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Current status and prospects of radiomics in the diagnosis of colorectal cancer
LONG Die  HUA Li  CHEN Shaojun  CHEN Haihui 

Cite this article as: LONG D, HUA L, CHEN S J, et al. Current status and prospects of radiomics in the diagnosis of colorectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(9): 211-217, 229. DOI:10.12015/issn.1674-8034.2024.09.037.


[Abstract] Radiomics can identify lesions that traditional imaging fails to detect by extracting a vast amount of information invisible to the human eye, and also provide further insights into the biological behavior of tumors. This assists physicians in more accurately determining the clinical staging of tumors and in guiding treatment plan selections. This study evaluates the role of radiomics in aiding the diagnosis of colorectal cancer, with a specific focus on pathological diagnosis and staging. The aim of this study was to offer new research directions for the diagnosis of colorectal cancer.
[Keywords] colorectal cancer；radiomics；diagnosis；magnetic resonance imaging

LONG Die1, 2   HUA Li1, 2   CHEN Shaojun1, 2*   CHEN Haihui3, 4*  

1 The Fourth Clinical Medical School of Guangxi medical University, Liuzhou 545005, China

2 Department of Oncology, Liuzhou Worker's Hospital, Liuzhou 545005, China

3 The Third Clinical Medical College of Guangxi University of Traditional Chinese Medicine, Liuzhou 545026, China

4 Department of Oncology, Liuzhou Traditional Chinese Medicine Hospital, Liuzhou 545026, China

Corresponding author: CHEN S J, E-mail: chenshaoiun388@163.com CHEN H H, E-mail: chenhh1595@163.com

Conflicts of interest   None.

Received  2024-05-29
Accepted  2024-08-09
DOI: 10.12015/issn.1674-8034.2024.09.037
Cite this article as: LONG D, HUA L, CHEN S J, et al. Current status and prospects of radiomics in the diagnosis of colorectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(9): 211-217, 229. DOI:10.12015/issn.1674-8034.2024.09.037.

