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Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer

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Abstract

Objectives

To examine the usefulness of the texture analysis (TA) of apparent diffusion coefficient (ADC) maps in predicting the chemoradiotherapy (CRT) response of muscle-invasive bladder cancer (MIBC).

Methods

We reviewed 45 MIBC patients who underwent cystectomy after CRT. CRT response was assessed through histologic evaluation of cystectomy specimens. Two radiologists determined the volume of interest for the index lesions on ADC maps of pretherapeutic 1.5-T MRI and performed TA using the LIFEx software. Forty-six texture features (TFs) were selected based on their contribution to the prediction of CRT sensitivity. To evaluate diagnostic performance, diagnostic models from the selected TFs were created using random forest (RF) and support vector machine (SVM), respectively.

Results

Twenty-three patients achieved pathologic complete response (pCR) to CRT. The feature selection identified first quartile ADC (Q1 ADC), gray-level co-occurrence matrix (GLCM) correlation, and GLCM homogeneity as important in predicting CRT response. Patients who achieved pCR showed significantly lower Q1 ADC and GLCM correlation values (0.66 × 10−3 mm2/s and 0.53, respectively) than those who did not (0.81 × 10−3 mm2/s and 0.70, respectively; p < 0.05 for both). The AUCs of the RF and SVM models incorporating the selected TFs were 0.82 (95% confidence interval [CI]: 0.67–0.97) and 0.96 (95% CI: 0.91–1.00), respectively, and the AUC of the SVM model was better than that of the mean ADC value (0.76, 95% CI: 0.61–0.90; p = 0.0037).

Conclusion

TFs can serve as imaging biomarkers in MIBC patients for predicting CRT sensitivity. TAs of ADC maps can potentially optimize patient selection for CRT.

Key Points

• Texture analysis of ADC maps and feature selection identified important texture features for classifying pathologic tumor response in patients with muscle-invasive bladder cancer.

• The machine learning model incorporating the texture features set, which included first quartile ADC, GLCM correlation, and GLCM homogeneity, showed high performance in predicting chemoradiotherapy response.

• Texture features could serve as imaging biomarkers that optimize eligible patient selection for chemoradiotherapy in muscle-invasive bladder cancer.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CI:

Confidence interval

CR:

Complete response

CRT:

Chemoradiotherapy

DWI:

Diffusion-weighted imaging

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLZLM:

Gray-level zone length matrix

IQR:

Interquartile range

MIBC:

Muscle-invasive bladder cancer

MRI:

Magnetic resonance imaging

NGLDM:

Neighborhood gray-level different matrix

pCR:

Pathologic CR

Q1:

First quartile

Q2:

Second quartile

RF:

Random forest

ROC:

Receiver operating characteristic

ROIs:

Regions of interest

SRE:

Short run emphasis

SVM:

Support vector machine

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

TA:

Texture analysis

TF:

Texture feature

TUR:

Transurethral resection

VOI:

Volume of interest

ZE:

Zone percentage

References

  1. Chang SS, Bochner BH, Chou R et al (2017) Treatment of non-metastatic muscle-invasive bladder cancer: AUA/ASCO/ASTRO/SUO guideline. J Urol 198:552–559

    Article  Google Scholar 

  2. Mitin T, George A, Zietman AL et al (2016) Long-term outcomes among patients who achieve complete or near-complete responses after the induction phase of bladder-preserving combined-modality therapy for muscle-invasive bladder cancer: a pooled analysis of NRG oncology/RTOG 9906 and 0233. Int J Radiat Oncol Biol Phys 94:67–74

    Article  Google Scholar 

  3. Ploussard G, Daneshmand S, Efstathiou JA et al (2014) Critical analysis of bladder sparing with trimodal therapy in muscle-invasive bladder cancer: a systematic review. Eur Urol 66:120–137

    Article  CAS  Google Scholar 

  4. Panebianco V, Narumi Y, Altun E et al (2018) Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system). Eur Urol 74:294–306

    Article  Google Scholar 

  5. Expert Panel on Urologic Imaging, Allen BC, Oto A et al (2019) ACR appropriateness criteria(R) post-treatment surveillance of bladder cancer. J Am Coll Radiol 16:S417–S427

  6. Zhu HB, Zhang XY, Zhou XH et al (2017) Assessment of pathological complete response to preoperative chemoradiotherapy by means of multiple mathematical models of diffusion-weighted MRI in locally advanced rectal cancer: a prospective single-center study. J Magn Reson Imaging 46:175–183

    Article  Google Scholar 

  7. Jones M, Hruby G, Stanwell P et al (2015) Multiparametric MRI as an outcome predictor for anal canal cancer managed with chemoradiotherapy. BMC Cancer 15:281

