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3,244 results on '"Combs, Stephanie"'

4. Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma

Author

Papargyriou, Aristeidis, Najajreh, Mulham, Cook, David P., Maurer, Carlo H., Bärthel, Stefanie, Messal, Hendrik A., Ravichandran, Sakthi K., Richter, Till, Knolle, Moritz, Metzler, Thomas, Shastri, Akul R., Öllinger, Rupert, Jasper, Jacob, Schmidleitner, Laura, Wang, Surui, Schneeweis, Christian, Ishikawa-Ankerhold, Hellen, Engleitner, Thomas, Mataite, Laura, Semina, Mariia, Trabulssi, Hussein, Lange, Sebastian, Ravichandra, Aashreya, Schuster, Maximilian, Mueller, Sebastian, Peschke, Katja, Schäfer, Arlett, Dobiasch, Sophie, Combs, Stephanie E., Schmid, Roland M., Bausch, Andreas R., Braren, Rickmer, Heid, Irina, Scheel, Christina H., Schneider, Günter, Zeigerer, Anja, Luecken, Malte D., Steiger, Katja, Kaissis, Georgios, van Rheenen, Jacco, Theis, Fabian J., Saur, Dieter, Rad, Roland, and Reichert, Maximilian

Published
2024
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5. Analysis of clinical, dosimetric and radiomic features for predicting local failure after stereotactic radiotherapy of brain metastases in malignant melanoma

Author

Hartong, Nanna E., Sachpazidis, Ilias, Blanck, Oliver, Etzel, Lucas, Peeken, Jan C., Combs, Stephanie E., Urbach, Horst, Zaitsev, Maxim, Baltas, Dimos, Popp, Ilinca, Grosu, Anca-Ligia, and Fechter, Tobias

Subjects
Physics - Medical Physics, Computer Science - Machine Learning
Abstract

Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM). Methods: In this multicenter, retrospective analysis, we reviewed 517 MBM from 130 patients treated with SRT (single fraction or hypofractionated). For each gross tumor volume (GTV) 1576 radiomic features (RF) were calculated (788 each for the GTV and for a 3 mm margin around the GTV). Clinical parameters, radiation dose and RF from pretherapeutic contrast-enhanced T1-weighted MRI from different institutions were evaluated with a feature processing and elimination pipeline in a nested cross-validation scheme. Results: Seventy-two (72) of 517 lesions (13.9%) showed a local failure (LF) after SRT. The processing pipeline showed clinical, dosimetric and radiomic features providing information for LF prediction. The most prominent ones were the correlation of the gray level co-occurrence matrix of the margin (hazard ratio (HR): 0.37, confidence interval (CI): 0.23-0.58) and systemic therapy before SRT (HR: 0.55, CI: 0.42-0.70). The majority of RF associated with LF was calculated in the margin around the GTV. Conclusions: Pretherapeutic MRI based RF connected with lesion-specific outcome after SRT could be identified, despite multicentric data and minor differences in imaging protocols. Image data analysis of the surrounding metastatic environment may provide therapy-relevant information with the potential to further individualize radiotherapy strategies.

Published
2024

6. Resolving spatial response heterogeneity in glioblastoma

Author

Ziegenfeuter, Julian, Delbridge, Claire, Bernhardt, Denise, Gempt, Jens, Schmidt-Graf, Friederike, Hedderich, Dennis, Griessmair, Michael, Thomas, Marie, Meyer, Hanno S, Zimmer, Claus, Meyer, Bernhard, Combs, Stephanie E, Yakushev, Igor, Metz, Marie-Christin, and Wiestler, Benedikt

Published
2024
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7. Full-body deep learning-based automated contouring of contrast-enhanced murine organs for small animal irradiator CBCT

Author

Cramer, Ethan, Dobiasch, Sophie, Liu, Xinmin, Combs, Stephanie E., and Wiersma, Rodney D.

