1. Akbari M, Nasiri H, Ziaee M. Male breast cancer biology, screening, treatment, and follow-up: a narrative review. Iran J Public Health. 2024;53(12): 2694-704. [DOI:10.18502/ijph.v53i12.17312] [PMID]

2. Garjani A, Rezazadeh H, Andalib S, Ziaee M, Doustar Y, Soraya H, et al. Ambivalent effects of atorvastatin on angiogenesis, epidermal cell proliferation and tumorgenesis in animal models. Iran Biomed J. 2012;16(2):59-67.

3. Mehri JS, Farnaz S, Ali DT, Parvin MG, Elaheh O, Marziyeh P, et al. Diagnostic value of tumor biomarkers CA125 and CA72-4 in differentiation of epithelial ovarian cancer and endometrioma. Biomed Res.(India). 2018;29(8):1697-701. [DOI:10.4066/biomedicalresearch.29-18-114]

4. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49. [DOI:10.3322/caac.21820] [PMID]

5. El-Sayes N, Vito A, Mossman K. Tumor heterogeneity: a great barrier in the age of cancer immunotherapy. Cancers. 2021;13(4):806. [DOI:10.3390/cancers13040806] [PMID] []

6. Naithani N, Sinha S, Misra P, Vasudevan B, Sahu R. Precision medicine: Concept and tools. Med J Armed Forces India. 2021;77(3):249-57. [DOI:10.1016/j.mjafi.2021.06.021] [PMID] []

7. Alanazi A. Using machine learning for healthcare challenges and opportunities. Inform Med Unlocked.2022;30:100924. [DOI:10.1016/j.imu.2022.100924]

8. Chua IS, Gaziel‐Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, et al. Artificial intelligence in oncology: Path to implementation. Cancer Med. 2021;10(12):4138-49. [DOI:10.1002/cam4.3935] [PMID] []

9. Samadi M, Kamrani A, Nasiri H, Shomali N, Heris JA, Shahabi P, et al. Cancer immunotherapy focusing on the role of interleukins: A comprehensive and updated study. Pathol Res Pract. 2023;249:154732. [DOI:10.1016/j.prp.2023.154732] [PMID]

10. Stein-Merlob AF, Rothberg MV, Holman P, Yang EH. Immunotherapy-associated cardiotoxicity of immune checkpoint inhibitors and chimeric antigen receptor T cell therapy: diagnostic and management challenges and strategies. Curr Cardiol Rep. 2021;23:1-11. [DOI:10.1007/s11886-021-01440-3] [PMID] []

11. Kamrani A, Hosseinzadeh R, Shomali N, Heris JA, Shahabi P, Mohammadinasab R, et al. New immunotherapeutic approaches for cancer treatment. Pathol Res Pract. 2023;248:154632. [DOI:10.1016/j.prp.2023.154632] [PMID]

12. Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol. 2024;15:1368749. [DOI:10.3389/fimmu.2024.1368749] [PMID] []

13. Sobhani N, Tardiel-Cyril DR, Chai D, Generali D, Li J-R, Vazquez-Perez J, et al. Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer. BJC Reports. 2024;2(1):4. [DOI:10.1038/s44276-023-00035-5] [PMID] []

14. Yang Z, Wei T, Liang Y, Yuan X, Gao R, Xia Y, et al. A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images. Nat Commun. 2025;16(1):2366. [DOI:10.1038/s41467-025-57587-y] [PMID] []

15. Tawfik OW, Subramanian J, Caughron S, Mana P, Ewing E, Aboudara M, et al. Challenges in pathology specimen processing in the new era of precision medicine. Arch Pathol Lab Med. 2022;146(5):603-10. [DOI:10.5858/arpa.2021-0089-OA] [PMID]

16. Lu H, Li L, Ong K, Wang Y, Jiao Y, Wang X, et al. AI-based computational pathology and its contribution to precision medicine. Frontiers in Bioimage Informatics Methodology: World Sci. 2024: 167-93. [DOI:10.1142/9789811286131_0005]

17. Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62-81. [DOI:10.1111/joim.13030] [PMID]

18. Wasinger G, Koeller MC, Compérat E. Pathology in the artificial intelligence era: practical insights for immunohistochemistry and molecular pathology. Diagn Histopathol. 2025;31(7):416-23. [DOI:10.1016/j.mpdhp.2025.04.003]