[1]
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
MORGAN E, ARNOLD M, GINI A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN[J]. Gut, 2023, 72(2): 338-344. DOI: 10.1136/gutjnl-2022-327736.
[3]
IMODOYE S O, ADEDOKUN K A, BELLO I O. From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy[J]. Histochem Cell Biol, 2024, 161(4): 299-323. DOI: 10.1007/s00418-023-02258-6.
[4]
HU Y H, SHEN F, YANG X, et al. Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity[J/OL]. Clin Epigenetics, 2023, 15(1): 161 [2024-06-18]. https://pubmed.ncbi.nlm.nih.gov/37821906/. DOI: 10.1186/s13148-023-01574-x.
[5]
YASUDA T, HONDA T, UTANO K, et al. Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study[J]. Jpn J Radiol, 2022, 40(8): 831-839. DOI: 10.1007/s11604-022-01266-1.
[6]
CHANDRAMOHAN A, MITTAL R, DSOUZA R, et al. Prognostic significance of MR identified EMVI, tumour deposits, mesorectal nodes and pelvic side wall disease in locally advanced rectal cancer[J]. Colorectal Dis, 2022, 24(4): 428-438. DOI: 10.1111/codi.16032.
[7]
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.
[8]
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.
[9]
LEE Y D, KIM H G, SEO M, et al. Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images[J/OL]. Int J Colorectal Dis, 2024, 39(1): 78 [2024-06-18]. https://pubmed.ncbi.nlm.nih.gov/38789861/. DOI: 10.1007/s00384-024-04651-6.
[10]
KNUTH F, TOHIDINEZHAD F, WINTER R M, et al. Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients[J/OL]. Sci Rep, 2024, 14(1): 258 [2024-06-18]. https://pubmed.ncbi.nlm.nih.gov/38167665/. DOI: 10.1038/s41598-023-50966-9.
[11]
JIA L L, CUI Y Q, HUANG G. Research progress of colorectal cancer radiogenomics[J]. Chin J Magn Reson Imag, 2022, 13(12): 159-162. DOI: 10.12015/issn.1674-8034.2022.12.030.
[12]
LIU L B, XU L H, WU D Q, et al. Impact of tumour stroma-immune interactions on survival prognosis and response to neoadjuvant chemotherapy in bladder cancer[J/OL]. EBioMedicine, 2024, 104: 105152 [2024-06-19]. https://pubmed.ncbi.nlm.nih.gov/38728838/. DOI: 10.1016/j.ebiom.2024.105152.
[13]
SU G H, XIAO Y, YOU C, et al. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets[J/OL]. Sci Adv, 2023, 9(40): eadf0837 [2024-06-19]. https://pubmed.ncbi.nlm.nih.gov/37801493/. DOI: 10.1126/sciadv.adf0837.
[14]
SCAPICCHIO C, GABELLONI M, BARUCCI A, et al. A deep look into radiomics[J]. Radiol Med, 2021, 126(10): 1296-1311. DOI: 10.1007/s11547-021-01389-x.
[15]
CUI Y F, YIN F F. Impact of image quality on radiomics applications[J/OL]. Phys Med Biol, 2022, 67(15) [2024-03-20]. https://pubmed.ncbi.nlm.nih.gov/35803254/. DOI: 10.1088/1361-6560/ac7fd7.DOI:10.1088/1361-6560/ac7fd7.
[16]
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.
[17]
ALHENAWI E, AL-SAYYED R, HUDAIB A, et al. Feature selection methods on gene expression microarray data for cancer classification: a systematic review[J/OL]. Comput Biol Med, 2022, 140: 105051 [2024-03-22]. https://pubmed.ncbi.nlm.nih.gov/34839186/. DOI: 10.1016/j.compbiomed.2021.105051.
[18]
GROSU S, WESP P, GRASER A, et al. Machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an asymptomatic screening population: a proof-of-concept study[J]. Radiology, 2021, 299(2): 326-335. DOI: 10.1148/radiol.2021202363.
[19]
TAN J X, GAO Y F, LIANG Z R, et al. 3D-GLCM CNN: a 3-dimensional gray-level co-occurrence matrix-based CNN model for polyp classification via CT colonography[J]. IEEE Trans Med Imaging, 2020, 39(6): 2013-2024. DOI: 10.1109/TMI.2019.2963177.
