Machine Learning Techniques for Renal Carcinoma Detection: A Review

The sixth most common malignant disease is renal cell carcinoma (RCC), which accounts for almost 90% of all cases. The probability of recovery can be greatly increased by early detection of kidney tumors. Imaging techniques are the most sought out non-invasive diagnostic techniques in recent times. In this review, the current method of image processing involves rapid identification of the tumor from CT scans by a combination of pre-processing, segmentation, feature extraction and classification methods. Machine learning (ML) techniques such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), convolutional neural networks (CNN), and Deep learning models can be used to analyze abdominal CT images for renal cancer detection. On the other hand, a probabilistic neural network (PNN) can also be used as a feedforward neural network that uses a probabilistic methodology by accurately classifying the class probability estimates. Various types of RCCs were defined in the histological study and diagnosed using various imaging methods such as ultrasound, MRI, CT, PET and angiography in the radiological part. Machine learning algorithms are used to identify tumors, extract relevant data from medical images, and help interpret imaging results. When these algorithms are integrated with medical imaging data such as computed tomography (CT) images, renal cancer detection models become more accurate. By being trained on labeled data sets, these algorithms can identify renal cancer. A detailed review has been done and after comparing the accuracy of several algorithms, CNN is considered to be the most suitable algorithm for identification of RCC.
{"references":["1.\tBray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), pp.394-424.","2.\tUdager, A.M. and Mehra, R., 2016. Morphologic, molecular, and taxonomic evolution of renal cell carcinoma: a conceptual perspective with emphasis on updates to the 2016 World Health Organization Classification. Archives of pathology & laboratory medicine, 140(10), pp.1026-1037.","3.\tRao A, Wiggins C, Lauer RC. Survival outcomes for advanced kidney cancer patients in the era of targeted therapies. Ann Transl Med. 2018 May;6(9):165. doi: 10.21037/atm.2018.04.44. PMID: 29911113; PMCID: PMC5985277.","4.\tDeja S, Litarski A, Mielko KA, Pudełko-Malik N, Wojtowicz W, Zabek A, Szydełko T, Młynarz P. Gender-Specific Metabolomics Approach to Kidney Cancer. Metabolites. 2021 Nov 10;11(11):767. doi: 10.3390/metabo11110767. PMID: 34822425; PMCID: PMC8624667.","5.\tGharaibeh, M., Alzu'bi, D., Abdullah, M., Hmeidi, I., Al Nasar, M.R., Abualigah, L. and Gandomi, A.H., 2022. Radiology imaging scans for early diagnosis of kidney tumors: a review of data analytics-based machine learning and deep learning approaches. Big Data and Cognitive Computing, 6(1), p.29.","6.\tAurna, N.F., Yousuf, M.A., Taher, K.A., Azad, A.K.M. and Moni, M.A., 2022. A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Computers in Biology and Medicine, 146, p.105539.","7.\tBarragán-Montero, A., Javaid, U., Valdés, G., Nguyen, D., Desbordes, P., Macq, B., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F. and Michiels, S., 2021. Artificial intelligence and machine learning for medical imaging: A technology review. Physica Medica, 83, pp.242-256.","8.\tCastiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D'Amico, N.C. and Sardanelli, F., 2021. AI applications to medical images: From machine learning to deep learning. Physica Medica, 83, pp.9-24.","9.\tBehura, A., 2021. The cluster analysis and feature selection: Perspective of machine learning and image processing. Data Analytics in Bioinformatics: A Machine Learning Perspective, pp.249-280.","10.\tUhm, K.H., Jung, S.W., Choi, M.H., Shin, H.K., Yoo, J.I., Oh, S.W., Kim, J.Y., Kim, H.G., Lee, Y.J., Youn, S.Y. and Hong, S.H., 2021. Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography. NPJ precision oncology, 5(1), p.54."]}