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Exploring Texture Analysis to Optimize Bladder Preservation in Muscle Invasive Bladder Cancer

Published:November 17, 2022DOI:https://doi.org/10.1016/j.clgc.2022.11.010

      ABSTRACT

      Purpose

      To explore if texture analysis of Muscle Invasive Bladder Cancer (MIBC) can aid in better patient selection for bladder preservation.

      Methods

      Pre-treatment non-contrast CT images of 41 patients of MIBC treated with bladder preservation were included. The visible tumor was contoured on all slices by a single observer. The primary endpoint was to identify texture parameters associated with disease recurrence post treatment. The secondary endpoints included intra and inter-observer variability, single and multi-slice analysis, and differentiating the texture features of normal bladder and tumor. For inter-observer variability of bladder tumour texture features, 3 observers contoured the visible tumour on all slices independently. Observer 1 contoured again at an interval of 1 month for intra-observer variability.

      Results

      The median follow-up was 30 months with 12 patients having a recurrence. In the primary endpoint analysis, the mean of the pixels at Spatial Scaling Filter (SSF) 2 for the no recurrence group and recurrence group was 6.44 v/s 13.73 respectively (p=0.031) and the same at SSF-3 was 11.95 and 22.32 respectively (p=0.034). The texture features that could significantly differentiate tumor and normal bladder were mean, standard deviation and kurtosis of the pixels at SSF-2 and entropy and kurtosis of the pixels at SSF-3. Overall, there was an excellent intra and interobserver concordance in texture features. Only multi-slice analysis and not single-slice could differentiate recurrence and no recurrence post treatment.

      Conclusions

      Texture analysis can be explored as a modality for patient selection for bladder preservation along with the established clinical parameters to improve outcomes

      Purpose

      To explore if texture analysis of Muscle Invasive Bladder Cancer (MIBC) can aid in better patient selection for bladder preservation.

      Overall result

      There was a significant difference in the mean of the pixels between the recurrence and the no recurrence group

      General significance

      Texture analysis can aid in better patient selection for bladder preservation

      Keywords

      Abbreviations:

      Ssf (spatial scaling factor), sd (standard deviation), mpp (mean of positive pixels)
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      REFERENCES

        • Cumberbatch MGK
        • Noon AP.
        Epidemiology, aetiology and screening of bladder cancer.
        Transl Androl Urol. 2019; 8: 5-11
        • Murthy V
        • Masodkar R
        • Kalyani N
        • Mahantshetty U
        • Bakshi G
        • Prakash G
        • et al.
        Clinical Outcomes With Dose-Escalated Adaptive Radiation Therapy for Urinary Bladder Cancer: A Prospective Study.
        Int J Radiat Oncol Biol Phys. 2016; 94: 60-66
        • Choudhury A
        • Nelson LD
        • Teo MT
        • Chilka S
        • Bhattarai S
        • Johnston CF
        • et al.
        MRE11 expression is predictive of cause-specific survival following radical radiotherapy for muscle-invasive bladder cancer.
        Cancer Res. 2010; 70: 7017-7026
        • Lubner MG
        • Smith AD
        • Sandrasegaran K
        • Sahani DV
        • Pickhardt PJ.
        CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.
        Radiographics. 2017; 37: 1483-1503
        • Alobaidli S
        • McQuaid S
        • South C
        • Prakash V
        • Evans P
        • Nisbet A.
        The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning.
        Br J Radiol. 2014; 8720140369
        • Cook GJ
        • Yip C
        • Siddique M
        • Goh V
        • Chicklore S
        • Roy A
        • et al.
        Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?.
        J Nucl Med. 2013; 54: 19-26
        • Nardone V
        • Reginelli A
        • Scala F
        • Carbone SF
        • Mazzei MA
        • Sebaste L
        • et al.
        Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation.
        Gastroenterol Res Pract. 2019; 20198505798
        • Meng Y
        • Zhang C
        • Zou S
        • Zhao X
        • Xu K
        • Zhang H
        • et al.
        MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer.
        Oncotarget. 2018; 9: 11999-12008
        • Meng J
        • Liu S
        • Zhu L
        • Zhu L
        • Wang H
        • Xie L
        • et al.
        Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT.
        Sci Rep. 2018; 8: 11399
        • Agarwal JP
        • Sinha S
        • Goda JS
        • Joshi K
        • Mhatre R
        • Kannan S
        • et al.
        Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers.
        Br J Radiol. 2020; 9320190857
        • Miles KA
        • Ganeshan B
        • Hayball MP.
        CT texture analysis using the filtration-histogram method: what do the measurements mean?.
        Cancer Imaging. 2013; 13: 400-406
        • Dennie C
        • Thornhill R
        • Souza CA
        • Odonkor C
        • Pantarotto JR
        • MacRae R
        • et al.
        Quantitative texture analysis on pre-treatment computed tomography predicts local recurrence in stage I non-small cell lung cancer following stereotactic radiation therapy.
        Quant Imaging Med Surg. 2017; 7: 614-622
        • Kuno H
        • Qureshi MM
        • Chapman MN
        • Li B
        • Andreu-Arasa VC
        • Onoue K
        • et al.
        CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.
        AJNR Am J Neuroradiol. 2017; 38: 2334-2340
        • Ganeshan B
        • Miles KA.
        Quantifying tumour heterogeneity with CT.
        Cancer Imaging. 2013; 13: 140-149
        • Shi Z
        • Yang Z
        • Zhang G
        • Cui G
        • Xiong X
        • Liang Z
        • et al.
        Characterization of texture features of bladder carcinoma and the bladder wall on MRI: initial experience.
        Acad Radiol. 2013; 20: 930-938
        • Kocak B
        • Durmaz ES
        • Kaya OK
        • Ates E
        • Kilickesmez O.
        Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility.
        AJR Am J Roentgenol. 2019; 213: 377-383
        • Leijenaar RT
        • Carvalho S
        • Velazquez ER
        • van Elmpt WJ
        • Parmar C
        • Hoekstra OS
        • et al.
        Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.
        Acta Oncol. 2013; 52: 1391-1397
        • Pavic M
        • Bogowicz M
        • Wurms X
        • Glatz S
        • Finazzi T
        • Riesterer O
        • et al.
        Influence of inter-observer delineation variability on radiomics stability in different tumor sites.
        Acta Oncol. 2018; 57: 1070-1074
        • Guezennec C
        • Bourhis D
        • Orlhac F
        • Robin P
        • Corre JB
        • Delcroix O
        • et al.
        Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer.
        PLoS One. 2019; 14e0214299
        • Lewis MA
        • Ganeshan B
        • Barnes A
        • Bisdas S
        • Jaunmuktane Z
        • Brandner S
        • et al.
        Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping.
        Eur J Radiol. 2019; 113: 116-123
        • Ng F
        • Kozarski R
        • Ganeshan B
        • Goh V.
        Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?.
        Eur J Radiol. 2013; 82: 342-348