[20]
DOHRN N, KLEIN M F. Colorectal cancer: current management and future perspectives[J]. Br J Surg, 2023, 110(10): 1256-1259. DOI: 10.1093/bjs/znad095.
[21]
FANG X. Differential diagnosis of colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma by multimodal imaging[D].Guilin: Guilin Medical University, 2022.
[22]
LUO X G, YIMIN Y M, WU J, et al. Analysis of prognostic factors in different grades of histologic differentiation in colorectal cancer patients receiving preoperative neoadjuvant chemotherapy and establishment of prognostic nomograms for moderately differentiated grade[J/OL]. Int J Colorectal Dis, 2023, 38(1): 237 [2024-03-25]. https://pubmed.ncbi.nlm.nih.gov/37747505/. DOI: 10.1007/s00384-023-04539-x.
[23]
ZHENG H D, HUANG Q Y, HUANG Q M, et al. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma[J]. World J Gastrointest Oncol, 2024, 16(3): 819-832. DOI: 10.4251/wjgo.v16.i3.819.
[24]
SONG J H, YU M N, KANG K M, et al. Significance of perineural and lymphovascular invasion in locally advanced rectal cancer treated by preoperative chemoradiotherapy and radical surgery: can perineural invasion be an indication of adjuvant chemotherapy?[J]. Radiother Oncol, 2019, 133: 125-131. DOI: 10.1016/j.radonc.2019.01.002.
[25]
CHEN Q L, CUI Y F, XUE T, et al. Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study[J]. Abdom Radiol (NY), 2022, 47(9): 3251-3263. DOI: 10.1007/s00261-022-03620-3.
[26]
LIU Y, SUN B J T, ZHANG C, et al. Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: a dual-center study[J]. World J Gastroenterol, 2024, 30(16): 2233-2248. DOI: 10.3748/wjg.v30.i16.2233.
[27]
ENTEE P D MC, SHOKUHI P, ROGERS A C, et al. Extramural venous invasion (EMVI) in colorectal cancer is associated with increased cancer recurrence and cancer-related death[J]. Eur J Surg Oncol, 2022, 48(7): 1638-1642. DOI: 10.1016/j.ejso.2022.02.013.
[28]
TONG P F, SUN D Q, CHEN G Q, et al. Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer[J/OL]. BMC Cancer, 2023, 23(1): 61 [2024-03-28]. https://pubmed.ncbi.nlm.nih.gov/36650498/. DOI: 10.1186/s12885-023-10534-w.
[29]
LI M M, GU H M, XUE T, et al. CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study[J/OL]. Br J Radiol, 2023, 96(1141): 20220568 [2024-03-27]. https://pubmed.ncbi.nlm.nih.gov/36318241/. DOI: 10.1259/bjr.20220568.
[30]
QU X T, ZHANG L, JI W N, et al. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics[J/OL]. Front Oncol, 2023, 13: 1267838 [2024-03-29]. https://pubmed.ncbi.nlm.nih.gov/37941552/. DOI: 10.3389/fonc.2023.1267838.
[31]
LI Z H, CHEN F Y, ZHANG S T, et al. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer[J]. Abdom Radiol, 2022, 47(1): 56-65. DOI: 10.1007/s00261-021-03311-5.
[32]
UENO H, NAGTEGAAL I D, QUIRKE P, et al. Tumor deposits in colorectal cancer: Refining their definition in the TNM system[J]. Ann Gastroenterol Surg, 2023, 7(2): 225-235. DOI: 10.1002/ags3.12652.
[33]
LI H, CHEN X L, LIU H, et al. MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study[J]. Eur Radiol, 2023, 33(11): 7561-7572. DOI: 10.1007/s00330-023-09723-9.
[34]
LI M M, XU G D, CHEN Q L, et al. Computed tomography-based radiomics nomogram for the preoperative prediction of tumor deposits and clinical outcomes in colon cancer: a multicenter study[J]. Acad Radiol, 2023, 30(8): 1572-1583. DOI: 10.1016/j.acra.2022.11.005.
[35]
TAIEB J, SVRCEK M, COHEN R, et al. Deficient mismatch repair/microsatellite unstable colorectal cancer: diagnosis, prognosis and treatment[J]. Eur J Cancer, 2022, 175: 136-157. DOI: 10.1016/j.ejca.2022.07.020.
