基于MRI影像组学和人工智能在前列腺癌诊疗中的研究进展
Research Progress in MRI Radiomics and Artificial Intelligence for the Diagnosis and Treatment of Prostate Cancer
DOI: 10.12677/acm.2025.15123564, PDF,   
作者: 孟 涵：济宁医学院临床医学院(附属医院)，山东 济宁；曾庆师*：山东第一医科大学第一附属医院(山东省千佛山医院)放射科，山东 济南
关键词: 前列腺癌；MRI；影像组学；人工智能；Prostate Cancer； MRI； Radiomics； Artificial Intelligence
摘要: 前列腺癌(prostate cancer, PCa)是男性常见的恶性肿瘤，其发病率呈持续上升趋势。早期诊断主要依赖前列腺特异性抗原(prostate specific antigen, PSA)和磁共振成像(magnetic resonance imaging, MRI)，但存在过度穿刺的问题。影像组学通过高通量提取影像的定量特征并挖掘传统影像中难以识别的潜在信息，结合人工智能(Artificial Intelligence, AI)辅助MRI图像的分析与评估，为前列腺癌的精准诊断、风险评估提供新的途径和方法，有助于优化临床决策，减少不必要的活检。本文主要综述了AI辅助下的影像组学在PCa的识别与诊断、PCa侵袭性评估和预后评价中的研究应用。
Abstract: Prostate cancer (PCa) is a prevalent malignancy in men, with its incidence rate showing a continuous upward trend. Early diagnosis primarily relies on prostate-specific antigen (PSA) and magnetic resonance imaging (MRI), yet faces dual challenges of excessive biopsy procedures. Imaging omics, through high-throughput extraction of quantitative imaging features and identification of latent information in traditional imaging, combined with artificial intelligence (AI) to assist MRI image analysis and evaluation, provides new approaches for precise diagnosis and risk assessment of prostate cancer. This contributes to optimizing clinical decision-making and reducing unnecessary biopsies. This article reviews the research applications of AI-assisted imaging omics in PCa diagnosis and grading, as well as the evaluation of PCa invasiveness, prognosis, and treatment.
文章引用：孟涵, 曾庆师. 基于MRI影像组学和人工智能在前列腺癌诊疗中的研究进展[J]. 临床医学进展, 2025, 15(12): 1555-1562. https://doi.org/10.12677/acm.2025.15123564

参考文献

[1] Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., et al. (2024) Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 74, 229-263. [Google Scholar] [CrossRef] [PubMed]
[2] 王少明, 郑荣寿, 韩冰峰, 等. 2022年中国人群恶性肿瘤发病与死亡年龄特征分析[J]. 中国肿瘤, 2024, 33(3): 165-174.
[3] Cornford, P., van den Bergh, R.C.N., Briers, E., Van den Broeck, T., Brunckhorst, O., Darraugh, J., et al. (2024) EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer—2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent. European Urology, 86, 148-163. [Google Scholar] [CrossRef] [PubMed]
[4] Loeb, S., Vellekoop, A., Ahmed, H.U., Catto, J., Emberton, M., Nam, R., et al. (2013) Systematic Review of Complications of Prostate Biopsy. European Urology, 64, 876-892. [Google Scholar] [CrossRef] [PubMed]
[5] Turkbey, B., Mani, H., Shah, V., Rastinehad, A.R., Bernardo, M., Pohida, T., et al. (2011) Multiparametric 3T Prostate Magnetic Resonance Imaging to Detect Cancer: Histopathological Correlation Using Prostatectomy Specimens Processed in Customized Magnetic Resonance Imaging Based Molds. Journal of Urology, 186, 1818-1824. [Google Scholar] [CrossRef] [PubMed]
[6] Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577. [Google Scholar] [CrossRef] [PubMed]
[7] 赵莹莹, 方陈, 吴声连, 等. 基于Bp-MRI影像组学预测前列腺病变良恶性的效能及风险评估[J]. 磁共振成像, 2022, 13(8): 43-47.