Subjects
Physics - Medical Physics
Abstract

Purpose. To alleviate the manual contouring burden, deep learning (DL) based automated contouring has been explored. However, due to the poor contrast resolution of preclinical irradiator CBCT, these methods have been limited to high contrast - minimally anatomically complex - structures such as the heart and lungs. Thus, low contrast abdominal CBCT DL-based segmentation has yet to be addressed. In this work we explore a DL-based model in conjunction with iodine-based contrast agent approach to allow precise automatic contouring of mouse abdominal, thorax, and skeletal structures in under a second. Methods. A DL U-net-like architecture was trained to contour mice organs in small animal radiation research platform CBCT scans. 41 mice were contoured by a human expert, using semi-automatic segmentation methods, after injection of iodine contrast agent, establishing a ground truth for the DL model. The model was trained on a dataset of 26 mice, while 2 mice were used for validation, tuning the model during training, and 15 mice used for performance evaluation testing. The model consists of a pre-processor, and a post-processor for volumetric reconstruction of the DL-predicted probability maps. Model performance was evaluated using both qualitative and distance metrics, including the dice similarity score, precision score, Hausdorff Distance (HD), and mean surface distance (MSD). Results. Performance of the DL-based iodine contrast-enhanced model provided high quality predicted contours in under a second, with the median for all organs being reported: dice $>$ 91\%, precision $>$ 95\%, HD50 $<$ 1.0 mm, and MSD $<$ 1.41 mm. Conclusion. The proposed combination of a DL-based and iodine contrast-enhanced model proved as a viable method to vastly improve efficiency of small animal CBCT image-guided RT preclinical trials.

Published
2024

12. All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions

Author

Erdur, Ayhan Can, Scholz, Daniel, Buchner, Josef A., Combs, Stephanie E., Rueckert, Daniel, and Peeken, Jan C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of patients having multiple BMs with stereo tactic radiosurgery necessitates accurate localization of the metastases. Neural networks can assist in this time-consuming and costly task that is typically performed by human experts. Particularly challenging is the detection of small lesions since they are often underrepresented in exist ing approaches. Yet, lesion detection is equally important for all sizes. In this work, we develop an ensemble of neural networks explicitly fo cused on detecting and segmenting small BMs. To accomplish this task, we trained several neural networks focusing on individual aspects of the BM segmentation problem: We use blob loss that specifically addresses the imbalance of lesion instances in terms of size and texture and is, therefore, not biased towards larger lesions. In addition, a model using a subtraction sequence between the T1 and T1 contrast-enhanced sequence focuses on low-contrast lesions. Furthermore, we train additional models only on small lesions. Our experiments demonstrate the utility of the ad ditional blob loss and the subtraction sequence. However, including the specialized small lesion models in the ensemble deteriorates segmentation results. We also find domain-knowledge-inspired postprocessing steps to drastically increase our performance in most experiments. Our approach enables us to submit a competitive challenge entry to the ASNR-MICCAI BraTS Brain Metastasis Challenge 2023.

Published
2023

13. Mobile App Delivery of the EORTC QLQ-C30 Questionnaire to Assess Health-Related Quality of Life in Oncological Patients: Usability Study

Author

Kessel, Kerstin A, Vogel, Marco ME, Alles, Anna, Dobiasch, Sophie, Fischer, Hanna, and Combs, Stephanie E

