EditorialEditorial
Open Access

Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis

Sunyi Zheng, Xiaonan Cui and Zhaoxiang Ye
Cancer Biology & Medicine January 2025, 22 (1) 6-13; DOI: https://doi.org/10.20892/j.issn.2095-3941.2024.0422

References

  1. 1.↵
    2. Siegel RL,
    3. Giaquinto AN,
    4. Jemal A.
    Cancer statistics, 2024. CA Cancer J Clin. 2024; 74: 12–49.
    OpenUrlCrossRefPubMed
  2. 2.↵
    2. Cui X,
    3. Zheng S,
    4. Heuvelmans MA,
    5. Du Y,
    6. Sidorenkov G,
    7. Fan S, et al.
    Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program. Eur J Radiol. 2022; 146: 110068.
  3. 3.
    2. Lotter W,
    3. Diab AR,
    4. Haslam B,
    5. Kim JG,
    6. Grisot G,
    7. Wu E, et al.
    Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med. 2021; 27: 244–9.
    OpenUrlCrossRefPubMed
  4. 4.
    2. Talukder MA,
    3. Islam MM,
    4. Uddin MA,
    5. Akhter A,
    6. Hasan KF,
    7. Moni MA.
    Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Syst Appl. 2022; 205: 117695.
  5. 5.↵
    2. Li C,
    3. Wang H,
    4. Jiang Y,
    5. Fu W,
    6. Liu X,
    7. Zhong R, et al.
    Advances in lung cancer screening and early detection. Cancer Biol Med. 2022; 19: 591–608.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    2. Reichenpfader D,
    3. Müller H,
    4. Denecke K.
    A scoping review of large language model based approaches for information extraction from radiology reports. NPJ Digit Med. 2024; 7: 222.
    OpenUrlPubMed
  7. 7.↵
    2. Zhang K,
    3. Zhou R,
    4. Adhikarla E,
    5. Yan Z,
    6. Liu Y,
    7. Yu J, et al.
    A generalist vision-language foundation model for diverse biomedical tasks. Nat Med. 2024; 30: 3129–41.
    OpenUrlPubMed
  8. 8.↵
    2. Zhang G,
    3. Xu L,
    4. Zhao L,
    5. Mao L,
    6. Li X,
    7. Jin Z, et al.
    CT-based radiomics to predict the pathological grade of bladder cancer. Eur Radiol. 2020; 30: 6749–56.
    OpenUrlCrossRefPubMed
  9. 9.↵
    2. Kniep HC,
    3. Madesta F,
    4. Schneider T,
    5. Hanning U,
    6. Schönfeld MH,
    7. Schön G, et al.
    Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology. 2019; 290: 479–87.
    OpenUrlCrossRefPubMed
  10. 10.↵
    2. Fan S,
    3. Li X,
    4. Cui X,
    5. Zheng L,
    6. Ren X,
    7. Ma W, et al.
    Computed tomography-based radiomic features could potentially predict microsatellite instability status in stage II colorectal cancer: a preliminary study. Acad Radiol. 2019; 26: 1633–40.
    OpenUrlPubMed
  11. 11.↵
    2. Venkadesh KV,
    3. Setio AAA,
    4. Schreuder A,
    5. Scholten ET,
    6. Chung K,
    7. Wille MM, et al.
    Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology. 2021; 300: 438–47.
    OpenUrlCrossRefPubMed
  12. 12.↵
    2. Chen K,
    3. Xu W,
    4. Li X.
    The potential of Gemini and GPTs for structured report generation based on free-text 18F-FDG PET/CT breast cancer reports. Acad Radiol. 2024. doi:10.1016/j.acra.2024.08.052.
    OpenUrlCrossRef
  13. 13.↵
    2. Li C,
    3. Wong C,
    4. Zhang S,
    5. Usuyama N,
    6. Liu H,
    7. Yang J, et al.
    LLaVA-Med: training a large language-and-vision assistant for biomedicine in one day. In: Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS’23); December 10-16, 2023; New Orleans, LA, USA. Curran Associates Inc.; 2017; Article No. 1240: 28541–64.
  14. 14.↵
    2. Selvaraju RR,
    3. Cogswell M,
    4. Das A,
    5. Vedantam R,
    6. Parikh D,
    7. Batra D.
    Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision; October 22-29, 2017; Venice, Italy. IEEE; 2017: 618–26.
  15. 15.↵
    2. Lundberg S.
    A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17); December 4-9, 2017; Long Beach, CA, USA. Curran Associates Inc.; 2017: 4768–77.
  16. 16.↵
    2. Song D,
    3. Yao J,
    4. Jiang Y,
    5. Shi S,
    6. Cui C,
    7. Wang L, et al.
    A new xai framework with feature explainability for tumors decision-making in ultrasound data: comparing with grad-cam. Comput Methods Programs Biomed. 2023; 235: 107527.
  17. 17.↵
    2. Islam MK,
    3. Rahman MM,
    4. Ali MS,
    5. Mahim S,
    6. Miah MS.
    Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with explainable AI: a deep learning approach. Image Vis Comput. 2024; 142: 104918.
  18. 18.↵
    2. Colen RR,
    3. Rolfo C,
    4. Ak M,
    5. Ayoub M,
    6. Ahmed S,
    7. Elshafeey N, et al.
    Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers. J Immunother Cancer. 2021; 9: e001752.
  19. 19.↵
    2. Antunovic L,
    3. De Sanctis R,
    4. Cozzi L,
    5. Kirienko M,
    6. Sagona A,
    7. Torrisi R, et al.
    PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2019; 46: 1468–77.
    OpenUrlCrossRefPubMed
  20. 20.↵
    2. Yoo C,
    3. Ahn JH,
    4. Jung KH,
