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2020 | OriginalPaper | Buchkapitel

Radiomics: A New Biomedical Workflow to Create a Predictive Model

verfasst von : Albert Comelli, Alessandro Stefano, Claudia Coronnello, Giorgio Russo, Federica Vernuccio, Roberto Cannella, Giuseppe Salvaggio, Roberto Lagalla, Stefano Barone

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

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Abstract

‘Radiomics’ is utilized to improve the prediction of patient overall survival and/or outcome. Target segmentation, feature extraction, feature selection, and classification model are the fundamental blocks of a radiomics workflow. Nevertheless, these blocks can be affected by several issues, i.e. high inter- and intra-observer variability. To overcome these issues obtaining reproducible results, we propose a novel radiomics workflow to identify a relevant prognostic model concerning a real clinical problem. In the specific, we propose an operator-independent segmentation system with the consequent automatic extraction of radiomics features, and a novel feature selection approach to create a relevant predictive model in 46 patients with prostate lesion underwent magnetic resonance imaging.
In the specific, using an operator-independent method of target segmentation based on an active contour, ad-hoc automated high-throughput analysis tool capable of calculating a total of 290 radiomics features for each imaging sequence, a novel statistical system for feature reduction and selection, and the discriminant analysis as a method for feature classification, we propose a performant and replicable radiomics workflow for the diagnosis of prostate cancer.
The proposed workflow revealed three and five relevant features on T2-weighted and apparent diffusion coefficient (ADC) maps images, respectively, that were significantly correlated with the histopathological results. In the specific, good performance in lesion discrimination was obtained using the combination of the selected features (accuracy 76.76% and 75.20%, for T2-weighted and ADC maps images, respectively) in an operator-independent and automatic way.

