Abstract
Objective
To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages.
Methods
Thirty-three Pca patients were included. All patients underwent pre- and post-IMRT T2-weighted (T2 W) and apparent diffusing coefficient (ADC) MRI. IMRT response was calculated in terms of changes in the ADC value, and patients were divided as responders and non-responders. A wide range of radiomic features from different feature sets were extracted from all T2 W and ADC images. Univariate radiomic analysis was performed to find highly correlated radiomic features with IMRT response, and a paired t test was used to find significant features between responders and non-responders. To find high predictive radiomic models, tenfold cross-validation as the criterion for feature selection and classification was applied on the pre-, post- and delta IMRT radiomic features, and area under the curve (AUC) of receiver operating characteristics was calculated as model performance value.
Results
Of 33 patients, 15 patients (45%) were found as responders. Univariate analysis showed 20 highly correlated radiomic features with IMRT response (20 ADC and 20 T2). Two and fifteen T2 and ADC radiomic features were found as significant (P-value ≤ 0.05) features between responders and non-responders, respectively. Several cross-combined predictive radiomic models were obtained, and post-T2 radiomic models were found as high predictive models (AUC 0.632) followed by pre-ADC (AUC 0.626) and pre-T2 (AUC 0.61). For GS prediction, T2 W radiomic models were found as more predictive (mean AUC 0.739) rather than ADC models (mean AUC 0.70), while for stage prediction, ADC models had higher prediction performance (mean AUC 0.675).
Conclusions
Radiomic models developed by MR image features and machine learning approaches are noninvasive and easy methods for personalized prostate cancer diagnosis and therapy.
Similar content being viewed by others
References
Zelefsky MJ, Fuks Z, Hunt M, Yamada Y, Marion C, Ling CC et al (2002) High-dose intensity modulated radiation therapy for prostate cancer: early toxicity and biochemical outcome in 772 patients. Int J Radiat Oncol Biol Phys 53(5):6–1111
Kumar V, Bora GS, Kumar R, Jagannathan NR (2018) Multiparametric (mp) MRI of prostate cancer. Prog Nucl Magn Reson 105:23–40
Wallace T, Torre T, Grob M, Yu J, Avital I, Brücher B et al (2014) Current approaches, challenges and future directions for monitoring treatment response in prostate cancer. J Cancer 5(1):3
Kelloff GJ, Choyke P, Coffey DS (2009) Challenges in clinical prostate cancer: role of imaging. Am J Roentgenol 192(6):70–1455
Mazaheri Y, Akin O, Hricak H (2017) Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: a review of current methods and applications. World J Radiol 9(12):416
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006
Abdollahi H, Mostafaei S, Cheraghi S, Shiri I, Mahdavi SR, Kazemnejad A (2018) Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: a machine learning and multi-variable modelling study. Phys Med 45:7–192
Stoyanova R, Takhar M, Tschudi Y, Ford JC, Solórzano G, Erho N et al (2016) Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res 5(4):432
X-k Niu, Z-f Chen, Chen L, Li J, Peng T, Li X (2018) Clinical application of biparametric MRI texture analysis for detection and evaluation of high-grade prostate cancer in zone-specific regions. Am J Roentgenol 210(3):56–549
Bates A, Miles K (2017) Prostate-specific membrane antigen PET/MRI validation of MR textural analysis for detection of transition zone prostate cancer. Eur Radiol 27(12):8–5290
Nketiah G, Elschot M, Kim E, Teruel JR, Scheenen TW, Bathen TF et al (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27(7):9–3050
Gnep K, Fargeas A, Gutiérrez-Carvajal RE, Commandeur F, Mathieu R, Ospina JD et al (2017) Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging 45(1):17–103
Stoyanova R, Pollack A, Takhar M, Lynne C, Parra N, Lam LL et al (2016) Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget 7(33):53362
Dzik-Jurasz A, Domenig C, George M, Wolber J, Padhani A, Brown G et al (2002) Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 360(9329):8–307
Mardor Y, Roth Y, Lidar Z, Jonas T, Pfeffer R, Maier SE et al (2001) Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 61(13):3–4971
Pearson R, Pieniazek P, Thelwall P, Maxwell R, Plummer R, Frew J (2018) Diffusion-weighted MRI for early response assessment in the treatment of bladder cancer. Clin Oncol 30(3):193
Thoeny HC, Ross BD (2010) Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 32(1):2–16
Decker G, Mürtz P, Gieseke J, Träber F, Block W, Sprinkart AM et al (2014) Intensity-modulated radiotherapy of the prostate: dynamic ADC monitoring by DWI at 3.0 T. Radiother Oncol 113(1):20–115
Desouza N, Reinsberg S, Scurr E, Brewster J, Payne G (2007) Magnetic resonance imaging in prostate cancer: the value of apparent diffusion coefficients for identifying malignant nodules. Br J Radiol 80(950):5–90