    Article  Google Scholar 

  8. Wang L, Liu L, Han C et al (2016) The diffusion-weighted magnetic resonance imaging (DWI) predicts the early response of esophageal squamous cell carcinoma to concurrent chemoradiotherapy. Radiother Oncol 121:246–251

    Article  Google Scholar 

  9. Yoshida S, Koga F, Kobayashi S et al (2012) Role of diffusion-weighted magnetic resonance imaging in predicting sensitivity to chemoradiotherapy in muscle-invasive bladder cancer. Int J Radiat Oncol Biol Phys 83:e21–e27

    Article  Google Scholar 

  10. Ji GW, Zhang YD, Zhang H et al (2019) Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 290:90–98

    Article  Google Scholar 

  11. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  12. Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol. https://doi.org/10.1007/s00330-019-06176-x

  13. Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206

    Article  CAS  Google Scholar 

  14. Shi Z, Yang Z, Zhang G et al (2013) Characterization of texture features of bladder carcinoma and the bladder wall on MRI: initial experience. Acad Radiol 20:930–938

    Article  Google Scholar 

  15. Zhang X, Xu X, Tian Q et al (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging 46:1281–1288

    Article  Google Scholar 

  16. Wang H, Hu D, Yao H et al (2019) Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol 29:6182–6190

    Article  Google Scholar 

  17. Xu X, Zhang X, Tian Q et al (2019) Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J Magn Reson Imaging 49:1489–1498

    Article  Google Scholar 

  18. Xu S, Yao Q, Liu G et al (2020) Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer. Eur Radiol 30:1804–1812

    Article  Google Scholar 

  19. Wang H, Xu X, Zhang X et al (2020) Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study. Eur Radiol 30:4816–4827

    Article  Google Scholar 

  20. Koga F, Yoshida S, Kawakami S et al (2008) Low-dose chemoradiotherapy followed by partial or radical cystectomy against muscle-invasive bladder cancer: an intent-to-treat survival analysis. Urology 72:384–388

    Article  Google Scholar 

  21. Koga F, Kihara K, Fujii Y et al (2009) Favourable outcomes of patients with clinical stage T3N0M0 bladder cancer treated with induction low-dose chemo-radiotherapy plus partial or radical cystectomy vs immediate radical cystectomy: a single-institutional retrospective comparative study. BJU Int 104:189–194

    Article  Google Scholar 

  22. Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    Article  CAS  Google Scholar 

  23. Kursa MB, Rudnicki WR (2010) Feature selection with the Boruta package. J Stat Softw 36:1–13

    Article  Google Scholar 

  24. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  25. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567

    Article  CAS  Google Scholar 

  26. Kobayashi S, Koga F, Yoshida S et al (2011) Diagnostic performance of diffusion-weighted magnetic resonance imaging in bladder cancer: potential utility of apparent diffusion coefficient values as a biomarker to predict clinical aggressiveness. Eur Radiol 21:2178–2186

    Article  Google Scholar 

  27. Yoshida S, Takahara T, Kwee TC, Waseda Y, Kobayashi S, Fujii Y (2017) DWI as an imaging biomarker for bladder cancer. AJR Am J Roentgenol 208:1218–1228

    Article  Google Scholar 

  28. de Haas RJ, Steyvers MJ, Futterer JJ (2014) Multiparametric MRI of the bladder: ready for clinical routine? AJR Am J Roentgenol 202:1187–1195

    Article  Google Scholar 

  29. Tuncbilek N, Kaplan M, Altaner S et al (2009) Value of dynamic contrast-enhanced MRI and correlation with tumor angiogenesis in bladder cancer. AJR Am J Roentgenol 192:949–955

    Article  Google Scholar 

  30. Tanaka H, Kijima T, Fujii Y (2020) Bladder preservation therapy in muscle-invasive bladder cancer: current evidence and future perspectives. AME Med J 5:16–16

    Article  Google Scholar 

Download references

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The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan

    Koichiro Kimura, Junichi Tsuchiya, Ichiro Yamada & Ukihide Tateishi

  2. Department of Urology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113–8510, Japan

    Soichiro Yoshida, Hajime Tanaka, Minato Yokoyama, Yoh Matsuoka & Yasuhisa Fujii

  3. Department of Radiation Therapeutics and Oncology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan

    Ryoichi Yoshimura

Authors
  1. Koichiro Kimura
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Correspondence to Soichiro Yoshida.

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Guarantor

The scientific guarantor of this publication is Yasuhisa Fujii.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study cohorts have been previously reported in International Journal of Radiation Oncology, Biology, Physics (https://doi.org/10.1016/j.ijrobp.2011.11.065).

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Kimura, K., Yoshida, S., Tsuchiya, J. et al. Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer. Eur Radiol 32, 671–679 (2022). https://doi.org/10.1007/s00330-021-08110-6

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