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Literatur
1.
Zurück zum Zitat Zhang, Z., Sejdić, E.: Radiological images and machine learning: trends, perspectives, and prospects. Comput. Biol. Med. 108, 354–370 (2019)CrossRef Zhang, Z., Sejdić, E.: Radiological images and machine learning: trends, perspectives, and prospects. Comput. Biol. Med. 108, 354–370 (2019)CrossRef
2.
Zurück zum Zitat Hatt, M., Tixier, F., Visvikis, D., Cheze Le Rest, C.: Radiomics in PET/CT: more than meets the eye? J. Nucl. Med. 58, 365–366 (2017) Hatt, M., Tixier, F., Visvikis, D., Cheze Le Rest, C.: Radiomics in PET/CT: more than meets the eye? J. Nucl. Med. 58, 365–366 (2017)
3.
Zurück zum Zitat Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J.D., Snyder, A., et al.: Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin. Radiol. 72, 3–10 (2017)CrossRef Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J.D., Snyder, A., et al.: Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin. Radiol. 72, 3–10 (2017)CrossRef
4.
Zurück zum Zitat Negrini, S., Gorgoulis, V.G., Halazonetis, T.D.: Genomic instability–an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11, 220–228 (2010)CrossRef Negrini, S., Gorgoulis, V.G., Halazonetis, T.D.: Genomic instability–an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11, 220–228 (2010)CrossRef
5.
Zurück zum Zitat Gerlinger, M., Swanton, C.: How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br. J. Cancer 103, 1139–1143 (2010)CrossRef Gerlinger, M., Swanton, C.: How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br. J. Cancer 103, 1139–1143 (2010)CrossRef
8.
Zurück zum Zitat Ugga, L., et al.: Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 61(12), 1365–1373 (2019) Ugga, L., et al.: Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 61(12), 1365–1373 (2019)
9.
Zurück zum Zitat Stefano, A., et al.: A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinform. (2020, in press) Stefano, A., et al.: A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinform. (2020, in press)
10.
Zurück zum Zitat Cuocolo, R., et al.: Prostate MRI technical parameters standardization: a systematic review on adherence to PI-RADSv2 acquisition protocol. Eur. J. Radiol. (2019) Cuocolo, R., et al.: Prostate MRI technical parameters standardization: a systematic review on adherence to PI-RADSv2 acquisition protocol. Eur. J. Radiol. (2019)
11.
Zurück zum Zitat Nioche, C., et al.: Lifex: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 78, 4786–4789 (2018)CrossRef Nioche, C., et al.: Lifex: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 78, 4786–4789 (2018)CrossRef
12.
Zurück zum Zitat Szczypiński, P.M., Strzelecki, M., Materka, A., Klepaczko, A.: MaZda-A software package for image texture analysis. Comput. Methods Programs Biomed. 94, 66–76 (2009)CrossRef Szczypiński, P.M., Strzelecki, M., Materka, A., Klepaczko, A.: MaZda-A software package for image texture analysis. Comput. Methods Programs Biomed. 94, 66–76 (2009)CrossRef
13.
Zurück zum Zitat Fang, Y.H.D., et al.: Development and evaluation of an open-source software package “cGITA” for quantifying tumor heterogeneity with molecular images. Biomed. Res. Int. (2014) Fang, Y.H.D., et al.: Development and evaluation of an open-source software package “cGITA” for quantifying tumor heterogeneity with molecular images. Biomed. Res. Int. (2014)
14.
Zurück zum Zitat Comelli, A., Agnello, L., Vitabile, S.: An ontology-based retrieval system for mammographic reports. In: Proceedings - IEEE Symposium on Computers and Communications (2016) Comelli, A., Agnello, L., Vitabile, S.: An ontology-based retrieval system for mammographic reports. In: Proceedings - IEEE Symposium on Computers and Communications (2016)
15.
Zurück zum Zitat Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. (2011) Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. (2011)
16.
Zurück zum Zitat Cuocolo, R., et al.: Machine learning applications in prostate cancer magnetic resonance imaging. Eur. Radiol. Exp. 3, 35 (2019)CrossRef Cuocolo, R., et al.: Machine learning applications in prostate cancer magnetic resonance imaging. Eur. Radiol. Exp. 3, 35 (2019)CrossRef
17.
Zurück zum Zitat Rizzo, S., et al.: Radiomics: the facts and the challenges of image analysis. Eur. Radiol. Exp. (2018) Rizzo, S., et al.: Radiomics: the facts and the challenges of image analysis. Eur. Radiol. Exp. (2018)
19.
Zurück zum Zitat Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. (2014) Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. (2014)
20.
Zurück zum Zitat Chandra, S.S., et al.: Patient specific prostate segmentation in 3-D magnetic resonance images. IEEE Trans. Med. Imaging (2012) Chandra, S.S., et al.: Patient specific prostate segmentation in 3-D magnetic resonance images. IEEE Trans. Med. Imaging (2012)
21.
Zurück zum Zitat Tsai, A., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging (2003) Tsai, A., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging (2003)
22.
Zurück zum Zitat Wang, B., et al.: Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med. Phys. (2019) Wang, B., et al.: Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med. Phys. (2019)
23.
Zurück zum Zitat Korsager, A.S., et al.: The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. Med. Phys. (2015) Korsager, A.S., et al.: The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. Med. Phys. (2015)
24.
Zurück zum Zitat Tian, Z., Liu, L., Fei, B.: A fully automatic multi-atlas based segmentation method for prostate MR images. In: Medical Imaging 2015: Image Processing (2015) Tian, Z., Liu, L., Fei, B.: A fully automatic multi-atlas based segmentation method for prostate MR images. In: Medical Imaging 2015: Image Processing (2015)
25.
Zurück zum Zitat Guo, Y., Gao, Y., Shao, Y., Price, T., Oto, A., Shen, D.: Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning. Med. Phys. (2014) Guo, Y., Gao, Y., Shao, Y., Price, T., Oto, A., Shen, D.: Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning. Med. Phys. (2014)
26.
Zurück zum Zitat Toth, R., Madabhushi, A.: Multifeature landmark-free active appearance models: application to prostate MRI segmentation. IEEE Trans. Med. Imaging (2012) Toth, R., Madabhushi, A.: Multifeature landmark-free active appearance models: application to prostate MRI segmentation. IEEE Trans. Med. Imaging (2012)
27.
Zurück zum Zitat Yang, X., et al.: 3D prostate segmentation in MR image using 3D deeply supervised convolutional neural networks. Med. Phys. (2018) Yang, X., et al.: 3D prostate segmentation in MR image using 3D deeply supervised convolutional neural networks. Med. Phys. (2018)
28.
Zurück zum Zitat Jia, H., Xia, Y., Song, Y., Cai, W., Fulham, M., Feng, D.D.: Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging. Neurocomputing (2018) Jia, H., Xia, Y., Song, Y., Cai, W., Fulham, M., Feng, D.D.: Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging. Neurocomputing (2018)
31.
Zurück zum Zitat Lankton, S., Nain, D., Yezzi, A., Tannenbaum, A.: Hybrid geodesic region-based curve evolutions for image segmentation. In: Hsieh, J., Flynn, M.J. (eds.) Medical Imaging 2007: Physics of Medical Imaging. International Society for Optics and Photonics, p. 65104U (2007). https://doi.org/10.1117/12.709700 Lankton, S., Nain, D., Yezzi, A., Tannenbaum, A.: Hybrid geodesic region-based curve evolutions for image segmentation. In: Hsieh, J., Flynn, M.J. (eds.) Medical Imaging 2007: Physics of Medical Imaging. International Society for Optics and Photonics, p. 65104U (2007). https://​doi.​org/​10.​1117/​12.​709700
32.
Zurück zum Zitat Cohen, J., Cohen, P., West, S., Aiken, L.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale (1983) Cohen, J., Cohen, P., West, S., Aiken, L.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale (1983)
35.
Zurück zum Zitat Armand, S., Watelain, E., Roux, E., Mercier, M., Lepoutre, F.X.: Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture. 25, 475–484 (2007)CrossRef Armand, S., Watelain, E., Roux, E., Mercier, M., Lepoutre, F.X.: Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture. 25, 475–484 (2007)CrossRef
Metadaten
Titel
Radiomics: A New Biomedical Workflow to Create a Predictive Model
verfasst von
Albert Comelli
Alessandro Stefano
Claudia Coronnello
Giorgio Russo
Federica Vernuccio
Roberto Cannella
Giuseppe Salvaggio
Roberto Lagalla
Stefano Barone
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-52791-4_22

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