19. Broomand Lomer N, Nouri A, Singh R, Asgari S. Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis. Abdom Radiol (NY). 2025:1-21. [DOI:10.1007/s00261-025-04933-9]

20. Tellez D, Litjens G, Bándi P, Bulten W, Bokhorst J-M, Ciompi F, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal. 2019;58:101544. [DOI:10.1016/j.media.2019.101544] [PMID]

21. Ziegler J, Hechtman JF, Rana S, Ptashkin RN, Jayakumaran G, Middha S, et al. A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing. Nat Commun. 2025;16(1):136. [DOI:10.1038/s41467-024-54970-z] [PMID] []

22. Sengupta D, Ali SN, Bhattacharya A, Mustafi J, Mukhopadhyay A, Sengupta K. A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. PloS one. 2022; ;17(1):e0261181. [DOI:10.1371/journal.pone.0261181] [PMID] []

23. Ho C, Zhao Z, Chen XF, Sauer J, Saraf SA, Jialdasani R, et al. A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Sci Rep. 2022;12(1):2222. [DOI:10.1038/s41598-022-06264-x] [PMID] []

24. Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, Lindholm P, et al. Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digit Health. 2020;2(9):e468-e74. [DOI:10.1016/S2589-7500(20)30185-0] [PMID]

25. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-15. [DOI:10.1038/s41571-019-0252-y] [PMID] []

26. Rodriguez JPM, Rodriguez R, Silva VWK, Kitamura FC, Corradi GCA, de Marchi ACB, et al. Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: a systematic review. J Pathol Inform. 2022;13:100138. [DOI:10.1016/j.jpi.2022.100138] [PMID] []

27. Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, et al., editors. Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol. 2022: ;84:129-143. [DOI:10.1016/j.semcancer.2021.02.011] [PMID]

28. Wang X, Jiang Y, Chen H, Zhang T, Han Z, Chen C, et al. Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning. Radiother Oncol. 2023;186:109793. [DOI:10.1016/j.radonc.2023.109793] [PMID]

29. Liao C-Y, Chen Y-M, Wu Y-T, Chao H-S, Chiu H-Y, Wang T-W, et al. Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning. Cancer Imaging. 2024;24(1):129. [DOI:10.1186/s40644-024-00779-4] [PMID] []

30. Zhang Y, Wang Y, Chen J, Xia Y, Huang Y. A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma. Front Immunol. 2023;14:1183230. [DOI:10.3389/fimmu.2023.1183230] [PMID] []

31. Zhang H, Chen L, Li L, Liu Y, Das B, Zhai S, et al. Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning. NPJ Precis Oncol. 2025;9(1):76. [DOI:10.1038/s41698-025-00866-0] [PMID] []

32. Hu J, Cui C, Yang W, Huang L, Yu R, Liu S, et al. Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images. Transl Oncol. 2021;14(1):100921. [DOI:10.1016/j.tranon.2020.100921] [PMID] []

33. Wang Y, Wang YG, Hu C, Li M, Fan Y, Otter N, et al. Cell graph neural networks enable the precise prediction of patient survival in gastric cancer. NPJ Precis Oncol. 2022;6(1):45. [DOI:10.1038/s41698-022-00285-5] [PMID] []

34. Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer. 2022;22(2):114-26. [DOI:10.1038/s41568-021-00408-3] [PMID] []

35. Gillies RJ, Beyer T. PET and MRI: is the whole greater than the sum of its parts? Cancer research. 2016;76(21):616. 6-3. [DOI:10.1158/0008-5472.CAN-16-2121] [PMID] []

36. Lambin P, Leijenaar RT, Deist TM, Peerlings J, De Jong EE, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-62. [DOI:10.1038/nrclinonc.2017.141] [PMID]

37. Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2(12):1636-42. [DOI:10.1001/jamaoncol.2016.2631] [PMID]

38. Jiang C, Cai Y-Q, Yang J-J, Ma C-Y, Chen J-X, Huang L, et al. Radiomics in the diagnosis and treatment of hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int. 2023;22(4):346-51. [DOI:10.1016/j.hbpd.2023.03.010] [PMID]

