Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Artificial Intelligence Practice
  4. Vol 9, Issue 1, 2026

Research Progress and Status of Radiomics and Deep Learning in Colorectal Cancer

Download as PDF

DOI: 10.23977/jaip.2026.090110 | Downloads: 1 | Views: 73

Author(s)

Wang Xinyuan 1, Zhu Xuanrui 1

Affiliation(s)

1 School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Wang Xinyuan

ABSTRACT

Colorectal cancer (CRC) continues to be a major contributor to global cancer mortality. Within China, its incidence ranks second and its mortality ranks fourth. Under these conditions, improving early diagnosis and performing quantitative evaluation of treatment efficacy become essential priorities in both clinical practice and engineering development. Medical imaging offers indispensable non-invasive information. In routine diagnostics, however, assessment still depends largely on radiologists subjective visual interpretation, which does not provide sub-visual pathological and immunological phenotypes. Radiomics was introduced as an approach to quantify tumor microenvironments. Yet, early machine-learning-based pipelines encountered persistent obstacles, including biases introduced by manual feature engineering and limited generalization across different centers. Deep learning (DL) has altered this workflow in a fundamental way. With end-to-end nonlinear feature mapping, DL architectures can bypass conventional bottlenecks, leading to more reliable model behavior and improved robustness and reproducibility. This paper surveys recent developments in DL-empowered radiomics for CRC across five key areas: automated tumor segmentation, neoadjuvant therapy response prediction, risk stratification, lymph node metastasis evaluation, and microsatellite instability (MSI) status mapping. In addition, it points to emerging engineering directions—specifically multi-modal fusion and explainable DL—that are required to bring these computational models into standard clinical workflows.

KEYWORDS

Colorectal tumor, Artificial intelligence, Deep learning, Radiomics, Automated segmentation, Multi-modal fusion, Precision oncology

CITE THIS PAPER

Wang Xinyuan, Zhu Xuanrui. Research Progress and Status of Radiomics and Deep Learning in Colorectal Cancer. Journal of Artificial Intelligence Practice (2026). Vol. 9, No. 1, 76-84. DOI: http://dx.doi.org/10.23977/jaip.2026.090110.

