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Koerber SA, Höcht S, Aebersold D, Albrecht C, Boehmer D, Ganswindt U, Schmidt-Hegemann NS, Hölscher T, Mueller AC, Niehoff P, Peeken JC, Pinkawa M, Polat B, Spohn SKB, Wolf F, Zamboglou C, Zips D, Wiegel T. Prostate cancer and elective nodal radiation therapy for cN0 and pN0-a never ending story? : Recommendations from the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO). Strahlenther Onkol 2024; 200:181-187. [PMID: 38273135 PMCID: PMC10876748 DOI: 10.1007/s00066-023-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024]
Abstract
For prostate cancer, the role of elective nodal irradiation (ENI) for cN0 or pN0 patients has been under discussion for years. Considering the recent publications of randomized controlled trials, the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO) aimed to discuss and summarize the current literature. Modern trials have been recently published for both treatment-naïve patients (POP-RT trial) and patients after surgery (SPPORT trial). Although there are more reliable data to date, we identified several limitations currently complicating the definitions of general recommendations. For patients with cN0 (conventional or PSMA-PET staging) undergoing definitive radiotherapy, only men with high-risk factors for nodal involvement (e.g., cT3a, GS ≥ 8, PSA ≥ 20 ng/ml) seem to benefit from ENI. For biochemical relapse in the postoperative situation (pN0) and no PSMA imaging, ENI may be added to patients with risk factors according to the SPPORT trial (e.g., GS ≥ 8; PSA > 0.7 ng/ml). If PSMA-PET/CT is negative, ENI may be offered for selected men with high-risk factors as an individual treatment approach.
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Affiliation(s)
- S A Koerber
- Department of Radiation Oncology, Barmherzige Brüder Hospital Regensburg, Prüfeninger Straße 86, 93049, Regensburg, Germany.
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
| | - S Höcht
- Department of Radiation Oncology, Ernst von Bergmann Hospital Potsdam, Charlottenstraße 72, 14467, Potsdam, Germany
| | - D Aebersold
- Department of Radiation Oncology, Inselspital-Bern University Hospital, University of Bern, Freiburgstraße 4, 3010, Bern, Switzerland
| | - C Albrecht
- Nordstrahl Radiation Oncology Unit, Nürnberg North Hospital, Prof.-Ernst-Nathan-Str. 1, 90149, Nürnberg, Germany
| | - D Boehmer
- Department of Radiation Oncology, University Hospital Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - U Ganswindt
- Department of Radiation Oncology, University Hospital Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - N-S Schmidt-Hegemann
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - T Hölscher
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Fiedlerstraße 19, 01307, Dresden, Germany
| | - A-C Mueller
- Department of Radiation Oncology, RKH Hospital Ludwigsburg, Posilipostraße 4, 71640, Ludwigsburg, Germany
| | - P Niehoff
- Department of Radiation Oncology, Sana Hospital Offenbach, Starkenburgring 66, 63069, Offenbach, Germany
| | - J C Peeken
- Department of Radiation Oncology, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - M Pinkawa
- Department of Radiation Oncology, Robert Janker Klinik, Villenstraße 8, 53129, Bonn, Germany
| | - B Polat
- Department of Radiation Oncology, University Hospital Würzburg, Josef-Schneider-Straße 11, 97080, Würzburg, Germany
| | - S K B Spohn
- Department of Radiation Oncology, University Hospital Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
| | - F Wolf
- Department of Radiation Oncology, Paracelsus Medical University of Salzburg, Müllner Hauptstraße 48, 5020, Salzburg, Austria
| | - C Zamboglou
- Department of Radiation Oncology, University Hospital Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Oncology Center, 1, Nikis Avenue, Agios Athanasios, 4108, Limassol, Cyprus
| | - D Zips
- Department of Radiation Oncology, University Hospital Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - T Wiegel
- Department of Radiation Oncology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
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Kraus KM, Oreshko M, Schnabel JA, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation. Lung Cancer 2024; 189:107507. [PMID: 38394745 DOI: 10.1016/j.lungcan.2024.107507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/08/2023] [Accepted: 02/14/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction. MATERIALS AND METHODS Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose. CONCLUSIONS Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany.
