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Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning

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Abstract

Purpose

The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients.

Methods

The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment.

Results

The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model’s performance, respectively.

Conclusion

This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.

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References

  1. Chen L-D, Wang W, Xu J-B, et al. Assessment of rectal tumors with shear-wave elastography before surgery: Comparison with endorectal US. Radiology. 2017;285(1):279–292.

    Article  PubMed  Google Scholar 

  2. Chen L Da, Liang JY, Wu H, et al. Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci. 2018;208:55–63.

    Article  Google Scholar 

  3. Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2019;29(3):1211–1220.

    Article  PubMed  Google Scholar 

  4. Pizzi AD, Chiarelli AM, Chiacchiaretta P, et al. OPEN MRI ‑ based clinical ‑ radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep. 2021;1–11.

  5. Chen L-D, Li W, Xian M-F, et al. Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network–based US radiomics model. Eur Radiol. 2020;30(4):1969–1979.

    Article  PubMed  Google Scholar 

  6. Kav T, Bayraktar Y. How useful is rectal endosonography in the staging of rectal cancer? World J Gastroenterol. 2010;16(6):691–697.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sun Q, Lin X, Zhao Y, et al. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don’t Forget the Peritumoral Region. Front Oncol. 2020;10:1–12.

  8. Cusumano D, Dinapoli N, Boldrini L, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Medica. 2018;123(4):286–295.

    Article  Google Scholar 

  9. Karaoğlu O, Bilge HŞ, Uluer İ. Removal of speckle noises from ultrasound images using five different deep learning networks. Eng Sci Technol an Int J. 2021.https://doi.org/10.1016/j.jestch.2021.06.010

    Article  Google Scholar 

  10. Singh P, Mukundan R, De Ryke R. Texture based quality analysis of simulated synthetic ultrasound images using local binary patterns. J Imaging. 2018;4(1):1–13.

    CAS  Google Scholar 

  11. Feng D, Wu W, Li H, Li Q. Speckle noise removal in ultrasound images using a deep convolutional neural network and a specially designed loss function. In: Q. Li et al. (ed) In International Workshop on Multiscale Multimodal Medical Imaging. Springer.Cham. 2019, pp 85–92.

  12. Kaur PP, Singh T. Speckle Noise Reduction in Ultrasound Images. NCCI 2010 -National Conference on Computational Instrumentation CSIO Chandigarh. 2010:198–203.

  13. Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J. MRI-based radiomics of rectal cancer: Preoperative assessment of the pathological features. BMC Med Imaging. 2019;19(1):1–7.

    Article  Google Scholar 

  14. Shiri I, Hajianfar G, Sohrabi A, et al. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test–retest and image registration analyses. Med Phys. 2020;47: 4265–4280.

    Article  PubMed  Google Scholar 

  15. Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev. 2022;42(1):426–440.

    Article  PubMed  Google Scholar 

  16. Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol. 2021;27(25):3802–3814.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang S, Huang TZ, Zhao X Le, Mei JJ, Huang J. Speckle noise removal in ultrasound images by first- and second-order total variation. Numer Algorithms. 2018;78(2):513–533.

    Article  Google Scholar 

  18. Saoji SU, Sarode M V. Speckle and Rician Noise Removal from Medical Images and Ultrasound Images. Int J Recent Technol Eng. 2020;8(5):1851–1854.

    Google Scholar 

  19. Duron L, Savatovsky J, Fournier L, Lecler A. Can we use radiomics in ultrasound imaging? Impact of preprocessing on feature repeatability. Diagn Interv Imaging. 2021;102(11):659–667.

    Article  PubMed  Google Scholar 

  20. Loizou CP, Theofanous C, Pantziaris M, Kasparis T. Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery. Comput Methods Programs Biomed. 2014;114(1):109–124.

    Article  PubMed  Google Scholar 

  21. Boldrini L, Cusumano D, Chiloiro G, et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Medica. 2019;124(2):145–153.

  22. Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, et al. MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep. 2021;11(1):5379-5390.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2018;45(3):365–375.

    Article  PubMed  Google Scholar 

  24. Wang J, Shen L, Zhong H, et al. Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients. Sci Rep. 2019;9(1):1–9.

    Google Scholar 

  25. Shayesteh SP, Alikhassi A, Farhan F, et al. Author Correction: Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients. J Gastrointest Cancer. 2020; 51(2):601-609.

    Article  PubMed  Google Scholar 

  26. Vanithamani R, Umamaheswari G. Speckle reduction in ultrasound images using Neighshrink and bilateral filtering. J Comput Sci. 2014;10(4):623–631.

    Article  Google Scholar 

  27. Jaybhay J, Shastri R. A Study of Speckle Noise Reduction Filters. Signal Image Process An Int J. 2015;6(3):71–80.

    Article  Google Scholar 

  28. Santoso AW, Bayuaji L, Sze LT, Lateh H, Zain JM. Comparison of various speckle noise reduction filters on synthetic aperture radar image. Int J Appl Eng Res. 2016;11(15):8760–8767.

    Google Scholar 

  29. Kupidura P. Comparison of filters dedicated to speckle suppression in SAR images. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch. 2016;41:269–276.

    Article  Google Scholar 

  30. Mohd Sagheer S V., George SN. A review on medical image denoising algorithms. Biomed Signal Process Control. 2020;61:102036.