[36]
KIM S, LEE J H, PARK E J, et al. Prediction of microsatellite instability in colorectal cancer using a machine learning model based on PET/CT radiomics[J]. Yonsei Med J, 2023, 64(5): 320-326. DOI: 10.3349/ymj.2022.0548.
[37]
MA Y Q, XU X R, LIN Y, et al. An integrative clinical and CT-based tumoral/peritumoral radiomics nomogram to predict the microsatellite instability in rectal carcinoma[J]. Abdom Radiol, 2024, 49(3): 783-790. DOI: 10.1007/s00261-023-04099-2.
[38]
YING M L, PAN J F, LU G H, et al. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer[J/OL]. BMC Cancer, 2022, 22(1): 524 [2024-05-04]. https://pubmed.ncbi.nlm.nih.gov/35534797/. DOI: 10.1186/s12885-022-09584-3.
[39]
BIAN X L, SUN Q, WANG M, et al. Preoperative prediction of microsatellite instability status in colorectal cancer based on a Multiphasic enhanced CT radiomics nomogram model[J/OL]. BMC Med Imaging, 2024, 24(1): 77 [2024-06-04]. https://pubmed.ncbi.nlm.nih.gov/38566000/. DOI: 10.1186/s12880-024-01252-1.
[40]
MA Y, LIN C S, LIU S, et al. Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer[J/OL]. Front Oncol, 2022, 12: 1020349 [2024-05-10]. https://pubmed.ncbi.nlm.Nih.gov/36276101/. DOI: 10.3389/fonc.2022.1020349.
[41]
XING X W, LI D X, PENG J X, et al. A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer[J/OL]. Sci Rep, 2024, 14(1): 11760 [2024-06-03]. https://pubmed.ncbi.nlm.nih.gov/38783014/. DOI: 10.1038/s41598-024-62584-0.
[42]
LI Z, ZHANG J, ZHONG Q, et al. Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study[J]. Eur Radiol, 2023, 33(3): 1835-1843. DOI: 10.1007/s00330-022-09160-0.
[43]
ZHANG W, HUANG Z X, ZHAO J, et al. Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer[J/OL]. Ann Transl Med, 2021, 9(2): 134 [2024-05-10]. https://pubmed.ncbi.nlm.nih.gov/33569436/. DOI: 10.21037/atm-20-7673.
[44]
VAN DER MEER R, JEUKEN J W M, BOSCH S L, et al. Biomarker concordance between primary colorectal cancer and ovarian metastases: a Dutch cohort study[J]. J Cancer Res Clin Oncol, 2023, 149(9): 5677-5685. DOI: 10.1007/s00432-022-04502-3.
[45]
GROTHEY A, VENOOK A P. Optimizing adjuvant therapy for localized colon cancer and treatment selection in advanced colorectal cancer[J]. J Natl Compr Canc Netw, 2018, 16(5S): 611-615. DOI: 10.6004/jnccn.2018.0038.
[46]
RÍOS-HOYO A, MONZONÍS X, VIDAL J, et al. Unveiling acquired resistance to anti-EGFR therapies in colorectal cancer: a long and winding road[J/OL]. Front Pharmacol, 2024, 15: 1398419 [2024-05-11]. https://pubmed.ncbi.nlm.nih.gov/38711991/. DOI: 10.3389/fphar.2024.1398419.
[47]
MAO C, WU X Y, YANG Z Y, et al. Concordant analysis of KRAS, BRAF, PIK3CA mutations, and PTEN expression between primary colorectal cancer and matched metastases[J/OL]. Sci Rep, 2015, 5: 8065 [2024-05-11]. https://pubmed.ncbi.nlm.nih.gov/25639985/. DOI: 10.1038/srep08065.
[48]
ZHAO H Y, SU Y X, WANG Y, et al. Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer[J/OL]. Cancer Imaging, 2024, 24(1): 26 [2024-05-11]. https://pubmed.ncbi.nlm.nih.gov/38342905/. DOI: 10.1186/s40644-024-00670-2.
[49]
RICCI LARA M A, ESPOSITO M I, AINESEDER M, et al. Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer[J/OL]. Surg Oncol, 2023, 51: 101986 [2024-05-11]. https://pubmed.ncbi.nlm.nih.gov/37729816/. DOI: 10.1016/j.suronc.2023.101986.
[50]
GUO X F, YANG W Q, YANG Q, et al. Feasibility of MRI radiomics for predicting KRAS mutation in rectal cancer[J]. Curr Med Sci, 2020, 40(6): 1156-1160. DOI: 10.1007/s11596-020-2298-6.
