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Year 2025, Volume: 12 Issue: 3, 894 - 917, 30.09.2025
https://doi.org/10.54287/gujsa.1768020

Abstract

References

  • Alataş, E., Kökkülünk, H. T., Tanyıldızı, H., & Alcın, G. (2023). Treatment prediction with machine learning in prostate cancer patients. Computer Methods in Biomechanics and Biomedical Engineering, 28(4), 572-580. https://doi.org/10.1080/10255842.2023.2298364
  • Alis, D., Bagcilar, O., Senli, Y. D., Yergin, M., Isler, C., Kocer, N., Islak, C., & Kizilkilic, O. (2020). Machine learning‑based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high‑grade gliomas. Japanese Journal of Radiology, 38, 135-143. https://doi.org/10.1007/s11604-019-00902-7
  • Arslan, R. U., Pamuk, Z., & Kaya, C. (2024). Usage of weka software based on machine learning algorithms for prediction of liver fibrosis/cirrhosis. Black Sea Journal of Engineering and Science, 7(3), 445-456. https://doi.org/10.34248/bsengineering.1351863
  • Ayyildiz, O., Aydin, Z., Yilmaz, B., Karaçavuş, S., Şenkaya, K., İçer, S., Taşdemir, E. A., & Kaya, E. (2020). Lung cancer subtype differentiation from positron emission tomography images. Turkish Journal of Electrical Engineering and Computer Sciences, 28(1), 19. https://doi.org/10.3906/elk-1810-154
  • Baysal, B., Baysal, H., Eser, M. B., Dogan, M. B., & Alimoglu, O. (2022). Radiomics features based on MRI-ADC maps of patients with breast cancer: Relationship with lesion size, features stability, and model accuracy. Medeniyet Medical Journal, 37(3), 277-288. https://doi.org/10.4274/mmj.galenos.2022.70094
  • Bicakci, M., Ayyildiz, O., Aydin, Z., Basturk, A., Karacavus, S., & Yilmaz, B. (2020). Metabolic imaging based sub-classification of lung cancer. IEEE Access, 8, 218470-218476. https://doi.org/10.1109/ACCESS.2020.3040155
  • Bramer, W. M., Giustini, D., Kramer, B. M. R., & Anderson, P. F. (2013). The comparative recall of Google Scholar versus PubMed in identical searches for biomedical systematic reviews: a review of searches used in systematic reviews. Systematic Reviews, 2, 115. https://doi.org/doi:10.1186/2046-4053-2-115
  • Bulut, G., Atilgan, H. I., C¸ınarer, G., Kılıc¸, K., Yıkar, D., & Parlar, T. (2023). Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT. PLoS ONE, 18(9), e0290543. https://doi.org/10.1371/journal.pone.0290543
  • Cakmak, A., Ayaz, H., Arıkan, S., Ibrahimzada, A. R., Demirkol, Ş., Sönmez, D., Hakan, M. T., Sürmen, S. T., Horozoğlu, C., Doğan, M. B., Küçükhüseyin, Ö., Cacına, C., Kıran, B., Zeybek, Ü., Baysan, M., & Yaylım, İ. (2023). Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models. Medical & Biological Engineering & Computing, 61, 243-258. https://doi.org/10.1007/s11517-022-02707-9
  • Canayaz, M., Şehribanoğlu, S., Ozgokçe, M., & Akıncı, M. B. (2024). A comprehensive exploration of deep learning approaches for pulmonary nodule classification and segmentation in chest CT images. Neural Computing and Applications, 36, 7245-7264. https://doi.org/10.1007/s00521-024-09457-9
  • Çayır, S., Solmaz, G., Kusetogullari, H., Tokat, F., Bozaba, E., Karakaya, S., Iheme, L. O., Tekin, E., Yazıcı, Ç., Özsoy, G., Ayaltı, S., Kayhan, C. K., İnce, Ü., Uzel, B., & Kılıç, O. (2022). MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue. Neural Computing and Applications, 34, 17837-17851. https://doi.org/10.1007/s00521-022-07441-9
  • Charilaou, P., & Battat, R. (2022). Machine learning models and over-fitting considerations. World Journal of Gastroenterology, 28(5), 605-607. https://doi.org/10.3748/wjg.v28.i5.605
  • Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. Nature Portfolio Journal Digital Medicine, 3, 81. https://doi.org/10.1038/s41746-020-0288-5
  • Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022). Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education, 22, 772. https://doi.org/10.1186/s12909-022-03852-3
  • Demir, R., Koc, S., Ozturk, D. G., Bilir, S., Ozata, H. İ., Williams, R., Christy, J., Akkoc, Y., Tinay, İ., Gunduz‑Demir, C., & Gozuacik, D. (2024). Artificial intelligence assisted patient blood and urine droplet pattern analysis for non‑invasive and accurate diagnosis of bladder cancer. Scientific Reports, 14, 2488. https://doi.org/10.1038/s41598-024-52728-7
  • Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009, June 20-25). ImageNet: A large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, (pp. 248-255), Miami, FL, USA. https://doi.org/10.1109/CVPR.2009.5206848
  • DergiPark (2025). ULAKBİM Journal Systems. About DergiPark. (Accessed: 11/06/2025) https://dergipark.org.tr/en/pub/page/about