REFERENCES

[1] SIEGEL R L, GIAQUINTO A N, JEMAL A. Cancer statistics, 2024 [J]. CA Cancer J Clin, 2024, 74(1): 12-49.
[2] HAN B, ZHENG R, ZENG H, et al. Cancer incidence and mortality in China, 2022 [J]. J Natl Cancer Cent, 2024, 4(1): 47-53.
[3] ZHUANG Y, YUAN W, et al. Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023) [J]. Front Oncol, 2024, 14: 1464104.
[4] WU L, WU H, LI C, et al. Radiomics in colorectal cancer [J]. iRADIOLOGY, 2023, 1(3): 236-244.
[5] HOLZINGER A, HAIBE-KAINS B, JURISICA I. Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data [J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2722-2730.
[6] QIN Y, ZHU L H, ZHAO W, et al. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer [J]. Front Oncol, 2022, 12: 913683.
[7] COBO M, MENéNDEZ FERNáNDEZ-MIRANDA P, BASTARRIKA G, et al. Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows [J]. Sci Data, 2023, 10(1): 732.
[8] EWING J N, GALA Z, VOYTIK M, et al. A cross-sectional survey investigating surgeon perceptions of pre-operative risk prediction models incorporating radiomic features [J]. HERNIA, 2025, 29(1).
[9] ZHANG W , GUO Y , JIN Q .Radiomics and Its Feature Selection: A Review[J].Symmetry (20738994), 2023, 15(10).DOI:10.3390/sym15101834.
[10] TRAVERSO A, KAZMIERSKI M, ZHOVANNIK I, et al. Machine learning helps identifying volume-confounding effects in radiomics [J]. Phys Med, 2020, 71: 24-30.
[11] INCHINGOLO R, MAINO C, CANNELLA R, et al. Radiomics in colorectal cancer patients [J]. World J Gastroenterol, 2023, 29(19): 2888.
[12] ALSHOHOUMI F, AL-HAMDANI A, HEDJAM R, et al. A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques [J]. Healthcare (Basel), 2022, 10(10).
[13] HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology [J]. Nat Rev Cancer, 2018, 18(8): 500-510.
[14] TRAN K A, KONDRASHOVA O, BRADLEY A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection [J]. Genome Medicine, 2021, 13(1): 152.
[15] YU H, YANG L T, ZHANG Q, et al. Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives [J]. Neurocomputing, 2021, 444: 92-110.
[16] PAPADIMITROULAS P, BROCKI L, CHRISTOPHER CHUNG N, et al. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization [J]. Phys Med, 2021, 83: 108-121.
[17] CHEN X, WANG X, ZHANG K, et al. Recent advances and clinical applications of deep learning in medical image analysis [J]. Med Image Anal, 2022, 79: 102444.
[18] VORONTSOV E, CERNY M, RéGNIER P, et al. Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases [J]. Radiol Artif Intell, 2019, 1(2): 180014.
[19] LONG J , SHELHAMER E , DARRELL T .Fully Convolutional Networks for Semantic Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.    
[20] SOOMRO M H , Cola G D , CONFORTO S ,et al.Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary study[C]//2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME).IEEE, 2018.DOI:10.1109/MECBME.2018.8402433.  
[21] RONNEBERGER O , FISCHER P , BROX T .U-Net: Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer International Publishing, 2015.DOI:10.1007/978-3-319-24574-4_28.
[22] HUANG Y J, DOU Q, WANG Z X, et al. 3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation [J]. IEEE Trans Cybern, 2021, 51(11): 5397-5408.
[23] CHEN C, ZHOU K, WANG H, et al. TMSF-Net: Multi-series fusion network with treeconnect for colorectal tumor segmentation [J]. Computer Methods and Programs in Biomedicine, 2022, 215: 106613.
[24] CHEN S, XIE F, CHEN S, et al. TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer [J]. Phys Med Biol, 2024, 69(5).
[25] YAO L, XIA Y, CHEN Z, et al. A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans [J]. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4): 7395-7406.
[26] ABBASPOUR E, MANSOORI B, KARIMZADHAGH S, et al. Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis [J]. Abdominal Radiology, 2024.
[27] ZHAO J, WANG H, ZHANG Y, et al. Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer [J]. Radiotherapy and Oncology, 2022, 167: 195-202.
[28] WAN L, HU J, CHEN S, et al. Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning [J]. Eur Radiol, 2023, 33(5): 3638-3646.
[29] WANG H, ZHANG J, LI Y, et al. Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis [J]. Clin Radiol, 2024, 79(9): e1152-e1158.
[30] YANG Y, HAN K, XU Z, et al. Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study [J]. Acad Radiol, 2024.
[31] XIA W, LI D, HE W, et al. Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI [J]. Radiol Artif Intell, 2024, 6(2): e230152.
[32] BENSON A B, VENOOK A P, ADAM M, et al. Colon Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology [J]. J Natl Compr Canc Netw, 2024, 22(2 d).
[33] LI M, XU G, CUI Y, et al. CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study [J]. Clinical Radiology, 2023, 78(10): e741-e751.
[34] ZHANG W, HUANG Z, ZHAO J, et al. Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer [J]. Ann Transl Med, 2021, 9(2): 134.
[35] PENG L, ZHANG X, ZHU Y, et al. T2WI and ADC radiomics combined with a nomogram based on clinicopathologic features to quantitatively predict microsatellite instability in colorectal cancer [J]. Acad Radiol, 2024.
[36] CHEN X, HE L, LI Q, et al. Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm–enhanced artificial neural network–based CT radiomics signature [J]. European Radiology, 2023, 33(1): 11-22.
[37] CAI Z, XU Z, CHEN Y, et al. Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study [J]. Int J Surg, 2024, 110(7): 4310-4319.
[38] CHEN W, ZHENG K, YUAN W, et al. A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study [J]. Radiol Med, 2024.
[39] TUERSUN A, HUO J, LV Z, et al. Establishment of a chemokine-based prognostic model and identification of CXCL10+ M1 macrophages as predictors of neoadjuvant therapy efficacy in colorectal cancer [J]. Front Immunol, 2024, 15: 1400722.
[40] SCOTT A J, KENNEDY E B, BERLIN J, et al. Management of Locally Advanced Rectal Cancer: ASCO Guideline [J]. J Clin Oncol, 2024, 42(28): 3355-3375.
[41] SHEN H, JIN Z, CHEN Q, 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.
[42] 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.
[43] LIU X, ZHANG D, LIU Z, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study [J]. EBioMedicine, 2021, 69: 103442.
[44] FENG L, LIU Z, LI C, et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study [J]. Lancet Digit Health, 2022, 4(1): e8-e17.
[45] QIN Q, GAN X, LIN P, et al. Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer [J]. BMC Med Imaging, 2024, 24(1): 65.
[46] CHENG X F, ZHAO F, CHEN D, et al. Current landscape of preoperative neoadjuvant therapies for initial resectable colorectal cancer liver metastasis [J]. World J Gastroenterol, 2024, 30(7): 663.
[47] WANG Q, NILSSON H, XU K, et al. Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification [J]. Eur J Radiol, 2024, 175: 111459.
[48] WEI J, CHENG J, GU D, et al. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases [J]. Med Phys, 2021, 48(1): 513-522.
[49] ZHOU S, SUN D, MAO W, et al. Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study [J]. EClinicalMedicine, 2023, 65: 102271.
[50] STAAL FCR, et al. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review [J]. Clin Colorectal Cancer, 2021, 20(1): 52-71.
[51] WANG R, DAI W, GONG J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients [J]. Journal of Hematology & Oncology, 2022, 15(1): 11.
[52] JIANG X, ZHAO H, SALDANHA O L, et al. An MRI Deep Learning Model Predicts Outcome in Rectal Cancer [J]. Radiology, 2023, 307(5): e222223.
[53] CAI C, HU T, RONG Z, et al. Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer [J]. Eur J Radiol, 2024, 170: 111254.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 26022
Visits: 746900

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.