Treatment with (chemo)-radiation in old patients (≥76 years of age) with newly diagnosed non-metastatic squamous cell cancer of the head and neck region: real-world data from a tertiary referral center.

Purpose: Treatment of patients with cancer of the head and neck region is in focus in a multitude of studies. Of these patients, one patient group, those aged 76 and more, is mostly underrepresented despite requiring thorough and well-reasoned treatment decisions to offer curative treatment. This study investigates real-world data on curative treatment of old (≥76 years) patients with newly diagnosed squamous cell carcinoma of the head and neck region (HNSCC). Patients and methods: Between January 2010 and December 2021, we identified 71 patients older than 76 years with newly diagnosed HNSCC and cM0 at the Department of Radiation Oncology of the University Hospital of Erlangen-Nuremberg. Using electronic medical records, we analyzed treatment patterns and outcomes in terms of overall survival (OS), progression-free survival (PFS), and locoregional control (LRC) rate. Additionally, we performed univariate risk analysis and Cox regression in order to identify predictive factors associated with the abovementioned treatment outcomes. Results: The median follow-up was 18 months. OS was 83%, 79%, and 72% after 1 year, 2 years, and 3 years, respectively. PFS was 69%, 54%, and 46% after 1 year, 2 years, and 3 years, respectively. A total of 34 (48%) patients were treated with standard therapy according to current guidelines. The reasons for deviation from standard therapy before or during treatment were as follows: unfitness for cisplatin-based chemotherapy (n = 37), reduction of chemotherapy (n = 3), and dose reduction/interruption of radiotherapy (n = 8). Carboplatin-based systemic therapy showed improved PFS compared to cisplatin or cetuximab (60 vs. 28 vs. 15 months, p = 0.037) but without impact on OS (83 vs. 52 vs. 38 months, p = 0.807). Oropharyngeal tumor localization (p = 0.026) and combined treatment (surgery and postoperative treatment) (p = 0.008) were significant predictors for a better OS. In multivariate analysis, oropharyngeal tumor localization (p = 0.011) and combined treatment (p = 0.041) showed significantly increased PFS. After 1 year, 2 years, and 3 years, the cumulative incidence of locoregional recurrences (LRRs) was 13%, 24%, and 27%, respectively, and was significantly decreased in patients with oropharyngeal tumor localization (p = 0.037). Conclusions: Adherence to treatment protocols for radiotherapy alone in old patients with HNSCC is good, whereas the application of concurrent chemotherapy often deviates from guidelines in terms of de-escalation. An important risk factor for decreased OS, PFS, and a higher rate of LRR appears to be non-oropharyngeal tumor location in old patients.

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data. METHODS: A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery. DISCUSSION: Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow. TRIAL REGISTRATION: NCT06106997.

Quality requirements for MRI simulation in cranial stereotactic radiotherapy: a guideline from the German Taskforce "Imaging in Stereotactic Radiotherapy".

Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning. For this guideline, an interdisciplinary panel including experts from the working group for radiosurgery and stereotactic radiotherapy of the German Society for Radiation Oncology (DEGRO), the working group for physics and technology in stereotactic radiotherapy of the German Society for Medical Physics (DGMP), the German Society of Neurosurgery (DGNC), the German Society of Neuroradiology (DGNR) and the German Chapter of the International Society for Magnetic Resonance in Medicine (DS-ISMRM) have defined minimum MRI quality requirements as well as advanced MRI simulation options for cranial SRT.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed. MATERIAL AND METHODS: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.5 T MRI-scan on nominal day 30 after implantation. For MRI-based seed visualization, DIXON-sequences were acquired and deep learning-based quantitative susceptibility maps (QSM) were generated from 3D-gradient-echo-sequences from 20 patients. Seed segmentations created on CT served as ground truth. For automatic seed segmentation on MRI, a 3D nnU-net model was trained using QSM and DIXON, both solely and combined. RESULTS: Of the implanted seeds 94.8 ± 2.4% were detected with deep learning automatic segmentation entrained on both QSM and DIXON data. Models trained on the individual sequence data-sets performed worse with detection rates of 87.5 ± 2.6% or 88.6 ± 7.5% for QSM and DIXON respectively. The seed centers identified on CT versus QSM and DIXON were on average 1.8 ± 1.3 mm apart. Postimplant dosimetry for evaluation of positioning inaccuracies revealed only small variations of up to 0.4 ± 4.26 Gy in D90 (dose 90% of the prostate receives) between the standard CT-approach and our MRI-only workflow. CONCLUSION: The proposed deep learning-based MRI-only workflow provided a promisingly accurate and robust seed localization and thus has the potential to compete with current state-of-the-art CT-based postimplant dosimetry in the future.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology. Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases. Results: For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Conclusion: Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising.

