Abstract
Pre-surgical and pre-treatment planning is a crucial step toward maximizing the patient’s chances of a successful outcome. The rational use of advanced imaging techniques will help the accurate targeting of the neurological pathology and minimize potential collateral damage to surrounding normal brain parenchyma. This chapter aims to summarize our current understanding of available techniques and help the clinician use them rationally.
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Keywords
Imaging Techniques Used in Pre-surgical and Preprocedural Planning in Neuro-Oncology
There are several imaging techniques with different strengths and weaknesses that can be used for preprocedural planning. These techniques are summarized in Table 49.1 and further described in the subsequent sections.
Computed Tomography (CT)
CT scanners are widely available and are often used as the initial imaging technique to diagnose and plan neuro-oncological patients’ treatment. CT imaging is a fast technique that provides essential anatomic information about skin and osseous landmarks and is often combined with the intracranial soft tissue information provided by MRI. CT angiography is used to visualize the arterial and/or venous system, providing crucial information for planning purposes. CT images can also be combined with PET for treatment planning purposes, particularly for systemic malignancies. CT myelography has a role in the accurate demarcation of the intradural contents and dural sac for some pathologies (e.g., chordoma) or patients with contraindications for MRI.
Conventional MRI Techniques
Magnetic resonance imaging uses different body tissues’ behavior while exposed to magnetic fields to create images that are particularly advantageous for the visualization of soft tissues and can be combined with the information obtained from other modalities such as CT or PET. Pre-surgical or pre-treatment planning often requires high-resolution MRI sequences that provide detailed anatomic information, clear visualization of the target pathology, and separation from the adjacent normal structures.
Standard MRI protocols for preoperative or pre-therapeutic planning often include the following MRI sequences:
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Pre-contrast T1-weighted sequences are useful for high-resolution visualization of normal anatomy and baseline pre-contrast assessment of the target pathology.
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Contrast-enhanced T1-weighted sequences help visualize vascular structures and define enhancing areas (secondary to gadolinium-containing agents’ leakage).
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T2-weighted sequences provide excellent visualization of water-containing structures such as CSF spaces or cystic components of the target pathology.
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Other MRI sequences used for pre-surgical or pre-radiation planning include fat-suppressed (T1- or T2-weighted) sequences for the visualization of skull base or orbital pathologies, susceptibility sequences (e.g., susceptibility-weighted imaging, SWI) for the identification of intralesional hemorrhage or mineralization, and advanced MRI sequences as detailed below.
DTI and Tractography
Diffusion tensor imaging is an MR imaging modality that uses the water diffusivity information obtained with particular echo-planar MR sequences for the definition and visualization of white matter tracts. Crucial white matter tracts can be reconstructed noninvasively utilizing this modality, and this information can have a significant impact on pre-surgical or pre-treatment planning [1,2,3,4,5,6,7]. Frequently used white matter structures include the corticospinal tracts, arcuate/superior longitudinal fasciculi, inferior fronto-occipital fasciculi, optic radiations, inferior longitudinal fasciculi, and frontal aslant tracts [2, 8]. Diffusion tensor imaging and other advanced diffusion techniques can also be used to characterize different tumoral components and their differentiation from non-infiltrated brain parenchyma [9].
Functional MRI (fMRI)
Echo-planar sequences can also be used to visualize cortical areas showing increased regional flow due to the blood-oxygen-level-dependent (BOLD) effect during functional tasks (task-based fMRI) [10] or spontaneously during rest (resting-state fMRI or rs-fMRI) [11,12,13,14]. Identifying eloquent areas noninvasively using fMRI allows the surgeon or radiation oncologist to avoid these areas during surgery or radiation planning and assess preprocedural risk before any intervention or treatment [11, 13].