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMobile apps are evolving in the medical field. However, ongoing discussions have questioned whether such apps are really valuable and whether patients will accept their use in day-to-day clinical life. Therefore, we initiated a usability study in our department. ObjectiveWe present our results of the first app prototype and patient testing of health-related quality of life (HRQoL) assessment in oncological patients. MethodsWe developed an app prototype for the iOS operating system within eight months in three phases: conception, initial development, and pilot testing. For the HRQoL assessment, we chose to implement only the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30; German version 3). Usability testing was conducted for three months. Participation was voluntary and pseudonymized. After completion of the QLQ-C30 questionnaire using iPads provided by our department, we performed a short survey with 10 questions. This survey inquired about patients’ opinions regarding general aspects, including technical advances in medicine, mobile and app assistance during cancer treatment, and the app-specific functions (eg, interface and navigation). ResultsAfter logging into the app, the user can choose between starting a questionnaire, reviewing answers (administrators only), and logging out. The questionnaire is displayed with the same information, questions, and answers as on the original QLQ-C30 sheet. No alterations in wording were made. Usability was tested with 81 patients; median age was 55 years. The median time for completing the HRQoL questionnaire on the iPad was 4.0 minutes. Of all participants, 84% (68/81) owned a mobile device. Similarly, 84% (68/81) of participants would prefer a mobile version of the HRQoL questionnaire instead of a paper-based version. Using the app in daily life during and after cancer treatment would be supported by 83% (67/81) of participants. In the prototype version of the app, data were stored on the device; in the future, 79% (64/81) of the patients would agree to transfer data via the Internet. ConclusionsOur usability test showed good results regarding attractiveness, operability, and understandability. Moreover, our results demonstrate a high overall acceptance of mobile apps and telemedicine in oncology. The HRQoL assessment via the app was accepted thoroughly by patients, and individuals are keen to use it in clinical routines, while data privacy and security must be ensured.

Published
2018
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15. Mobile Health in Oncology: A Patient Survey About App-Assisted Cancer Care

Author

Kessel, Kerstin Anne, Vogel, Marco ME, Kessel, Carmen, Bier, Henning, Biedermann, Tilo, Friess, Helmut, Herschbach, Peter, von Eisenhart-Rothe, Rüdiger, Meyer, Bernhard, Kiechle, Marion, Keller, Ulrich, Peschel, Christian, Schmid, Roland M, and Combs, Stephanie E

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundIn the last decade, the health care sector has been enriched by numerous innovations such as apps and connected devices that assist users in weight reduction and diabetes management. However, only a few native apps in the oncological context exist, which support patients during treatment and aftercare. ObjectiveThe objective of this study was to analyze patients’ acceptance regarding app use and to investigate the functions of an oncological app that are most required, and the primary reasons for patients to refuse app-assisted cancer care. MethodsWe designed and conducted a survey with 23 questions, inquiring patients about their technical knowledge and equipment, as well as the possible advantages and disadvantages, data transfer, and general functionality of an app. ResultsA total of 375 patients participated; the participation rate was 60.7% (375/618). Gender distribution was about 3:4 (female:male) with a median age of 59 years (range 18-92 years). Whereas 69.6% (261/375) of patients used mobile devices, 16.3% (61/375) did not own one, and 9.1% (34/375) only used a personal computer (PC). About half of the patients rated their usability skills as very good and good (18.9% 71/375; 35.2% 132/375), 23.5% (88/375) described their skills as intermediate, and 14.4% (54/375) as bad. Of all patients, 182 (48.5%, 182/375) were willing to send data to their treating clinic via an app, that is, to a server (61.0% 111/182) or as email (33.5%, 61/182). About two-thirds (68.7%, 125/182) believed that additional and regularly sent data would be an ideal complement to the standard follow-up procedure. Additionally, 86.8% (158/182) wished to be contacted by a physician when entered data showed irregularities. Because of lack of skills (34.4%, 56/163), concerns about the use of data (35.0%, 57/163), lack of capable devices (25.8%, 42/163), and the wish for personal contact with the treating physician (47.2%, 77/163), a total of 163 (43.5%, 163/375) patients refused to use an app. Pearson correlation showed a significant but mild relationship between age and app use (P=.03, r=−.12), favoring younger age; male gender correlated as well (P=.04; r=−.11). ConclusionsThe results show that the introduction of mobile apps needs to follow different strategies depending on the patients’ attitude. Age and gender seem to be the strongest predictive factors. For oncology patients, our survey showed that about half of the patients were willing to send data via an app supporting their treatment. In the future, clinical data such as quality of life and treatment satisfaction recorded by mobile health (mHealth) devices could be used to evaluate and improve therapy workflow. Furthermore, apps could support classical visits, document adverse effects, and remind patients of treatment dates or drug intake.