Wolf MB, Edler C, Tichy D, Röthke MC, Schlemmer HP, Herfarth K et al (2017) Diffusion-weighted MRI treatment monitoring of primary hypofractionated proton and carbon ion prostate cancer irradiation using raster scan technique. J Magn Reson Imaging 46(3):60–850
Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087
Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272
Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom K et al (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am J Neuroradiol 39(2):16–208
Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L et al (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:7–21
Song I, Kim CK, Park BK, Park W (2010) Assessment of response to radiotherapy for prostate cancer: value of diffusion-weighted MRI at 3 T. Am J Roentgenol 194(6):W82–W477
Wahba MH, Morad MM (2015) The role of diffusion-weighted MRI: in assessment of response to radiotherapy for prostate cancer. Egypt J Radiol Nucl Med 46(1):8–183
Belli P, Costantini M, Ierardi C, Bufi E, Amato D, Mule A et al (2011) Diffusion-weighted imaging in evaluating the response to neoadjuvant breast cancer treatment. Breast J 17(6):9–610
Khalil RF, Abdelhamid AEM, Darwish AMA, Hassan HHM (2017) Diffusion weighted imaging in early prediction of neoadjuvant chemotherapy response in breast cancer. Egypt J Radiol Nucl Med 48(2):35–529
Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22(1):81–91
Bollineni VR, Widder J, Pruim J, Langendijk JA, Wiegman EM (2012) Residual 18F-FDG-PET uptake 12 weeks after stereotactic ablative radiotherapy for stage I non-small-cell lung cancer predicts local control. Int J Radiat Oncol Biol Phys 83(4):e5–e551
Liu Z, Zhang X-Y, Shi Y-J, Wang L, Zhu H-T, Tang Z-C, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017:clincanres. 1038.2017
Aerts HJ, Grossmann P, Tan Y, Oxnard GR, Rizvi N, Schwartz LH et al (2016) Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 6:33860
Yue Y, Osipov A, Fraass B, Sandler H, Zhang X, Nissen N et al (2017) Identifying prognostic intratumor heterogeneity using pre-and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. J Gastrointest Oncol 8(1):127
Rao S-X, Lambregts DM, Schnerr RS, Beckers RC, Maas M, Albarello F et al (2016) CT texture analysis in colorectal liver metastases: a better way than size and volume measurements to assess response to chemotherapy? United Eur Gastroenterol J 4(2):63–257
Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261(1):71–165
Cunliffe A, Armato SG III, Castillo R, Pham N, Guerrero T, Al-Hallaq HA (2015) Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys 91(5):56–1048
Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D et al (2017) Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 7(1):588
Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW et al (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119(3):6–480
Mattonen SA, Palma DA, Haasbeek CJ, Senan S, Ward AD (2014) Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys 41(3):033502
Lucia F, Visvikis D, Desseroit M-C, Miranda O, Malhaire J-P, Robin P et al (2018) Prediction of outcome using pretreatment 18 F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 45(5):86–768
Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S et al (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:26–1418
Parmar C, Leijenaar RT, Grossmann P, Velazquez ER, Bussink J, Rietveld D et al (2015) Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 5:11044
Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA 112(46):E73–E6265
Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D et al (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25(10):50–2840
Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A (2017) The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 27(11):509–4498
Shiri I, Abdollahi H, Shaysteh S, Mahdavi SR (2017) Test-retest reproducibility and robustness analysis of recurrent glioblastoma MRI radiomics texture features. Iran J Radiol. https://doi.org/10.5812/iranjradiol.48035
Saeedi E, Dejkam A, Beigi J, Rastegar S, Yousei Z, Mehdipour A et al (2018) Radiomic feature robustness and reproducibility in quantitative bone radiography: a study on radiologic parameter changes. J Clin Densitom. https://doi.org/10.1016/j.jocd.2018.06.004
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed 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.
Informed consent
Informed consent was obtained from all participants included in the study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Abdollahi, H., Mofid, B., Shiri, I. et al. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol med 124, 555–567 (2019). https://doi.org/10.1007/s11547-018-0966-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11547-018-0966-4