| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Allies not enemies—creating a more empathetic and uplifting patient experience through technology and art
|
Tagliaferri, Luca
|
|
|
201 |
3 |
p. 316-332
|
artikel
|
| 2 |
Artificial intelligence for response prediction and personalisation in radiation oncology
|
Zwanenburg, Alex
|
|
|
201 |
3 |
p. 266-273
|
artikel
|
| 3 |
Artificial intelligence for treatment delivery: image-guided radiotherapy
|
Rabe, Moritz
|
|
|
201 |
3 |
p. 283-297
|
artikel
|
| 4 |
Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives
|
Erdur, Ayhan Can
|
|
|
201 |
3 |
p. 236-254
|
artikel
|
| 5 |
Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose–volume metrics: a two-center study
|
Kong, Yan
|
|
|
201 |
3 |
p. 274-282
|
artikel
|
| 6 |
Gekommen, um zu bleiben? Nivolumab in der Erstlinientherapie des Hodgkin-Lymphoms im fortgeschrittenen Stadium – eine strahlentherapeutische Nachlese der SWOG-S1826-Studie
|
Oertel, Michael
|
|
|
201 |
3 |
p. 349-351
|
artikel
|
| 7 |
Kann die stereotaktische Radiotherapie der Makula die Lebensqualität bei der altersbedingten feuchten Makuladegeneration (STAR Trial) verbessern?
|
Göller, Stephanie
|
|
|
201 |
3 |
p. 346-348
|
artikel
|
| 8 |
MR-linac: role of artificial intelligence and automation
|
Psoroulas, Serena
|
|
|
201 |
3 |
p. 298-305
|
artikel
|
| 9 |
Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer
|
Zamanian, Maryam
|
|
|
201 |
3 |
p. 306-315
|
artikel
|
| 10 |
Olaparib bei biochemischem Rezidiv des High-risk-Prostatakarzinoms nach Prostatektomie
|
Hintelmann, Katharina
|
|
|
201 |
3 |
p. 343-345
|
artikel
|
| 11 |
Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy
|
Trapp, Christian
|
|
|
201 |
3 |
p. 333-342
|
artikel
|
| 12 |
Principles of artificial intelligence in radiooncology
|
Huang, Yixing
|
|
|
201 |
3 |
p. 210-235
|
artikel
|
| 13 |
The increasing role of artificial intelligence in radiation oncology: how should we navigate it?
|
Putz, Florian
|
|
|
201 |
3 |
p. 207-209
|
artikel
|
| 14 |
The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning
|
Putz, Florian
|
|
|
201 |
3 |
p. 255-265
|
artikel
|
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