Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study

Radiation Oncology

Dag R Olsen¹  Anne H Ree³  Therese Seierstad⁴  Manish Kakar⁴  Kathrine Røe² 

[1] University of Bergen, Bergen, Norway;Institute of Clinical Medicine, University of Oslo, Oslo, Norway;Department of Oncology, Akershus University Hospital, Lorenskog, Norway;Department of Radiation Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4953 Nydalen, 0424 Oslo, Norway

关键词: radiotherapy;    androgen-deprivation therapy;    prostate cancer;    dynamic contrast-enhanced magnetic resonance imaging;    diffusion weighted magnetic resonance imaging;    back propagation neural network;    artificial neural network;   

Others  :  1224361
DOI  :  10.1186/1748-717X-6-65

 received in 2011-02-01, accepted in 2011-06-08,  发布年份 2011

【 摘 要 】

Background

In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.

Methods

We challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker K^trans, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.

Results

ADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while K^trans, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, K^trans, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).

Conclusions

We have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.

【 授权许可】

   
2011 Røe et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Mitchell RE, Chang SS: Current controversies in the treatment of high-risk prostate cancer. Curr Opin Urol 2008, 18:263-268.
• [2]Barry MJ: Screening for prostate cancer--the controversy that refuses to die. N Engl J Med 2009, 360:1351-1354.
• [3]Bammer R: Basic principles of diffusion-weighted imaging. Eur J Radiol 2003, 45:169-184.
• [4]Koh DM, Padhani AR: Diffusion-weighted MRI: a new functional clinical technique for tumour imaging. Br J Radiol 2006, 79:633-635.
• [5]Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ, Port RE, Taylor J, Weisskoff RM: Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999, 10:223-232.
• [6]Zahra MA, Hollingsworth KG, Sala E, Lomas DJ, Tan LT: Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. Lancet Oncol 2007, 8:63-74.
• [7]Seierstad T, Folkvord S, Røe K, Flatmark K, Skretting A, Olsen DR: Early changes in apparent diffusion coefficient predict the quantitative antitumoral activity of capecitabine, oxaliplatin, and irradiation in HT29 xenografts in athymic nude mice. Neoplasia 2007, 9:392-400.
• [8]Theilmann RJ, Borders R, Trouard TP, Xia G, Outwater E, Ranger-Moore J, Gillies RJ, Stopeck A: Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 2004, 6:831-837.
• [9]Moffat BA, Chenevert TL, Lawrence TS, Meyer CR, Johnson TD, Dong Q, Tsien C, Mukherji S, Quint DJ, Gebarski SS, Robertson PL, Junck LR, Rehemtulla A, Ross BD: Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci USA 2005, 102:5524-5529.
• [10]Cui Y, Zhang XP, Sun YS, Tang L, Shen L: Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 2008, 248:894-900.
• [11]Kakar M, Seierstad T, Røe K, Olsen DR: Artificial neural networks for prediction of response to chemoradiation in HT29 xenografts. Int J Radiat Oncol Biol Phys 2009, 75:506-511.
• [12]Kim CK, Park BK, Lee HM, Kim SS, Kim E: MRI techniques for prediction of local tumor progression after high-intensity focused ultrasonic ablation of prostate cancer. AJR Am J Roentgenol 2008, 190:1180-1186.
• [13]Kurhanewicz J, Vigneron D, Carroll P, Coakley F: Multiparametric magnetic resonance imaging in prostate cancer: present and future. Curr Opin Urol 2008, 18:71-77.
• [14]Lisboa PJ, Taktak AF: The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw 2006, 19:408-415.
• [15]Ramesh AN, Kambhampati C, Monson JR, Drew PJ: Artificial intelligence in medicine. Ann R Coll Surg Engl 2004, 86:334-338.
• [16]Rumelhart DE, Hinton GE, Williams RJ: Learning representations by back-propagating errors. Nature 1986, 323:533-536.
• [17]Gulliford SL, Webb S, Rowbottom CG, Corne DW, Dearnaley DP: Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother Oncol 2004, 71:3-12.
• [18]Gamito EJ, Crawford ED: Artificial neural networks for predictive modeling in prostate cancer. Curr Oncol Rep 2004, 6:216-221.
• [19]Tewari A, Issa M, El-Galley R, Stricker H, Peabody J, Pow-Sang J, Shukla A, Waisman Z, Rubin M, Wei J, Montie J, Demers R, Johnson CC, Lamerato L, Divine GW, Crawford ED, Gamito EJ, Farah R, Naravan P, Carlson G, Menon M: Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study. Mol Urol 2001, 5:163-169.
• [20]Finne P, Finne R, Auvinen A, Juusela H, Aro J, Määttänen L, Hakama M, Rannikko S, Tammela TL, Stenman U: Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 2000, 56:418-422.
• [21]Stephan C, Kahrs AM, Cammann H, Lein M, Schrader M, Deger S, Miller K, Jung K: A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases. Prostate 2009, 69:198-207.
• [22]Røe K, Seierstad T, Kristian A, Mikalsen LT, Mælandsmo GM, van der Kogel AJ, Ree AH, Olsen DR: Longitudinal magnetic resonance imaging-based assessment of vascular changes and radiation response in androgen-sensitive prostate carcinoma xenografts under androgen-exposed and androgen-deprived conditions. Neoplasia 2010, 12:818-825.
• [23]Seierstad T, Røe K, Høvik B: Construction of a modified capacitive overlap MR coil for imaging of small animals and objects in a clinical whole-body scanner. Phys Med Biol 2007, 52:N513-N522.
• [24]Hittmair K, Gomiscek G, Langenberger K, Recht M, Imhof H, Kramer J: Method for the quantitative assessment of contrast agent uptake in dynamic contrast-enhanced MRI. Magn Reson Med 1994, 31:567-571.
• [25]Geman S, Bienenstock E, Doursat R: Neural networks and the bias/variance dilemma. Neural Comput 1992, 4:1-58.