Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques

Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA: a cancer journal for clinicians 2024 Jan 1;74(1).

Google Scholar 

Islami F, Baeker Bispo J, Lee H, Wiese D, Yabroff KR, Bandi P, Sloan K, Patel AV, Daniels EC, Kamal AH, Guerra CE. American Cancer Society’s report on the status of cancer disparities in the United States, 2023. CA: A Cancer Journal for Clinicians 2024; 74(2):136-66.

PubMed  Google Scholar 

Kiri S, Ryba T. Cancer, metastasis, and the epigenome. Molecular Cancer 2024; 23(1):154.

PubMed  PubMed Central  Google Scholar 

Patel VV, Yadav AR. A review on kidney tumor segmentation and detection using different artificial intelligence algorithms. In AIP Conference Proceedings 2024 (Vol. 3107, No. 1), AIP Publishing.

Google Scholar 

Distante A, Marandino L, Bertolo R, Ingels A, Pavan N, Pecoraro A, Marchioni M, Carbonara U, Erdem S, Amparore D, Campi R. Artificial intelligence in renal cell carcinoma histopathology: Current applications and future perspectives. Diagnostics 2023; 13(13):2294.

PubMed  PubMed Central  Google Scholar 

Wang X, Zhu Z. Context understanding in computer vision: A survey. Computer Vision and Image Understanding 2023;229:103646.

Google Scholar 

Yasuda Y, Zhang JH, Attawettayanon W, Rathi N, Wilkins L, Roversi G, Zhang A, Accioly JP, Shah S, Munoz-Lopez C, Palacios DA. Comprehensive management of renal masses in solitary kidneys. European Urology Oncology 2023;6(1):84-94.

PubMed  Google Scholar 

Hora M, Albiges L, Bedke J, Campi R, Capitanio U, Giles RH, Ljungberg B, Marconi L, Klatte T, Volpe A, Abu-Ghanem Y. European Association of Urology guidelines panel on renal cell carcinoma update on the new World Health Organization classification of kidney tumors 2022: the urologist’s point of view. Eur Urol 2023; 83(2):97-100.

PubMed  Google Scholar 

Jiang P, Ali SN, Peta A, Arada RB, Brevik A, Xie L, Okhunov Z, Clayman RV, Landman J. A review of the recommendations and strength of evidence for clinical practice guidelines on the management of small renal masses. Journal of Endourology 2023; 37(8):903-13.

PubMed  Google Scholar 

Williamson SR, Taneja K, Cheng L. Renal cell carcinoma staging: pitfalls, challenges, and updates. Histopathology 2019; 74(1):18-30.

PubMed  Google Scholar 

Hussain S, Mubeen I, Ullah N, Shah SS, Khan BA, Zahoor M, Ullah R, Khan FA, Sultan MA. Modern diagnostic imaging technique applications and risk factors in the medical field: a review. BioMed research international 2022;2022(1):5164970.

PubMed  PubMed Central  Google Scholar 

Kaur R, Juneja M. A Survey of Different Imaging Modalities for Renal Cancer. Indian Journal of Science and Technology 2016;9(44).

Kaur R, Juneja M, Mandal AK. Computer-aided diagnosis of renal lesions in CT images: a comprehensive survey and future prospects. Computers & Electrical Engineering 2019;77:423-34.

Kaur R, Juneja M. A survey of kidney segmentation techniques in CT images. Current Medical Imaging 2018;14(2):238-50.

Google Scholar 

Hossain E, Hossain MS, Hossain MS, Al Jannat S, Huda M, Alsharif S, Faragallah OS, Eid M, Rashed AN. Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network. Computers, Materials & Continua 2022;72(3).

Inthiyaz S, Altahan BR, Ahammad SH, Rajesh V, Kalangi RR, Smirani LK, Hossain MA, Rashed AN. Skin disease detection using deep learning. Advances in Engineering Software 2023; 175:103361.

Google Scholar 

Soundararajan R, Prabu AV, Routray S, Malla PP, Ray AK, Palai G, Faragallah OS, Baz M, Abualnaja MM, Eid MM, Rashed AN. Deeply trained real-time body sensor networks for analyzing the symptoms of Parkinson’s disease. IEEE Access 2022; 10:63403-21.

Google Scholar 

Sun HY, Kim JH, Hwang J, Hong SS, Doo SW, Yang WJ, Cho YJ, Song YS. Diagnostic accuracy of contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging of small renal masses in real practice: sensitivity and specificity according to subjective radiologic interpretation. World journal of surgical oncology 2016; 14(1):260.

PubMed  PubMed Central  Google Scholar 

MG Linguraru, S. Wang, F. Shah, R. Gautam, J. Peterson, WM. Linehan, and RM Summers, Automated noninvasive classification of renal cancer on multiphase CT. Medical physics, 38(2011), pp. 5738-5746.