| | - Maksym Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Medical Faculty, University Hospital, LMU Munich, 80539 Munich, Germany
| | - Julia Anne Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Computation, Information and Technology, Technical University of Munich, Germany; Institute of Machine Learning in Biomedical Imaging, Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
| | - Jan Caspar Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), 80336 Munich, Germany
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Buchner JA, Kofler F, Mayinger MC, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, Shafie RE, Rogers S, Schulze K, Blanck O, Zamboglou C, Grosu A, Combs SE, Bernhardt D, Wiestler B, Peeken JC. What MRI Sequences are Necessary for Automated Neural Network-Based Metastasis Segmentation - An Ablation Study. Int J Radiat Oncol Biol Phys 2023; 117:e704-e705. [PMID: 37786065 DOI: 10.1016/j.ijrobp.2023.06.2195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Brain metastasis (BM) delineation is a time-consuming process in both daily clinical practice and research. Automated BM segmentation algorithms can be used to assist in this task. Most approaches to brain tumor segmentation, such as algorithms trained on the BraTS challenge, use four magnetic resonance imaging (MRI) sequences as input, making them susceptible to missing or corrupted sequences and increase the number of sequences necessary for MRI RT planning. The goal of this project is to compare neural networks with different combinations of input sequences for the segmentation of the contrast-enhancing metastasis and the surrounding FLAIR hyperintense edema. All models were tested in a multicenter international external test cohort. This allows us to determine which MRI sequences are needed for effective automated segmentations. MATERIALS/METHODS In total, we had T1-weighted sequences without (T1) and with contrast enhancement (T1-CE), T2-weighted sequences (T2), and T2 fluid-attenuated inversion recovery (FLAIR) sequences from 339 patients with at least one brain metastasis from seven centers available. Preprocessing yielded co-registered, skull-stripped sequences with an isotropic resolution of 1 millimeter. The contrast-enhancing metastasis as well as the surrounding FLAIR hyperintense edema were manually segmented to create reference labels. A baseline 3D U-Net with all four sequences as well as six additional U-Nets with different clinically plausible combinations (T1-CE; T1; FLAIR; T1-CE+FLAIR; T1-CE+T1+FLAIR; T1-CE+T1) of input sequences were trained on a cohort of 239 patients from two centers and subsequently tested on an external cohort of 100 patients from the remaining five centers. RESULTS All models that included T1-CE in their selected sequences showed similar performance for metastasis segmentation with a median Dice similarity coefficient (DSC) of 0.93-0.96. T1-CE alone likewise achieved a performance of 0.96 (IQR 0.93-0.97). The model trained with only FLAIR performed worse (DSC = 0.73, IQR 0.54-0.84). For edema segmentation, models that included both T1-CE and FLAIR performed best (median DSC = 0.93), while the remaining four models without simultaneous inclusion of these two sequences (T1-CE; T1; FLAIR; T1-CE+T1) reached a median DSC of 0.81-0.89. CONCLUSION Automatic segmentation of brain metastases with less than four input sequences is feasible with minimal or no loss of quality. A T1-CE-only protocol suffices for metastasis segmentation. In contrast, for edema segmentation, the combination of T1-CE and FLAIR seems to be important. Missing either T1-CE or FLAIR decreases performance. These findings may improve future imaging routines by omitting unnecessary sequences, thus speeding up procedures in daily clinical practice while allowing for optimal neural network-based target definitions.
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Affiliation(s)
- J A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - F Kofler
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany; Department of Informatics, Technical University of Munich, Munich, Germany
| | - M C Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - T B Brunner
- Medical University of Graz, Dept. of Radiation Oncology, Graz, Austria; Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - A Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - B Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - C Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - B Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - R El Shafie
- Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - S Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - K Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - O Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - C Zamboglou
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus; Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - A Grosu
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - S E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - D Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany
| | - B Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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Kraus KM, Oreshko M, Bernhardt D, Combs SE, Peeken JC. The Value of Equivalent Dose Calculation for Dosiomics and Radiomics-Based Prediction of Pneumonitis after Thoracic Radiotherapy with Immune Checkpoint Inhibition. Int J Radiat Oncol Biol Phys 2023; 117:e473. [PMID: 37785503 DOI: 10.1016/j.ijrobp.2023.06.1683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Post-therapy pneumonitis (PTP) is a relevant side effect after thoracic radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICI). The impact of the combination of both is unclear. We aim to improve risk estimation by prediction of PTP with and without ICI therapy. To analyze the influence of different fractionation schemes, the value of voxel-wise 2 Gy equivalent dose (EQD2) is investigated. MATERIALS/METHODS Clinical data from 100 patients who received fractionated RT (single dose ≤ 3Gy) RT were collected. 36 patients received additional ICI therapy. PTP of all grades were monitored. Planning Computed tomographies (CTs), segmentations and 3D dose data were extracted and converted to EQD2. Dosiomics and radiomics features were extracted using 1000-fold bootstrapping using Pearson intercorrelation and the Boruta algorithm for 5 single and 4 combined predictive models. Machine learning algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using a 5-fold nested cross validation approach and Synthetic Minority Oversampling Technique resampling in R. Analysis was performed using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS All investigated models predicted PTP better than random (AUC>.5) (Table 1). Dosiomics+Radiomics models based on EQD2 using rf classifier resulted in the highest predictive performance (AUC = .83 (95% Confidence Interval .83-.84)) and performed worse on physical dose data (AUC = .72 (.71-.73)). For single models, radiomics and dosiomics achieved the best prediction (AUC = .73 (.72-.74), AUC = .8 (.79-.81)) for physical dose and EQD2, respectively. Clinical factors and ICI therapy (AUC = .6 (.59-.62)) had minor impact on PTP prediction. Table 1: AUC and 95% confidence intervals (CI) for all investigated Machine Learning models for EQD2 and physical doses (D). CONCLUSION Dosiomics+Radiomics machine learning models have strong capability of PTP prediction and could contribute to pre-treatment decision making. Fractionation schemes should be considered for dose-based prediction strategies. Additional ICI therapy has limited impact on PTP prediction.
