Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. Patient Data
2.2. Single Modality
- Selected T1w-MRI and T2w-MRI features (supplementary materials, Figures S1 and S2) belonged to the groups of first order, textural and shape features.
- Selected CT features (supplementary materials, Figure S3) described various image properties, such as the distribution of low and high HU values (first order 10th percentile and GLRLM High Gray Level Run Emphasis, HGLRE), or their variability (e.g., first order robust Mean Absolute Deviation, rMAD). Additionally, regional (e.g., GLSZM Large Area Emphasis) and volume-confounded descriptors (e.g., first order Energy) were selected.
- Selected dosiomic features (Figure 1, supplementary materials, Figure S4) mostly described heterogeneity at different spatial scales (GLRLM run entropy, RE; GLCM Joint Energy, JEg; GLCM Joint Entropy, JEp; GLCM sum entropy; first-order entropy) and shape properties (elongation, flatness).
2.3. Combined Modalities
2.4. Survival Analysis
3. Discussion
3.1. Technical Evaluation
3.2. Single Modality
3.3. Combined Modalities
3.4. Validity and Limitations of the Proposed Work
4. Materials and Methods
4.1. Patient Data and Clinical Features
4.2. Data Preparation
4.3. Feature Extraction and Selection
4.4. Survival Models
4.5. Experiments
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Durante, M.; Orecchia, R.; Loeffler, J.S. Charged-particle therapy in cancer: Clinical uses and future perspectives. Nat. Rev. Clin. Oncol. 2017, 14, 483–495. [Google Scholar] [CrossRef] [PubMed]
- Schardt, D.; Elsässer, T.; Schulz-Ertner, D. Heavy-ion tumor therapy: Physical and radiobiological benefits. Rev. Mod. Phys. 2010, 82, 383–425. [Google Scholar] [CrossRef]
- Frezza, A.M.; Botta, L.; Trama, A.; Dei Tos, A.P.; Stacchiotti, S. Chordoma: Update on disease, epidemiology, biology and medical therapies. Curr. Opin. Oncol. 2019, 31, 114–120. [Google Scholar] [CrossRef] [PubMed]
- Mizoe, J. Review of carbon ion radiotherapy for skull base tumors (especially chordomas). Reports Pract. Oncol. Radiother. 2016, 21, 356–360. [Google Scholar] [CrossRef] [Green Version]
- Stacchiotti, S.; Gronchi, A.; Fossati, P.; Akiyama, T.; Alapetite, C.; Baumann, M.; Blay, J.Y.; Bolle, S.; Boriani, S.; Bruzzi, P.; et al. Best practices for the management of local-regional recurrent chordoma: A position paper by the Chordoma Global Consensus Group. Ann. Oncol. 2017, 28, 1230–1242. [Google Scholar] [CrossRef]
- Zhou, J.; Yang, B.; Wang, X.; Jing, Z. Comparison of the Effectiveness of Radiotherapy with Photons and Particles for Chordoma After Surgery: A Meta-Analysis. World Neurosurg. 2018, 117, 46–53. [Google Scholar] [CrossRef]
- Uhl, M.; Mattke, M.; Welzel, T.; Roeder, F.; Oelmann, J.; Habl, G.; Jensen, A.; Ellerbrock, M.; Jäkel, O.; Haberer, T.; et al. Highly effective treatment of skull base chordoma with carbon ion irradiation using a raster scan technique in 155 patients: First long-term results. Cancer 2014, 120, 3410–3417. [Google Scholar] [CrossRef]
- Zou, M.-X.; Lv, G.-H.; Zhang, Q.-S.; Wang, S.-F.; Li, J.; Wang, X.-B. Prognostic Factors in Skull Base Chordoma: A Systematic Literature Review and Meta-Analysis. World Neurosurg. 2018, 109, 307–327. [Google Scholar] [CrossRef]