  • Digital Transformation Office. (2021). National Artificial Intelligence Strategy 2021 - 2025 - Türkiye. Presidency of the Republic of Türkiye, Digital Transformation Office. (Accessed: 13/06/2025) https://cbddo.gov.tr/en/nais
  • Dirican, E., & Akkuş, Z. (2021). Performance comparison of support vector machines, random forest and artificial neural networks in binary classification: Descriptive comparison study. Turkiye Klinikleri Journal of Biostatistics, 13(3), 236-251. https://doi.org/10.5336/biostatic.2021-81105
  • Doğaner, A., Çolak, C., Küçükdurmaz, F., & Ölmez, C. (2021). Prediction of renal cell carcinoma based on ensemble learning methods. Middle Black Sea Journal of Health Science, 7(1), 104-114. https://doi.org/10.19127/mbsjohs.889492
  • Ekşi, M., Evren, İ., Akkaş, F., Arıkan, Y., Özdemir, O., Özlü, D. N., Ayten, A., Sahin, S., Tuğcu, V., & Taşçı, A. İ. (2021). Machine learning algorithms can more efficiently predict biochemical recurrence after robot‐assisted radical prostatectomy. The Prostate, 81, 913‐920. https://doi.org/10.1002/pros.24188
  • Emiroglu, M., Esin, H., Erdogan, M., Ugurlu, L., Dursun, A., Mertoglu, S., Kiziloglu, I., & Karaali, C. (2022). National study on use of artificial intelligence in breast disease and cancer. Bratislava Medical Journal, 123(3), 191-196. https://doi.org/10.4149/BLL_2022_032
  • Erturk, H., Eser, M. B., Yaşar, A. B., Ayaz, M., Atalay, B., Tatoglu, M. T., & Caymaz, I. (2023). Low‑dose CT radiomics features‑based neural networks predict lymphoma types. Egyptian Journal of Radiology and Nuclear Medicine, 54, 135. https://doi.org/10.1186/s43055-023-01084-z
  • Etiz, D., Yakar, M., Ak, G., Kütri, D., Çelik, Ö., & Metintaş, M. (2023). Response prediction in lung SBRT with artificial intelligence. Turkish Journal of Oncology, 38(3), 288-294. https://doi.org/10.5505/tjo.2023.4008
  • Gencer, K., & Gencer, G. (2024). Investigation of the Status of Artificial Intelligence Courses in Medical Education Curriculum in Turkey. Bilgisayar Bilimleri ve Teknolojileri Dergisi, 5(2), 67-76. https://doi.org/10.54047/bibted.1520315
  • Gultekin, M. A., Peker, A. A., Oktay, A. B., Turk, H. M., Cesme, D. H., Shbair, A. T. M., Yilmaz, T. F., Kaya, A., Yasin, A. I., Seker, M., Mayadagli, A., & Alkan, A. (2023). Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks. Journal of Clinical Ultrasound: Sonography and other Imaging Techniques, 51(9), 1579‐1586. https://doi.org/10.1002/jcu.23558
  • Hamyoon, H., Chan, W. Y., Mohammadi, A., Kuzan, T. Y., Mirza-Aghazadeh-Attari, M., Leong, W. L., Altintoprak, K. M., Vijayananthan, A., Rahmat, K., Mumin, N. A., Leong, S. S., Ejtehadifar, S., Faeghi, F., Abolghasemi, J., Ciaccio, E. J., Acharya, U. R., & Ardakani, A. A. (2022). Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts. European Journal of Radiology, 157, 110591. https://doi.org/10.1016/j.ejrad.2022.110591
  • Ikizceli, T., Karacavus, S., Erbay, H., & Yurttakal, A. H. (2021). Discrimination of malignant and benign breast masses using computer-aided diagnosis from dynamic contrast-enhanced magnetic resonance imaging. The Medical Bulletin of Haseki, 59, 190-195. https://doi.org/10.4274/haseki.galenos.2021.6819
  • Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203-211. https://doi.org/10.1038/s41592-020-01008-z
  • Kabakçı, K. A., Çakır, A., Türkmen, İ., Töreyin, B. U., & Çapar, A. (2021). Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images. Biomedical Signal Processing and Control, 69, 102924. https://doi.org/10.1016/j.bspc.2021.102924
  • Karagoz, A., Alis, D., Seker, M. E., Zeybel, G., Yergin, M., Oksuz, I., & Karaarslan, E. (2023). Anatomically guided self‑adapting deep neural network for clinically significant prostate cancer detection on bi‑parametric MRI: a multi‑center study. Insights into Imaging, 14, 110. https://doi.org/10.1186/s13244-023-01439-0
  • Karagöz, M. A., Nalbantoğlu, Ö. U., Karaboğa, D., Akay, B., Baştürk, A., Ulutabanca, H., Doğan, S., Coşkun, D., & Demİr, O. (2024). Deep learning based breast cancer diagnosis with multiview of mammography screening to reduce false positive recall rate. Turkish Journal of Electrical Engineering and Computer Sciences, 32(3), 3. https://doi.org/10.55730/1300-0632.4076
  • Kelly, M., Longjohn, R., & Nottingham, K. (2024). The UCI machine learning repository. (Accessed: 02/05/2025) https://archive.ics.uci.edu
  • Kinar, Y., Akiva, P., Choman, E., Kariv, R., Shalev, V., Levin, B., Narod, S. A., & Goshen, R. (2017). Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer. PLoS ONE, 12(2), e0171759. https://doi.org/10.1371/journal.pone.0171759
  • Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences, 117(23), 12592-12594. https://doi.org/10.1073/pnas.1919012117