Published
2017
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16. Comparison of first-line radiosurgery for small-cell and non-small cell lung cancer brain metastases (CROSS-FIRE).

Author

Rusthoven, Chad, Staley, Alyse, Gao, Dexiang, Yomo, Shoji, Bernhardt, Denise, Wandrey, Narine, El Shafie, Rami, Kraemer, Anna, Padilla, Oscar, Chiang, Veronica, Faramand, Andrew, Palmer, Joshua, Zacharia, Brad, Wegner, Rodney, Hattangadi-Gluth, Jona, Levy, Antonin, Bernstein, Kenneth, Mathieu, David, Cagney, Daniel, Chan, Michael, Grills, Inga, Lee, Cheng-Chia, Sheehan, Jason, Kluwe, Christien, Patel, Samir, Halasz, Lia, Andratschke, Nicolaus, Deibert, Christopher, Verma, Vivek, Trifiletti, Daniel, Cifarelli, Christopher, Debus, Jürgen, Combs, Stephanie, Sato, Yasunori, Higuchi, Yoshinori, Aoyagi, Kyoko, Brown, Paul, Alami, Vida, Niranjan, Ajay, Lunsford, L, Kondziolka, Douglas, Camidge, D, Kavanagh, Brian, Robin, Tyler, Serizawa, Toru, Yamamoto, Masaaki, and Braunstein, Steve

Subjects
Humans, Carcinoma, Non-Small-Cell Lung, Lung Neoplasms, Retrospective Studies, Radiosurgery, Prospective Studies, Small Cell Lung Carcinoma, ErbB Receptors, Brain Neoplasms
Abstract

INTRODUCTION: Historical reservations regarding stereotactic radiosurgery (SRS) for small-cell lung cancer (SCLC) brain metastases include concerns for short-interval and diffuse central nervous system (CNS) progression, poor prognoses, and increased neurological mortality specific to SCLC histology. We compared SRS outcomes for SCLC and non-small cell lung cancer (NSCLC) where SRS is well established. METHODS: Multicenter first-line SRS outcomes for SCLC and NSCLC from 2000 to 2022 were retrospectively collected (n = 892 SCLC, n = 4785 NSCLC). Data from the prospective Japanese Leksell Gamma Knife Society (JLGK0901) clinical trial of first-line SRS were analyzed as a comparison cohort (n = 98 SCLC, n = 814 NSCLC). Overall survival (OS) and CNS progression were analyzed using Cox proportional hazard and Fine-Gray models, respectively, with multivariable adjustment for cofactors including age, sex, performance status, year, extracranial disease status, and brain metastasis number and volume. Mutation-stratified analyses were performed in propensity score-matched retrospective cohorts of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) positive NSCLC, mutation-negative NSCLC, and SCLC. RESULTS: OS was superior for patients with NSCLC compared to SCLC in the retrospective dataset (median OS = 10.5 vs 8.6 months; P

Published
2023

18. Radiation therapy for cancer is potentially associated with reduced growth of concomitant abdominal aortic aneurysm

Author

Becker von Rose, Aaron, Kobus, Kathrin, Bohmann, Bianca, Lindquist-Lilljequist, Moritz, Eilenberg, Wolf, Kapalla, Marvin, Bassermann, Florian, Reeps, Christian, Eckstein, Hans-Henning, Neumayer, Christoph, Brostjan, Christine, Roy, Joy, von Heckel, Korbinian, Hultgren, Rebecka, Schwaiger, Benedikt J., Combs, Stephanie E., Busch, Albert, and Schiller, Kilian

Published
2024
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19. DEGRO guideline for personalized radiotherapy of brain metastases and leptomeningeal carcinomatosis in patients with breast cancer