Additional Advanced MRI Sequences
Some specialized MR sequences can provide additional information that improves surgical resection or radiation treatment accuracy [15,16,17,18]. These sequences include the following:
Perfusion Imaging
MR imaging can also be used to assess the volume of saturated protons or intravascular contrast entering a volume of interest over a defined amount of time, providing metrics of tissue perfusion. MR perfusion can be obtained without contrast using specialized subtraction techniques before and after magnetic saturation of intravascular protons. Perfusion techniques using contrast rely on the decrease of signal when using echo-planar images (T2* or DSC perfusion) or increased T1 relaxation produced by the leakage of contrast (T1 or DCE perfusion). Perfusion imaging can result in a more accurate demarcation of tumoral margins in high-grade neoplastic pathologies [19].
MR Spectroscopy (MRS)
MRS uses the properties of different chemical compounds in the MR environment to characterize intracranial pathologies and improve the visualization of subtle areas of pathological involvement that may not be distinctly seen with conventional MR images [20, 21]. MR spectroscopy can also help identify oncometabolites, such as 2-hydroxy-glutarate (2HG), that can be used for pre-surgical mapping or pre-radiation planning [22].
Positron-Emission Tomography (PET)
The high contrast-to-noise ratio produced by some radiopharmaceutical tracers makes the use of PET imaging an attractive technique to improve the accuracy of pre-surgical and pre-radiation planning. Radiotracers such as a fluoro-deoxy-glucose (FDG) will have a high background uptake due to the cerebral cortex’s high metabolic rate and glucose consumption, limiting their value for preprocedural planning for intracranial pathologies. However, PET imaging is beneficial outside of the central nervous system due to the relatively low background uptake in most soft tissues. Other radiotracers have shown their clinical value for the diagnosis and treatment planning of brain tumors, particularly radiolabeled amino acids such as C11-methionine [23], O-(2-[18F]fluoroethyl)-L-tyrosine (FET) [24, 25], and others [26, 27] (Table 49.2).
Combined PET-MRI
The prospective real-time and accurate co-registration produced by the simultaneous acquisition of MRI and PET images can further improve the planning steps’ accuracy before surgery or radiotherapy [28,29,30].
The Use of Imaging Techniques in Specific Clinical Scenarios
Pre-surgical Planning for Intracranial Tumors
The combination of high-resolution CT (without or with contrast) and high-resolution contrast-enhanced brain MRI is frequently used as the imaging work-frame for pre-surgical planning. These pre-surgical imaging datasets are typically fused using specialized software, and surgical trajectories can be planned before the procedure. Preoperative imaging has a significant positive effect on the surgical procedure’s performance and outcome [48].
Lesions that are near eloquent areas (e.g., Wernicke’s or Broca’s areas, peri-Rolandic regions, primary visual cortex) can be particularly challenging and would benefit from the use of fMRI [49, 50] and DTI [1, 2, 6,7,8, 51] for pre-surgical planning (Fig. 49.1). These techniques will help the surgeon avoid functionally eloquent cortical areas or white matter tracts near the target lesion. A significant number of studies show the crucial clinical value of fMRI and DTI in the pre-surgical planning of patients with brain tumors [17, 49, 50, 52,53,54,55]. Identifying tumoral infiltration beyond the expected margins based on conventional sequences using MR perfusion and MR spectroscopy can also impact pre-surgical planning. Still, these sequences’ use for this purpose is more variable in clinical practice [56]. Despite the additional information provided by these sequences, the surgical debulking is often limited by the potential morbidity produced by more extensive resections.
Pre-radiation Planning for Intracranial and Skull Base Tumors
The accurate demarcation of the target pathological volume, exclusion of normal tissues adjacent to the target, and definition of areas for different radiation exposure are the core principles of pre-radiation planning. The following terms are often used in pre-radiation planning and are necessary to understand the role of imaging during pre-radiation planning:
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Gross tumor volume (GTV) represents the total neoplastic volume shown on conventional MRI, CT, or PET images.
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Clinical target volume (CTV) includes the GTV and suspected surrounding areas likely containing microscopic tumoral infiltration.
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Internal target volume (ITV) includes the CTV and a margin of error due to motion. This volume is often dismissed for cranial radiosurgery if motion artifacts are minimal.
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Planning target volume (PTV) includes the ITV and a potential margin of error due to geometrical errors during the planning process.
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Treated volume (TV) is the total tissue volume that receives the planned radiation dose and is the same as the prescription isodose volume for practical purposes.
