Abnormality Classification in PET Images of Prostate Tumour using Neural Network Methods
Keywords:PET, Prostrate, cancer, medical image, machine learning, convolutional neural networks
The second most commonly affecting disease among males across the world is prostate cancer. The occurrence of prostate cancer has shown significant variation across the world. Many countries have done extensive study on the distribution of the disease and its characteristics to find the vital parameters and methods that could enable timely and precise diagnosis and therapy.Thisincludes the analysis of information obtained from different methods in decision making. Most commonly blood tests to find PSA levels is the primary test which is generally followed by MRI guided biopsies. In this study, application of computer aided diagnostic method is implemented and the outcomes are analysed with the intention that the use of AI would reduce the subjectivity of the findings at large. Use of Artificial Intelligence in cancer diagnosis help for early diagnosis, treatment and monitoring enabling remote access to experts. Functional imaging techniques when combined with CT or MRI scans should facilitate quick diagnosis times to allow for rapid response and treatment. However, positron emission tomography, when used for prostate cancer imaging, still relies on manual contour drawing and feature extraction to pinpoint tumour location. This process can take up to several days.
In this paper, an attempt has been made to streamlinethe process by performing automated tumour location pinpointing using image processing techniques. We have also used machine learning techniques and compared the results to automatically diagnose the tumour as benign or malignant. The back propagation method, Support Vector Machine approach and Convoluted Neural network models have been performed.
The automated process with specialist oversight may allow and enable faster response time and more effective treatment enabling computer assisted diagnostic approach facilitating both the qualitative and quantitative aspects.
Hassanipour, S., Delam, H., Arab-Zozani, M., Abdzadeh, E., Hosseini, S. A., Nikbakht, H. A., Malakoutikhah, M., Ashoobi, M. T., Fathalipour, M., Salehiniya, H., &Riahi, S. (2020). Survival Rate of Prostate Cancer in Asian Countries: A Systematic Review and Meta-Analysis. Annals of global health, 86(1), 2. https://doi.org/10.5334/aogh.2607
Hossein Jadvar.“Molecular Imaging of Prostate Cancer: PET Radiotracers , Nuclear Medicine and Molecular Imaging • Review , http://doi.org/10.2214/AJR.12.8816
Unterrainer, M., Eze, C., Ilhan, H. et al.” Recent advances of PET imaging in clinical radiation oncology.” Radiat Oncol 15, 88 (2020). http://doi.org/10.1186/s13014-020-01519-1
Matteo Bauckneht, Francesco Bertagna, Maria Isabella Donegani, RexhepDurmo, Alberto Miceli, Vincenzo De Biasi, Riccardo Laudicella, Giuseppe Fornarini, Alfredo Berruti, Sergio Baldari, AnnibaleVersari, Raffaele Giubbini, GianmarioSambuceti, Silvia Morbelli& Domenico Albano, “The prognostic power of 18F-FDG PET/CT extends to estimating systemic treatment response duration in metastatic castration-resistant prostate cancer (mCRPC) patients.” Prostate Cancer Prostatic Dis (2021). https://doi.org/10.1038/s41391-021-00391-8
Steven Korevaar, Ruwan Tennakoon, Mark Page, Peter Brotchie, John Thangarajah, Cosmin Florescu, Tom Sutherland, Ning Mao Kam & Alireza Bab-Hadiasharl.“ Incidental detection of prostate cancer with computed tomography scans.” Sci Rep 11, 7956 (2021), http://doi.org/10.1038/s41598-021-86972-y
Nickols N, Anand A, Johnsson K, Brynolfsson J, Borrelli P, Juarez J, Parikh N, Jafari L, Eiber M, Rettig MB.”aPROMISE: A Novel Automated-PROMISE platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL (PSMA) images of Veterans with Prostate Cancer.” J Nucl Med. 2021, http://doi.org/10.2967/jnumed.120.261863
Ntakolia, Charis &Diamantis, Dimitris &Papandrianos, Nikolaos &Moustakidis, Serafeim&Papageorgiou, Elpiniki. (2020). “A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients.” Healthcare.8(4)493. http://doi.org/10.3390/healthcare8040493.
Aphinives, C., Aphinives, P. “Artificial intelligence development for detecting prostate cancer in MRI.” Egypt J RadiolNucl Med 52, 87 (2021),http://doi.org/10.1186/s43055-021-00467-4
Baratto, L., Duan, H., Laudicella, R. et al. Physiological 68Ga-RM2 uptake in patients with biochemically recurrent prostate cancer: an atlas of semi-quantitative measurements. Eur J Nucl Med Mol Imaging 47, 115–122 (2020). https://doi.org/10.1007/s00259-019-04503-4
Morand, G. B., Vital, D. G., Kudura, K., Werner, J., Stoeckli, S. J., Huber, G. F., &Huellner, M. W. (2018). “Maximum Standardized Uptake Value (SUVmax) of Primary Tumor Predicts Occult Neck Metastasis in Oral Cancer. “Scientific reports, 8(1), 11817. doi:10.1038/s41598-018-30111-7
MR. Mohana Priya, P. Venkatesan, “An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM, Microprocessors and Microsystems, Volume 82, 2021, 103958, ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2021.103958.