- Bai, J.; Shi, J.; Zhang, S.; Zhang, C.; Zhai, Y.; Wang, S.; Li, M.; Li, C.; Zhao, P.; Geng, S.; et al. MRI signal intensity and electron ultrastructure classification predict the long-term outcome of skull base chordomas. Am. J. Neuroradiol. 2020, 41, 852–858. [Google Scholar] [CrossRef]
- Tian, K.; Wang, L.; Ma, J.; Wang, K.; Li, D.; Du, J.; Jia, G.; Wu, Z.; Zhang, J. MR Imaging Grading System for Skull Base Chordoma. Am. J. Neuroradiol. 2017, 38, 1206–1211. [Google Scholar] [CrossRef] [Green Version]
- Santegoeds, R.G.C.; Temel, Y.; Beckervordersandforth, J.C.; Van Overbeeke, J.J.; Hoeberigs, C.M. State-of-the-Art Imaging in Human Chordoma of the Skull Base. Curr. Radiol. Rep. 2018, 6, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lambin, P.; Leijenaar, R.T.H.; Deist, T.M.; Peerlings, J.; de Jong, E.E.C.; van Timmeren, J.; Sanduleanu, S.; Larue, R.T.H.M.; Even, A.J.G.; Jochems, A.; et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749–762. [Google Scholar] [CrossRef] [PubMed]
- Gillies, R.J.; Kinahan, P.E.; Hricak, H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016, 278, 563–577. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Limkin, E.J.; Sun, R.; Dercle, L.; Zacharaki, E.I.; Robert, C.; Reuzé, S.; Schernberg, A.; Paragios, N.; Deutsch, E.; Ferté, C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann. Oncol. 2017, 28, 1191–1206. [Google Scholar] [CrossRef] [PubMed]
- Aerts, H.J.W.L.; Velazquez, E.R.; Leijenaar, R.T.H.; Parmar, C.; Grossmann, P.; Carvalho, S.; Bussink, J.; Monshouwer, R.; Haibe-Kains, B.; Rietveld, D.; et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014, 5, 4006. [Google Scholar] [CrossRef] [PubMed]
- Parr, E.; Du, Q.; Zhang, C.; Lin, C.; Kamal, A.; McAlister, J.; Liang, X.; Bavitz, K.; Rux, G.; Hollingsworth, M.; et al. Radiomics-based outcome prediction for pancreatic cancer following stereotactic body radiotherapy. Cancers 2020, 12, 1051. [Google Scholar] [CrossRef]
- Cook, G.J.R.; Siddique, M.; Taylor, B.P.; Yip, C.; Chicklore, S.; Goh, V. Radiomics in PET: Principles and applications. Clin. Transl. Imag. 2014, 2, 269–276. [Google Scholar] [CrossRef] [Green Version]
- Astaraki, M.; Wang, C.; Buizza, G.; Toma-Dasu, I.; Lazzeroni, M.; Smedby, Ö. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys. Med. 2019, 60, 58–65. [Google Scholar] [CrossRef]
- Traverso, A.; Wee, L.; Dekker, A.; Gillies, R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int. J. Radiat. Oncol. 2018, 102, 1143–1158. [Google Scholar] [CrossRef] [Green Version]
- Lohmann, P.; Galldiks, N.; Kocher, M.; Heinzel, A.; Filss, C.P.; Stegmayr, C.; Mottaghy, F.M.; Fink, G.R.; Jon Shah, N.; Langen, K.-J. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods 2020. [Google Scholar] [CrossRef]
- Zhou, H.; Vallières, M.; Bai, H.X.; Su, C.; Tang, H.; Oldridge, D.; Zhang, Z.; Xiao, B.; Liao, W.; Tao, Y.; et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro. Oncol. 2017, 19, 862–870. [Google Scholar] [CrossRef] [PubMed]
- Zhou, M.; Chaudhury, B.; Hall, L.O.; Goldgof, D.B.; Gillies, R.J.; Gatenby, R.A. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J. Magn. Reson. Imaging 2017, 46, 115–123. [Google Scholar] [CrossRef] [PubMed]
- Rossi, L.; Bijman, R.; Schillemans, W.; Aluwini, S.; Cavedon, C.; Witte, M.; Incrocci, L.; Heijmen, B. Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy. Radiother. Oncol. 2018, 129, 548–553. [Google Scholar] [CrossRef] [PubMed]