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Irradiated volume includes all tissues exposed to a significant level of radiation.
A similar combination of high-resolution CT and structural MRI datasets is often used for pre-radiotherapy planning for intracranial malignancies [57, 58]. There is preliminary evidence of a potential beneficial role of MRS [56, 59,60,61,62], MR perfusion [19], and DTI [18, 63, 64] techniques to define areas of microscopic tumoral infiltration outside of the GTV target. Radiation planning can also benefit from structural and functional connectivity information to minimize the collateral damage adjacent to the treated areas [65,66,67]. Recent publications explore the possibility of using deep learning and combined multimodal imaging to improve radiation planning’s accuracy and effectiveness [68,69,70] (Fig. 49.2).
References
Zhang H, Feng Y, Cheng L, Liu J, Li H, Jiang H. Application of diffusion tensor tractography in the surgical treatment of brain tumors located in functional areas. Oncol Lett. 2020;19(1):615–22. https://doi.org/10.3892/ol.2019.11167.
Yu Q, Lin K, Liu Y, Li X. Clinical uses of diffusion tensor imaging fiber tracking merged neuronavigation with lesions adjacent to corticospinal tract : a retrospective cohort study. J Korean Neurosurg Soc. 2020;63(2):248–60. https://doi.org/10.3340/jkns.2019.0046.
Rahmat R, Saednia K, Haji Hosseini Khani MR, Rahmati M, Jena R, Price SJ. Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Comput Biol Med. 2020;123:103815. https://doi.org/10.1016/j.compbiomed.2020.103815.
Henderson F, Abdullah KG, Verma R, Brem S. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus. 2020;48(2):E6. https://doi.org/10.3171/2019.11.FOCUS19785.
Panesar SS, Abhinav K, Yeh FC, Jacquesson T, Collins M, Fernandez-Miranda J. Tractography for surgical neuro-oncology planning: towards a gold standard. Neurotherapeutics. 2019;16(1):36–51. https://doi.org/10.1007/s13311-018-00697-x.
Soni N, Mehrotra A, Behari S, Kumar S, Gupta N. Diffusion-tensor imaging and tractography application in pre-operative planning of intra-axial brain lesions. Cureus. 2017;9(10):e1739. https://doi.org/10.7759/cureus.1739.
Caverzasi E, Hervey-Jumper SL, Jordan KM, Lobach IV, Li J, Panara V, et al. Identifying pre-operative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas. J Neurosurg. 2016;125(1):33–45. https://doi.org/10.3171/2015.6.JNS142203.
Voets NL, Bartsch A, Plaha P. Brain white matter fibre tracts: a review of functional neuro-oncological relevance. J Neurol Neurosurg Psychiatry. 2017;88(12):1017–25. https://doi.org/10.1136/jnnp-2017-316170.
Masjoodi S, Hashemi H, Oghabian MA, Sharifi G. Differentiation of edematous, tumoral and normal areas of brain using diffusion tensor and neurite orientation dispersion and density imaging. J Biomed Phys Eng. 2018;8(3):251–60.
Morrison MA, Churchill NW, Cusimano MD, Schweizer TA, Das S, Graham SJ. Reliability of task-based fMRI for preoperative planning: a test-retest study in brain tumor patients and healthy controls. PLoS One. 2016;11(2):e0149547. https://doi.org/10.1371/journal.pone.0149547.
Voets NL, Plaha P, Parker Jones O, Pretorius P, Bartsch A. Presurgical localization of the primary sensorimotor cortex in gliomas : when is resting state FMRI beneficial and sufficient? Clin Neuroradiol. 2020. https://doi.org/10.1007/s00062-020-00879-1.
Sparacia G, Parla G, Cannella R, Perri A, Lo Re V, Mamone G, et al. Resting-state functional magnetic resonance imaging for brain tumor surgical planning: feasibility in clinical setting. World Neurosurg. 2019;131:356–63. https://doi.org/10.1016/j.wneu.2019.07.022.
Metwali H, Samii A. Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a feasibility study. World Neurosurg. 2019;128:e165–e76. https://doi.org/10.1016/j.wneu.2019.04.073.
