Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method

International Journal of Radiation Oncology Biology Physics

Volumn 97, Issue 5, 2017, Pages 1087-1094

  Hall, David C ^a   Trofimov, Alexei V ^a   Winey, Brian A ^a   Liebsch, Norbert J ^a   Paganetti, Harald ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; CORRELATION METHODS; EYE PROTECTION; FORECASTING; GEOMETRY; KNOWLEDGE BASED SYSTEMS; PROTON BEAM THERAPY; PROTON BEAMS; TUMORS;

ALTERNATIVE THERAPY; CONFIDENCE INTERVAL; DISTANCE TO TARGETS; GENERALIZED EQUIVALENT UNIFORM DOSE (GEUD); GEOMETRIC COMPLEXITY; INTENSITY-MODULATED; METHODS AND MATERIALS; PEARSON'S CORRELATION COEFFICIENTS;

PATIENT TREATMENT;

ARTICLE; BRAIN STEM; CANCER RADIOTHERAPY; CLINICAL DECISION MAKING; CLINICAL TRIAL (TOPIC); COCHLEA; HUMAN; KNOWLEDGE; OPTIC CHIASM; OPTIC NERVE; ORGAN AT RISK; PAROTID GLAND; PATIENT SELECTION; PHYSICIAN; PRIORITY JOURNAL; PROTON THERAPY; RADIATION DOSE; RADIOLOGICAL PARAMETERS; RISK ASSESSMENT; SKULL BASE TUMOR; SPINAL CORD; TREATMENT PLANNING; VELOCITY; BIOLOGICAL MODEL; COMPUTER SIMULATION; KNOWLEDGE BASE; MACHINE LEARNING; PATIENT CARE; PROCEDURES; PROGNOSIS; RADIATION RESPONSE; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING SYSTEM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SKULL BASE NEOPLASMS;

COMPUTER SIMULATION; DOSE-RESPONSE RELATIONSHIP, RADIATION; HUMANS; KNOWLEDGE BASES; MACHINE LEARNING; MODELS, BIOLOGICAL; PATIENT-CENTERED CARE; PROGNOSIS; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SKULL BASE NEOPLASMS;

EID: 85015347575     PISSN: 03603016     EISSN: 1879355X     Source Type: Journal    
DOI: 10.1016/j.ijrobp.2017.01.236     Document Type: Article

References (19)

1. 1 Jermann, M., Particle therapy statistics in 2014. Int J Particle Ther 2 (2015), 50–54.
2. 2 Crellin, A.M., Burnet, N.G., Proton beam therapy: The context, future direction and challenges become clearer. Clin Oncol 26 (2014), 736–738.
3. 3 Patel, S., Parliament, M., Kostaras, X., et al. Recommendations for the referral of patients for proton-beam therapy, an Alberta Health Services report: A model for Canada?. Curr Oncol 21 (2014), 251–262.
4. 4 Lühr, A., Löck, S., Roth, K., et al. Concept for individualized patient allocation: ReCompare—Remote comparison of particle and photon treatment plans. Radiat Oncol, 9, 2014, 59.
5. 5 Jakobi, A., Bandurska-Luque, A., Stützer, K., et al. Identification of patient benefit from proton therapy for advanced head and neck cancer patients based on individual and subgroup normal tissue complication probability analysis. Int J Radiat Oncol Biol Phys 92 (2015), 1165–1174.
6. 6 Langendijk, J.A., Lambin, P., De Ruysscher, D., et al. Selection of patients for radiotherapy with protons aiming at reduction of side effects: The model-based approach. Radiother Oncol 107 (2013), 267–273.
7. 7 Widder, J., Van Der Schaaf, A., Lambin, P., et al. The quest for evidence for proton therapy: Model-based approach and precision medicine. Int J Radiat Oncol Biol Phys 95 (2016), 30–36.
8. 8 Wu, B., Ricchetti, F., Sanguineti, G., et al. Patient geometry-driven information retrieval for IMRT treatment plan quality control. Med Phys 36 (2009), 5497–5505.
9. 9 Zhu, X., Ge, Y., Li, T., et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys 38 (2011), 719–726.
10. 10 Appenzoller, L.M., Michalski, J.M., Thorstad, W.L., et al. Predicting dose-volume histograms for organs-at-risk in IMRT planning. Med Phys 39 (2012), 7446–7461.
11. 11 Petit, S.F., van Elmpt, W., Accurate prediction of target dose-escalation and organ-at-risk dose levels for non-small cell lung cancer patients. Radiother Oncol 117 (2015), 453–458.
12. 12 Wang, Y., Breedveld, S., Heijmen, B., et al. Evaluation of plan quality assurance models for prostate cancer patients based on fully automatically generated Pareto-optimal treatment plans. Phys Med Biol 4268 (2016), 1–21.
13. 13 Paganetti, H., Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys Med Biol 57 (2012), R99–R177.
14. 14 Gay, H.A., Niemierko, A., A free program for calculating EUD-based NTCP and TCP in external beam radiotherapy. Phys Med 23 (2007), 115–125.
15. 15 De Marzi, L., Feuvret, L., Boule, T., et al. Use of gEUD for predicting ear and pituitary gland damage following proton and photon radiation therapy. Br J Radiol, 88, 2015, 20140413.
16. 16 Bhandare, N., Jackson, A., Eisbruch, A., et al. Radiation therapy and hearing loss. Int J Radiat Oncol Biol Phys 76 (2010), S50–S57.
17. 17 Dijkema, T., Raaijmakers, C.P.J., Ten Haken, R.K., et al. Parotid gland function after radiotherapy: The combined Michigan and Utrecht experience. Int J Radiat Oncol Biol Phys 78 (2010), 449–453.
18. 18 Marucci, L., Niemierko, A., Liebsch, N.J., et al. Spinal cord tolerance to high-dose fractionated 3D conformal proton-photon irradiation as evaluated by equivalent uniform dose and dose volume histogram analysis. Int J Radiat Oncol Biol Phys 59 (2004), 551–555.
19. 19 Cheng, Q., Roelofs, E., Ramaekers, B.L., et al. Development and evaluation of an online three-level proton vs photon decision support prototype for head and neck cancer—Comparison of dose, toxicity and cost-effectiveness. Radiother Oncol 118 (2016), 281–285.