Sequential Optimization Scripts to Facilitate Treatment Planning for Robotic Radiosurgery Clinical Studies for Prostate and Lung Cancers

Author(s):  
E. Lessard ◽  
W. Kilby ◽  
J. Dooley ◽  
C. Sims ◽  
A. Schlaefer ◽  
...  
2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer

AbstractAlthough robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.


2021 ◽  
Vol 10 ◽  
Author(s):  
Tobias Greve ◽  
Felix Ehret ◽  
Theresa Hofmann ◽  
Jun Thorsteinsdottir ◽  
Franziska Dorn ◽  
...  

ObjectiveCyberKnife offers CT- and MRI-based treatment planning without the need for stereotactically acquired DSA. The literature on CyberKnife treatment of cerebral AVMs is sparse. Here, a large series focusing on cerebral AVMs treated by the frameless CyberKnife stereotactic radiosurgery (SRS) system was analyzed.MethodsIn this retrospective study, patients with cerebral AVMs treated by CyberKnife SRS between 2005 and 2019 were included. Planning was MRI- and CT-based. Conventional DSA was not coregistered to the MRI and CT scans used for treatment planning and was only used as an adjunct. Obliteration dynamics and clinical outcome were analyzed.Results215 patients were included. 53.0% received SRS as first treatment; the rest underwent previous surgery, embolization, SRS, or a combination. Most AVMs were classified as Spetzler-Martin grade I to III (54.9%). Hemorrhage before treatment occurred in 46.0%. Patients suffered from headache (28.8%), and seizures (14.0%) in the majority of cases. The median SRS dose was 18 Gy and the median target volume was 2.4 cm³. New neurological deficits occurred in 5.1% after SRS, with all but one patient recovering. The yearly post-SRS hemorrhage incidence was 1.3%. In 152 patients who were followed-up for at least three years, 47.4% showed complete AVM obliteration within this period. Cox regression analysis revealed Spetzler-Martin grade (P = 0.006) to be the only independent predictor of complete obliteration.ConclusionsAlthough data on radiotherapy of AVMs is available, this is one of the largest series, focusing exclusively on CyberKnife treatment. Safety and efficacy compared favorably to frame-based systems. Non-invasive treatment planning, with a frameless SRS robotic system might provide higher patient comfort, a less invasive treatment option, and lower radiation exposure.


Author(s):  
Rhea Tombropoulos ◽  
Achim Schweikard ◽  
Jean-Claude Latombe ◽  
John R. Adler

2016 ◽  
Vol 17 (3) ◽  
pp. 313-330 ◽  
Author(s):  
Oliver Blanck ◽  
Lei Wang ◽  
Wolfgang Baus ◽  
Jimm Grimm ◽  
Thomas Lacornerie ◽  
...  

2008 ◽  
Vol 33 (3) ◽  
pp. 175-179 ◽  
Author(s):  
Arjun Sahgal ◽  
Cynthia Chuang ◽  
David Larson ◽  
Kim Huang ◽  
Paula Petti ◽  
...  

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 3092-3092
Author(s):  
Yataro Daigo ◽  
Atsushi Takano ◽  
Yusuke Nakamura

3092 Background: Oncoantigens are defined to be proteins that are very specifically expressed in cancer cells and that have the oncogenic activity and high immunogenicity, and are considered to be promising targets for immunotherapy such as therapeutic cancer vaccines. Methods: We have established a strategy as follows to identify new oncoantigens; i) screening of highly transactivated genes in the majority of 120 lung cancers using cDNA microarray representing 27,648 genes coupled with enrichment of tumor cells by laser microdissection, ii) verification of no expression of each candidate gene in normal tissues by northern-blot analysis, iii) validation of the clinicopathological significance of its high level of expression with tissue microarray containing 300 lung cancers, iv) verification of a critical role of each gene in the growth or invasiveness of cancer cells by RNAi and cell growth/invasion assays, v) screening of the epitope peptides recognized by HLA-A*0201- or A*2402-restricted cytotoxic T lymphocyte (CTL). We conducted phase I clinical trials of these therapeutic peptide vaccines for lung cancer patients. Results: We identified 35 oncoantigens and screened dozens of 10-amino-acid peptides, each of which corresponded to a part of TTK, LY6K, IMP-3, CDCA1, KIF20A, CDC45L, and FOXM1, and was a candidate to be presented on the surface of HLA-A*0201 or HLA-A*2402 that induced in vitro CTL response. Phase I clinical studies indicated that five epitope peptides could strongly induce the CTL activity in cancer patients. For example, we conducted a phase I study for HLA-A*2402-positive, advanced non-small cell lung cancer patients who failed to standard therapy, using the combination of 1, 2 or 3 mg/body of each peptides from LY6K, CDCA1, and KIF20A mixed with adjuvant once a week. This cancer vaccine therapy demonstrated tolerability and had very high immunogenicity of even 1 mg/body dose to induce antigen-specific CTLs in cancer patients. Conclusions: Through systematic genomics-based approach and clinical study, we have identified five epitope peptides, which could induce CTLs very effectively in cancer patients, and therefore it warrants further clinical studies. Clinical trial information: NCT01069575.


Sign in / Sign up

Export Citation Format

Share Document