[8] Solari, E.L., Gafita, A., Schachoff, S., Bogdanović, B., Villagrán Asiares, A., Amiel, T., et al. (2021) The Added Value of PSMA PET/MR Radiomics for Prostate Cancer Staging. European Journal of Nuclear Medicine and Molecular Imaging, 49, 527-538. [Google Scholar] [CrossRef] [PubMed]
[9] Zhao, L., Liu, Z., Xie, W., Shao, L., Lu, J., Tian, J., et al. (2023) What Benefit Can Be Obtained from Magnetic Resonance Imaging Diagnosis with Artificial Intelligence in Prostate Cancer Compared with Clinical Assessments? Military Medical Research, 10, Article No. 29. [Google Scholar] [CrossRef] [PubMed]
[10] Twilt, J.J., Saha, A., Bosma, J.S., Padhani, A.R., Bonekamp, D., Giannarini, G., et al. (2025) AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images. JAMA Network Open, 8, e2515672. [Google Scholar] [CrossRef] [PubMed]
[11] Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G.P.M., Granton, P., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
[12] Zhu, X., Shao, L., Liu, Z., Liu, Z., He, J., Liu, J., et al. (2023) MRI-Derived Radiomics Models for Diagnosis, Aggressiveness, and Prognosis Evaluation in Prostate Cancer. Journal of Zhejiang University-SCIENCE B, 24, 663-681. [Google Scholar] [CrossRef] [PubMed]
[13] Zhao, W., Hou, M., Wang, J., Song, D. and Niu, Y. (2024) Interpretable Machine Learning Model for Predicting Clinically Significant Prostate Cancer: Integrating Intratumoral and Peritumoral Radiomics with Clinical and Metabolic Features. BMC Medical Imaging, 24, Article No. 353. [Google Scholar] [CrossRef] [PubMed]
[14] Padhani, A.R., Barentsz, J., Villeirs, G., Rosenkrantz, A.B., Margolis, D.J., Turkbey, B., et al. (2019) PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-Directed Biopsy Pathway. Radiology, 292, 464-474. [Google Scholar] [CrossRef] [PubMed]
[15] Zeng, X., Liu, C., Liu, S., Wang, Z., Yu, K., Feng, C., et al. (2018) Using the Prostate Imaging Reporting and Data System Version 2 (PI-RIDS V2) to Detect Prostate Cancer Can Prevent Unnecessary Biopsies and Invasive Treatment. Asian Journal of Andrology, 20, 459-464. [Google Scholar] [CrossRef] [PubMed]
[16] Lu, Y., Li, B., Huang, H., Leng, Q., Wang, Q., Zhong, R., et al. (2022) Biparametric MRI-Based Radiomics Classifiers for the Detection of Prostate Cancer in Patients with PSA Serum Levels of 4-10 ng/ml. Frontiers in Oncology, 12, Article 1020317. [Google Scholar] [CrossRef] [PubMed]
[17] Qi, Y., Zhang, S., Wei, J., Zhang, G., Lei, J., Yan, W., et al. (2019) Multiparametric MRI‐Based Radiomics for Prostate Cancer Screening with PSA in 4-10 ng/ml to Reduce Unnecessary Biopsies. Journal of Magnetic Resonance Imaging, 51, 1890-1899. [Google Scholar] [CrossRef] [PubMed]
[18] Cai, J.C., Nakai, H., Kuanar, S., Froemming, A.T., Bolan, C.W., Kawashima, A., et al. (2024) Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology, 312, e232635. [Google Scholar] [CrossRef] [PubMed]
[19] Saha, A., Bosma, J.S., Twilt, J.J., van Ginneken, B., Bjartell, A., Padhani, A.R., et al. (2024) Artificial Intelligence and Radiologists in Prostate Cancer Detection on MRI (PI-CAI): An International, Paired, Non-Inferiority, Confirmatory Study. The Lancet Oncology, 25, 879-887. [Google Scholar] [CrossRef] [PubMed]
[20] Lee, Y.J., Moon, H.W., Choi, M.H., Eun Jung, S., Park, Y.H., Lee, J.Y., et al. (2025) MRI-Based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study. Radiology, 314, e232788. [Google Scholar] [CrossRef] [PubMed]
[21] Zhuang, H., Chatterjee, A., Fan, X., Qi, S., Qian, W. and He, D. (2023) A Radiomics Based Method for Prediction of Prostate Cancer Gleason Score Using Enlarged Region of Interest. BMC Medical Imaging, 23, Article No. 205. [Google Scholar] [CrossRef] [PubMed]