Author

Borm, Kai J., Behzadi, Sophie T., Hörner-Rieber, Juliane, Krug, David, Baumann, Rene, Corradini, Stefanie, Duma, Marciana Nona, Dunst, Jürgen, Fastner, Gerd, Feyer, Petra, Fietkau, Rainer, Haase, Wulf, Harms, Wolfgang, Hehr, Thomas, Matuschek, Christiane, Piroth, Marc D., Schmeel, Leonard Christopher, Souchon, Rainer, Strnad, Vratislav, Budach, Wilfried, and Combs, Stephanie E.

Published
2024
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20. All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions

Author

Erdur, Ayhan Can, Scholz, Daniel, Buchner, Josef A., Combs, Stephanie E., Rueckert, Daniel, Peeken, Jan C., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Baid, Ujjwal, editor, Dorent, Reuben, editor, Malec, Sylwia, editor, Pytlarz, Monika, editor, Su, Ruisheng, editor, Wijethilake, Navodini, editor, Bakas, Spyridon, editor, and Crimi, Alessandro, editor

Published
2024
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21. Vestibular Schwannoma

Author

Mayinger, Michael, Lo, Simon S., Combs, Stephanie E., Chang, Eric L., editor, Brown, Paul D., editor, Lo, Simon S., editor, Sahgal, Arjun, editor, and Suh, John H., editor

Published
2024
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22. Radiosurgery Society Case-Based Guide to Stereotactic Body Radiation Therapy for Challenging Cases of Spinal Metastases

Author

Aziz, Khaled, Koffler, Daniel, Vassantachart, April, Rattani, Abbas, Ankrah, Nii-Kwanchie, Gogineni, Emile, Andraos, Therese Y., Sahgal, Arjun, Vellayappan, Balamurugan, Dunne, Emma M., Siva, Shankar, Moraes, Fabio Y., Guckenberger, Matthias, Lubelski, Daniel, Chao, Samuel, Combs, Stephanie, Chang, Eric, Amin, Anubhav G., Foote, Matthew, Gibbs, Iris, Kim, Minsun, Palmer, Joshua, Lo, Simon, and Redmond, Kristin J.

Published
2025
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25. A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Author

Navarro, Fernando, Sasahara, Guido, Shit, Suprosanna, Ezhov, Ivan, Peeken, Jan C., Combs, Stephanie E., and Menze, Bjoern H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of $0.9260\pm 0.18 \%$ on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.

Published
2022

28. Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas

Author

Peeken, Jan C., Etzel, Lucas, Tomov, Tim, Münch, Stefan, Schüttrumpf, Lars, Shaktour, Julius H., Kiechle, Johannes, Knebel, Carolin, Schaub, Stephanie K., Mayr, Nina A., Woodruff, Henry C., Lambin, Philippe, Gersing, Alexandra S., Bernhardt, Denise, Nyflot, Matthew J., Menze, Bjoern, Combs, Stephanie E., and Navarro, Fernando

Published
2024
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31. Carbon ion radiotherapy of hepatocellular carcinoma provides excellent local control: The prospective phase I PROMETHEUS trial

Author

Hoegen-Saßmannshausen, Philipp, Naumann, Patrick, Hoffmeister-Wittmann, Paula, Ben Harrabi, Semi, Seidensaal, Katharina, Weykamp, Fabian, Mielke, Thomas, Ellerbrock, Malte, Habermehl, Daniel, Springfeld, Christoph, Dill, Michael T., Longerich, Thomas, Schirmacher, Peter, Mehrabi, Arianeb, Chang, De-Hua, Hörner-Rieber, Juliane, Jäkel, Oliver, Haberer, Thomas, Combs, Stephanie E., Debus, Jürgen, Herfarth, Klaus, and Liermann, Jakob

Published
2024
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32. Evaluating the Robustness of Self-Supervised Learning in Medical Imaging

Author

Navarro, Fernando, Watanabe, Christopher, Shit, Suprosanna, Sekuboyina, Anjany, Peeken, Jan C., Combs, Stephanie E., and Menze, Bjoern H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared to fully-supervised learning in the context of medical imaging. Our experiments on pneumonia detection in X-rays and multi-organ segmentation in CT yield consistent results exposing the hidden benefits of self-supervision for learning robust feature representations.