  • Luchini, C., Pea, A., & Scarpa, A. (2022). Artificial intelligence in oncology: current applications and future perspectives. British Journal of Cancer, 126, 4-9. https://doi.org/10.1038/s41416-021-01633-1
  • Lunit Inc. (2024). Lunit INSIGHT MMG. (Accessed: 02/05/2025) https://www.lunit.io/en/products/mmg
  • Mahmood, H., Shaban, M., Rajpoot, N., & Khurram, S. A. (2021). Artificial intelligence-based methods in head and neck cancer diagnosis: an overview. British Journal of Cancer, 124, 1934-1940. https://doi.org/10.1038/s41416-021-01386-x
  • Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Gansau, J. A., & Goh, L. P. W. (2023). The role of machine learning and deep learning approaches for the detection of skin cancer. Healthcare, 11(3), 415. https://doi.org/10.3390/healthcare11030415
  • Mese, I., Inan, N. G., Kocadagli, O., Salmaslioglu, A., & Yildirim, D. (2023). ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artificial intelligence. Medical Ultrasonography, 25(4), 375-383. https://doi.org/10.11152/mu-4306
  • Naqa, I. E., Karolak, A., Luo, Y., Folio, L., Tarhini, A. A., Rollison, D., & Parodi, K. (2023). Translation of AI into oncology clinical practice. Oncogene, 42, 3089-3097. https://doi.org/10.1038/s41388-023-02826-z
  • Nettel, P. F., Hankins, E., Stirling, R., Cirri, G., Grau, G., Rahim, S., & Crampton, E. (2024). Government AI Readiness Index 2024. Oxford Insights. (Accessed: 13/06/2025) https://oxfordinsights.com/wp-content/uploads/2024/12/2024-Government-AI-Readiness-Index-2.pdf
  • Orman, A., & Sebetci, Ö. (2022). Artificial intelligence (AI) studies in the TR index: A systematic review. Düzce University Journal of Science & Technology, 10(1), 465-475. https://doi.org/10.29130/dubited.964460
  • Ozbozduman, K., Loc, I., Durmaz, S., Atasoy, D., Kilic, M., Yildirim, H., Esen, T., Vural, M., & Unlu, M. B. (2024). Machine learning prediction of Gleason grade group upgrade between in‑bore biopsy and radical prostatectomy pathology. Scientific Reports, 14(1), 5849. https://doi.org/10.1038/s41598-024-56415-5
  • Ozer, E., Bilecen, A. E., Ozer, N. B., & Yanikoglu, B. (2023). Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model. Cytopathology, 34, 113-119. https://doi.org/10.1111/cyt.13192
  • Özgül, H. A., Akin, I. B., Mutlu, U., & Balci, A. (2023). Diagnostic value of machine learning‑based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. Skeletal Radiology, 52, 1703-1711. https://doi.org/10.1007/s00256-023-04333-4
  • Özsandikcioğlu, Ü., & Atasoy, A. (2023). Breath analysis for detection of lung cancer with hybrid sensor-based electronic nose. Turkish Journal of Electrical Engineering and Computer Sciences, 31(3), 5. https://doi.org/10.55730/1300-0632.4001
  • Öztürk, E. M. A., Ünsal, G., Erişir, F., & Orhan, K. (2024). Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model. European Archives of Oto-Rhino-Laryngology, 281, 6585-6597. https://doi.org/10.1007/s00405-024-08862-z
  • Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. https://doi.org/10.1038/s41591-021-01614-0
  • Sacli-Bilmez, B., Danyeli, A. E., Yakicier, M. C., Aras, F. K., Pamir, M. N., Özduman, K., Dinçer, A., & Ozturk-Isik, E. (2023). Magnetic resonance spectroscopic correlates of progression free and overall survival in “glioblastoma, IDH-wildtype, WHO grade-4”. Frontiers in Neuroscience, 17, 1149292. https://doi.org/10.3389/fnins.2023.1149292
  • Seker, M. E., Koyluoglu, Y. O., Ozaydin, A. N., Gurdal, S. O., Ozcinar, B., Cabioglu, N., Ozmen, V., & Aribal, E. (2024). Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program. European Radiology, 34, 6145-6157. https://doi.org/10.1007/s00330-024-10661-3
  • Şen, N. P. K., Aksu, A., & Kaya, G. Ç. (2021). A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Annals of Nuclear Medicine, 35, 1030-1037. https://doi.org/10.1007/s12149-021-01638-z
  • Seven, G., Silahtaroglu, G., Seven, O. O., & Senturk, H. (2022a). Differentiating gastrointestinal stromal tumors from leiomyomas using a neural network trained on endoscopic ultrasonography images. Digestive Diseases, 40, 427-435. https://doi.org/10.1159/000520032
  • Seven, G., Silahtaroglu, G., Kochan, K., Ince, A. T., Arici, D. S., & Senturk, H. (2022b). Use of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors. Digestive Diseases and Sciences, 67, 273-281. https://doi.org/10.1007/s10620-021-06830-9
  • Şimşek, C., Güven, D. C., Ceylan, F., Çakir, I. Y., Şahin, T. K., Dizdar, Ö., Balaban, Y., & Yalçin, Ş. (2021). Machine learning models predict early postoperative relapse in pancreatic cancer. Journal of Oncological Sciences, 7(3), 115-124. https://doi.org/10.37047/jos.2021-84326
