Advanced search
Publication Cover
Cancer Management and Research Volume 11, 2019 - Issue
Submit an article Journal homepage
109
Views
16
CrossRef citations to date
0
Altmetric
Original Research

Imaging-Based Individualized Response Prediction Of Carbon Ion Radiotherapy For Prostate Cancer Patients

Shuang Wu1 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, People’s Republic of China;2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China
,
Yining Jiao3 Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
,
Yafang Zhang1 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, People’s Republic of China;2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China
,
Xuhua Ren3 Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0003-3903-1333
ORCID Icon,
Ping Li2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China;4 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, People’s Republic of China
,
Qi Yu1 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, People’s Republic of China;2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China
,
Qing Zhang2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China;4 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, People’s Republic of China
,
Qian Wang3 Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-3490-3836
ORCID Icon &
Shen Fu1 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, People’s Republic of China;2 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China;5 Key Laboratory of Nuclear Physics and Ion-beam Application MOE, Fudan University, Shanghai, People’s Republic of China;6 Department of Radiation Oncology, Shanghai Concord Cancer Hospital, Shanghai, People’s Republic of ChinaCorrespondence[email protected]
 show all
Pages 9121-9131 | Published online: 24 Oct 2019

References

  • Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68(1):7–30. doi:10.3322/caac.2144229313949
  • Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–132. doi:10.3322/caac.2133826808342
  • Kupelian PA, Elshaikh M, Reddy CA, Zippe C, Klein EA. Comparison of the efficacy of local therapies for localized prostate cancer in the prostate-specific antigen era: a large single-institution experience with radical prostatectomy and external-beam radiotherapy. J Clin Oncol. 2002;20(16):3376–3385. doi:10.1200/JCO.2002.01.15012177097
  • Hamdy FC, Donovan JL, Lane JA, et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med. 2016;375(15):1415–1424. doi:10.1056/NEJMoa160622027626136
  • Donovan JL, Hamdy FC, Lane JA, et al. Patient-reported outcomes after monitoring, surgery, or radiotherapy for prostate cancer. N Engl J Med. 2016;375(15):1425–1437. doi:10.1056/NEJMoa160622127626365
  • Shioyama Y, Tsuji H, Suefuji H, et al. Particle radiotherapy for prostate cancer. Int J Urol. 2015;22(1):33–39. doi:10.1111/iju.1264025308767
  • Schulz-Ertner D, Tsujii H. Particle radiation therapy using proton and heavier ion beams. J Clin Oncol. 2007;25(8):953–964. doi:10.1200/JCO.2006.09.781617350944
  • Akakura K, Tsujii H, Morita S, et al. Phase I/II clinical trials of carbon ion therapy for prostate cancer. Prostate. 2004;58(3):252–258. doi:10.1002/pros.1032814743464
  • Nomiya T, Tsuji H, Kawamura H, et al. A multi-institutional analysis of prospective studies of carbon ion radiotherapy for prostate cancer: a report from the Japan Carbon ion Radiation Oncology Study Group (J-CROS). Radiother Oncol. 2016;121(2):288–293. doi:10.1016/j.radonc.2016.10.00927836119
  • Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446. doi:10.1016/j.ejca.2011.11.03622257792
  • Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi:10.1038/ncomms597224892406
  • Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology. 2016;281(3):947–957. doi:10.1148/radiol.201615223427347764
  • Rios Velazquez E, Parmar C, Liu Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 2017;77(14):3922–3930. doi:10.1158/0008-5472.CAN-17-012228566328
  • 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. doi:10.1200/JCO.2015.65.912827138577
  • Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–5264. doi:10.1158/1078-0432.CCR-15-299727185368
  • Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016;2:16012. doi:10.1038/npjbcancer.2016.1227853751