39. Chen Z, Chen X, Ju L, Li Y, Li W, Pang H. Establishing a predictive model for tumor mutation burden status based on 18F-FDG PET/CT and clinical features of non-small cell lung cancer patients. Transl Lung Cancer Res.2024;13(9):2269. [DOI:10.21037/tlcr-24-416] [PMID] []

40. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. The Lancet Oncology. 2018;19(9):1180-91. [DOI:10.1016/S1470-2045(18)30413-3] [PMID]

41. Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer. 2022;10(9):e005292. [DOI:10.1136/jitc-2022-005292] [PMID] []

42. Mardis ER. Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif). 2013;6(1):287-303. [DOI:10.1146/annurev-anchem-062012-092628] [PMID]

43. Xie F, Zhang J, Wang J, Reuben A, Xu W, Yi X, et al. Multifactorial deep learning reveals pan-cancer genomic tumor clusters with distinct immunogenomic landscape and response to immunotherapy. Clin Cancer Res. 2020;26(12):2908-20. [DOI:10.1158/1078-0432.CCR-19-1744] [PMID] []

44. Xie J, Zou Y, Ye F, Zhao W, Xie X, Ou X, et al. A novel platelet-related gene signature for predicting the prognosis of triple-negative breast cancer. Front Cell Dev Biol. 2022;9:795600. [DOI:10.3389/fcell.2021.795600] [PMID] []

45. Nikoo M, Rudiansyah M, Bokov DO, Jainakbaev NT, Suksatan W, Ansari MJ, et al. Potential of chimeric antigen receptor (CAR)‐redirected immune cells in breast cancer therapies: Recent advances. J Cell Mol Med. 2022;26(15):4137-56. [DOI:10.1111/jcmm.17465] [PMID] []

46. Hugo W, Shi H, Sun L, Piva M, Song C, Kong X, et al. Non-genomic and immune evolution of melanoma acquiring MAPKi resistance. Cell. 2015;162(6):127. [DOI:10.1016/j.cell.2015.07.061] [PMID] []

47. Moshrefiravasjani R, Kamrani A, Nazari N, Jafari F, Nasiri H, Jahanban-Esfahlan R, et al. Exosome-mediated tumor metastasis: biology, molecular targets and immuno-therapeutic options. Pathol Res Pract.2024;254:155083. [DOI:10.1016/j.prp.2023.155083] [PMID]

48. Yadav M, Jhunjhunwala S, Phung QT, Lupardus P, Tanguay J, Bumbaca S, et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature. 2014;515(7528):572-6. [DOI:10.1038/nature14001] [PMID]

49. Mo X, Tang C, Niu Q, Ma T, Du Y, Fu H. HTiP: high-throughput immunomodulator phenotypic screening platform to reveal IAP antagonists as anti-cancer immune enhancers. Cell Chem Biol. 2019;26(3):331-9. e3. [DOI:10.1016/j.chembiol.2018.11.011] [PMID] []

50. Garcia-Prieto CA, Villanueva L, Bueno-Costa A, Davalos V, González-Navarro EA, Juan M, et al. Epigenetic profiling and response to CD19 chimeric antigen receptor T-cell therapy in B-cell malignancies. J Natl Cancer Inst. 2022;114(3):436-45. [DOI:10.1093/jnci/djab194] [PMID] []

51. Mardi A, Shirokova AV, Mohammed RN, Keshavarz A, Zekiy AO, Thangavelu L, et al. Biological causes of immunogenic cancer cell death (ICD) and anti-tumor therapy; combination of oncolytic virus-based immunotherapy and CAR T-cell therapy for ICD induction. Cancer Cell Int. 2022;22(1):168. [DOI:10.1186/s12935-022-02585-z] [PMID] []

52. Shen W, Nguyen TH, Li MMR, Huang Y, Moon I, Nair N, et al. Generalizable AI predicts immunotherapy outcomes across cancers and treatments. medRxiv. 2025:2025.05. 01.25326820. [DOI:10.1101/2025.05.01.25326820]

53. Harel M, Ortenberg R, Varanasi SK, Mangalhara KC, Mardamshina M, Markovits E, et al. Proteomics of melanoma response to immunotherapy reveals mitochondrial dependence. Cell. 2019;179(1):236-50. [DOI:10.1016/j.cell.2019.08.012] [PMID] []

54. Bio P. Cutting-edge proteomics meets AI to rejuvenate cancer immunotherapy [updated 2022. Available from: https://www.protica.bio.