    Article  Google Scholar 

  31. Forman G, Scholz M. Apples-to-Apples in Cross-Validation Studies : Pitfalls in Classifier Performance Measurement. Acm Sigkdd Explorations Newsletter. 2010;12(1):49–57.

    Article  Google Scholar 

  32. Berrar D. Cross-Validation Cross-validation. Encyclopedia of Bioinformatics and Computational Biology 2018;1:542–545.

    Google Scholar 

  33. Purushotham S, Tripathy BK. Evaluation of Classifier Models Using Stratified Tenfold Cross Validation Techniques. International Conference on Computing and Communication Systems. 2012;680–690.

  34. Lee SE, Han K, Kwak JY, Lee E, Kim EK. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep. 2018;8(1):2–9.

    Google Scholar 

  35. Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23(23):7253–7262.

    Article  CAS  PubMed  Google Scholar 

  36. Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol. 2019;44(11):3764–3774.

    Article  Google Scholar 

  37. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–2164.

    Article  PubMed  Google Scholar 

  38. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563–577.

    Article  PubMed  Google Scholar 

  39. Chen H, Shi L, Nam K, et al. MRI Radiomics for Prediction of Tumor Response and Downstaging in Rectal Cancer Patients after Preoperative Chemoradiation. Advancesradonc. 2020;5(6):1–10.

    Google Scholar 

  40. Jin J, Zhu H, Zhang J, Ai Y, Zhang J, Teng Y. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer. Frontiers in Oncology. 2021;18(10):1–8.

    Google Scholar 

  41. Zhou H, Jin Y, Dai L, et al. Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images. Eur. J. Radiol. 2020; 127: 108992.

    Article  PubMed  Google Scholar 

  42. Qiu X, Jiang Y, Zhao Q, et al. Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?,” J. Ultrasound Med. 2020; 39(10):1897–1905.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang Y, Yue W, Li X, et al. Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images. IEEE Access. 2020; 8: 52010–52017.

    Article  Google Scholar 

  44. DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study,” Cancer Med.2020; 9(16):5798–5806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dasgupta A, Brade S, Sannachi L, et al. Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer. Oncotarget. 2020;11(42):3782–3792.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Rahman MM, Azim M, Mina, Uddin S. Speckle noise reduction in ultrasound images by wavelet thresholding based on subband mean difference. Int J Tomogr Stat. 2012;20(2):91–97.

  47. Bhuiyan MIH, Ahmad MO, Swamy MNS. New spatially adaptive wavelet-based method for the despeckling of medical ultrasound images. Proc - IEEE Int Symp Circuits Syst. 2007;2347–2350.

  48. Theek B, Opacic T, Magnuska Z, Lammers T, Kiessling F. Radiomic analysis of contrast-enhanced ultrasound data. Sci Rep. 2018;8(1):1–9.

    Article  CAS  Google Scholar 

  49. Shayesteh SP, Alikhassi A, Fard Esfahani A, et al. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Medica. 2019;62:111–119.

    Article  Google Scholar 

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Acknowledgements

This study was extracted from a Ph.D. thesis from the first author approved by the Tehran University of Medical Sciences. The authors would like to thank the staff of the Medical Imaging and Radiotherapy Oncology centers in Firouzgar Hospital of Iran University of Medical Sciences (Tehran, Iran).

Funding

This work was supported by the Tehran University of Medical Sciences, under Grant No. 49134.

Author information

Authors and Affiliations

  1. Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Samira Abbaspour & Mohammadreza Ay

  2. Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran

    Samira Abbaspour & Mohammadreza Ay

  3. Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran

    Hamid Abdollahi

  4. Research Center for Science and Technology in Medicine (RCSTM), Tehran University of Medical Sciences, Tehran, Iran

    Hossein Arabalibeik

  5. Department of Radiation Oncology, Firoozgar Hospital, Firoozgar Clinical Research Development Center (FCRDC), Iran University of Medical Sciences, Tehran, Iran

    Maedeh Barahman & Amir Mohammad Arefpour

  6. Department of Radiation Oncology, Iran University of Medical Sciences, Tehran, Iran

    Pedram Fadavi

  7. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

    Seied Rabi Mahdavi

  8. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada

    Hamid Abdollahi

Authors
  1. Samira Abbaspour
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Contributions

SA, HAb, and MA contributed to conceptualization, SA, HAb, and SRM contributed to methodology, SA, HAb, HAr, and SRM contributed to formal analysis and investigation, SA contributed to writing and preparation of the original draft, HAb, HAr, MB, AMA, PF, MA, and SRM contributed to writing, reviewing, and editing of the manuscript, SA, MA, and SRM contributed to funding acquisition, AMA and PF provided resources, MA, SRM, and HAr performed supervision.

Corresponding authors

Correspondence to Mohammadreza Ay or Seied Rabi Mahdavi.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Tehran University of Medical Sciences (No. IR.TUMS.MEDICINE.REC.1399.244).

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Abbaspour, S., Abdollahi, H., Arabalibeik, H. et al. Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning. Abdom Radiol 47, 3645–3659 (2022). https://doi.org/10.1007/s00261-022-03625-y

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  • DOI: https://doi.org/10.1007/s00261-022-03625-y

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