  • Sümer, E., Tek, E., Türe, O. A., Şengöz, M., Dinçer, A., Özcan, A., Pamir, M. N., Özduman, K., & Ozturk‑Isik, E. (2022). The effect of tumor shape irregularity on Gamma Knife treatment plan quality and treatment outcome: an analysis of 234 vestibular schwannomas. Scientific Reports, 12, 21809. https://doi.org/10.1038/s41598-022-25422-9
  • TÜBİTAK ULAKBİM TR Dizin. (2025). About TR Dizin. (Accessed: 11/06/2025) https://trdizin.gov.tr/en/about/
  • Tuominen, V. J., Tolonen, T. T., & Isola, J. (2012). ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology, 60, 758-767. https://doi.org/10.1111/j.1365-2559.2011.04142.x
  • Turk, G., Ozdemir, M., Zeydan, R., Turk, Y., Bilgin, Z., & Zeydan, E. (2021). On the identification of thyroid nodules using semi-supervised deep learning. International Journal for Numerical Methods in Biomedical Engineering, 37(3), e3433. https://doi.org/10.1002/cnm.3433
  • Türkiye Health Data Research and Artificial Intelligence Applications Institute. (2025). TÜYZE. (Accessed: 02/05/2025) https://tuyze.tuseb.gov.tr/
  • Uygun İlikhan, S., Özer, M., Tanberkan, H., & Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences, 54(3), 2. https://doi.org/10.55730/1300-0144.5814
  • Yardimci, A. H., Kocak, B., Sel, I., Bulut, H., Bektas, C. T., Cin, M., Dursun, N., Bektas, H., Mermut, O., Yardimci, V. H., & Kilickesmez, O. (2023). Radiomics of locally advanced rectal cancer: machine learning‑based prediction of response to neoadjuvant chemoradiotherapy using pre‑treatment sagittal T2‑weighted MRI. Japanese Journal of Radiology, 41, 71-82. https://doi.org/10.1007/s11604-022-01325-7
  • Yazici, H., Odemis, D. A., Aksu, D., Erdogan, O. S., Tuncer, S. B., Avsar, M., Kilic, S., Turkcan, G. K., Celik, B., & Aydin, M. A. (2020). New approach for risk estimation algorithms of BRCA1/2 negativeness detection with modelling supervised machine learning techniques. Disease Markers, 7, 8594090. https://doi.org//10.1155/2020/8594090
  • Yildirim, K., Yildirim, M., Eryesil, H., Talo, M., Yildirim, O., Karabatak, M., Ogras, M. S., Artas, H., & Acharya, U. R. (2022). Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging. Computers and Electrical Engineering, 102, 108275. https://doi.org/10.1016/j.compeleceng.2022.108275
  • Yılmaz, H. H., Yazıhan, N., Tunca, D., Sevinc¸, A., Olcayto, E. O., Ozgul, N., & Tuncer, M. (2011). Cancer trends and incidence and mortality patterns in turkey. Japanese Journal of Clinical Oncology, 41(1), 10-16. https://doi.org/10.1093/jjco/hyq075
  • Yirgin, I. K., Koyluoglu, Y. O., Seker, M. E., Gurdal, S. O., Ozaydin, A. N., Ozcinar, B., Cabioğlu, N., Ozmen, V., & Aribal, E. (2022). Diagnostic performance of AI for cancers registered in a mammography screening program: A retrospective analysis. Technology in Cancer Research & Treatment, 21, 1-11. https://doi.org/10.1177/15330338221075172
  • Yurttakal, A. H., Erbay, H., Ikizceli, T., & Karacavus, S. (2020). Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications, 79, 15555-15573. https://doi.org/10.1007/s11042-019-7479-6

The Application of AI in Oncology Research in Türkiye: Impact and Future Directions

Year 2025, Volume: 12 Issue: 3, 894 - 917, 30.09.2025
https://doi.org/10.54287/gujsa.1768020

Abstract

Artificial Intelligence (AI) techniques such as machine learning and deep learning have seen increasing application in medical diagnosis. The field of oncology has seen the application of AI in cancer detection and diagnosis, treatment selection and survival prediction for patients. This study evaluates recent research in the application of AI in oncology and its potential impact on the Turkish health and medical sectors through a review-based study. We identified 41 research studies from 2020 to 2024 applying AI in oncology utilizing datasets curated from Turkish medical data. Our analysis showed that all studies were retrospective, with the majority being diagnosis studies. Patient datasets were unicentric, relatively small and not publicly available. Thus, most of the reported results cannot be generalized until the models are validated in larger, more diverse studies. The majority of studies concentrated on model accuracy, with limited evidence of model integration in clinical settings or within the health industry. Our findings indicate that more work is required in order to develop more advanced approaches for human-AI collaboration, i.e., clinician–in-the-loop or patient-in-the-loop approaches. An important step toward achieving this is to create and maintain a national dataset for AI in oncology research in Türkiye. Although this study is specific to Türkiye, we anticipate that its findings may be relevant to countries with similar research environments.