  • Diehn M, Nardini C, Wang DS, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A. 2008;105(13):5213–5218. doi:10.1073/pnas.080127910518362333
  • Gevaert O, Mitchell LA, Achrol AS, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2014;273(1):168–174. doi:10.1148/radiol.1413173124827998
  • Kickingereder P, Gotz M, Muschelli J, et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res. 2016;22(23):5765–5771. doi:10.1158/1078-0432.CCR-16-070227803067
  • Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712–721. doi:10.1109/42.79628410534053
  • van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21): e104-e107. doi:10.1158/0008-5472.CAN-17-0339
  • Gamer M, Lemon J, Singh I F P. irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84. 2012.
  • Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–163. doi:10.1016/j.jcm.2016.02.01227330520
  • McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods. 1996;1(1):30–46. doi:10.1037/1082-989X.1.1.30
  • Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol. 2012;8(1):23–34.22833776
  • Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1–3):389–422. doi:10.1023/A:1012487302797
  • Jones E, Oliphant T, Peterson P. SciPy: Open Source Scientific Tools for Python. 2001 Availble from: http://www.scipy.org/. Accessed October 17, 2019.
  • D’Amico AV, Chen MH, de Castro M, et al. Surrogate endpoints for prostate cancer-specific mortality after radiotherapy and androgen suppression therapy in men with localised or locally advanced prostate cancer: an analysis of two randomised trials. Lancet Oncol. 2012;13(2):189–195. doi:10.1016/S1470-2045(11)70295-922112793
  • Loeffler JS, Durante M. Charged particle therapy – optimization, challenges and future directions. Nat Rev Clin Oncol. 2013;10(7):411–424. doi:10.1038/nrclinonc.2013.7923689752
  • Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2(12):1636–1642. doi:10.1001/jamaoncol.2016.263127541161
  • Bowen SR, Yuh WTC, Hippe DS, et al. Tumor radiomic heterogeneity: multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging. 2018;47(5):1388–1396. doi:10.1002/jmri.2587429044908
  • Altazi BA, Fernandez DC, Zhang GG, et al. Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes. Phys Med. 2018;46:180–188. doi:10.1016/j.ejmp.2017.10.00929475772
  • Kwan JYY, Su J, Huang SH, et al. Radiomic biomarkers to refine risk models for distant metastasis in HPV-related oropharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2018;102(4):1107–1116. doi:10.1016/j.ijrobp.2018.01.05729506884
  • Head MDACC, Neck Quantitative Imaging Working G. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep. 2018;8(1):1524. doi:10.1038/s41598-017-14687-029367653
  • Gnep K, Fargeas A, Gutierrez-Carvajal RE, et al. Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging. 2017;45(1):103–117. doi:10.1002/jmri.2533527345946
  • Cusumano D, Dinapoli N, Boldrini L, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2018;123(4):286–295. doi:10.1007/s11547-017-0838-329230678
  • Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287(3):833–843. doi:10.1148/radiol.201817230029514017
  • Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep. 2017;7:46349. doi:10.1038/srep4634928418006
  • Li Q, Kim J, Balagurunathan Y, et al. Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys. 2017;44(8):4341–4349. doi:10.1002/mp.1230928464316
  • Li Q, Kim J, Balagurunathan Y, et al. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol. 2017;12(1):158. doi:10.1186/s13014-017-0892-y28946909
  • Huynh E, Coroller TP, Narayan V, et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol. 2016;120(2):258–266. doi:10.1016/j.radonc.2016.05.02427296412
  • Moran A, Daly ME, Yip SSF, Yamamoto T. Radiomics-based assessment of radiation-induced lung injury after stereotactic body radiotherapy. Clin Lung Cancer. 2017;18(6): e425–e431. doi:10.1016/j.cllc.2017.05.014
  • Shiradkar R, Ghose S, Jambor I, et al. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging. 2018;48(6):1626–1636. doi:10.1002/jmri.2617829734484
  • Pavic M, Bogowicz M, Wurms X, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol. 2018;57:1–5.29297250

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.