ROLE OF TEXTURE ANALYSIS IN PREDICTING HISTOPATHOLOGICAL OUTCOME IN PATIENTS WITH BRAIN MASSES USING CT AND MRI IMAGING
DOI:
https://doi.org/10.22159/ajpcr.2025v18i1.53049Keywords:
Texture analysis, Low-grade gliomas, High-grade gliomas, Computed tomography scan, Magnetic resonance imagingAbstract
Objectives: Brain tumors, particularly gliomas, are difficult to differentiate radiologically, whether they are benign or malignant, which usually requires histopathological examination. Texture analysis (TA), a method for quantification of heterogeneity of the tumor, can be used as a tool for this differentiation. This study aims to elucidate possible associations between computed tomography (CT) scan or magnetic resonance imaging TA (MRI TA) of brain tumors and their histopathological diagnosis.
Methods: A total of 20 patients with brain tumor were retrospectively studied. A detailed history was taken so that only pre-treatment CT/MRI scans were included to avoid heterogeneity of the sample. Patients from all age groups and sexes were included. Postcontrast images with the largest cross-section of the tumor were processed for TA (using texRAD software).
Results: In this study, it was found that for World Health Organisation (WHO) grade I and II brain tumors, mean and mean of positive pixel (MPP) are high and Kurtosis is low when compared with WHO grade III and IV. The strongest differences on unfiltered images were found for mean and MPP (p=0.049) and on medium-level filter for Kurtosis (p=0.049).
Conclusion: TA has a great potential to improve the diagnosis and stratification of patients of brain tumors. It can also give information regarding the underlying growth patterns, and hormonal/tumor markers, may add inputs in decisions regarding therapeutic efficacy, follow-up before and after treatment and prognosis, thus helping in the management of the patient.
Objectives: Brain tumors, particularly gliomas, are difficult to differentiate radiologically, whether they are benign or malignant, which usually requires histopathological examination. Texture analysis (TA), a method for quantification of heterogeneity of the tumor, can be used as a tool for this differentiation. This study aims to elucidate possible associations between computed tomography (CT) scan or magnetic resonance imaging TA (MRI TA) of brain tumors and their histopathological diagnosis.
Methods: A total of 20 patients with brain tumor were retrospectively studied. A detailed history was taken so that only pre-treatment CT/MRI scans were included to avoid heterogeneity of the sample. Patients from all age groups and sexes were included. Postcontrast images with the largest cross-section of the tumor were processed for TA (using texRAD software).
Results: In this study, it was found that for World Health Organisation (WHO) grade I and II brain tumors, mean and mean of positive pixel (MPP) are high and Kurtosis is low when compared with WHO grade III and IV. The strongest differences on unfiltered images were found for mean and MPP (p=0.049) and on medium-level filter for Kurtosis (p=0.049).
Conclusion: TA has a great potential to improve the diagnosis and stratification of patients of brain tumors. It can also give information regarding the underlying growth patterns, and hormonal/tumor markers, may add inputs in decisions regarding therapeutic efficacy, follow-up before and after treatment and prognosis, thus helping in the management of the patient.
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References
Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, et al. Influence of image processing on radiomic features from magnetic resonance imaging. Invest Radiol. 2023 Mar 1;58(3):199- 208. doi: 10.1097/RLI.0000000000000921, PMID 36070524
Li S, Zhou B. A review of radiomics and genomics applications in cancers: The way towards precision medicine. Radiat Oncol. 2022 Dec 30;17(1):217. doi: 10.1186/s13014-022-02192-2, PMID 36585716
Panico C, Avesani G, Zormpas-Petridis K, Rundo L, Nero C, Sala E. Radiomics and radiogenomics of ovarian cancer: Implications for treatment monitoring and clinical management. Radiol Clin North Am. 2023 Jul 1;61(4):749-60. doi: 10.1016/j.rcl.2023.02.006, PMID 37169435
Xue C, Zhou Q, Zhang P, Zhang B, Sun Q, Li S, et al. MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma. NeuroImage Clin. 2023 Jan 1;37:103353. doi: 10.1016/j. nicl.2023.103353, PMID 36812768
Patkulkar PA, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping spatiotemporal heterogeneity in tumor profiles by integrating high-throughput imaging and omics analysis. ACS Omega. 2023 Feb 7;8(7):6126-38. doi: 10.1021/acsomega.2c06659, PMID 36844580
Ganeshan B, Miles KA, Young RCD, et al. Texture analysis in non-contract enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver. European Journal of Radiology 70 (2009): 101-110
Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi SJ, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med. 2023 Jan 1;152:106405. doi: 10.1016/j. compbiomed.2022.106405, PMID 36512875
Nair M, Varghese C, Swaminathan R. Cancer: Current Scenario, Intervention Strategies and Projections for 2015. NCMH Background Paper; 2015.
Ostrom QT, Francis SS, Barnholtz-Sloan JS. Epidemiology of brain and other CNS tumors. Curr Neurol Neurosci Rep. 2021 Dec;21(12):68. doi: 10.1007/s11910-021-01152-9, PMID 34817716
Ghosh MM, Trivedi V, Chauhan R, Shubham S, Muneer A. Clinical profile of patients with brain metastasis-a single institutional retrospective study. Int J Contemp Med Res. 2017;4(2):372-6.