- Liang, B.; Yan, H.; Tian, Y.; Chen, X.; Yan, L.; Zhang, T.; Zhou, Z.; Wang, L.; Dai, J. Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis. Front. Oncol. 2019, 9, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Lee, S.H.; Han, P.; Hales, R.K.; Voong, K.R.; Noro, K.; Sugiyama, S.; Haller, J.W.; McNutt, T.R.; Lee, J. Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy. Phys. Med. Biol. 2020, 65, 195015. [Google Scholar] [CrossRef]
- Kalasauskas, D.; Kronfeld, A.; Renovanz, M.; Kurz, E.; Leukel, P.; Krenzlin, H.; Brockmann, M.A.; Sommer, C.J.; Ringel, F.; Keric, N. Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features. Cancers 2020, 12, 2942. [Google Scholar] [CrossRef]
- Li, L.; Wang, K.; Ma, X.; Liu, Z.; Wang, S.; Du, J.; Tian, K.; Zhou, X.; Wei, W.; Sun, K.; et al. Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. Eur. J. Radiol. 2019, 118, 81–87. [Google Scholar] [CrossRef]
- Wei, W.; Wang, K.; Liu, Z.; Tian, K.; Wang, L.; Du, J.; Ma, J.; Wang, S.; Li, L.; Zhao, R.; et al. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiother. Oncol. 2019, 141, 239–246. [Google Scholar] [CrossRef]
- Funaki, T.; Matsushima, T.; Peris-Celda, M.; Valentine, R.J.; Joo, W.; Rhoton, A.L. Focal Transnasal Approach to the Upper, Middle, and Lower Clivus. Oper. Neurosurg. 2013, 73, ons155–ons191. [Google Scholar] [CrossRef]
- Chatterjee, A.; Vallieres, M.; Dohan, A.; Levesque, I.R.; Ueno, Y.; Bist, V.; Saif, S.; Reinhold, C.; Seuntjens, J. An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets. IEEE Trans. Radiat. Plasma Med. Sci. 2019, 3, 201–209. [Google Scholar] [CrossRef]
- Leger, S.; Zwanenburg, A.; Pilz, K.; Lohaus, F.; Linge, A.; Zöphel, K.; Kotzerke, J.; Schreiber, A.; Tinhofer, I.; Budach, V.; et al. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Sci. Rep. 2017, 7, 13206. [Google Scholar] [CrossRef] [PubMed]
- Bologna, M.; Corino, V.; Mainardi, L. Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med. Phys. 2019, 46, 5116–5123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Molina, D.; Pérez-Beteta, J.; Martínez-González, A.; Martino, J.; Velasquez, C.; Arana, E.; Pérez-García, V.M. Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization. PLoS ONE 2017, 12, e0178843. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Buizza, G.; Molinelli, S.; D’Ippolito, E.; Fontana, G.; Anemoni, L.; Preda, L.; Baroni, G.; Valvo, F.; Paganelli, C. PV-0311 MRI-based tumour control probability model in particle therapy. Radiother. Oncol. 2019, 133, S159–S160. [Google Scholar] [CrossRef]
- Kurz, C.; Buizza, G.; Landry, G.; Kamp, F.; Rabe, M.; Paganelli, C.; Baroni, G.; Reiner, M.; Keall, P.J.; van den Berg, C.A.T.; et al. Medical physics challenges in clinical MR-guided radiotherapy. Radiat. Oncol. 2020, 15, 93. [Google Scholar] [CrossRef] [PubMed]
- Iannalfi, A.; D’Ippolito, E.; Riva, G.; Molinelli, S.; Gandini, S.; Viselner, G.; Fiore, M.R.; Vischioni, B.; Vitolo, V.; Bonora, M.; et al. Proton and carbon ion radiotherapy in skull base chordomas: A prospective study based on a dual particle and a patient-customized treatment strategy. Neuro. Oncol. 2020, 1–11. [Google Scholar] [CrossRef]