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Affiliation(s)
- K M Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
| | - M Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Medical Faculty, University Hospital, LMU Munich, Munich, Germany
| | - D Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Germany, Munich, Germany
| | - S E Combs
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany; Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
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Bernhardt D, Peeken JC, Kehl V, Eitz K, Guckenberger M, Andratschke N, Mayinger MC, Lindel K, Dieckmann K, El Shafie R, Debus J, Riesterer O, Rogers S, Blanck O, Wolff R, Grosu A, Bilger A, Henkenberens C, Schulze K, Gani C, Müller AC, Radlanski K, Janssen S, Ferentinos K, Combs SE. Post-Operative Stereotactic Radiotherapy for Resected Brain Metastases: Results of the Multicenter Analysis (AURORA) of the German Working Group "Stereotactic Radiotherapy". Int J Radiat Oncol Biol Phys 2023; 117:e87-e88. [PMID: 37786203 DOI: 10.1016/j.ijrobp.2023.06.842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) While the results of prospective studies support the use of postoperative stereotactic radiotherapy (RT) to the resection cavity (RC) as the standard of care after surgery, there are several issues that need to be investigated such as factors for improving local control, risk of leptomeningeal disease and radiation necrosis. Further, the optimal dose and fractionation is still under debate. MATERIALS/METHODS The working group "Stereotactic Radiotherapy" of the German Society of Radiation Oncology (DEGRO) analyzed its multi-institutional database with 661 patients who received postoperative stereotactic RT to the RC. Treatment was performed at 13 centers between 2008 and 2021. Patient characteristics, treatment details, and follow-up data including overall survival (OS), local control (LC) were evaluated. Cox Regression and Kaplan-Meier curves with Log-rank Tests were calculated for selected variables. RESULTS In this retrospective study, overall survival was 61.5% at 1 year, 47.6% at 2 years, and 35.5% at 3 years, and local control was 84.6% at 1 year, 74.8% at 2 years, and 72.8% at 3 years. 96% of patients were treated with hypofractionated stereotactic radiotherapy (HSRT), only 26 patients received single fraction radiosurgery (4%). Prognostic factors associated with overall survival were Karnofsky Performance Status, RPA and GPA class, controlled primary tumor and absence of extracranial metastases, whereas prognostic factor associated with local control was planning target volume (23 mL or less). CONCLUSION HSRT is the most common fractionation form in the treatment of RCs in this multicenter analysis. This approach results in excellent OS and LC outcomes. OS in patients with resected brain metastases is mainly influenced by performance status. In regard to local control, RT of large cavities remain a challenge with significantly worse outcome.
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Affiliation(s)
- D Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, Germany, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - V Kehl
- Institute for AI and Informatics in Medicine, Munich, NA, Germany
| | - K Eitz
- Department of Radiation Oncology - Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital Zurich (USZ), University of Zurich (UZH), Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - M C Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - K Lindel
- Municipal Hospital, Department for Radiation Oncology, Karlsruhe, Germany
| | - K Dieckmann
- Department of Radiation Oncology, Vienna, Austria
| | - R El Shafie
- 8Department of Radiation Oncology, University Hospital Göttingen, Göttingen, Germany
| | - J Debus
- CCU Translational Radiation Oncology, German Cancer Consortium (DKTK) Core-Center Heidelberg, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Heidelberg, Germany; Radiation Oncology University Hospital Heidelberg, Heidelberg, Germany
| | - O Riesterer
- Center for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - S Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - O Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - R Wolff
- University Hospital Frankfurt, Department of Neurosurgery, Frankfurt, Germany
| | - A Grosu
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - A Bilger
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - C Henkenberens
- Department of Radiotherapy and Special Oncology, Medical School Hannover, Hannover, Germany
| | - K Schulze
- Klinikum Fulda, 36251 Bad Hersfeld, Germany
| | - C Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
| | - A C Müller
- Department of Radiotherapy, Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - K Radlanski
- Radiation Oncology and Radiotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - S Janssen
- Department of Radiation Oncology, University of Lübeck, Lübeck, Germany
| | - K Ferentinos
- Radiation Oncology Department, German Oncology Center, Limassol, Cyprus
| | - S E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
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6
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Kraus KM, Oreshko M, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition. Front Oncol 2023; 13:1124592. [PMID: 37007119 PMCID: PMC10050584 DOI: 10.3389/fonc.2023.1124592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionPneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.MethodsWe investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.ResultsResults were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.ConclusionOur results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
- *Correspondence: Kim Melanie Kraus,
| | - Maksym Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Medical Faculty, University hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Jan Caspar Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
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Abstract
INTRODUCTION Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. RESULTS Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. DISCUSSION Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. CONCLUSION This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.
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Affiliation(s)
- Jan Caspar Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany.
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
- Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
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