Lee MH, Miller-Thomas MM, Benzinger TL, Marcus DS, Hacker CD, Leuthardt EC, et al. Clinical resting-state fMRI in the pre-operative setting: are we ready for prime time? Top Magn Reson Imaging. 2016;25(1):11–8. https://doi.org/10.1097/RMR.0000000000000075.
Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Neurosurgery. 2017;81(3):397–415. https://doi.org/10.1093/neuros/nyx103.
Brahimaj BC, Kochanski RB, Pearce JJ, Guryildirim M, Gerard CS, Kocak M, et al. Structural and functional imaging in glioma management. Neurosurgery. 2020. https://doi.org/10.1093/neuros/nyaa360.
Verburg N, de Witt Hamer PC. State-of-the-art imaging for glioma surgery. Neurosurg Rev. 2020. https://doi.org/10.1007/s10143-020-01337-9.
Nabavizadeh SA, Ware JB, Wolf RL. Emerging techniques in imaging of glioma microenvironment. Top Magn Reson Imaging. 2020;29(2):103–14. https://doi.org/10.1097/RMR.0000000000000232.
Munshi A, Ganesh T, Gupta RK, Vaishya S, Patir R, Sarkar B, et al. Perfusion magnetic resonance imaging in contouring of glioblastoma patients: preliminary experience from a single institution. J Cancer Res Ther. 2020;16(6):1488–94. https://doi.org/10.4103/jcrt.JCRT_1151_19.
Payne GS. Clinical applications of in vivo magnetic resonance spectroscopy in oncology. Phys Med Biol. 2018;63(21):21TR02. https://doi.org/10.1088/1361-6560/aae61e.
Verburg N, Hoefnagels FWA, Barkhof F, Boellaard R, Goldman S, Guo J, et al. Diagnostic accuracy of neuroimaging to delineate diffuse gliomas within the brain: a meta-analysis. AJNR Am J Neuroradiol. 2017;38(10):1884–91. https://doi.org/10.3174/ajnr.A5368.
Chen R, Ravindra VM, Cohen AL, Jensen RL, Salzman KL, Prescot AP, et al. Molecular features assisting in diagnosis, surgery, and treatment decision making in low-grade gliomas. Neurosurg Focus. 2015;38(3):E2. https://doi.org/10.3171/2015.1.FOCUS14745.
Yano H, Shinoda J, Iwama T. Clinical utility of positron emission tomography in patients with malignant glioma. Neurol Med Chir (Tokyo). 2017;57(7):312–20. https://doi.org/10.2176/nmc.ra.2016-0312.
Stegmayr C, Stoffels G, Filss C, Heinzel A, Lohmann P, Willuweit A, et al. Current trends in the use of O-(2-[(18)F]fluoroethyl)-L-tyrosine ([(18)F]FET) in neurooncology. Nucl Med Biol. 2020. https://doi.org/10.1016/j.nucmedbio.2020.02.006.
Stegmayr C, Willuweit A, Lohmann P, Langen KJ. O-(2-[18F]-Fluoroethyl)-L-tyrosine (FET) in neurooncology: a review of experimental results. Curr Radiopharm. 2019;12(3):201–10. https://doi.org/10.2174/1874471012666190111111046.
Suchorska B, Albert NL, Tonn JC. Role of amino-tracer PET for decision-making in neuro-oncology. Curr Opin Neurol. 2018;31(6):720–6. https://doi.org/10.1097/WCO.0000000000000616.
Miyake K, Ogawa D, Okada M, Hatakeyama T, Tamiya T. Usefulness of positron emission tomographic studies for gliomas. Neurol Med Chir (Tokyo). 2016;56(7):396–408. https://doi.org/10.2176/nmc.ra.2015-0305.
Widhalm G, Traub-Weidinger T, Hainfellner JA, Bienkowski M, Wolfsberger S, Czech T. Bioimaging and surgery of brain tumors. Handb Clin Neurol. 2017;145:535–45. https://doi.org/10.1016/B978-0-12-802395-2.00033-X.
