Share：
Share this content in WeChat
X
[Chinese][PDF]551
• Review •
MRI-Based Artificial Intelligence in Lymph Node Metastasis of Rectal Cancer
YANG Xinyue  WEN Zhibo 

Cite this article as: YANG X Y, WEN Z B. MRI-Based Artificial Intelligence in Lymph Node Metastasis of Rectal Cancer[J]. Chin J Magn Reson Imaging, 2024, 15(10): 205-210. DOI:10.12015/issn.1674-8034.2024.10.035.


[Abstract] Rectal cancer is one of the most common malignancies in the digestive tract. Cancer cells usually disseminate from rectal tumors to distant sites via lymphatic vessels. Thus, lymph node involvement, which influences treatment and prognosis, plays a crucial role in patients with rectal cancer. High resolution MRI has been used to estimate lymph node metastasis in rectal cancer. However, the morphological criteria were influenced by the subjective judgement of different observers. Artificial intelligence (AI) can mine and learn quantitative features from medical images, thus providing a new method for us to distinguish metastatic lymph nodes. In this review, we summarize the research progress of MRI-based AI in the evaluation of nodal metastasis with rectal cancer before and after the neoadjuvant chemoradiotherapy. We further discuss the challenges and provide prospects of AI research to help researchers understand the limitations of MRI-based AI in evaluation of nodal involvement in rectal cancer and offer guidance for future prospective, multi-center, big-data AI research.
[Keywords] rectal cancer；lymph node；magnetic resonance imaging；artificial intelligence

YANG Xinyue   WEN Zhibo*  

Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China

Corresponding author: WEN Z B, E-mail: zhibowen@163.com

Conflicts of interest   None.