    5. Kim SB,
    6. Kim HH,
    7. Shin HJ, et al.
    Impact of immunohistochemistry-based molecular subtype on chemosensitivity and survival in patients with breast cancer following neoadjuvant chemotherapy. J Breast Cancer. 2012; 15: 203–10.
    OpenUrlPubMed
  21. 21.↵
    2. Vanguri RS,
    3. Luo J,
    4. Aukerman AT,
    5. Egger JV,
    6. Fong CJ,
    7. Horvat N, et al.
    Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022; 3: 1151–64.
    OpenUrlPubMed
  22. 22.↵
    2. Zheng S,
    3. Guo J,
    4. Langendijk JA,
    5. Both S,
    6. Veldhuis RNJ,
    7. Oudkerk M, et al.
    Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning. Radiother Oncol. 2023; 180: 109483.
  23. 23.↵
    2. Leger S,
    3. Zwanenburg A,
    4. Pilz K,
    5. Zschaeck S,
    6. Zöphel K,
    7. Kotzerke J, et al.
    CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. Radiother Oncol. 2019; 130: 10–17.
    OpenUrlPubMed
  24. 24.↵
    2. Zhou Y,
    3. He L,
    4. Huang Y,
    5. Chen S,
    6. Wu P,
    7. Ye W, et al.
    CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol. 2017; 42: 1695–704.
    OpenUrl
  25. 25.↵
    2. Wei W,
    3. Rong Y,
    4. Liu Z,
    5. Zhou B,
    6. Tang Z,
    7. Wang S, et al.
    Radiomics: a novel CT-based method of predicting postoperative recurrence in ovarian cancer. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); July 18-21, 2018; Honolulu, HI, USA. IEEE; 2018: 4130–3.
  26. 26.↵
    2. Badic B,
    3. Da-Ano R,
    4. Poirot K,
    5. Jaouen V,
    6. Magnin B,
    7. Gagnière J, et al.
    Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study. Eur Radiol. 2022; 32: 405–14.
    OpenUrlPubMed
  27. 27.↵
    2. Kirillov A,
    3. Mintun E,
    4. Ravi N,
    5. Mao H,
    6. Rolland C,
    7. Gustafson L, et al.
    Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV); October 01-06, 2023; Paris, France. IEEE; 2023: 4015–26.
  28. 28.↵
    2. Kapoor N,
    3. Lacson R,
    4. Khorasani R.
    Workflow applications of artificial intelligence in radiology and an overview of available tools. J Am Coll Radiol. 2020; 17: 1363–70.
    OpenUrlPubMed
  29. 29.↵
    2. Prabhod KJ.
    The role of artificial intelligence in reducing healthcare costs and improving operational efficiency. Q J Emerg Technol Innovat. 2024; 9: 47–59.
    OpenUrl
  30. 30.↵
    2. Kottlors J,
    3. Bratke G,
    4. Rauen P,
    5. Kabbasch C,
    6. Persigehl T,
    7. Schlamann M, et al.
    Feasibility of differential diagnosis based on imaging patterns using a large language model. Radiology. 2023; 308: e231167.
  31. 31.↵
    2. Kinney R,
    3. Anastasiades C,
    4. Authur R,
    5. Beltagy I,
    6. Bragg J,
    7. Buraczynski A, et al.
    The semantic scholar open data platform. 2023; arXiv: 2301.10140.
  32. 32.↵
    2. Alexander R,
    3. Waite S,
    4. Bruno MA,
    5. Krupinski EA,
    6. Berlin L,
    7. Macknik S, et al.
    Mandating limits on workload, duty, and speed in radiology. Radiology. 2022; 304: 274–82.
    OpenUrlCrossRefPubMed
  33. 33.↵
    2. Jing X,
    3. Wielema M,
    4. Monroy-Gonzalez AG,
    5. Stams TRG,
    6. Mahesh SVK,
    7. Oudkerk M, et al.
    Automated breast density assessment in MRI using deep learning and radiomics: strategies for reducing inter-observer variability. J Magn Reson Imaging. 2024; 60: 80–91.
    OpenUrlPubMed
  34. 34.↵
    2. Hirsch L,
    3. Huang Y,
    4. Luo S,
    5. Rossi Saccarelli C,
    6. Lo Gullo R,
    7. Daimiel Naranjo I, et al.
    Radiologist-level performance by using deep learning for segmentation of breast cancers on MRI scans. Radiol Artif Intell. 2021; 4: e200231.
  35. 35.↵
    2. Schmidt RA,
    3. Seah JCY,
    4. Cao K,
    5. Lim L,
    6. Lim W,
    7. Yeung J.
    Generative large language models for detection of speech recognition errors in radiology reports. Radiol Artif Intell. 2024; 6: e230205.
  36. 36.↵
    2. Wismüller A,
    3. Stockmaster L,
    4. Vosoughi MA.
    Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS). Pattern Recognit Track. 2022; 12101: 117–22.
    OpenUrl
  37. 37.↵
    2. Zhang Q,
    3. Yang M,
    4. Zhang P,
    5. Wu B,
    6. Wei X,
    7. Li S.
    Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer Biol Med. 2023; 21: 312–30.
    OpenUrlPubMed
  38. 38.↵
    2. Guo Y,
    3. Lin L,
    4. Zhao S,
    5. Sun G,
    6. Chen Y,
    7. Xue K, et al.
    Myocardial fibrosis assessment at 3-T versus 5-T myocardial late gadolinium enhancement MRI: early results. Radiology. 2024; 313: e233424.
  39. 39.↵
    2. Greffier J,
    3. Villani N,
    4. Defez D,
    5. Dabli D,
    6. Si-Mohamed S.
    Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging. 2023; 104: 167–77.
    OpenUrlPubMed
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