[22] Qiao, X., Gu, X., Liu, Y., Shu, X., Ai, G., Qian, S., et al. (2023) MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer. Cancers, 15, Article 4536. [Google Scholar] [CrossRef] [PubMed]
[23] Yang, Y., Zheng, B., Zou, B., Liu, R., Yang, R., Chen, Q., et al. (2025) MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer. Academic Radiology, 32, 5303-5316. [Google Scholar] [CrossRef] [PubMed]
[24] Shao, L., Liang, C., Yan, Y., Zhu, H., Jiang, X., Bao, M., et al. (2025) An MRI-Pathology Foundation Model for Noninvasive Diagnosis and Grading of Prostate Cancer. Nature Cancer, 6, 1621-1637. [Google Scholar] [CrossRef] 
[25] He, D., Wang, X., Fu, C., Wei, X., Bao, J., Ji, X., et al. (2021) MRI-Based Radiomics Models to Assess Prostate Cancer, Extracapsular Extension and Positive Surgical Margins. Cancer Imaging, 21, Article No. 46. [Google Scholar] [CrossRef] [PubMed]
[26] Bai, H., Xia, W., Ji, X., He, D., Zhao, X., Bao, J., et al. (2021) Multiparametric Magnetic Resonance Imaging‐Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension with Prostate Cancer. Journal of Magnetic Resonance Imaging, 54, 1222-1230. [Google Scholar] [CrossRef] [PubMed]
[27] Cuocolo, R., Stanzione, A., Faletti, R., Gatti, M., Calleris, G., Fornari, A., et al. (2021) MRI Index Lesion Radiomics and Machine Learning for Detection of Extraprostatic Extension of Disease: A Multicenter Study. European Radiology, 31, 7575-7583. [Google Scholar] [CrossRef] [PubMed]
[28] Moroianu, Ş.L., Bhattacharya, I., Seetharaman, A., Shao, W., Kunder, C.A., Sharma, A., et al. (2022) Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers, 14, Article 2821. [Google Scholar] [CrossRef] [PubMed]
[29] Khosravi, P., Saikali, S., Alipour, A., Mohammadi, S., Boger, M., Diallo, D.M., et al. (2025) AutoRadAI: A Versatile Artificial Intelligence Framework Validated for Detecting Extracapsular Extension in Prostate Cancer. Biology Methods and Protocols, 10, bpaf032. [Google Scholar] [CrossRef] [PubMed]
[30] Zheng, H., Miao, Q., Liu, Y., Mirak, S.A., Hosseiny, M., Scalzo, F., et al. (2022) Multiparametric MRI-Based Radiomics Model to Predict Pelvic Lymph Node Invasion for Patients with Prostate Cancer. European Radiology, 32, 5688-5699. [Google Scholar] [CrossRef] [PubMed]
[31] Bourbonne, V., Jaouen, V., Nguyen, T.A., Tissot, V., Doucet, L., Hatt, M., et al. (2021) Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients. Cancers, 13, Article 5672. [Google Scholar] [CrossRef] [PubMed]
[32] 姬健智, 张倩, 牛猛, 等. 联合临床、MRT2WI及表观弥散系数图影像组学特征列线图预测初发前列腺癌骨转移[J]. 中国医学影像技术, 2022, 38(7): 1050-1055.
[33] 李克建, 张濬韬, 任凯旋, 等. 基于MRI影像组学的机器学习模型预测前列腺癌骨转移的价值[J]. 磁共振成像, 2023, 14(1): 100-104, 115.
[34] Xinyang, S., Tianci, S., Xiangyu, H., Shuang, Z., Yangyang, W., Mengying, D., et al. (2024) A Semi-Automatic Deep Learning Model Based on Biparametric MRI Scanning Strategy to Predict Bone Metastases in Newly Diagnosed Prostate Cancer Patients. Frontiers in Oncology, 14, Article 1298516. [Google Scholar] [CrossRef] [PubMed]
[35] Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A.S., et al. (2018) Radiomic Features from Pretreatment Biparametric MRI Predict Prostate Cancer Biochemical Recurrence: Preliminary Findings. Journal of Magnetic Resonance Imaging, 48, 1626-1636. [Google Scholar] [CrossRef] [PubMed]
[36] Lee, H.W., Kim, E., Na, I., Kim, C.K., Seo, S.I. and Park, H. (2023) Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy. Cancers, 15, Article 3416. [Google Scholar] [CrossRef] [PubMed]
[37] Gu, W., Liu, Z., Yang, Y., Zhang, X., Chen, L., Wan, F., et al. (2023) A Deep Learning Model, NAFNet, Predicts Adverse Pathology and Recurrence in Prostate Cancer Using MRIs. npj Precision Oncology, 7, Article No. 134. [Google Scholar] [CrossRef] [PubMed]

为你推荐