PubMed  PubMed Central  Google Scholar 

SP Raman, Y Chen, JL Schroeder, P Huang, and EK Fishman, CT Texture Analysis of Renal Masses: Pilot Study Using Random Forest Classification for Prediction of Pathology. Academic radiology 21(2014), pp. 1587-1596.

PubMed  PubMed Central  Google Scholar 

J. Liu, S. Wang, MG Linguraru, J. Yao and RM Summers, Computer-aided detection of exophytic renal lesions on non-contrast CT images. Medical image analysis, 19(2015), pp.15-29.

PubMed  Google Scholar 

He QH, Feng JJ, Lv FJ, Jiang Q, Xiao MZ. Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions. Insights into Imaging 2023;14(1):6.

PubMed  PubMed Central  Google Scholar 

Xu J, He X, Shao W, Bian J, Terry R. Classification of Benign and Malignant Renal Tumors Based on CT Scans and Clinical Data Using Machine Learning Methods. In Informatics 2023 (Vol. 10, No. 3, p. 55). MDPI.

Google Scholar 

Miskin N, Qin L, Silverman SG, Shinagare AB. Differentiating benign from malignant cystic renal masses: a feasibility study of computed tomography texture-based machine learning algorithms. Journal of computer assisted tomography 2023; 47(3):376-81.

PubMed  Google Scholar 

Han JH, Kim BW, Kim TM, Ko JY, Choi SJ, Kang M, Kim SY, Cho JY, Ku JH, Kwak C, Kim YG. Fully automated segmentation and classification of renal tumors on CT scans via machine learning. BMC cancer 2025; 25(1):173.

PubMed  PubMed Central  Google Scholar 

Kaur R, Juneja M, Mandal AK. Machine learning based quantitative texture analysis of CT images for diagnosis of renal lesions. Biomedical Signal Processing and Control 2021;64:102311.

Google Scholar 

Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, Liu Q, Wang W. Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma. European radiology 2018; 28:1625-33.

PubMed  Google Scholar 

Sarvestani ZM, Jamali J, Taghizadeh M, Dindarloo MH. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images. Journal of Cancer Research and Clinical Oncology 2023; 149(9):6151-70.

PubMed  Google Scholar 

Chen A, Karwoski RA, Gierada DS, Bartholmai BJ, Koo CW. Quantitative CT analysis of diffuse lung disease. Radiographics 2020 Jan;40(1):28-43.

PubMed  Google Scholar 

Ramola A, Shakya AK, Van Pham D. Study of statistical methods for texture analysis and their modern evolutions. Engineering Reports 2020; 2(4):e12149.

Google Scholar 

Yap FY, Varghese BA, Cen SY, Hwang DH, Lei X, Desai B, Lau C, Yang LL, Fullenkamp AJ, Hajian S, Rivas M. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses. European radiology 2021; 31:1011-21.

PubMed  Google Scholar 

Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Systems with Applications 2020 15;158:113514.

Google Scholar 

Ghalati MK, Nunes A, Ferreira H, Serranho P, Bernardes R. Texture analysis and its applications in biomedical imaging: A survey. IEEE Reviews in Biomedical Engineering 2021 27;15:222-46.

Google Scholar 

Varghese BA, Fields BK, Hwang DH, Duddalwar VA, Matcuk Jr. GR, Cen SY. Spatial assessments in texture analysis: what the radiologist needs to know. Frontiers in Radiology 2023 24;3:1240544.

Google Scholar 

Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2022 27;2:927312.

Google Scholar 

Uğuz H. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems 2011 ;24(7):1024-32.

Google Scholar 

Pasha SJ, Mohamed ES. Advanced hybrid ensemble gain ratio feature selection model using machine learning for enhanced disease risk prediction. Informatics in Medicine Unlocked 2022 1;32:101064.

Google Scholar 

Jetti CR, Shaik R, Shaik S, Sanagapalli S. Disease prediction using Naïve Bayes—machine learning algorithm. International Journal of Science & Healthcare Research 2021.

Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, Alrashed S, Olatunji SO. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. Computers in biology and medicine 2019;109:101-11.

PubMed  Google Scholar 

Swarupa AN, Sree VH, Nookambika S, Kishore YK, Teja UR. Disease prediction: smart disease prediction system using random forest algorithm. In 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) 2021 (pp. 48-51). IEEE.

Google Scholar 

Uddin S, Haque I, Lu H, Moni MA, Gide E. Comparative performance analysis of K-nearest neighbor (KNN) algorithm and its different variants for disease prediction. Scientific Reports 2022;12(1):6256.

CAS  PubMed  PubMed Central  Google Scholar 

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