Classification of Glioma Grades on Diffusion-Weighted MRI Using Random Forest in Machine Learning
Main Article Content
Background: Glioma, a type of brain tumor, is classified into two grades, LGG and HGG, and requires early detection due to the high mortality rate from brain tumors in Indonesia, with 5,323 new cases and 4,229 deaths. For patients with kidney disorders, gadolinium-based contrast MRI is unsuitable, making DWI images and AI-based systems viable alternatives for detection.
Objective: This study aims to develop a machine learning (ML) model using the random forest (RF) algorithm for automatic glioma grade classification from DWI MRI Brain images and to evaluate its performance.
Method: Purposive sampling was used to collect 1,848 DWI MRI brain images. A quasi-experimental design with a post-test control group was employed. The model's validity was assessed by media experts using a Likert scale to evaluate aspects such as user interface design, system performance, flexibility, usability, and reliability. Reliability was tested using Fleiss' Kappa for inter-rater reliability.
Results: The ML model achieved 82% accuracy and a micro-AUC of 0.96, excelling in Normal and Grade 4 classifications but needing better recall for Grades 1 and 2. IT experts rated it positively: 84% for User Interface Design, 82% for Usability, 84.4% for System Performance, and 68.9% and 73.3% for Flexibility and Reliability. The Wilcoxon test found no significant differences from respondents (p > 0.05), with Fleiss' Kappa at 0.85 and 92% Observed Agreement.
Conclusion: This study successfully developed and tested an RF model for glioma classification, demonstrating consistent and accurate results.
Keywords:
MRI, DWI, Machine Learning, Random Forest, Digital Imaging Processing
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Winangun PP, Widyantara IM, Hartati RS. Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru. Maj. Ilm. Teknol. Elektro. 2020 Oct;19(1):83.
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Vijithananda SM, Jayatilake ML, Gonçalves TC, Rato LM, Weerakoon BS, Kalupahana TD, Silva AD, Dissanayake K, Hewavithana PB. Texture feature analysis of MRI- ADC images to differentiate glioma grades using machine learning techniques. Scientific Reports. 2023 Sep 22;13(1):15772.
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Maekawa T, Hori M, Murata K, Feiweier T, Kamiya K, Andica C, Hagiwara A, Fujita S, Koshino S, Akashi T, Kamagata K. Differentiation of high-grade and low-grade intra- axial brain tumors by time-dependent diffusion MRI. Magnetic Resonance Imaging. 2020 Oct 1;72:34-41.
Phuttharak W, Thammaroj J, Wara-Asawapati S, Panpeng K. Grading gliomas capability: comparison between visual assessment and apparent diffusion coefficient (ADC) value measurement on diffusion-weighted imaging (DWI). Asian Pacific Journal of Cancer Prevention: APJCP. 2020;21(2):385.
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Elmao?lu M, Çelik A. MRI handbook: MR physics, patient positioning, and protocols. Springer Science & Business Media; 2012.
Chandra S, Jeniyanti NP, Budiati TA. Prosedur Teknik Pemeriksaan Magnetic Resonance Spectroscopy Pada Kasus Tumor Otak Di Instalasi Radiologi RS Pusat Pertamina Jakarta. Jurnal Ilmu Kesehatan dan Gizi. 2024;2(1):162-79.
Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology. 2000 Nov;217(2):331-45.
Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, Okuda T, Liang L, Ge Y, Komohara Y, Ushio Y. Usefulness of diffusion?weighted MRI with echo?planar technique in the evaluation of cellularity in gliomas. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine. 1999 Jan;9(1):53-60.
Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R. The role of diffusion-weighted imaging in patients with brain tumors. American journal of neuroradiology. 2001 Jun 1;22(6):1081-8. 12318. Bozgeyik Z, Onur MR, Poyraz AK. The role of diffusion weighted magnetic resonance imaging in oncologic settings. Quantitative imaging in medicine and surgery. 2013 Oct;3(5):269.
Sensusiati AD. Apparent Diffusion Coefficient of Diffusion Weighted Imaging haveStrong Correlation with the Malignancy Grading of Intracranial Tumor. InJournal of Physics: Conference Series 2020 Jan (Vol. 1445, No. 1, p. 012019). IOP Publishing.
Rahman H, Sukemi S. Noise Removal Pada Citra Digital Menggunakan Metode Gaussian filter. InAnnual Research Seminar (ARS) 2020 Feb 7 (Vol. 5, No. 1, pp. 152-156).
Febrianti AS, Sardjono TA, Babgei AF. Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine. Jurnal Teknik ITS. 2020 Jul 24;9(1):A118-23.
Winangun PP, Widyantara IM, Hartati RS. Pendekatan Diagnostik Berbasis Extreme Learning Machine dengan Kernel Linear untuk Mengklasifikasi Kelainan Paru-Paru. Maj. Ilm. Teknol. Elektro. 2020 Oct;19(1):83.
Jakhar D, Kaur I. Artificial intelligence, machine learning and deep learning: definitions and differences. Clinical and experimental dermatology. 2020 Jan 1;45(1):131-2.
Tech Wire Asia, “Medical diagnosis AI in Beijing beats real doctors,” Tech Wire Asia, 2018. [Online]. Available: https://techwireasia.com/2018/07/medical-diagnosis-ai-in- beijingbeats-real-doctors/.
Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A. Glioma grading using a machine?learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. Journal of Magnetic Resonance Imaging. 2019 Oct;50(4):1295-306.
Fatni Z. Klasifikasi Citra Magnetic Resonance Imaging (MRI) Otak Dalam Mengidentifikasi Tumor Menggunakan Algoritma Random Forest (Doctoral dissertation, Universitas Islam Negeri Sultan Syarif Kasim Riau).
Vijithananda SM, Jayatilake ML, Gonçalves TC, Rato LM, Weerakoon BS, Kalupahana TD, Silva AD, Dissanayake K, Hewavithana PB. Texture feature analysis of MRI- ADC images to differentiate glioma grades using machine learning techniques. Scientific Reports. 2023 Sep 22;13(1):15772.
Kemenkes. 2020. Laporan Riset Kesehatan Dasar (Riskesdas). 2020. Jakarta: KemenkesRI.
Chalik R. Anatomi fisiologi manusia. Jakarta: Kementrian Kesehatan Republik Indonesia. 2016 Dec. Sherwood L. Fisiologi manusia dari sel ke sistem.
VanPutte C, Regan J, Russo A. ISE Seeley's Anatomy & Physiology. McGraw-Hill; 2022 Feb 16.
Moeller, T. B., & Reif, E. (2014). Pocket Atlas of Sectional Anatomy Volume I: Head and Neck.
Lampignano, J.P. and Kendric, L.E. (2018) Radiographic Positioning and Related 90 Anatomy Ninth Edition, Elsevier Health Sciences
VanPutte, C., Regan, J., & Russo, A. (2017). Seeley’s Essentials of Anatomy and Physiology. In Journal of Thoracic Disease (Vol. 9, Nomor 8)
Pope WB, editor. Glioma imaging: physiologic, metabolic, and molecular approaches.
Hashemi M, Gharaylou Z, Sepand MR, Hamedi SS, Raminfard S, Alimohamadi M, 2019;6(1). Springer Nature; 2019 Nov 11.
Sherkatkhamene N, Zarepour L, Hadjighassem M. Apoptosis induced by Viola odorata extract in human glioblastoma multiforme. Archives of Neuroscience. 12437. Kim HS, Lee DY. Nanomedicine in clinical photodynamic therapy for the treatment of brain tumors. Biomedicines. 2022 Jan 3;10(1):96.
Luo T, Li Y. Research and Analysis of Brain Glioma Imaging Based on Deep Learning. Journal of Healthcare Engineering. 2021 Nov 18;2021:1-8.
Pope WB, editor. Glioma imaging: physiologic, metabolic, and molecular approaches. Springer Nature; 2019 Nov 11.
Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of artificial intelligence based on medical imaging in glioma: Current state and future challenges. Frontiers in Oncology. 2022 Jul 27;12:892056.
Shukla G, Alexander GS, Bakas S, Nikam R, Talekar K, Palmer JD, Shi W. Advanced magnetic resonance imaging in glioblastoma: a review. Chinese clinical oncology. 2017 Aug;6(4):40-
https://radiopaedia.org/cases/37605/studies/39483?lang=us. Diakses pada 12 Mei 2024
Zeng Q, Jiang B, Shi F, Ling C, Dong F, Zhang J. Bright edge sign on high b-value diffusion-weighted imaging as a new imaging biomarker to predict poor prognosis in glioma patients: a retrospective pilot study. Frontiers in Oncology. 2019 May 21;9:424.
Campero A, Ajler P, Fernandez J, Isolan G, Paiz M, Rivadeneira C. Hemangioblastomas de fosa posterior: Reporte de 16 casos y revisión de la literatura. Surgical Neurology International. 2016;7(Suppl 36): S855.
Maekawa T, Hori M, Murata K, Feiweier T, Kamiya K, Andica C, Hagiwara A, Fujita S, Koshino S, Akashi T, Kamagata K. Differentiation of high-grade and low-grade intra- axial brain tumors by time-dependent diffusion MRI. Magnetic Resonance Imaging. 2020 Oct 1;72:34-41.
Phuttharak W, Thammaroj J, Wara-Asawapati S, Panpeng K. Grading gliomas capability: comparison between visual assessment and apparent diffusion coefficient (ADC) value measurement on diffusion-weighted imaging (DWI). Asian Pacific Journal of Cancer Prevention: APJCP. 2020;21(2):385.
Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. American Journal of Neuroradiology. 2004 Feb 1;25(2):201-9.
Kartawiguna D. Tomografi resonansi magnetik inti : teori dasar, pembentukan gambar dan instrumentasi perangkat kerasnya . 2015. 131–132 p.
Ma JJ, Nakarmi U, Kin CY, Sandino CM, Cheng JY, Syed AB, Wei P, Pauly JM, Vasanawala SS. Diagnostic image quality assessment and classification in medical imaging: Opportunities and challenges. In2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020 Apr 3 (pp. 337-340). IEEE.
https://marketing.webassets.siemenshealthineers.com/1800000001348689/b76f3f5ee066/MRI_49_Graessner_18000000 01348689.pdf diakses pada tanggal 28 Januari 2024
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