Fink JR, Muzi M, Peck M, Krohn KA. Multimodality brain tumor imaging: MR imaging, PET, and PET/MR imaging. J Nucl Med. 2015;56(10):1554–61. https://doi.org/10.2967/jnumed.113.131516.
Tsiouris S, Bougias C, Fotopoulos A. Principles and current trends in the correlative evaluation of glioma with advanced MRI techniques and PET. Hell J Nucl Med. 2019;22(3):206–19.
Abouzied MM, Crawford ES, Nabi HA. 18F-FDG imaging: pitfalls and artifacts. J Nucl Med Technol. 2005;33(3):145–55; quiz 62-3.
Hoberuck S, Michler E, Zophel K, Platzek I, Kotzerke J, Brogsitter C. Brain metastases of a neuroendocrine tumor visualized by 68Ga-DOTATATE PET/CT. Clin Nucl Med. 2019;44(1):50–2. https://doi.org/10.1097/RLU.0000000000002341.
Nguyen NC, Moon CH, Muthukrishnan A, Furlan A. 68Ga-DOTATATE PET/MRI for neuroendocrine tumors: a pictorial review. Clin Nucl Med. 2020;45(9):e406–e10. https://doi.org/10.1097/RLU.0000000000003085.
Ivanidze J, Roytman M, Lin E, Magge RS, Pisapia DJ, Liechty B, et al. Gallium-68 DOTATATE PET in the evaluation of intracranial meningiomas. J Neuroimaging. 2019;29(5):650–6. https://doi.org/10.1111/jon.12632.
Dadgar H, Norouzbeigi N, Ahmadzadehfar H, Assadi M. 68Ga-DOTATATE and 18F-FDG PET/CT for the Management of Esthesioneuroblastoma of the sphenoclival region. Clin Nucl Med. 2020;45(8):e363–e4. https://doi.org/10.1097/RLU.0000000000003133.
Xiao J, Zhu Z, Zhong D, Ma W, Wang R. Improvement in diagnosis of metastatic pituitary carcinoma by 68Ga DOTATATE PET/CT. Clin Nucl Med. 2015;40(2):e129–31. https://doi.org/10.1097/RLU.0000000000000462.
Telli T, Lay Ergun E, Volkan Salanci B, Ozgen KP. The complementary role of 68Ga-DOTATATE PET/CT in neuroblastoma. Clin Nucl Med. 2020;45(4):326–9. https://doi.org/10.1097/RLU.0000000000002961.
Ito K, Matsuda H, Kubota K. Imaging spectrum and pitfalls of (11)C-methionine positron emission tomography in a series of patients with intracranial lesions. Korean J Radiol. 2016;17(3):424–34. https://doi.org/10.3348/kjr.2016.17.3.424.
de Zwart PL, van Dijken BRJ, Holtman GA, Stormezand GN, Dierckx R, Jan van Laar P, et al. Diagnostic accuracy of PET tracers for the differentiation of tumor progression from treatment-related changes in high-grade glioma: a systematic review and metaanalysis. J Nucl Med. 2020;61(4):498–504. https://doi.org/10.2967/jnumed.119.233809.
Jacobs A. Amino acid uptake in ischemically compromised brain tissue. Stroke. 1995;26(10):1859–66. https://doi.org/10.1161/01.str.26.10.1859.
Becherer A, Karanikas G, Szabo M, Zettinig G, Asenbaum S, Marosi C, et al. Brain tumour imaging with PET: a comparison between [18F]fluorodopa and [11C]methionine. Eur J Nucl Med Mol Imaging. 2003;30(11):1561–7. https://doi.org/10.1007/s00259-003-1259-1.
Fuenfgeld B, Machler P, Fischer DR, Esposito G, Rushing EJ, Kaufmann PA, et al. Reference values of physiological 18F-FET uptake: implications for brain tumor discrimination. PLoS One. 2020;15(4):e0230618. https://doi.org/10.1371/journal.pone.0230618.
Chondrogiannis S, Marzola MC, Al-Nahhas A, Venkatanarayana TD, Mazza A, Opocher G, et al. Normal biodistribution pattern and physiologic variants of 18F-DOPA PET imaging. Nucl Med Commun. 2013;34(12):1141–9. https://doi.org/10.1097/MNM.0000000000000008.
