Abstract
Radiomics has shown great potential for outcome prognosis and presents a promising approach for improving personalized cancer treatment. In radiomic analyses, features of different complexity are extracted from clinical imaging datasets, which are correlated to the endpoints of interest using machine-learning approaches. However, it is generally unclear if more complex features have a higher prognostic value and show a robust performance in external validation. Therefore, in this study, we developed and validated radiomic signatures for outcome prognosis after neoadjuvant radiochemotherapy in locally advanced rectal cancer (LARC) using computed tomography (CT) and T2-weighted magnetic resonance imaging (MRI) of two independent institutions (training/validation: 94/28 patients). For the prognosis of tumor response and freedom from distant metastases (FFDM), we used different imaging features extracted from the gross tumor volume: less complex morphological and first-order (MFO) features, more complex second-order texture (SOT) features, and both feature classes combined. Analyses were performed for both imaging modalities separately and combined. Performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumor response and FFDM, respectively. Overall, radiomic features showed prognostic value for both endpoints. Combining MFO and SOT features led to equal or higher performance in external validation compared to MFO and SOT features alone. The best results were observed after combining MRI and CT features (AUC = 0.76, CI = 0.65). In conclusion, promising biomarker signatures combining MRI and CT were developed for outcome prognosis in LARC. Further external validation is pending before potential clinical application.
E. G. C. Troost and S. Löck—Shared last authorship.
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References
Dossa, F., Chesney, T.R., Acuna, S.A., Baxter, N.N.: A watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis. Lancet Gastroenterol. Hepatol. 2(7), 501–513 (2017)
Das, P., Skibber, J.M., Rodriguez-Bigas, M.A., Feig, B.W., Chang, G.J., Wolff, R.A., et al.: Predictors of tumor response and downstaging in patients who receive preoperative chemoradiation for rectal cancer. Cancer 109(9), 1750–1755 (2007)
Ryan, J.E., Warrier, S.K., Lynch, A.C., Ramsay, R.G., Phillips, W.A., Heriot, A.G.: Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Dis. 18(3), 234–246 (2016)
Ojima, E., Inoue, Y., Miki, C., Mori, M., Kusunoki, M.: Effectiveness of gene expression profiling for response prediction of rectal cancer to preoperative radiotherapy. J. Gastroenterol. 42(9), 730–736 (2007)
Watanabe, T., Komuro, Y., Kiyomatsu, T., Kanazawa, T., Kazama, Y., Tanaka, J., et al.: Prediction of sensitivity of rectal cancer cells in response to preoperative radiotherapy by DNA microarray analysis of gene expression profiles. Can. Res. 66(7), 3370–3374 (2006)
Parmar, C., Grossmann, P., Bussink, J., Lambin, P., Aerts, H.J.: Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 5(1), 1–11 (2015)
Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563–577 (2016)
Song, J., Yin, Y., Wang, H., Chang, Z., Liu, Z., Cui, L.: A review of original articles published in the emerging field of radiomics. Eur. J. Radiol. 127, 108991 (2020)
Antunes, J.T., Ofshteyn, A., Bera, K., Wang, E.Y., Brady, J.T., et al.: Radiomic features of primary rectal cancers on baseline T2-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: a multisite study. J. Magn. Reson. Imaging 52(5), 1531–1541 (2020)
Dinapoli, N., et al.: Magnetic resonance, vendor-independent, intensity histogram analysis predicting pathologic complete response after radiochemotherapy of rectal cancer. Int. J. Radiat. Oncol. Biol. Phys. 102(4), 765–774 (2018)
Yi, X., Pei, Q., Zhang, Y., Zhu, H., Wang, Z., Chen, C., et al.: MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front. Oncol. 9, 552 (2019)
Horvat, N., Veeraraghavan, H., Khan, M., Blazic, I., Zheng, J., Capanu, M., et al.: MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287(3), 833–843 (2018)
Nie, K., Shi, L., Chen, Q., Hu, X., Jabbour, S.K., Yue, N., et al.: Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin. Cancer Res. 22(21), 5256–5264 (2016)
Jeon, S.H., Song, C., Chie, E.K., Kim, B., Kim, Y.H., Chang, W., et al.: Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat. Oncol. 14(1), 1–10 (2019)
Chee, C.G., Kim, Y.H., Lee, K.H., Lee, Y.J., Park, J.H., Lee, H.S., et al.: CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS ONE 12(8), e0182883 (2017)
Bibault, J.E., Giraud, P., Housset, M., Durdux, C., Taieb, J., Berger, A., et al.: Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci. Rep. 8(1), 1–8 (2018)
Li, Z.Y., Wang, X.D., Li, M., Liu, X.J., Ye, Z., Song, B., et al.: Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J. Gastroenterol. 26(19), 2388 (2020)
Zhang, Y., He, K., Guo, Y., Liu, X., Yang, Q., Zhang, C., et al.: A novel multimodal radiomics model for preoperative prediction of lymphovascular invasion in rectal cancer. Front. Oncol. 10, 457 (2020)
Zwanenburg, A., Vallières, M., Abdalah, M.A., Aerts, H.J., Andrearczyk, V., et al.: The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2), 328–338 (2020)
Dworak, O., Keilholz, L., Hoffmann, A.: Pathological features of rectal cancer after preoperative radiochemotherapy. Int. J. Colorectal Dis. 12(1), 19–23 (1997)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Zwanenburg, A., Leger, S., Starke, S.: GitHub-oncoray/mirp: medical image radiomics processor. https://github.com/oncoray/mirp. Accessed January 1 2021
Zwanenburg, A., et al.: Assessing robustness of radiomic features by image perturbation. Sci. Rep. 9(1), 1–10 (2020)
Schoenfeld, D.: Partial residuals for the proportional hazards regression model. Biometrika 69(1), 239–241 (1982)
De Cecco, C.N., et al.: Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom. Radiol. 41(9), 1728–1735 (2016)
Meng, Y., et al.: MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget 9(15), 11999 (2018)
Aker, M., Ganeshan, B., Afaq, A., Wan, S., Groves, A.M., Arulampalam, T.: Magnetic resonance texture analysis in identifying complete pathological response to neoadjuvant treatment in locally advanced rectal cancer. Dis. Colon Rectum 62(2), 163–170 (2019)
Liu, Z., Zhang, X.Y., Shi, Y.J., Wang, L., Zhu, H.T., Tang, Z., et al.: Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin. Cancer Res. 23(23), 7253–7262 (2017)
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Shahzadi, I. et al. (2021). Do We Need Complex Image Features to Personalize Treatment of Patients with Locally Advanced Rectal Cancer?. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_73
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