Received  2024-06-03
Accepted  2024-10-10
DOI: 10.12015/issn.1674-8034.2024.10.035
Cite this article as: YANG X Y, WEN Z B. MRI-Based Artificial Intelligence in Lymph Node Metastasis of Rectal Cancer[J]. Chin J Magn Reson Imaging, 2024, 15(10): 205-210. DOI:10.12015/issn.1674-8034.2024.10.035.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023[J]. CA A Cancer J Clinicians, 2023, 73(1): 17-48. DOI: 10.3322/caac.21763.
[3]
XIA C F, DONG X S, LI H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants[J]. Chin Med J, 2022, 135(5): 584-590. DOI: 10.1097/CM9.0000000000002108.
[4]
BORGHERESI A, MUZIO F D, AGOSTINI A, et al. Lymph nodes evaluation in rectal cancer: where do we stand and future perspective[J/OL]. J Clin Med, 2022, 11(9): 2599 [2024-10-21]. https://www.mdpi.com/2077-0383/11/9/2599. DOI: 10.3390/jcm11092599.
[5]
BENSON A B, VENOOK A P, AL-HAWARY M M, et al. Rectal cancer, version 2.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2022, 20(10): 1139-1167. DOI: 10.6004/jnccn.2022.0051.
[6]
LEE S, KASSAM Z, BAHETI A D, et al. Rectal cancer lexicon 2023 revised and updated consensus statement from the Society of Abdominal Radiology Colorectal and Anal Cancer Disease-Focused Panel[J]. Abdom Radiol, 2023, 48(9): 2792-2806. DOI: 10.1007/s00261-023-03893-2.
[7]
CRIMÌ F, CABRELLE G, CAMPI C, et al. Nodal staging with MRI after neoadjuvant chemo-radiotherapy for locally advanced rectal cancer: a fast and reliable method[J]. Eur Radiol, 2024, 34(5): 3205-3214. DOI: 10.1007/s00330-023-10265-3.
[8]
PEACOCK O, MANISUNDARAM N, KIM Y, et al. Therapeutic lateral pelvic lymph node dissection in rectal cancer: when to dissect? Size is not everything[J]. Br J Surg, 2023, 110(8): 985-986. DOI: 10.1093/bjs/znad115.
[9]
LANGMAN G, PATEL A, BOWLEY D M. Size and distribution of lymph nodes in rectal cancer resection specimens[J]. Dis Colon Rectum, 2015, 58(4): 406-414. DOI: 10.1097/DCR.0000000000000321.
[10]
FAN J H, ZHANG S C. Research progress of magnetic resonance imaging in evaluating regional lymph node metastasis of rectal cancer[J]. Chin J Magn Reson Imag, 2023, 14(12): 187-191. DOI: 10.12015/issn.1674-8034.2023.12.034.
[11]
SHEN H, JIN Z, CHEN Q Y, et al. Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis[J]. Radiol Med, 2024, 129(4): 598-614. DOI: 10.1007/s11547-024-01796-w.
[12]
WONG C, FU Y, LI M Y, et al. MRI-based artificial intelligence in rectal cancer[J]. J Magn Reson Imaging, 2023, 57(1): 45-56. DOI: 10.1002/jmri.28381.
[13]
INCHINGOLO R, MAINO C, CANNELLA R, et al. Radiomics in colorectal cancer patients[J]. World J Gastroenterol, 2023, 29(19): 2888-2904. DOI: 10.3748/wjg.v29.i19.2888.
[14]
GUO X F, HE Y Y, YUAN Z L, et al. Association analysis between intratumoral and peritumoral MRI radiomics features and overall survival of neoadjuvant therapy in rectal cancer[J/OL]. J Magn Reson Imaging, 2024 [2024-10-21]. https://pubmed.ncbi.nlm.nih.gov/38733601. DOI: 10.1002/jmri.29396.
[15]
CARTER D, BYKHOVSKY D, HASKY A, et al. Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds[J/OL]. Tech Coloproctol, 2024, 28(1): 44 [2024-10-21]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10984882. DOI: 10.1007/s10151-024-02917-3.
[16]
JIANG X F, ZHAO H Y, SALDANHA O L, et al. An MRI deep learning model predicts outcome in rectal cancer[J/OL]. Radiology, 2023, 307(5): e222223 [2024-10-21]. https://pubs.rsna.org/doi/10.1148/radiol.222223?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed. DOI: 10.1148/radiol.222223.
[17]
ZHANG H T, YANG X T, LI D A, et al. Dual parallel net: a novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior[J/OL]. J Biomed Inform, 2023, 139: 104304 [2024-10-21]. https://www.sciencedirect.com/science/article/pii/S1532046423000254?via%3Dihub. DOI: 10.1016/j.jbi.2023.104304.
[18]
LI J, SONG Y, WU Y C, et al. Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy[J]. Front Oncol, 2023, 13: 1158315 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1158315/full. DOI: 10.3389/fonc.2023.1158315.