[51]
HE K, LIU X M, LI M Y, et al. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging[J/OL]. BMC Med Imaging, 2020, 20(1): 59 [2024-05-11]. https://pubmed.ncbi.nlm.nih.gov/32487083/. DOI: 10.1186/s12880-020-00457-4.
[52]
CAO Y T, ZHANG J, HUANG L L, et al. Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics[J]. Jpn J Radiol, 2023, 41(11): 1236-1246. DOI: 10.1007/s11604-023-01458-3.
[53]
ALSHUHRI M S, ALDUHYYIM A, AL-MUBARAK H, et al. Investigating the feasibility of predicting KRAS status, tumor staging, and extramural venous invasion in colorectal cancer using inter-platform magnetic resonance imaging radiomic features[J/OL]. Diagnostics, 2023, 13(23): 3541 [2024-05-12]. https://pubmed.ncbi.nlm.nih.gov/3806 6782/. DOI: 10.3390/diagnostics13233541.
[54]
ZHANG Z Y, SHEN L J, WANG Y, et al. MRI radiomics signature as a potential biomarker for predicting KRAS status in locally advanced rectal cancer patients[J/OL]. Front Oncol, 2021, 11: 614052 [2024-05-12]. https://pubmed.ncbi.nlm.nih.gov/34026605/. DOI: 10.3389/fonc.2021.614052.
[55]
CUI Y F, LIU H H, REN J L, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer[J]. Eur Radiol, 2020, 30(4): 1948-1958. DOI: 10.1007/s00330-019-06572-3.
[56]
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 [2024-05-12]. https://pubmed.ncbi.nlm.nih.gov/34337027/. DOI: 10.1155/2021/5566885.
[57]
CHENG Y, YU Q, MENG W Y, et al. Clinico-radiologic nomogram using multiphase CT to predict lymph node metastasis in colon cancer[J]. Mol Imaging Biol, 2022, 24(5): 798-806. DOI: 10.1007/s11307-022-01730-4.
[58]
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]. Chung Kuo Yen Cheng Yen Chiu, 2019, 31(6): 984-992. DOI: 10.21147/j.issn.1000-9604.2019.06.14.
[59]
KRUSKAL J B, THOMAS P, KANE R A, et al. Hepatic perfusion changes in mice livers with developing colorectal cancer metastases[J]. Radiology, 2004, 231(2): 482-490. DOI: 10.1148/radiol.2312030160.
[60]
TAGHAVI M, TREBESCHI S, SIMÕES R, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases[J]. Abdom Radiol, 2021, 46(1): 249-256. DOI: 10.1007/s00261-020-02624-1.
[61]
BECKERS R C J, LAMBREGTS D M J, SCHNERR R S, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases-a multicentre study[J]. Eur J Radiol, 2017, 92: 64-71. DOI: 10.1016/j.ejrad.2017.04.019.
[62]
HU T D, WANG S P, HUANG L, et al. A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules[J]. Eur Radiol, 2019, 29(1): 439-449. DOI: 10.1007/s00330-018-5539-3.
[63]
JIN J L, ZHOU H Y, SUN S L, et al. Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI[J/OL]. Front Oncol, 2023, 13: 1121594 [2024-05-12]. https://pubmed.ncbi.nlm.nih.gov/37035167/. DOI: 10.3389/fonc.2023.1121594.
[64]
BERA K, BRAMAN N, GUPTA A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology[J]. Nat Rev Clin Oncol, 2022, 19(2): 132-146. DOI: 10.1038/s41571-021-00560-7.
[65]
PAPADIMITROULAS P, BROCKI L, CHUNG N C, et al. Artificial intelligence: deep learning in oncological radiomics and challenges of interpretability and data harmonization[J]. Phys Med, 2021, 83: 108-121. DOI: 10.1016/j.ejmp.2021.03.009.
[66]
LIU Z Y, 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.
[67]
LIU Y, WEI X Q, FENG X, et al. Repeatability of radiomics studies in colorectal cancer: a systematic review[J/OL]. BMC Gastroenterol, 2023, 23(1): 125 [2024-06-18]. https://pubmed.ncbi.nlm.nih.gov/37059990/. DOI: 10.1186/s12876-023-02743-1.

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