Status epilepticus in patients with glioblastoma: Clinical characteristics, risk factors, and epileptological outcome.

PURPOSE: Epilepsy is a common comorbidity in patients with glioblastoma, however, clinical data on status epilepticus (SE) in these patients is sparse. We aimed to investigate the risk factors associated with the occurrence and adverse outcomes of SE in glioblastoma patients. METHODS: We retrospectively analysed electronic medical records of patients with de-novo glioblastoma treated at our institution between 01/2006 and 01/2020 and collected data on patient, tumour, and SE characteristics. RESULTS: In the final cohort, 292/520 (56.2 %) patients developed seizures, with 48 (9.4 % of the entire cohort and 16.4 % of patients with epilepsy, PWE) experiencing SE at some point during the course of their disease. SE was the first symptom of the tumour in 6 cases (1.2 %) and the first manifestation of epilepsy in 18 PWE (6.2 %). Most SE episodes occurred postoperatively (n = 37, 77.1 %). SE occurrence in PWE was associated with postoperative seizures and drug-resistant epilepsy. Adverse outcome (in-house mortality or admission to palliative care, 10/48 patients, 20.8 %), was independently associated with higher status epilepticus severity score (STESS) and Charlson Comorbidity Index (CCI), but not tumour progression. 32/48 SE patients (66.7 %) were successfully treated with first- and second-line agents, while escalation to third-line agents was successful in 6 (12.5 %) cases. CONCLUSION: Our data suggests a link between the occurrence of SE, postoperative seizures, and drug-resistant epilepsy. Despite the dismal oncological prognosis, SE was successfully treated in 79.2 % of the cases. Higher STESS and CCI were associated with adverse SE outcomes.

Non-Professional Phagocytosis Increases in Melanoma Cells and Tissues with Increasing E-Cadherin Expression.

Non-professional phagocytosis in cancer has been increasingly studied in recent decades. In malignant melanoma metastasis, cell-in-cell structures have been described as a sign of cell cannibalism. To date, only low rates of cell-in-cell structures have been described in patients with malignant melanoma. To investigate these findings further, we examined twelve primary melanoma cell lines in both adherent and suspended co-incubation for evidence of engulfment. In addition, 88 malignant melanoma biopsies and 16 healthy tissue samples were evaluated. E-cadherin levels were determined in the cell lines and tissues. All primary melanoma cell lines were capable of phagocytosis, and phagocytosis increased when cells were in suspension during co-incubation. Cell-in-cell structures were also detected in most of the tissue samples. Early T stages and increasingly advanced N and M stages have correspondingly lower rates of cell-in-cell structures. Non-professional phagocytosis was also present in normal skin tissue. Non-professional phagocytosis appears to be a ubiquitous mechanism in malignant melanoma. The absence of phagocytosis in metastases may be one reason for the high rate of metastasis in malignant melanoma.

Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems.

PURPOSE: Auxiliary devices such as immobilization systems should be considered in synthetic CT (sCT)-based treatment planning (TP) for MRI-only brain radiotherapy (RT). A method for auxiliary device definition in the sCT is introduced, and its dosimetric impact on the sCT-based TP is addressed. METHODS: T1-VIBE DIXON was acquired in an RT setup. Ten datasets were retrospectively used for sCT generation. Silicone markers were used to determine the auxiliary devices' relative position. An auxiliary structure template (AST) was created in the TP system and placed manually on the MRI. Various RT mask characteristics were simulated in the sCT and investigated by recalculating the CT-based clinical plan on the sCT. The influence of auxiliary devices was investigated by creating static fields aimed at artificial planning target volumes (PTVs) in the CT and recalculated in the sCT. The dose covering 50% of the PTV (D50) deviation percentage between CT-based/recalculated plan (∆D50[%]) was evaluated. RESULTS: Defining an optimal RT mask yielded a ∆D50[%] of 0.2 ± 1.03% for the PTV and between -1.6 ± 3.4% and 1.1 ± 2.0% for OARs. Evaluating each static field, the largest ∆D50[%] was delivered by AST positioning inaccuracy (max: 3.5 ± 2.4%), followed by the RT table (max: 3.6 ± 1.2%) and the RT mask (max: 3.0 ± 0.8% [anterior], 1.6 ± 0.4% [rest]). No correlation between ∆D50[%] and beam depth was found for the sum of opposing beams, except for (45°â€¯+ 315°). CONCLUSION: This study evaluated the integration of auxiliary devices and their dosimetric influence on sCT-based TP. The AST can be easily integrated into the sCT-based TP. Further, we found that the dosimetric impact was within an acceptable range for an MRI-only workflow.