Fernandez P, Zanotti-Fregonara P, Eimer S, Gimbert E, Monteil P, Penchet G, et al. Combining 3'-Deoxy-3′-[18F] fluorothymidine and MRI increases the sensitivity of glioma volume detection. Nucl Med Commun. 2019;40(10):1066–71. https://doi.org/10.1097/MNM.0000000000001056.
Ferdova E, Ferda J, Baxa J, Tupy R, Mracek J, Topolcan O, et al. Assessment of grading in newly-diagnosed glioma using 18F-fluorothymidine PET/CT. Anticancer Res. 2015;35(2):955–9.
Tripathi M, Sharma R, D’Souza M, Jaimini A, Panwar P, Varshney R, et al. Comparative evaluation of F-18 FDOPA, F-18 FDG, and F-18 FLT-PET/CT for metabolic imaging of low grade gliomas. Clin Nucl Med. 2009;34(12):878–83. https://doi.org/10.1097/RLU.0b013e3181becfe0.
Wei Y, Zhao W, Huang Y, Yu Q, Zhu S, Wang S, et al. A comparative study of noninvasive hypoxia imaging with 18F-fluoroerythronitroimidazole and 18F-fluoromisonidazole PET/CT in patients with lung cancer. PLoS One. 2016;11(6):e0157606. https://doi.org/10.1371/journal.pone.0157606.
Mahvash M, Boettcher I, Petridis AK, Besharati TL. Image guided surgery versus conventional brain tumor and craniotomy localization. J Neurosurg Sci. 2017;61(1):8–13. https://doi.org/10.23736/S0390-5616.16.03142-8.
Stopa BM, Senders JT, Broekman MLD, Vangel M, Golby AJ. Pre-operative functional MRI use in neurooncology patients: a clinician survey. Neurosurg Focus. 2020;48(2):E11. https://doi.org/10.3171/2019.11.FOCUS19779.
Vysotski S, Madura C, Swan B, Holdsworth R, Lin Y, Rio AMD, et al. Preoperative FMRI associated with decreased mortality and morbidity in brain tumor patients. Interdiscip Neurosurg. 2018;13:40–5. https://doi.org/10.1016/j.inat.2018.02.001.
Meyer EJ, Gaggl W, Gilloon B, Swan B, Greenstein M, Voss J, et al. The impact of intracranial tumor proximity to white matter tracts on morbidity and mortality: a retrospective diffusion tensor imaging study. Neurosurgery. 2017;80(2):193–200. https://doi.org/10.1093/neuros/nyw040.
Lorenzen A, Groeschel S, Ernemann U, Wilke M, Schuhmann MU. Role of pre-surgical functional MRI and diffusion MR tractography in pediatric low-grade brain tumor surgery: a single-center study. Childs Nerv Syst. 2018;34(11):2241–8. https://doi.org/10.1007/s00381-018-3828-4.
Dubey A, Kataria R, Sinha VD. Role of diffusion tensor imaging in brain tumor surgery. Asian J Neurosurg. 2018;13(2):302–6. https://doi.org/10.4103/ajns.AJNS_226_16.
Brennan NP, Peck KK, Holodny A. Language mapping using fMRI and direct cortical stimulation for brain tumor surgery: the good, the bad, and the questionable. Top Magn Reson Imaging. 2016;25(1):1–10. https://doi.org/10.1097/RMR.0000000000000074.
D’Andrea G, Trillo G, Picotti V, Raco A. Functional magnetic resonance imaging (fMRI), pre-intraoperative tractography in neurosurgery: the experience of Sant’ Andrea Rome University Hospital. Acta Neurochir Suppl. 2017;124:241–50. https://doi.org/10.1007/978-3-319-39546-3_36.
Zhang J, Zhuang DX, Yao CJ, Lin CP, Wang TL, Qin ZY, et al. Metabolic approach for tumor delineation in glioma surgery: 3D MR spectroscopy image-guided resection. J Neurosurg. 2016;124(6):1585–93. https://doi.org/10.3171/2015.6.JNS142651.