- Fossati, P.; Matsufuji, N.; Kamada, T.; Karger, C.P. Radiobiological issues in prospective carbon ion therapy trials. Med. Phys. 2018, 45, e1096–e1110. [Google Scholar] [CrossRef] [Green Version]
- Molinelli, S.; Magro, G.; Mairani, A.; Matsufuji, N.; Kanematsu, N.; Inaniwa, T.; Mirandola, A.; Russo, S.; Mastella, E.; Hasegawa, A.; et al. Dose prescription in carbon ion radiotherapy: How to compare two different RBE-weighted dose calculation systems. Radiother. Oncol. 2016, 120, 307–312. [Google Scholar] [CrossRef]
- Dale, J.E.; Molinelli, S.; Vitolo, V.; Vischioni, B.; Bonora, M.; Magro, G.; Pettersen, H.E.S.; Mairani, A.; Hasegawa, A.; Dahl, O.; et al. Optic nerve constraints for carbon ion RT at CNAO—Reporting and relating outcome to European and Japanese RBE. Radiother. Oncol. 2019, 140, 175–181. [Google Scholar] [CrossRef]
- Zwanenburg, A.; Löck, S. Why validation of prognostic models matters? Radiother. Oncol. 2018, 127, 370–373. [Google Scholar] [CrossRef]
- Da-ano, R.; Masson, I.; Lucia, F.; Doré, M.; Robin, P.; Alfieri, J.; Rousseau, C.; Mervoyer, A.; Reinhold, C.; Castelli, J.; et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci. Rep. 2020, 10, 10248. [Google Scholar] [CrossRef] [PubMed]
- Garau, N.; Paganelli, C.; Summers, P.; Choi, W.; Alam, S.; Lu, W.; Fanciullo, C.; Bellomi, M.; Baroni, G.; Rampinelli, C. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis. Med. Phys. 2020, 47, 4125–4136. [Google Scholar] [CrossRef] [PubMed]
- Welch, M.L.; McIntosh, C.; Haibe-Kains, B.; Milosevic, M.F.; Wee, L.; Dekker, A.; Huang, S.H.; Purdie, T.G.; O’Sullivan, B.; Aerts, H.J.W.L.; et al. Vulnerabilities of radiomic signature development: The need for safeguards. Radiother. Oncol. 2019, 130, 2–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- O’Connor, J.P.B.; Aboagye, E.O.; Adams, J.E.; Aerts, H.J.W.L.; Barrington, S.F.; Beer, A.J.; Boellaard, R.; Bohndiek, S.E.; Brady, M.; Brown, G.; et al. Imaging biomarker roadmap for cancer studies. Nat. Rev. Clin. Oncol. 2017, 14, 169–186. [Google Scholar] [CrossRef] [PubMed]
- Kramer, M.; Scholz, M. Treatment planning for heavy-ion radiotherapy: Calculation and optimization of biologically effective dose. Phys. Med. Biol. 2000, 45, 3319–3330. [Google Scholar] [CrossRef]
- Tustison, N.J.; Avants, B.B.; Cook, P.A.; Zheng, Y.; Egan, A.; Yushkevich, P.A.; Gee, J.C. N4ITK: Improved N3 Bias Correction. IEEE Trans. Med. Imag. 2010, 29, 1310–1320. [Google Scholar] [CrossRef] [Green Version]
- Reinhold, J.C.; Dewey, B.E.; Carass, A.; Prince, J.L. Evaluating the impact of intensity normalization on MR image synthesis. In Proceedings of the Medical Imaging 2019: Image Processing; 2019; Volume 10949, p. 109493H. [Google Scholar]
- Shah, M.; Xiao, Y.; Subbanna, N.; Francis, S.; Arnold, D.L.; Collins, D.L.; Arbel, T. Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Med. Image Anal. 2011, 15, 267–282. [Google Scholar] [CrossRef]
- van Griethuysen, J.J.M.; Fedorov, A.; Parmar, C.; Hosny, A.; Aucoin, N.; Narayan, V.; Beets-Tan, R.G.H.; Fillion-Robin, J.-C.; Pieper, S.; Aerts, H.J.W.L. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017, 77, e104–e107. [Google Scholar] [CrossRef] [Green Version]
- Zwanenburg, A.; Leger, S.; Vallières, M.; Löck, S. Image biomarker standardisation initiative. arXiv 2016. Available online: https://arxiv.org/abs/1612.07003v11 (accessed on 18 January 2021).