Time-dependent risk factors for epileptic seizures in glioblastoma patients: A retrospective analysis of 520 cases.

OBJECTIVE: Epilepsy is a common comorbidity of glioblastoma. Seizures may occur in various phases of the disease. We aimed to assess potential risk factors for seizures in accordance with the point in time at which they occurred. METHODS: We retrospectively analyzed medical files of adult patients with de novo glioblastoma treated at our institution between January 2006 and January 2020. We categorized seizures as preoperative seizures (POS), early postoperative seizures (EPS; before initiation of radio[chemo]therapy [RCT]), seizures during radiotherapy (SDR; during or <30 days after RCT), and posttherapeutic seizures (PTS; ≥30 days after completion of RCT). We addressed associations between patients' characteristics and their seizures. RESULTS: In the final cohort (N = 520), 292 patients experienced seizures. POS, EPS, SDR, and/or PTS occurred in 29.6% (154/520), 6.0% (31/520), 13.8% (70/509), and 36.1% (152/421) of patients, respectively. POS occurred more frequently in patients with higher Karnofsky Performance Scale scores (odds ratio [OR] = 3.27, p = .001) and tumor location in the temporal lobe (OR = 1.51, p = .034). None of the parameters we analyzed was related to the occurrence of EPS. SDR were independently associated with tumor location (parietal lobe, OR = 1.86, p = .027) and POS, but not EPS, and were independent of RCT. PTS were independently associated with tumor progression (OR = 2.32, p < .001) and with occurrence of SDR (OR = 3.36, p < .001), and negatively correlated with temporal lobe location (OR = .58, p < .014). In patients with tumors exclusively located in the temporal lobe, complete tumor resection was associated with a decreased risk of postoperative seizures. SIGNIFICANCE: Seizures in glioblastoma patients have various, time-dependent risk factors. Temporal lobe localization was a risk factor for preoperative seizures; surgery may have had a protective effect in these patients. RCT did not have dose-dependent pro- or anticonvulsive effects. PTS were associated with tumor progression.

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Background: Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

Usability of magnetic resonance images acquired at a novel low-field 0.55 T scanner for brain radiotherapy treatment planning.

Background and Purpose: Low-field magnetic resonance imaging (MRI) may offer specific advantages over high-field MRI, e.g. lower susceptibility-dependent distortions and simpler installation. The study aim was to evaluate if a novel 0.55 T MRI scanner provides sufficient image accuracy and quality for radiotherapy (RT) treatment planning. Material and methods: The geometric accuracy of images acquired at a low-field MRI scanner was evaluated in phantom measurements regarding gradient non-linearity-related distortions. Patient-induced B0-susceptibility changes were investigated via B0-field-mapping in ten volunteers. Patients were positioned in RT-setup using a 3D-printed insert for the head/neck-coil that was tested for sufficient signal-to-noise-ratio (SNR). The suitability of the MRI-system for detection of metastases was evaluated in eleven patients. In comparison to diagnostic images, acquired at ≥1.5 T, three physicians evaluated the detectability of metastases by counting them in low- and high-field-images, respectively. Results: The phantom measurements showed a high imaging fidelity after 3D-distortion-correction with (1.2 ± 0.9) mm geometric distortion in 10 cm radius from isocentre. At the edges remaining distortions were greater than at 1.5 T. The mean susceptibility-induced distortions in the head were (0.05 ± 0.05) mm and maximum 0.69 mm. SNR analysis showed that optimised positioning of RT-patients without signal loss in the head/neck-coil was possible with the RT-insert. No significant differences (p = 0.48) in detectability of metastases were found. Conclusion: The 0.55 T MRI system provided sufficiently geometrically accurate and high-resolution images that can be used for RT-planning for brain metastases. Hence, modern low-field MRI may contribute to simply access MRI for RT-planning after further investigations.

Low-dose radiotherapy of osteoarthritis: from biological findings to clinical effects-challenges for future studies.

Osteoarthritis (OA) is one of the most common and socioeconomically relevant diseases, with rising incidence and prevalence especially with regard to an ageing population in the Western world. Over the decades, the scientific perception of OA has shifted from a simple degeneration of cartilage and bone to a multifactorial disease involving various cell types and immunomodulatory factors. Despite a wide range of conventional treatment modalities available, a significant proportion of patients remain treatment refractory. Low-dose radiotherapy (LDRT) has been used for decades in the treatment of patients with inflammatory and/or degenerative diseases and has proven a viable option even in cohorts of patients with a rather poor prognosis. While its justification mainly derives from a vast body of empirical evidence, prospective randomized trials have until now failed to prove the effectiveness of LDRT. Nevertheless, over the decades, adaptions of LDRT treatment modalities have evolved using lower dosages with establishment of different treatment schedules for which definitive clinical proof is still pending. Preclinical research has revealed that the immune system is modulated by LDRT and very recently osteoimmunological mechanisms have been described. Future studies and investigations further elucidating the underlying mechanisms are an essential key to clarify the optimal patient stratification and treatment procedure, considering the patients' inflammatory status, age, and sex. The present review aims not only to present clinical and preclinical knowledge about the mechanistic and beneficial effects of LDRT, but also to emphasize topics that will need to be addressed in future studies. Further, a concise overview of the current status of the underlying radiobiological knowledge of LDRT for clinicians is given, while seeking to stimulate further translational research.

Normofractionated irradiation and not temozolomide modulates the immunogenic and oncogenic phenotype of human glioblastoma cell lines.

PURPOSE: Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, with an overall poor prognosis after diagnosis. Conventional treatment includes resection, chemotherapy with temozolomide (TMZ), and concomitant radiotherapy (RT). The recent success of immunotherapy approaches in other tumor entities, particularly with immune checkpoint inhibitors, could not be clinically transferred to GBM treatment so far. Therefore, preclinical analyses of the expression of both immune-suppressive and immune-stimulatory checkpoint molecules following treatment of human glioblastoma cells with RT and/or temozolomide is needed to design feasible radio(chemo)immunotherapy trials for GBM in the future. METHODS: Five human glioblastoma cell lines (H4, HROG-06, U118, U138, U251) were analyzed regarding their clonogenic survival and cell death forms after chemotherapy (CT) with TMZ and/or normofractionated RT (5â€¯× 2 Gy) via multicolor flow cytometry. Further, the tumor cell surface expression of immune-activating (OX40L, CD137L, CD70, and ICOSL) and immune-suppressive (PD-L1, PD-L2, HVEM) checkpoint molecules and of an oncogenic molecule (EGFR) were measured via multicolor flow cytometry after CT and RT alone or after RCT. RESULTS: Normofractionated RT and not TMZ was the trigger of induction of predominantly necrosis in the glioblastoma cells. Notably, clonogenicity did not correlate with cell death induction by RT. The basal expression level of immune-suppressive PD-L1, PD-L2, and HVEM varied in the analyzed glioblastoma cells. RT, but not TMZ, resulted in a significant upregulation of PD-L1 and PD-L2 in all tumor cells investigated. Also, the expression of HVEM was increased after RT in most of the GBM cell lines. In contrast, normofractionated RT individually modulated expression of the stimulating immune checkpoint molecules CD70, CD137L, OX40L, and ICOSL1. The oncogenic factor EGFR was significantly increased by irradiation in all examined cell lines, albeit to a different extent. None of the investigated molecules were downregulated after the treatments. CONCLUSION: Normofractionated radiotherapy modulates the immunogenic as well as the oncogenic phenotype of glioblastoma cells, partly individually. Therefore, not only PD-L1 and PD-L2, but also other immunogenic molecules expressed on the surface of glioblastoma cells could serve as targets for immune checkpoint blockade in combination with RT in the future.

End-to-end testing for stereotactic radiotherapy including the development of a Multi-Modality phantom.

PURPOSE: A new insert for a commercially available end-to-end test phantom was designed and in-house manufactured by 3D printing. Subsequently, the insert was tested for different stereotactic radiation therapy workflows (SRS, SBRT, FSRT, and Multimet) also in comparison to the original insert. MATERIAL AND METHODS: Workflows contained imaging (MR, CT), treatment planning, positioning, and irradiation. Positioning accuracy was evaluated for non-coplanar x-ray, kV- and MV-CBCT systems, as well as surface guided radiation therapy. Dosimetric accuracy of the irradiation was measured with an ionization chamber at four different linear accelerators including dynamic tumor tracking for SBRT. RESULTS: CT parameters of the insert were within the specification. For MR images, the new insert allowed quantitative analysis of the MR distortion. Positioning accuracy of the phantom with the new insert using the imaging systems of the different linacs was < 1 mm/degree also for MV-CBCT and a non-coplanar imaging system which caused > 3 mm deviation with the original insert. Deviation of point dose values was <3% for SRS, FSRT, and SBRT for both inserts. For the Multimet plans deviations exceeded 10% because the ionization chamber was not positioned in each metastasis, but in the center of phantom and treatment plan. CONCLUSION: The in-house manufactured insert performed well in all steps of four stereotactic treatment end-to-end tests. Advantages over the commercially available alternative were seen for quantitative analysis of deformation correction in MR images, applicability for non-coplanar x-ray imaging, and dynamic tumor tracking.

Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning.

Background and purpose: Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this study was to evaluate a commercially available artificial intelligence (AI)-based sCT generation for dose calculation and 2D/2D kV-image daily positioning for brain RT workflow. Materials and methods: T1-VIBE DIXON was acquired at the 1.5 T MRI for 26 patients in RT setup for sCTs generation. For each patient, a volumetric modulated arc therapy (VMAT) plan was optimized on the CT, then recalculated on the sCT; and vice versa. sCT-based digitally reconstructed radiographs (DRRs) were fused with stereoscopic X-ray images recorded as image guidance for clinical treatments. Dosimetric differences between planned/recalculated doses and the differences between the calculated and recorded clinical couch shift/rotation were evaluated. Results: Mean ΔD50 between planned/recalculated doses for target volumes ranged between -0.2 % and 0.2 %; mean ΔD50 and ΔD0.01ccm were -0.6 % and 1.6 % and -1.4 % and 1.0 % for organ-at-risks, respectively. Differences were tested for clinical equivalence using intervals ±2 % (dose), ±1mm (translation), and ±1° (rotation). Dose equivalence was found using ±2 % interval (p < 0.001). The median differences between lat./long./vert. couch shift between CT-based/sCT-based DRRs were 0.3 mm/0.2 mm/0.3 mm (p < 0.05); median differences between lat./long./vert. couch rotation were -1.5°/0.1°/0.1° (after improvement of RT setup: -0.4°/-0.1°/-0.4°, p < 0.05). Conclusions: This in-silico study showed that the AI-based sCT provided equivalent results to the CT for dose calculation and daily stereoscopic X-ray positioning when using an optimal RT setup during MRI acquisition.

Differences and Similarities in the Pattern of Early Metabolic and Morphologic Response after Induction Chemo-Immunotherapy versus Induction Chemotherapy Alone in Locally Advanced Squamous Cell Head and Neck Cancer.

Background: In head and neck cancer patients, parameters of metabolic and morphologic response of the tumor to single-cycle induction chemotherapy (IC) with docetaxel, cis- or carboplatin are used to decide the further course of treatment. This study investigated the effect of adding a double immune checkpoint blockade (DICB) of tremelimumab and durvalumab to IC on imaging parameters and their significance with regard to tumor cell remission. Methods: Response variables of 53 patients treated with IC+DICB (ICIT) were compared with those of 104 who received IC alone. Three weeks after one cycle, pathologic and, in some cases, clinical and endoscopic primary tumor responses were evaluated and correlated with a change in 18F-FDG PET and CT/MRI-based maximum-standardized uptake values (SUVmax) before (SUVmaxpre), after treatment (SUVmaxpost) and residually (resSUVmax in % of SUVmaxpre), and in maximum tumor diameter (Dmax) before (Dmaxpre) and after treatment (Dmaxpost) and residually (resD). Results: Reduction of SUVmax and Dmax occurred in both groups; values were SUVmaxpre: 14.4, SUVmaxpost: 6.6, Dmaxpre: 30 mm and Dmaxpost: 23 mm for ICIT versus SUVmaxpre: 16.5, SUVmaxpost: 6.4, Dmaxpre: 21 mm, and Dmaxpost: 16 mm for IC alone (all p < 0.05). ResSUVmax was the best predictor of complete response (IC: AUC: 0.77; ICIT: AUC: 0.76). Metabolic responders with resSUVmax ≤ 40% tended to have a higher rate of CR to ICIT (88%; n = 15/17) than to IC (65%; n = 30/46; p = 0.11). Of the metabolic nonresponders (resSUVmax > 80%), 33% (n = 5/15) achieved a clinical CR to ICIT versus 6% (n = 1/15) to IC (p = 0.01). Conclusions: ICIT and IC quickly induce a response and 18F-FDG PET is the more accurate modality for identifying complete remission. The rate of discrepant response, i.e., pCR with metabolic nonresponse after ICIT was >30%.

Effects of Hippocampal Sparing Radiotherapy on Brain Microstructure-A Diffusion Tensor Imaging Analysis.

Hippocampal-sparing radiotherapy (HSR) is a promising approach to alleviate cognitive side effects following cranial radiotherapy. Microstructural brain changes after irradiation have been demonstrated using Diffusion Tensor Imaging (DTI). However, evidence is conflicting for certain parameters and anatomic structures. This study examines the effects of radiation on white matter and hippocampal microstructure using DTI and evaluates whether these may be mitigated using HSR. A total of 35 tumor patients undergoing a prospective randomized controlled trial receiving either conventional or HSR underwent DTI before as well as 6, 12, 18, 24, and 30 (±3) months after radiotherapy. Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were measured in the hippocampus (CA), temporal, and frontal lobe white matter (TL, FL), and corpus callosum (CC). Longitudinal analysis was performed using linear mixed models. Analysis of the entire patient collective demonstrated an overall FACC decrease and RDCC increase compared to baseline in all follow-ups; ADCC decreased after 6 months, and MDCC increased after 12 months (p ≤ 0.001, 0.001, 0.007, 0.018). ADTL decreased after 24 and 30 months (p ≤ 0.004, 0.009). Hippocampal FA increased after 6 and 12 months, driven by a distinct increase in ADCA and MDCA, with RDCA not increasing until 30 months after radiotherapy (p ≤ 0.011, 0.039, 0.005, 0.040, 0.019). Mean radiation dose correlated positively with hippocampal FA (p < 0.001). These findings may indicate complex pathophysiological changes in cerebral microstructures after radiation, insufficiently explained by conventional DTI models. Hippocampal microstructure differed between patients undergoing HSR and conventional cranial radiotherapy after 6 months with a higher ADCA in the HSR subgroup (p ≤ 0.034).

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data.

PURPOSE: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small BM are very difficult to detect manually. With the recent development of deep learning technologies, several res earchers have reported promising results in automated brain metastasis detection. However, the detection sensitivity is still not high enough for tiny BM, and integration into clinical practice in regard to differentiating true metastases from false positives (FPs) is challenging. METHODS: The DeepMedic network with the binary cross-entropy (BCE) loss is used as our baseline method. To improve brain metastasis detection performance, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates metastasis detection sensitivity and specificity at a (sub)volume level. As sensitivity and precision are always a trade-off, either a high sensitivity or a high precision can be achieved for brain metastasis detection by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as FP metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Combining a high-sensitivity VSS loss and a high specificity loss for DeepMedic+, the majority of true positive metastases are confirmed with high specificity, while additional metastases candidates in each patient are marked with high sensitivity for detailed expert evaluation. RESULTS: Our proposed VSS loss improves the sensitivity of brain metastasis detection, increasing the sensitivity from 85.3% for DeepMedic with BCE to 97.5% for DeepMedic with VSS. Alternatively, the precision is improved from 69.1% for DeepMedic with BCE to 98.7% for DeepMedic with VSS. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the FP metastases are reduced in the high-sensitivity model and the precision reaches 99.6% for the high-specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high-sensitivity and high-specificity models, on average only 1.5 FP metastases per patient need further check, while the majority of true positive metastases are confirmed. CONCLUSIONS: Our proposed VSS loss and temporal prior improve brain metastasis detection sensitivity and precision. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice. This facilitates metastasis detection and segmentation for neuroradiologists in diagnostic and radiation oncologists in therapeutic clinical applications.