References

  • Alataş, E., Kökkülünk, H. T., Tanyıldızı, H., & Alcın, G. (2023). Treatment prediction with machine learning in prostate cancer patients. Computer Methods in Biomechanics and Biomedical Engineering, 28(4), 572-580. https://doi.org/10.1080/10255842.2023.2298364
  • Alis, D., Bagcilar, O., Senli, Y. D., Yergin, M., Isler, C., Kocer, N., Islak, C., & Kizilkilic, O. (2020). Machine learning‑based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high‑grade gliomas. Japanese Journal of Radiology, 38, 135-143. https://doi.org/10.1007/s11604-019-00902-7
  • Arslan, R. U., Pamuk, Z., & Kaya, C. (2024). Usage of weka software based on machine learning algorithms for prediction of liver fibrosis/cirrhosis. Black Sea Journal of Engineering and Science, 7(3), 445-456. https://doi.org/10.34248/bsengineering.1351863
  • Ayyildiz, O., Aydin, Z., Yilmaz, B., Karaçavuş, S., Şenkaya, K., İçer, S., Taşdemir, E. A., & Kaya, E. (2020). Lung cancer subtype differentiation from positron emission tomography images. Turkish Journal of Electrical Engineering and Computer Sciences, 28(1), 19. https://doi.org/10.3906/elk-1810-154
  • Baysal, B., Baysal, H., Eser, M. B., Dogan, M. B., & Alimoglu, O. (2022). Radiomics features based on MRI-ADC maps of patients with breast cancer: Relationship with lesion size, features stability, and model accuracy. Medeniyet Medical Journal, 37(3), 277-288. https://doi.org/10.4274/mmj.galenos.2022.70094
  • Bicakci, M., Ayyildiz, O., Aydin, Z., Basturk, A., Karacavus, S., & Yilmaz, B. (2020). Metabolic imaging based sub-classification of lung cancer. IEEE Access, 8, 218470-218476. https://doi.org/10.1109/ACCESS.2020.3040155
  • Bramer, W. M., Giustini, D., Kramer, B. M. R., & Anderson, P. F. (2013). The comparative recall of Google Scholar versus PubMed in identical searches for biomedical systematic reviews: a review of searches used in systematic reviews. Systematic Reviews, 2, 115. https://doi.org/doi:10.1186/2046-4053-2-115
  • Bulut, G., Atilgan, H. I., C¸ınarer, G., Kılıc¸, K., Yıkar, D., & Parlar, T. (2023). Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT. PLoS ONE, 18(9), e0290543. https://doi.org/10.1371/journal.pone.0290543
  • Cakmak, A., Ayaz, H., Arıkan, S., Ibrahimzada, A. R., Demirkol, Ş., Sönmez, D., Hakan, M. T., Sürmen, S. T., Horozoğlu, C., Doğan, M. B., Küçükhüseyin, Ö., Cacına, C., Kıran, B., Zeybek, Ü., Baysan, M., & Yaylım, İ. (2023). Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models. Medical & Biological Engineering & Computing, 61, 243-258. https://doi.org/10.1007/s11517-022-02707-9
  • Canayaz, M., Şehribanoğlu, S., Ozgokçe, M., & Akıncı, M. B. (2024). A comprehensive exploration of deep learning approaches for pulmonary nodule classification and segmentation in chest CT images. Neural Computing and Applications, 36, 7245-7264. https://doi.org/10.1007/s00521-024-09457-9
  • Çayır, S., Solmaz, G., Kusetogullari, H., Tokat, F., Bozaba, E., Karakaya, S., Iheme, L. O., Tekin, E., Yazıcı, Ç., Özsoy, G., Ayaltı, S., Kayhan, C. K., İnce, Ü., Uzel, B., & Kılıç, O. (2022). MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue. Neural Computing and Applications, 34, 17837-17851. https://doi.org/10.1007/s00521-022-07441-9
  • Charilaou, P., & Battat, R. (2022). Machine learning models and over-fitting considerations. World Journal of Gastroenterology, 28(5), 605-607. https://doi.org/10.3748/wjg.v28.i5.605
  • Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. Nature Portfolio Journal Digital Medicine, 3, 81. https://doi.org/10.1038/s41746-020-0288-5
  • Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022). Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education, 22, 772. https://doi.org/10.1186/s12909-022-03852-3
  • Demir, R., Koc, S., Ozturk, D. G., Bilir, S., Ozata, H. İ., Williams, R., Christy, J., Akkoc, Y., Tinay, İ., Gunduz‑Demir, C., & Gozuacik, D. (2024). Artificial intelligence assisted patient blood and urine droplet pattern analysis for non‑invasive and accurate diagnosis of bladder cancer. Scientific Reports, 14, 2488. https://doi.org/10.1038/s41598-024-52728-7
  • Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009, June 20-25). ImageNet: A large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, (pp. 248-255), Miami, FL, USA. https://doi.org/10.1109/CVPR.2009.5206848