Published
2021

35. Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Author

Lang, Daniel M., Peeken, Jan C., Combs, Stephanie E., Wilkens, Jan J., and Bartzsch, Stefan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine tuned such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet the video pre-trained model performed best., Comment: 15 pages, 2 figures

Published
2020

36. Deep Reinforcement Learning for Organ Localization in CT

Author

Navarro, Fernando, Sekuboyina, Anjany, Waldmannstetter, Diana, Peeken, Jan C., Combs, Stephanie E., and Menze, Bjoern H.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis. In contrast to current solutions based on exhaustive search or region proposals, which require large amounts of annotated data, we propose a deep reinforcement learning approach for organ localization in CT. In this work, an artificial agent is actively self-taught to localize organs in CT by learning from its asserts and mistakes. Within the context of reinforcement learning, we propose a novel set of actions tailored for organ localization in CT. Our method can use as a plug-and-play module for localizing any organ of interest. We evaluate the proposed solution on the public VISCERAL dataset containing CT scans with varying fields of view and multiple organs. We achieved an overall intersection over union of 0.63, an absolute median wall distance of 2.25 mm, and a median distance between centroids of 3.65 mm., Comment: Accepted paper in MIDL 2020

Published
2020

39. Impact of the SARS-CoV-2 pandemic on the survival of patients with high-grade glioma and best practice recommendations

Author

Vogel, Marco M. E., Wagner, Arthur, Gempt, Jens, Krenzlin, Harald, Zeyen, Thomas, Drexler, Richard, Voss, Martin, Nettekoven, Charlotte, Abboud, Tammam, Mielke, Dorothee, Rohde, Veit, Timmer, Marco, Goldbrunner, Roland, Steinbach, Joachim P., Dührsen, Lasse, Westphal, Manfred, Herrlinger, Ulrich, Ringel, Florian, Meyer, Bernhard, and Combs, Stephanie E.

Published
2023
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41. The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts

Author

Linge, Annett, Patil, Shivaprasad, Grosser, Marianne, Lohaus, Fabian, Gurtner, Kristin, Kemper, Max, Gudziol, Volker, Haim, Dominik, Nowak, Alexander, Tinhofer, Inge, Zips, Daniel, Guberina, Maja, Stuschke, Martin, Balermpas, Panagiotis, Rödel, Claus, Schäfer, Henning, Grosu, Anca-Ligia, Abdollahi, Amir, Debus, Jürgen, Ganswindt, Ute, Belka, Claus, Pigorsch, Steffi, Combs, Stephanie E., Boeke, Simon, Gani, Cihan, Jöhrens, Korinna, Baretton, Gustavo B., Löck, Steffen, Baumann, Michael, and Krause, Mechthild

Published
2024
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45. Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

Author

Spohn, Simon K. B., Schmidt-Hegemann, Nina-Sophie, Ruf, Juri, Mix, Michael, Benndorf, Matthias, Bamberg, Fabian, Makowski, Marcus R., Kirste, Simon, Rühle, Alexander, Nouvel, Jerome, Sprave, Tanja, Vogel, Marco M. E., Galitsnaya, Polina, Gschwend, Jürgen E., Gratzke, Christian, Stief, Christian, Löck, Steffen, Zwanenburg, Alex, Trapp, Christian, Bernhardt, Denise, Nekolla, Stephan G., Li, Minglun, Belka, Claus, Combs, Stephanie E., Eiber, Matthias, Unterrainer, Lena, Unterrainer, Marcus, Bartenstein, Peter, Grosu, Anca-L., Zamboglou, Constantinos, and Peeken, Jan C.

