-04-05]. https://pubmed.ncbi.nlm.nih.gov/34986842/. DOI: 10.1186/s12957-021-02464-3.
[48]
SHU Z Y, MAO D W, SONG Q W, et al. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer[J]. Eur Radiol, 2022, 32(2): 1002-1013. DOI: 10.1007/s00330-021-08242-9.
[49]
LIU S Y, YU X P, YANG S H, et al. Machine learning-based radiomics nomogram for detecting extramural venous invasion in rectal cancer[J/OL]. Front Oncol, 2021, 11: 610338 [2023-04-06]. https://pubmed.ncbi.nlm.nih.gov/33842316/. DOI: 10.3389/fonc.2021.610338.
[50]
ZHAO L, LIANG M, WANG S C, et al. Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI[J]. Abdom Radiol, 2021, 46(8): 3815-3825. DOI: 10.1007/s00261-021-03021-y.

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