  • DergiPark (2025). ULAKBİM Journal Systems. About DergiPark. (Accessed: 11/06/2025) https://dergipark.org.tr/en/pub/page/about
  • Digital Transformation Office. (2021). National Artificial Intelligence Strategy 2021 - 2025 - Türkiye. Presidency of the Republic of Türkiye, Digital Transformation Office. (Accessed: 13/06/2025) https://cbddo.gov.tr/en/nais
  • Dirican, E., & Akkuş, Z. (2021). Performance comparison of support vector machines, random forest and artificial neural networks in binary classification: Descriptive comparison study. Turkiye Klinikleri Journal of Biostatistics, 13(3), 236-251. https://doi.org/10.5336/biostatic.2021-81105
  • Doğaner, A., Çolak, C., Küçükdurmaz, F., & Ölmez, C. (2021). Prediction of renal cell carcinoma based on ensemble learning methods. Middle Black Sea Journal of Health Science, 7(1), 104-114. https://doi.org/10.19127/mbsjohs.889492
  • Ekşi, M., Evren, İ., Akkaş, F., Arıkan, Y., Özdemir, O., Özlü, D. N., Ayten, A., Sahin, S., Tuğcu, V., & Taşçı, A. İ. (2021). Machine learning algorithms can more efficiently predict biochemical recurrence after robot‐assisted radical prostatectomy. The Prostate, 81, 913‐920. https://doi.org/10.1002/pros.24188
  • Emiroglu, M., Esin, H., Erdogan, M., Ugurlu, L., Dursun, A., Mertoglu, S., Kiziloglu, I., & Karaali, C. (2022). National study on use of artificial intelligence in breast disease and cancer. Bratislava Medical Journal, 123(3), 191-196. https://doi.org/10.4149/BLL_2022_032
  • Erturk, H., Eser, M. B., Yaşar, A. B., Ayaz, M., Atalay, B., Tatoglu, M. T., & Caymaz, I. (2023). Low‑dose CT radiomics features‑based neural networks predict lymphoma types. Egyptian Journal of Radiology and Nuclear Medicine, 54, 135. https://doi.org/10.1186/s43055-023-01084-z
  • Etiz, D., Yakar, M., Ak, G., Kütri, D., Çelik, Ö., & Metintaş, M. (2023). Response prediction in lung SBRT with artificial intelligence. Turkish Journal of Oncology, 38(3), 288-294. https://doi.org/10.5505/tjo.2023.4008
  • Gencer, K., & Gencer, G. (2024). Investigation of the Status of Artificial Intelligence Courses in Medical Education Curriculum in Turkey. Bilgisayar Bilimleri ve Teknolojileri Dergisi, 5(2), 67-76. https://doi.org/10.54047/bibted.1520315
  • Gultekin, M. A., Peker, A. A., Oktay, A. B., Turk, H. M., Cesme, D. H., Shbair, A. T. M., Yilmaz, T. F., Kaya, A., Yasin, A. I., Seker, M., Mayadagli, A., & Alkan, A. (2023). Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks. Journal of Clinical Ultrasound: Sonography and other Imaging Techniques, 51(9), 1579‐1586. https://doi.org/10.1002/jcu.23558
  • Hamyoon, H., Chan, W. Y., Mohammadi, A., Kuzan, T. Y., Mirza-Aghazadeh-Attari, M., Leong, W. L., Altintoprak, K. M., Vijayananthan, A., Rahmat, K., Mumin, N. A., Leong, S. S., Ejtehadifar, S., Faeghi, F., Abolghasemi, J., Ciaccio, E. J., Acharya, U. R., & Ardakani, A. A. (2022). Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts. European Journal of Radiology, 157, 110591. https://doi.org/10.1016/j.ejrad.2022.110591
  • Ikizceli, T., Karacavus, S., Erbay, H., & Yurttakal, A. H. (2021). Discrimination of malignant and benign breast masses using computer-aided diagnosis from dynamic contrast-enhanced magnetic resonance imaging. The Medical Bulletin of Haseki, 59, 190-195. https://doi.org/10.4274/haseki.galenos.2021.6819
  • Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203-211. https://doi.org/10.1038/s41592-020-01008-z
  • Kabakçı, K. A., Çakır, A., Türkmen, İ., Töreyin, B. U., & Çapar, A. (2021). Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images. Biomedical Signal Processing and Control, 69, 102924. https://doi.org/10.1016/j.bspc.2021.102924
  • Karagoz, A., Alis, D., Seker, M. E., Zeybel, G., Yergin, M., Oksuz, I., & Karaarslan, E. (2023). Anatomically guided self‑adapting deep neural network for clinically significant prostate cancer detection on bi‑parametric MRI: a multi‑center study. Insights into Imaging, 14, 110. https://doi.org/10.1186/s13244-023-01439-0
  • Karagöz, M. A., Nalbantoğlu, Ö. U., Karaboğa, D., Akay, B., Baştürk, A., Ulutabanca, H., Doğan, S., Coşkun, D., & Demİr, O. (2024). Deep learning based breast cancer diagnosis with multiview of mammography screening to reduce false positive recall rate. Turkish Journal of Electrical Engineering and Computer Sciences, 32(3), 3. https://doi.org/10.55730/1300-0632.4076