[19]
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 Imag, 2023, 14(9): 176-180. DOI: 10.12015/issn.1674-8034.2023.09.032.
[20]
YOO R N, CHO H M, KYE B H, et al. Reappraisal of the lymphatic drainage system of the distal rectum: functional lymphatic flow into the presacral space and its clinical implication in rectal cancer treatment[J/OL]. Biomedicines, 2023, 11(2): 274 [2024-10-21]. https://www.mdpi.com/2227-9059/11/2/274. DOI: 10.3390/biomedicines11020274.
[21]
Department of Medical Administration of National Health Comimission, Chinese Society of Oncology. Chinese protocol of diagnosis and treatment of colorectal cancer of the National Health Commission (2023 edition)[J]. Chin J Gastrointest Surg, 2023, 26(6): 505-528. DOI: 10.3760/cma.j.cn441530-20230525-00182.
[22]
FERNANDES M C, GOLLUB M J, BROWN G. The importance of MRI for rectal cancer evaluation[J/OL]. Surg Oncol, 2022, 43: 101739 [2024-10-21]. https://www.sciencedirect.com/science/article/pii/S0960740422000329?via%3Dihub. DOI: 10.1016/j.suronc.2022.101739.
[23]
YANG L Q, LIU D, FANG X, et al. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?[J]. Eur Radiol, 2019, 29(12): 6469-6476. DOI: 10.1007/s00330-019-06328-z.
[24]
MENG X C, XIA W, XIE P Y, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer[J]. Eur Radiol, 2019, 29(6): 3200-3209. DOI: 10.1007/s00330-018-5763-x.
[25]
ZHOU Y P, LI S, ZHANG X X, et al. High definition MRI rectal lymph node aided diagnostic system based on deep neural network[J]. Chin J Surg, 2019, 57(2): 108-113. DOI: 10.3760/cma.j.issn.0529-5815.2019.02.007.
[26]
LI J, ZHOU Y, WANG X X, et al. An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer[J]. Abdom Radiol, 2021, 46(5): 1816-1824. DOI: 10.1007/s00261-020-02863-2.
[27]
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 [2024-10-21]. https://www.sciencedirect.com/science/article/pii/S2352396420301559?via%3Dihub. DOI: 10.1016/j.ebiom.2020.102780.
[28]
SONG L R, YIN J D. Application of texture analysis based on sagittal fat-suppression and oblique axial T2-weighted magnetic resonance imaging to identify lymph node invasion status of rectal cancer[J/OL]. Front Oncol, 2020, 10: 1364 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.01364/full. DOI: 10.3389/fonc.2020.01364.
[29]
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 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.585767/full. DOI: 10.3389/fonc.2020.585767.
[30]
LI C L, YIN J D. Radiomics based on T2-weighted imaging and apparent diffusion coefficient images for preoperative evaluation of lymph node metastasis in rectal cancer patients[J/OL]. Front Oncol, 2021, 11: 671354 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.671354/full. DOI: 10.3389/fonc.2021.671354.
[31]
ZHOU Y, YANG R, WANG Y, et al. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma[J/OL]. BMC Med Imaging, 2021, 21(1): 176 [2024-10-21]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-021-00706-0. DOI: 10.1186/s12880-021-00706-0.
[32]
JIA H D, JIANG X Y, ZHANG K Y, et al. A nomogram of combining IVIM-DWI and MRI radiomics from the primary lesion of rectal adenocarcinoma to assess nonenlarged lymph node metastasis preoperatively[J]. J Magn Reson Imaging, 2022, 56(3): 658-667. DOI: 10.1002/jmri.28068.
[33]
ATRE I D, EURBOONYANUN K, NODA Y, et al. Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort[J]. Abdom Radiol, 2021, 46(2): 459-468. DOI: 10.1007/s00261-020-02653-w.
[34]
SU Y X, ZHAO H Y, LIU P F, et al. A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer[J]. Abdom Radiol, 2022, 47(12): 4103-4114. DOI: 10.1007/s00261-022-03672-5.
[35]
WEI Q R, YUAN W J, JIA Z Q, et al. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma[J]. Abdom Radiol, 2023, 48(2): 458-470. DOI: 10.1007/s00261-022-03731-x.
[36]