Saha A, Ghosh SK, Roy C, Choudhury KB, Chakrabarty B, Sarkar R. Demographic and clinical profile of patients with brain metastases: A retrospective study. Asian J Neurosurg. 2013 Sep;8(3):157-61. doi: 10.4103/1793-5482.121688, PMID 24403959
Shehu IA, Islam M, Singh V. Nose-to-brain delivery, a route of choice for targeting brain tumors. Int J Appl Pharm. 2021 May 7;13:39-46. doi: 10.22159/ijap.2021v13i3.40602
Satapathy BS, Panda J. Carmustine loaded nanosize lipid vesicles showed preferential cytotoxicity and internalization in U87mg cell line along with improved pharmacokinetic profile in mice: A strategy for treatment of glioma. Int J Appl Pharm. 2020 Sep 7;12(5):240-8. doi: 10.22159/ijap.2020v12i5.37885
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella- Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of tumors of the central nervous system: A summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016- 1545-1, PMID 27157931
Tessamma T, Ananda Resmi S. Texture description of low grade and high grade glioma using statistical features in brain MRIs. United States: ACEEE.
Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim JH, Sohn CH. Glioma: Application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLOS One. 2014 Sep 30;9(9):e108335. doi: 10.1371/journal.pone.0108335, PMID 25268588
Wang Q, Lei D, Yuan Y, Zhao H. Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: Systematic review and meta-analysis. BMJ Open. 2019 Sep 1;9(9):e027144. doi: 10.1136/bmjopen-2018-027144, PMID 31492777
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009 Dec;62(6):1609-18. doi: 10.1002/mrm.22147, PMID 19859947
Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, et al. Influence of image processing on radiomic features from magnetic resonance imaging. Invest Radiol. 2023 Mar 1;58(3):199- 208. doi: 10.1097/RLI.0000000000000921, PMID 36070524
Li S, Zhou B. A review of radiomics and genomics applications in cancers: The way towards precision medicine. Radiat Oncol. 2022 Dec 30;17(1):217. doi: 10.1186/s13014-022-02192-2, PMID 36585716
Panico C, Avesani G, Zormpas-Petridis K, Rundo L, Nero C, Sala E. Radiomics and radiogenomics of ovarian cancer: Implications for treatment monitoring and clinical management. Radiol Clin North Am. 2023 Jul 1;61(4):749-60. doi: 10.1016/j.rcl.2023.02.006, PMID 37169435
Xue C, Zhou Q, Zhang P, Zhang B, Sun Q, Li S, et al. MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma. NeuroImage Clin. 2023 Jan 1;37:103353. doi: 10.1016/j. nicl.2023.103353, PMID 36812768
Patkulkar PA, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping spatiotemporal heterogeneity in tumor profiles by integrating high-throughput imaging and omics analysis. ACS Omega. 2023 Feb 7;8(7):6126-38. doi: 10.1021/acsomega.2c06659, PMID 36844580
Ganeshan B, Miles KA, Young RCD, et al. Texture analysis in non-contract enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver. European Journal of Radiology 70 (2009): 101-110
Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi SJ, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med. 2023 Jan 1;152:106405. doi: 10.1016/j. compbiomed.2022.106405, PMID 36512875
Nair M, Varghese C, Swaminathan R. Cancer: Current Scenario, Intervention Strategies and Projections for 2015. NCMH Background Paper; 2015.
Ostrom QT, Francis SS, Barnholtz-Sloan JS. Epidemiology of brain and other CNS tumors. Curr Neurol Neurosci Rep. 2021 Dec;21(12):68. doi: 10.1007/s11910-021-01152-9, PMID 34817716
Ghosh MM, Trivedi V, Chauhan R, Shubham S, Muneer A. Clinical profile of patients with brain metastasis-a single institutional retrospective study. Int J Contemp Med Res. 2017;4(2):372-6.
Saha A, Ghosh SK, Roy C, Choudhury KB, Chakrabarty B, Sarkar R. Demographic and clinical profile of patients with brain metastases: A retrospective study. Asian J Neurosurg. 2013 Sep;8(3):157-61. doi: 10.4103/1793-5482.121688, PMID 24403959
Shehu IA, Islam M, Singh V. Nose-to-brain delivery, a route of choice for targeting brain tumors. Int J Appl Pharm. 2021 May 7;13:39-46. doi: 10.22159/ijap.2021v13i3.40602
Satapathy BS, Panda J. Carmustine loaded nanosize lipid vesicles showed preferential cytotoxicity and internalization in U87mg cell line along with improved pharmacokinetic profile in mice: A strategy for treatment of glioma. Int J Appl Pharm. 2020 Sep 7;12(5):240-8. doi: 10.22159/ijap.2020v12i5.37885
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella- Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of tumors of the central nervous system: A summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016- 1545-1, PMID 27157931
Tessamma T, Ananda Resmi S. Texture description of low grade and high grade glioma using statistical features in brain MRIs. United States: ACEEE.
Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim JH, Sohn CH. Glioma: Application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLOS One. 2014 Sep 30;9(9):e108335. doi: 10.1371/journal.pone.0108335, PMID 25268588
Wang Q, Lei D, Yuan Y, Zhao H. Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: Systematic review and meta-analysis. BMJ Open. 2019 Sep 1;9(9):e027144. doi: 10.1136/bmjopen-2018-027144, PMID 31492777
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009 Dec;62(6):1609-18. doi: 10.1002/mrm.22147, PMID 19859947
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