55. Beck L, Geiger T. MS-based technologies for untargeted single-cell proteomics. Curr Opin Biotechnol. 2022;76:102736. [DOI:10.1016/j.copbio.2022.102736] [PMID]

56. Deng Z, Liu J, Yu YV, Jin YN. Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma. Front Immunol. 2024;15:1451103. [DOI:10.3389/fimmu.2024.1451103] [PMID] []

57. Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, 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(10):1151-64. [DOI:10.1038/s43018-022-00416-8] [PMID] []

58. Yang Y, Yang J, Shen L, Chen J, Xia L, Ni B, et al. A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer. Am J Transl Res. 2021;13(2):743.

59. Prelaj A, Rebuzzi SE, Pizzutilo P, Bilancia M, Montrone M, Pesola F, et al. EPSILoN: A prognostic score using clinical and blood biomarkers in advanced non-small-cell lung cancer treated with immunotherapy. Clin Lung Cancer. 2020;21(4):365-77. e5. [DOI:10.1016/j.cllc.2019.11.017] [PMID]

60. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot A, Mezquita L, et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann Oncol. 2019;30(8):1321-8. [DOI:10.1093/annonc/mdz167] [PMID] []

61. 61 . Zhao Q, Chen Y, Huang W, Zhou H, Zhang W. Drug-microbiota interactions: an emerging priority for precision medicine. Signal Transduct Target Ther. 2023;8(1):1-27. [DOI:10.1038/s41392-023-01619-w] [PMID] []

62. Xiang H, Kasajima R, Azuma K, Tagami T, Hagiwara A, Nakahara Y, et al. Multi-omics analysis-based clinical and functional significance of a novel prognostic and immunotherapeutic gene signature derived from amino acid metabolism pathways in lung adenocarcinoma. Front Immunol. 2024;15:1361992. [DOI:10.3389/fimmu.2024.1361992] [PMID] []

63. Neal JT, Li X, Zhu J, Giangarra V, Grzeskowiak CL, Ju J, et al. Organoid modeling of the tumor immune microenvironment. Cell. 2018;175(7):1972-88. e16. [DOI:10.1016/j.cell.2018.11.021] [PMID] []

64. Wang H, Li X, You X, Zhao G. Harnessing the power of artificial intelligence for human living organoid research. Bioact Mater. 2024;42:140-64. [DOI:10.1016/j.bioactmat.2024.08.027] [PMID] []

65. Monem S, Hassanien AE, Abdel-Hamid AH. A multi-task graph deep learning model to predict drugs combination of synergy and sensitivity scores. BMC Bioinformatics. 2024;25(1):327. [DOI:10.1186/s12859-024-05925-0] [PMID] []

66. Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021;11(4):900-15. [DOI:10.1158/2159-8290.CD-21-0090] [PMID] []

67. He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol. 2023;88:187-200. [DOI:10.1016/j.semcancer.2022.12.009] [PMID]

68. Liao J, Li X, Gan Y, Han S, Rong P, Wang W, et al. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023;12:998222. [DOI:10.3389/fonc.2022.998222] [PMID] []

69. Ahmed L, Constantinidou A, Chatzittofis A. Patients' perspectives related to ethical issues and risks in precision medicine: a systematic review. Front Med (Lausanne). 2023;10:1215663. [DOI:10.3389/fmed.2023.1215663] [PMID] []

70. Huang L, Wu X, You J, Jin Z, He W, Sun J, et al. Artificial intelligence can predict personalized immunotherapy outcomes in cancer. Cancer Immunol Res. 2025;13(7):964-77. [DOI:10.1158/2326-6066.CIR-24-1270] [PMID]

71. Esteva A, Topol E. Can skin cancer diagnosis be transformed by AI? The Lancet. 2019;394(10211):1795. [DOI:10.1016/S0140-6736(19)32726-6]

72. Sadeghi Z, Alizadehsani R, Cifci MA, Kausar S, Rehman R, Mahanta P, et al. A review of explainable artificial intelligence in healthcare. Comput Electr Eng. 2024;118:109370. [DOI:10.1016/j.compeleceng.2024.109370]