- Larue, R.T.H.M.; Defraene, G.; De Ruysscher, D.; Lambin, P.; van Elmpt, W. Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures. Br. J. Radiol. 2017, 90, 20160665. [Google Scholar] [CrossRef]
- Parmar, C.; Grossmann, P.; Bussink, J.; Lambin, P.; Aerts, H.J.W.L. Machine Learning methods for Quantitative Radiomic Biomarkers. Sci. Rep. 2015, 5, 13087. [Google Scholar] [CrossRef]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Pölsterl, S.; Navab, N.; Katouzian, A. Fast Training of Support Vector Machines for Survival Analysis. In Proceedings of the Machine Lerning and Knowledge Discovery in Databases: European Conference, ECML PKDD, Porto, Portugal, 7–11 September 2015; Springer: Cham, Switzerland, 2015; pp. 243–259, ISBN 978-3-319-23525-7. [Google Scholar]
- Simon, N.; Friedman, J.; Hastie, T.; Tibshirani, R. Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent. J. Stat. Softw. 2011, 39, 1–13. [Google Scholar] [CrossRef] [PubMed]
- CamDavidsonPilon/lifelines: v0.23.0. Available online: https://doi.org/10.5281/zenodo.3544808 (accessed on 18 January 2021).
- Harrell, F.E.; Lee, K.L.; Mark, D.B. Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors. In Tutorials in Biostatistics; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 1996; Volume 15, pp. 361–387. [Google Scholar]
Continuous Variables | Median (Range) | |
---|---|---|
Age (Years) | 58 (17–81) | |
GTV (cm3) | 14.48 (0.39–194.70) | |
Categorical Variables | Occurrence | |
Gender | Female | 22 |
Male | 35 | |
Histology | Conventional | 47 |
Chondroid | 4 | |
Dedifferentiated | 1 | |
n.a. | 5 | |
Anatomical location | 1 | 6 |
2 | 4 | |
3 | 2 | |
1+2 | 27 | |
2+3 | 6 | |
1+2+3 | 11 | |
n.a. | 1 | |
Brainstem involvement | Yes | 14 |
No | 42 | |
n.a. | 1 | |
Optic pathway involvement | Yes | 10 |
No | 46 | |
n.a. | 1 | |
Outcome | Occurrence | |
LC | Favorable (censored) | 40 |
Adverse (adverse event) | 17 |
Model | Feature Selection Routine | T1w-MRI | T2w-MRI | CT | Dose | ComboAll | Clinical |
---|---|---|---|---|---|---|---|
s-SVM | Routine n. 1 | 0.58/0.17 | 0.50/0.22 | 0.61/0.24 | 0.73/0.19 | 0.69/0.27 | |
Routine n. 2 | 0.58/0.17 | 0.45/0.24 | 0.62/0.19 | 0.74/0.25 | 0.60/0.20 | ||
Routine n. 3 | 0.36/0.21 | 0.60/0.27 | 0.77/0.24 ^ | 0.73/0.22 | 0.69/0.33 | ||
Routine n. 4 | 0.36/0.21 | 0.64/0.33 | 0.63/0.24 | 0.77/0.21 | 0.69/0.33 | ||