  • Kelly, M., Longjohn, R., & Nottingham, K. (2024). The UCI machine learning repository. (Accessed: 02/05/2025) https://archive.ics.uci.edu
  • Kinar, Y., Akiva, P., Choman, E., Kariv, R., Shalev, V., Levin, B., Narod, S. A., & Goshen, R. (2017). Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer. PLoS ONE, 12(2), e0171759. https://doi.org/10.1371/journal.pone.0171759
  • Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences, 117(23), 12592-12594. https://doi.org/10.1073/pnas.1919012117
  • Luchini, C., Pea, A., & Scarpa, A. (2022). Artificial intelligence in oncology: current applications and future perspectives. British Journal of Cancer, 126, 4-9. https://doi.org/10.1038/s41416-021-01633-1
  • Lunit Inc. (2024). Lunit INSIGHT MMG. (Accessed: 02/05/2025) https://www.lunit.io/en/products/mmg
  • Mahmood, H., Shaban, M., Rajpoot, N., & Khurram, S. A. (2021). Artificial intelligence-based methods in head and neck cancer diagnosis: an overview. British Journal of Cancer, 124, 1934-1940. https://doi.org/10.1038/s41416-021-01386-x
  • Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Gansau, J. A., & Goh, L. P. W. (2023). The role of machine learning and deep learning approaches for the detection of skin cancer. Healthcare, 11(3), 415. https://doi.org/10.3390/healthcare11030415
  • Mese, I., Inan, N. G., Kocadagli, O., Salmaslioglu, A., & Yildirim, D. (2023). ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artificial intelligence. Medical Ultrasonography, 25(4), 375-383. https://doi.org/10.11152/mu-4306
  • Naqa, I. E., Karolak, A., Luo, Y., Folio, L., Tarhini, A. A., Rollison, D., & Parodi, K. (2023). Translation of AI into oncology clinical practice. Oncogene, 42, 3089-3097. https://doi.org/10.1038/s41388-023-02826-z
  • Nettel, P. F., Hankins, E., Stirling, R., Cirri, G., Grau, G., Rahim, S., & Crampton, E. (2024). Government AI Readiness Index 2024. Oxford Insights. (Accessed: 13/06/2025) https://oxfordinsights.com/wp-content/uploads/2024/12/2024-Government-AI-Readiness-Index-2.pdf
  • Orman, A., & Sebetci, Ö. (2022). Artificial intelligence (AI) studies in the TR index: A systematic review. Düzce University Journal of Science & Technology, 10(1), 465-475. https://doi.org/10.29130/dubited.964460
  • Ozbozduman, K., Loc, I., Durmaz, S., Atasoy, D., Kilic, M., Yildirim, H., Esen, T., Vural, M., & Unlu, M. B. (2024). Machine learning prediction of Gleason grade group upgrade between in‑bore biopsy and radical prostatectomy pathology. Scientific Reports, 14(1), 5849. https://doi.org/10.1038/s41598-024-56415-5
  • Ozer, E., Bilecen, A. E., Ozer, N. B., & Yanikoglu, B. (2023). Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model. Cytopathology, 34, 113-119. https://doi.org/10.1111/cyt.13192
  • Özgül, H. A., Akin, I. B., Mutlu, U., & Balci, A. (2023). Diagnostic value of machine learning‑based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. Skeletal Radiology, 52, 1703-1711. https://doi.org/10.1007/s00256-023-04333-4
  • Özsandikcioğlu, Ü., & Atasoy, A. (2023). Breath analysis for detection of lung cancer with hybrid sensor-based electronic nose. Turkish Journal of Electrical Engineering and Computer Sciences, 31(3), 5. https://doi.org/10.55730/1300-0632.4001
  • Öztürk, E. M. A., Ünsal, G., Erişir, F., & Orhan, K. (2024). Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model. European Archives of Oto-Rhino-Laryngology, 281, 6585-6597. https://doi.org/10.1007/s00405-024-08862-z
  • Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. https://doi.org/10.1038/s41591-021-01614-0
  • Sacli-Bilmez, B., Danyeli, A. E., Yakicier, M. C., Aras, F. K., Pamir, M. N., Özduman, K., Dinçer, A., & Ozturk-Isik, E. (2023). Magnetic resonance spectroscopic correlates of progression free and overall survival in “glioblastoma, IDH-wildtype, WHO grade-4”. Frontiers in Neuroscience, 17, 1149292. https://doi.org/10.3389/fnins.2023.1149292
  • Seker, M. E., Koyluoglu, Y. O., Ozaydin, A. N., Gurdal, S. O., Ozcinar, B., Cabioglu, N., Ozmen, V., & Aribal, E. (2024). Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program. European Radiology, 34, 6145-6157. https://doi.org/10.1007/s00330-024-10661-3
  • Şen, N. P. K., Aksu, A., & Kaya, G. Ç. (2021). A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Annals of Nuclear Medicine, 35, 1030-1037. https://doi.org/10.1007/s12149-021-01638-z