Van Booven J, Kuchakulla M, Pai R, Frech FS, Ramasahayam R, Reddy P, Parmar M, Ramasamy R, Arora H. “A Systematic Review of Artificial Intelligence in Prostate Cancer.” Res Rep Urol. 2021;13:31-, 39, http://doi.org/10.2147/RRU.S268596
Young Sympascho, Metser Ur, Sistani Golmehr, Langer Deanna L., Bauman Glenn. “Establishing a Provincial Registry for Recurrent Prostate Cancer: Providing Access to PSMA PET/CT in Ontario, Canada” ,Front. Oncol., 02 August 2021 ,http://doi.org/10.3389/fonc.2021.722430
Mi Jung Rho, Jihwan Park, HyongWoo Moon, Chanjung Lee, Sejin Nam, Dongbum Kim, Choung-Soo Kim, Seong Soo Jeon, Minyong Kang, Ji Youl Lee Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service,Published:August 5, 2020,http://doi.org/10.1371/journal.pone.0236553
Andrew Janowczyk , Patrick Leo ,Mark A Rubin, “ Clinical deployment of AI for prostate cancer diagnosis” The Lancet Digital Health ,VOLUME 2, ISSUE 8, E383-E384,August, 2020 , http://doi.org/10.1016/S2589-7500(20)30163-1
Peter Ström, Kimmo Kartasalo, Henrik Olsson,et.al, “Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study”, The Lancet Oncology,vol 21, Issue 2, 2020, http://doi.org/10.1016/S1470-2045(19)30738-7.
Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. “Artificial intelligence at the intersection of pathology and radiology in prostate cancer.” Diagn IntervRadiol. 2019;25(3):183-188. http://doi.org/10.5152/dir.2019.19125
Hadavand, M. A., Mayer, D., Chen, W., Wnorowski, A., & Siddiqui, M. M. (2020). Role of metabolic imaging in diagnosis of primary, metastatic, and recurrent prostate cancer. Current opinion in oncology, 32(3), 223–231. https://doi.org/10.1097/CCO.0000000000000625
Capobianco, N., Sibille, L., Chantadisai, M. et al. Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning. Eur J Nucl Med Mol Imaging (2021). https://doi.org/10.1007/s00259-021-05473-2
Rachid Sammouda, Abdu Gumaei, Ali El-Zaart, "Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions", Mathematical Problems in Engineering, vol. 2021, Ar.ID 9955174, 17 pages, 2021,http://doi.org/10.1155/2021/9955174
Detection of Prostate Cancer Using Deep Learning Framework Abhishek Patel , Sanjay Kumar Singh and Aditya Khamparia. ICCRDA 2020 IOP Conf. Series: Materials Science and Engineering 1022 (2021) doi:10.1088/1757-899X/1022/1/012073
Bayerschmidt, S.; Uprimny, C.; Kroiss, A.S.; Fritz, J.; Nilica, B.; Svirydenka, H.; Decristoforo, C.; von Guggenberg, E.; Horninger, W.; Virgolini, I.J. Comparison of Early Imaging and Imaging 60 min Post-Injection after Forced Diuresis with Furosemide in the Assessment of Local Recurrence in Prostate Cancer Patients with Biochemical Recurrence Referred for 68Ga-PSMA-11 PET/CT. Diagnostics 2021, 11, 1191, http://doi.org/10.3390/diagnostics11071191
Walter Jentzen, Lutz Freudenberg, Ernst G. Eising, Melanie Heinze, Wolfgang Brandau, Andreas Bockisch , “Segmentation of PET Volumes by Iterative Image Thresholding”, Journal of Nuclear Medicine Jan 2007, 48 (1) 108-114 . Rachid Sammouda, Abdu Gumaei, Ali El-Zaart, "Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions", MathematicalProblems in Engineering, vol. 2021, Article ID 9955174,17 pages, 2021 ,https://doi.org/10.1155/2021/9955174
Abdelmaksoud, I.R.; Shalaby, A.; Mahmoud, A.; Elmogy, M.; Aboelfetouh, A.; Abou El-Ghar, M.; El-Melegy, M.; Alghamdi, N.S.; El-Baz, A. Precise Identification of Prostate Cancer from DWI Using Transfer Learning. Sensors 2021, 21, 3664. https://doi.org/10.3390/ s21113664
EldadRubinstein, MosheSalhov, MeitalNidam-Leshem et.al. “Unsupervised tumor detection in Dynamic PET/CT imaging of the prostate,” Medical Image Analysis , Volume 55, July 2019, Pages 27-40 , http://doi.org/10.1016/j.media.2019.04.001
Hartenstein, A., Lübbe, F., Baur, A.D.J. et al. Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone. Sci Rep 10, 3398 (2020). https://doi.org/10.1038/s41598-020-60311-z]
Ayyad, S.M.; Shehata, M.; Shalaby, A.; Abou El-Ghar, M.; Ghazal, M.; El-Melegy, M.; Abdel-Hamid, N.B.; Labib, L.M.; Ali, H.A.; El-Baz, A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors 2021, 21, 2586. https://doi.org/10.3390/s21082586
Barwal, R. K. ., Raheja, N. ., Bhiyana, M. ., & Rani, D. . (2023). Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 23–42. https://doi.org/10.17762/ijritcc.v11i1s.5991
Juan Garcia, Guðmundsdóttir Anna, Maria Jansen, Johansson Anna, Anna Wagner. Exploring Decision Trees and Random Forests for Decision Science Applications. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/211
Gupta, R., Mane, M., Bhardwaj, S., Nandekar, U., Afaq, A., Dhabliya, D., Pandey, B.K. Use of artificial intelligence for image processing to aid digital forensics: Legislative challenges (2023) Handbook of Research on Thrust Technologies? Effect on Image Processing, pp. 433-447.
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