Routine n. 5 | 0.60/0.24 ^ | 0.60/0.25 | 0.58/0.27 | 0.67/0.20 | 0.70/0.24 | ||
Routine n. 6 | 0.42/0.22 | 0.67/0.23 ^ | 0.68/0.27 | 0.80/0.24 ^ | 0.46/0.21 | ||
Routine n. 7 | 0.54/0.24 | 0.63/0.22 | 0.50/0.24 | 0.74/0.23 | 0.58/0.25 | ||
Routine n. 8 | 0.56/0.23 | 0.41/0.18 | 0.54/0.27 | 0.23/0.24 | 0.54/0.25 | ||
Routine n. 9 | 0.40/0.18 | 0.47/0.19 | 0.55/0.31 | 0.62/0.30 | 0.73/0.30 ^ | ||
Routine n. 10 | 0.42/0.30 | 0.41/0.30 | 0.60/0.35 | 0.64/0.30 | 0.55/0.15 | ||
None | 0.69/0.23 | ||||||
r-Cox | Routine n. 1 | 0.60/0.18 | 0.60/0.27 | 0.62/0.35 | 0.62/0.22 | 0.63/0.33 | |
Routine n. 2 | 0.60/0.18 | 0.57/0.27 | 0.62/0.35 | 0.59/0.20 | 0.62/0.30 | ||
Routine n. 3 | 0.62/0.28 | 0.43/0.23 | 0.64/0.28 | 0.74/0.20 | 0.69/0.30 | ||
Routine n. 4 | 0.62/0.28 | 0.57/0.27 | 0.64/0.28 ^ | 0.69/0.24 | 0.69/0.30 | ||
Routine n. 5 | 0.64/0.20 | 0.57/0.32 | 0.54/0.20 | 0.72/0.27 | 0.68/0.33 | ||
Routine n. 6 | 0.53/0.38 | 0.50/0.19 | 0.54/0.18 | 0.79/0.26 ^ | 0.75/0.28 ^ | ||
Routine n. 7 | 0.65/0.21 | 0.50/0.24 | 0.48/0.25 | 0.73/0.25 | 0.57/0.32 | ||
Routine n. 8 | 0.65/0.21 ^ | 0.60/0.30 | 0.54/0.30 | 0.73/0.25 | 0.57/0.62 | ||
Routine n. 9 | 0.40/0.29 | 0.63/0.27 ^ | 0.53/0.19 | 0.65/0.22 | 0.75/0.27 ^ | ||
Routine n. 10 | 0.56/0.37 | 0.59/0.26 | 0.53/0.24 | 0.67/0.24 | 0.75/0.27 | ||
None | 0.64/0.26 |
Model | T1w-MRI | T2w-MRI | CT | Dose | ComboAll | Clinical |
---|---|---|---|---|---|---|
s-SVM | 0.273 | 0.176 * | 0.176 * | 0.002 ** | 0.067 | 0.101 * |
r-Cox | 0.361 | 0.067 | 0.213 | 0.101 | 0.101 | 0.213 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Buizza, G.; Paganelli, C.; D’Ippolito, E.; Fontana, G.; Molinelli, S.; Preda, L.; Riva, G.; Iannalfi, A.; Valvo, F.; Orlandi, E.; et al. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers 2021, 13, 339. https://doi.org/10.3390/cancers13020339
Buizza G, Paganelli C, D’Ippolito E, Fontana G, Molinelli S, Preda L, Riva G, Iannalfi A, Valvo F, Orlandi E, et al. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers. 2021; 13(2):339. https://doi.org/10.3390/cancers13020339
Chicago/Turabian StyleBuizza, Giulia, Chiara Paganelli, Emma D’Ippolito, Giulia Fontana, Silvia Molinelli, Lorenzo Preda, Giulia Riva, Alberto Iannalfi, Francesca Valvo, Ester Orlandi, and et al. 2021. "Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma" Cancers 13, no. 2: 339. https://doi.org/10.3390/cancers13020339