DONG X, REN G, CHEN Y H, et al. Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer[J/OL]. Front Oncol, 2023, 13: 1194120 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1194120/full. DOI: 10.3389/fonc.2023.1194120.
[37]
ZHUANG Z X, ZHANG Y, YANG X Y, et al. T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer[J]. Abdom Radiol, 2024, 49(6): 2008-2016. DOI: 10.1007/s00261-024-04209-8.
[38]
MENG Y, AI Q, HU Y, et al. Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer[J]. Abdom Radiol, 2024, 49(6): 1805-1815. DOI: 10.1007/s00261-024-04204-z.
[39]
XIA W, LI D D, HE W G, et al. Multicenter evaluation of a weakly supervised deep learning model for lymph node diagnosis in rectal cancer at MRI[J/OL]. Radiol Artif Intell, 2024, 6(2): e230152 [2024-10-21]. https://pubs.rsna.org/doi/10.1148/ryai.230152?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed. DOI: 10.1148/ryai.230152.
[40]
ZHAO W, XU H, ZHAO R, et al. MRI-based radiomics model for preoperative prediction of lateral pelvic lymph node metastasis in locally advanced rectal cancer[J]. Acad Radiol, 2024, 31(7): 2753-2772. DOI: 10.1016/j.acra.2023.07.016.
[41]
TAKAO M, KAWAI K, NAKANO D, et al. Recurrence of rectal cancer on the pelvic sidewall after lateral lymph node dissection[J]. Int J Colorectal Dis, 2024, 39(1): 80-88. DOI: 10.1007/s00384-024-04650-7.
[42]
SLUCKIN T C, HEKHUIS M, KOL S Q, et al. A deep learning framework with explainability for the prediction of lateral locoregional recurrences in rectal cancer patients with suspicious lateral lymph nodes[J/OL]. Diagnostics, 2023, 13(19): 3099 [2024-10-21]. https://www.mdpi.com/2075-4418/13/19/3099. DOI: 10.3390/diagnostics13193099.
[43]
HASHIGUCHI Y, MURO K, SAITO Y, et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer[J]. Int J Clin Oncol, 2020, 25(1): 1-42. DOI: 10.1007/s10147-019-01485-z.
[44]
ZHENG Y Z, YAN F F, LUO L X. Feasibility and limitations of combined treatment for lateral pelvic lymph node metastases in rectal cancer[J]. World J Clin Oncol, 2024, 15(5): 591-593. DOI: 10.5306/wjco.v15.i5.591.
[45]
YAN H, YANG H J, JIANG P S, et al. A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer[J]. Asian J Surg, 2024, 47(1): 450-458. DOI: 10.1016/j.asjsur.2023.09.156.
[46]
YANG H J, JIANG P S, DONG L C, et al. Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer[J]. Updates Surg, 2023, 75(8): 2225-2234. DOI: 10.1007/s13304-023-01618-0.
[47]
HINDSON J. Organ preservation versus radical surgery for early-stage rectal cancer[J/OL]. Nat Rev Gastroenterol Hepatol, 2021, 18(2): 82 [2024-10-21]. https://www.nature.com/articles/s41575-021-00414-8. DOI: 10.1038/s41575-021-00414-8.
[48]
FRANCA A L, MUTTILLO E M, MADAFFARI I, et al. Lymph node metastasis in extraperitoneal rectal cancer after neoadjuvant therapy: an unsolved problem?[J]. Anticancer Res, 2023, 43(6): 2813-2820. DOI: 10.21873/anticanres.16450.
[49]
FANG Z, PU H, CHEN X L, et al. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer[J]. Abdom Radiol, 2023, 48(7): 2270-2283. DOI: 10.1007/s00261-023-03910-4.
[50]
ZHOU X Z, YI Y J, LIU Z Y, et al. Radiomics-based preoperative prediction of lymph node status following neoadjuvant therapy in locally advanced rectal cancer[J/OL]. Front Oncol, 2020, 10: 604 [2024-10-21]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00604/full. DOI: 10.3389/fonc.2020.00604.
[51]
ZHANG S Y, TANG B, YU M R, et al. Development and validation of a radiomics model based on lymph-node regression grading after neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Int J Radiat Oncol Biol Phys, 2023, 117(4): 821-833. DOI: 10.1016/j.ijrobp.2023.05.027.
[52]
ZHU H T, ZHANG X Y, LI X T, et al. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy[J]. Chin J Cancer Res, 2019, 31(6): 984-992. DOI: 10.21147/j.issn.1000-9604.2019.06.14.

PREV Research progress in MRI evaluation of the therapeutic effect of ablation in hepatocellular carcinoma
NEXT Evaluation mechanism and research progress of radiogenomics in urinary tumors
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn