GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Frontiers Media SA  (6)
  • Semrau, Sabine  (6)
  • 1
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 10 ( 2020-10-29)
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-10-21)
    Abstract: To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma. Methods Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance. Results Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p & lt; 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914). Conclusions DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 10 ( 2020-9-30)
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-9-14)
    Abstract: 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 & amp; eye, pediatrics, biology, and physics than knowledge of bone & amp; 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.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    Online Resource
    Online Resource
    Frontiers Media SA ; 2024
    In:  Frontiers in Oncology Vol. 14 ( 2024-4-25)
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 14 ( 2024-4-25)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 6
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-2-16)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...