Grunert M, Kassubek R, Danz B, Klemenz B, Hasslacher S, Stroh S, et al. Radiation and brain tumors: an overview. Crit Rev Oncog. 2018;23(1–2):119–38. https://doi.org/10.1615/CritRevOncog.2018025927.
Ajithkumar T, Horan G, Padovani L, Thorp N, Timmermann B, Alapetite C, et al. SIOPE - brain tumor group consensus guideline on craniospinal target volume delineation for high-precision radiotherapy. Radiother Oncol. 2018;128(2):192–7. https://doi.org/10.1016/j.radonc.2018.04.016.
Cordova JS, Kandula S, Gurbani S, Zhong J, Tejani M, Kayode O, et al. Simulating the effect of spectroscopic MRI as a metric for radiation therapy planning in patients with glioblastoma. Tomography. 2016;2(4):366–73. https://doi.org/10.18383/j.tom.2016.00187.
Cordova JS, Shu HK, Liang Z, Gurbani SS, Cooper LA, Holder CA, et al. Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients. Neuro Oncol. 2016;18(8):1180–9. https://doi.org/10.1093/neuonc/now036.
Jafari-Khouzani K, Loebel F, Bogner W, Rapalino O, Gonzalez GR, Gerstner E, et al. Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients. Neuro Oncol. 2016;18(11):1569–78. https://doi.org/10.1093/neuonc/now100.
Gurbani S, Weinberg B, Cooper L, Mellon E, Schreibmann E, Sheriff S, et al. The Brain Imaging Collaboration Suite (BrICS): a cloud platform for integrating whole-brain spectroscopic MRI into the radiation therapy planning workflow. Tomography. 2019;5(1):184–91. https://doi.org/10.18383/j.tom.2018.00028.
Rahmat R, Brochu F, Li C, Sinha R, Price SJ, Jena R. Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps. Br J Radiol. 2020;93(1108):20190441. https://doi.org/10.1259/bjr.20190441.
Hathout L, Patel V. Estimating subthreshold tumor on MRI using a 3D-DTI growth model for GBM: an adjunct to radiation therapy planning. Oncol Rep. 2016;36(2):696–704. https://doi.org/10.3892/or.2016.4878.
Duffau H. Why brain radiation therapy should take account of the individual structural and functional connectivity: toward an irradiation “a la carte”. Crit Rev Oncol Hematol. 2020;154:103073. https://doi.org/10.1016/j.critrevonc.2020.103073.
Yahya N, Manan HA. Utilisation of diffusion tensor imaging in intracranial radiotherapy and radiosurgery planning for white matter dose optimization: a systematic review. World Neurosurg. 2019;130:e188–e98. https://doi.org/10.1016/j.wneu.2019.06.027.
Scranton RA, Hsiao KY, Sadrameli SS, Wang HC, Thong Y, Garcia Luzardo P, et al. Combinatorial anatomic and functional neural tract mapping for stereotactic radiosurgery planning. Cureus. 2019;11(11):e6161. https://doi.org/10.7759/cureus.6161.
Liu F, Yadav P, Baschnagel AM, McMillan AB. MR-based treatment planning in radiation therapy using a deep learning approach. J Appl Clin Med Phys. 2019;20(3):105–14. https://doi.org/10.1002/acm2.12554.
Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C, et al. Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference. IEEE Trans Med Imaging. 2019;38(8):1875–84. https://doi.org/10.1109/TMI.2019.2902044.
Florez E, Nichols T, Parker EE, Lirette ST, Howard CM, Fatemi A. Multiparametric magnetic resonance imaging in the assessment of primary brain tumors through radiomic features: a metric for guided radiation treatment planning. Cureus. 2018;10(10):e3426. https://doi.org/10.7759/cureus.3426.
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Rapalino, O. (2022). Treatment Planning. In: Franceschi, A.M., Franceschi, D. (eds) Hybrid PET/MR Neuroimaging. Springer, Cham. https://doi.org/10.1007/978-3-030-82367-2_49
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