Published
2023
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46. Identifying core MRI sequences for reliable automatic brain metastasis segmentation

Author

Buchner, Josef A, Peeken, Jan C, Etzel, Lucas, Ezhov, Ivan, Mayinger, Michael, Christ, Sebastian M, Brunner, Thomas B, Wittig, Andrea, Menze, Bjoern H, Zimmer, Claus, Meyer, Bernhard, Guckenberger, Matthias, Andratschke, Nicolaus, El Shafie, Rami A, Debus, Jürgen, Rogers, Susanne, Riesterer, Oliver, Schulze, Katrin, Feldmann, Horst J, Blanck, Oliver, Zamboglou, Constantinos, Ferentinos, Konstantinos, Bilger, Angelika, Grosu, Anca L, Wolff, Robert, Kirschke, Jan S, Eitz, Kerstin A, Combs, Stephanie E, Bernhardt, Denise, Rueckert, Daniel, Piraud, Marie, Wiestler, Benedikt, and Kofler, Florian

Published
2023
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47. Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification

Author

Thammasorn, Phawis, Hippe, Daniel, Chaovalitwongse, Wanpracha, Spraker, Matthew, Wootton, Landon, Nyflot, Matthew, Combs, Stephanie, Peeken, Jan, and Ford, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep representation learning using triplet network for classification suffers from a lack of theoretical foundation and difficulty in tuning both the network and classifiers for performance. To address the problem, local-margin triplet loss along with local positive and negative mining strategy is proposed with theory on how the strategy integrate nearest-neighbor hyper-parameter with triplet learning to increase subsequent classification performance. Results in experiments with 2 public datasets, MNIST and Cifar-10, and 2 small medical image datasets demonstrate that proposed strategy outperforms end-to-end softmax and typical triplet loss in settings without data augmentation while maintaining utility of transferable feature for related tasks. The method serves as a good performance baseline where end-to-end methods encounter difficulties such as small sample data with limited allowable data augmentation., Comment: Triplet Network, Representation Learning, Transfer Learning, Nearest Neighbor, Medical Image Classification

Published
2019

48. Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

Author

Navarro, Fernando, Shit, Suprosanna, Ezhov, Ivan, Paetzold, Johannes, Gafita, Andrei, Peeken, Jan, Combs, Stephanie, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis. We address this problem from an organ-specific shape-prior learning perspective. We introduce the idea of complementary-task learning to enforce shape-prior leveraging the existing target labels. We propose two complementary-tasks namely i) distance map regression and ii) contour map detection to explicitly encode the geometric properties of each organ. We evaluate the proposed solution on the public VISCERAL dataset containing CT scans of multiple organs. We report a significant improvement of overall dice score from 0.8849 to 0.9018 due to the incorporation of complementary-task learning., Comment: Accepted in MLMI Workshop 2019 MICCAI

Published
2019

50. The maximum standardized uptake value in patients with recurrent or persistent prostate cancer after radical prostatectomy and PSMA-PET-guided salvage radiotherapy—a multicenter retrospective analysis

Author

Spohn, Simon K. B., Farolfi, Andrea, Schandeler, Sarah, Vogel, Marco M. E., Ruf, Juri, Mix, Michael, Kirste, Simon, Ceci, Francesco, Fanti, Stefano, Lanzafame, Helena, Serani, Francesca, Gratzke, Christian, Sigle, August, Combs, Stephanie E., Bernhardt, Denise, Gschwend, Juergen E., Buchner, Josef A., Trapp, Christian, Belka, Claus, Bartenstein, Peter, Unterrainer, Lena, Unterrainer, Marcus, Eiber, Matthias, Nekolla, Stephan G., Schiller, Kilian, Grosu, Anca L., Schmidt-Hegemann, Nina-Sophie, Zamboglou, Constantinos, and Peeken, Jan C.

Published
2022
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