  • Seven, G., Silahtaroglu, G., Seven, O. O., & Senturk, H. (2022a). Differentiating gastrointestinal stromal tumors from leiomyomas using a neural network trained on endoscopic ultrasonography images. Digestive Diseases, 40, 427-435. https://doi.org/10.1159/000520032
  • Seven, G., Silahtaroglu, G., Kochan, K., Ince, A. T., Arici, D. S., & Senturk, H. (2022b). Use of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors. Digestive Diseases and Sciences, 67, 273-281. https://doi.org/10.1007/s10620-021-06830-9
  • Şimşek, C., Güven, D. C., Ceylan, F., Çakir, I. Y., Şahin, T. K., Dizdar, Ö., Balaban, Y., & Yalçin, Ş. (2021). Machine learning models predict early postoperative relapse in pancreatic cancer. Journal of Oncological Sciences, 7(3), 115-124. https://doi.org/10.37047/jos.2021-84326
  • Sümer, E., Tek, E., Türe, O. A., Şengöz, M., Dinçer, A., Özcan, A., Pamir, M. N., Özduman, K., & Ozturk‑Isik, E. (2022). The effect of tumor shape irregularity on Gamma Knife treatment plan quality and treatment outcome: an analysis of 234 vestibular schwannomas. Scientific Reports, 12, 21809. https://doi.org/10.1038/s41598-022-25422-9
  • TÜBİTAK ULAKBİM TR Dizin. (2025). About TR Dizin. (Accessed: 11/06/2025) https://trdizin.gov.tr/en/about/
  • Tuominen, V. J., Tolonen, T. T., & Isola, J. (2012). ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology, 60, 758-767. https://doi.org/10.1111/j.1365-2559.2011.04142.x
  • Turk, G., Ozdemir, M., Zeydan, R., Turk, Y., Bilgin, Z., & Zeydan, E. (2021). On the identification of thyroid nodules using semi-supervised deep learning. International Journal for Numerical Methods in Biomedical Engineering, 37(3), e3433. https://doi.org/10.1002/cnm.3433
  • Türkiye Health Data Research and Artificial Intelligence Applications Institute. (2025). TÜYZE. (Accessed: 02/05/2025) https://tuyze.tuseb.gov.tr/
  • Uygun İlikhan, S., Özer, M., Tanberkan, H., & Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences, 54(3), 2. https://doi.org/10.55730/1300-0144.5814
  • Yardimci, A. H., Kocak, B., Sel, I., Bulut, H., Bektas, C. T., Cin, M., Dursun, N., Bektas, H., Mermut, O., Yardimci, V. H., & Kilickesmez, O. (2023). Radiomics of locally advanced rectal cancer: machine learning‑based prediction of response to neoadjuvant chemoradiotherapy using pre‑treatment sagittal T2‑weighted MRI. Japanese Journal of Radiology, 41, 71-82. https://doi.org/10.1007/s11604-022-01325-7
  • Yazici, H., Odemis, D. A., Aksu, D., Erdogan, O. S., Tuncer, S. B., Avsar, M., Kilic, S., Turkcan, G. K., Celik, B., & Aydin, M. A. (2020). New approach for risk estimation algorithms of BRCA1/2 negativeness detection with modelling supervised machine learning techniques. Disease Markers, 7, 8594090. https://doi.org//10.1155/2020/8594090
  • Yildirim, K., Yildirim, M., Eryesil, H., Talo, M., Yildirim, O., Karabatak, M., Ogras, M. S., Artas, H., & Acharya, U. R. (2022). Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging. Computers and Electrical Engineering, 102, 108275. https://doi.org/10.1016/j.compeleceng.2022.108275
  • Yılmaz, H. H., Yazıhan, N., Tunca, D., Sevinc¸, A., Olcayto, E. O., Ozgul, N., & Tuncer, M. (2011). Cancer trends and incidence and mortality patterns in turkey. Japanese Journal of Clinical Oncology, 41(1), 10-16. https://doi.org/10.1093/jjco/hyq075
  • Yirgin, I. K., Koyluoglu, Y. O., Seker, M. E., Gurdal, S. O., Ozaydin, A. N., Ozcinar, B., Cabioğlu, N., Ozmen, V., & Aribal, E. (2022). Diagnostic performance of AI for cancers registered in a mammography screening program: A retrospective analysis. Technology in Cancer Research & Treatment, 21, 1-11. https://doi.org/10.1177/15330338221075172
  • Yurttakal, A. H., Erbay, H., Ikizceli, T., & Karacavus, S. (2020). Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications, 79, 15555-15573. https://doi.org/10.1007/s11042-019-7479-6
There are 67 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Artificial Intelligence
Authors

Rasha Osman 0000-0002-1112-1284

Hilal Arslan 0000-0002-6449-6952

Publication Date September 30, 2025
Submission Date August 19, 2025
Acceptance Date September 22, 2025
Published in Issue Year 2025 Volume: 12 Issue: 3

Cite

APA Osman, R., & Arslan, H. (2025). The Application of AI in Oncology Research in Türkiye: Impact and Future Directions. Gazi University Journal of Science Part A: Engineering and Innovation, 12(3), 894-917. https://doi.org/10.54287/gujsa.1768020