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Yin R, Dou Z, Wang Y, Zhang Q, Guo Y, Wang Y, Chen Y, Zhang C, Li H, Jian X, Qi L, Ma W. Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00231-9. [PMID: 38693025 DOI: 10.1016/j.acra.2024.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 05/03/2024]
Abstract
RATIONALE AND OBJECTIVES Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC. METHODS In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis. RESULTS The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632). CONCLUSION The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC.
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Affiliation(s)
- Rui Yin
- National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China; School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin 300203, China
| | - Zhaoxiang Dou
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Yanyan Wang
- Department of CT and MRI, Shanxi Tumor Hospital, Taiyuan 030013, China
| | - Qian Zhang
- Department of Radiology, Baoding No. 1 Central Hospital, Baoding 071030, China
| | - Yijun Guo
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Yigeng Wang
- Department of Radiology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Ying Chen
- Department of Gynecologic Oncology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Chao Zhang
- Department of Bone Cancer, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Huiyang Li
- Department of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiqi Jian
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin 300203, China
| | - Lisha Qi
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wenjuan Ma
- Department of Breast Imaging, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
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2
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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3
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Bousquet J, Bedbrook A, Czarlewski W, De Carlo G, Fonseca JA, González Ballester MA, Illario M, Koskinen S, Laatikainen T, Onorato GL, Palkonen S, Patella V, Pham-Thi N, Puggioni F, Ventura MT, Joos G, Kuna P, Louis R, Makris M, Zalud P, Zuberbier T, Bachert C, Brussino L, Carreiro-Martins P, Carrion Y Ribas C, Chalubinski M, Costa EM, de Vries G, Gemicioglu B, Gennimata D, Micheli Y, Niedoszytko M, Regateiro FS, Romantowski J, Taborda-Barata L, Toppila-Salmi S, Tsiligianni I, Viart F, Laune D. Digital Health Europe (DHE) Twinning on severe asthma-kick-off meeting report. J Thorac Dis 2021; 13:3215-3225. [PMID: 34164213 PMCID: PMC8182538 DOI: 10.21037/jtd-21-792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Jean Bousquet
- Charité Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany.,University Hospital Montpellier, Montpellier, France.,Maladies Chroniques pour un Viellissement Actif, (Macvia-France), Montpellier, France
| | - Anna Bedbrook
- Maladies Chroniques pour un Viellissement Actif, (Macvia-France), Montpellier, France.,Allergic Rhinitis and its Impact on Asthma (ARIA), Montpellier, France.,Mobile Airways Sentinel nekworK (MASK-air), Montpellier, France
| | - Wienczyslawa Czarlewski
- Allergic Rhinitis and its Impact on Asthma (ARIA), Montpellier, France.,Mobile Airways Sentinel nekworK (MASK-air), Montpellier, France.,Medical Consulting Czarlewski, Levallois, France
| | - Giuseppe De Carlo
- European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium
| | - Joao A Fonseca
- Center for Research in Health Technology and Information Systems, Faculdade de Medicina da Universidade do Porto, Porto, Portugal.,Medida, Lda Porto, Portugal
| | - Miguel A González Ballester
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain, ICREA, Barcelona, Spain
| | - Maddalena Illario
- Division for Health Innovation, Campania Region and Federico II University Hospital Naples (R&D Unit and Department of Public Health), Naples, Italy
| | - Seppo Koskinen
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Gabrielle L Onorato
- Maladies Chroniques pour un Viellissement Actif, (Macvia-France), Montpellier, France
| | - Susanna Palkonen
- European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium
| | - Vincenzo Patella
- Division of Allergy and Clinical Immunology, Department of Medicine, Agency of Health ASL Salerno, "Santa Maria della Speranza" Hospital, Battipaglia, Salerno, Italy
| | - Nhân Pham-Thi
- Ecole Polytechnique Palaiseau, IRBA (Institut de Recherche bio-Médicale des Armées), Bretigny, France
| | - Francesca Puggioni
- Personalized Medicine Clinic Asthma & Allergy, Humanitas Clinical and Research Center IRCCS, Rozzano, MI, Italy.,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy
| | - Maria Teresa Ventura
- University of Bari Medical School, Unit of Geriatric Immunoallergology, Bari, Italy
| | - Guy Joos
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Piotr Kuna
- Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Lodz, Poland
| | - Renaud Louis
- Department of Pulmonary Medicine, CHU Sart-Tilman, and GIGA I3 Research Group, Liege, Belgium
| | - Michael Makris
- Allergy Unit "D Kalogeromitros", 2nd Department of Dermatology and Venereology, National & Kapodistrian University of Athens, "Attikon" University Hospital, Athens, Greece
| | | | - Torsten Zuberbier
- Charité Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Claus Bachert
- Upper Airways Research Laboratory, ENT Department, Ghent University Hospital, Ghent, Belgium.,International Airway Research Center, First Affiliated Hospital, Sun Yat-sen University, Guangzou, China.,Division of ENT Diseases, CLINTEC, Karolinska Institutet, Stockholm, Sweden.,Department of ENT Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Luisa Brussino
- Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino & Mauriziano Hospital, Torino, Italy
| | - Pedro Carreiro-Martins
- Serviço de Imunoalergologia, Hospital de Dona Estefânia, Centro Hospitalar de Lisboa Central, Lisbon, Portugal.,CEDOC, Faculdade de Ciências Médicas (FCM), Universidade Nova de Lisboa, Lisbon, Portugal
| | - Carme Carrion Y Ribas
- School of Health Sciences and UOC eHealth Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maciej Chalubinski
- Department of Immunology and Allergy, Medical University of Lodz, Lodz, Poland
| | - Elisio M Costa
- Faculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing), Porto, Portugal
| | | | - Bilun Gemicioglu
- Department of Pulmonary Diseases, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Istanbul, Turkey
| | - Dimitra Gennimata
- Department of Pharmacy, Athens General Hospital "Korgialenio-Benakio" Hellenic Red Cross, Athens, Greece
| | | | - Marek Niedoszytko
- Medical University of Gdańsk, Department of Allergology, Gdańsk, Poland
| | - Frederico S Regateiro
- Allergy and Clinical Immunology Unit, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Institute of Immunology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Jan Romantowski
- Medical University of Gdańsk, Department of Allergology, Gdańsk, Poland
| | - Luis Taborda-Barata
- Health Sciences, University of Beira Interior, Covilhã, Portugal.,Department of Immunoallergology, Cova da Beira University Hospital Centre, Covilhã, Portugal
| | - Sanna Toppila-Salmi
- Skin and Allergy Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Ioanna Tsiligianni
- Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Crete, Greece.,International Primary Care Respiratory Group IPCRG, Aberdeen, Scotland
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Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8811056. [PMID: 33791381 PMCID: PMC7984886 DOI: 10.1155/2021/8811056] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 11/05/2020] [Accepted: 03/03/2021] [Indexed: 11/17/2022]
Abstract
Objectives To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors. Materials and Methods In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. Results The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement. Conclusions This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.
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6
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Electronic health records for the diagnosis of rare diseases. Kidney Int 2020; 97:676-686. [DOI: 10.1016/j.kint.2019.11.037] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 11/15/2019] [Accepted: 11/22/2019] [Indexed: 01/13/2023]
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7
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Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease. Med Image Anal 2020. [DOI: 10.1016/j.media.2019.101625 10.1016/j.media.2019.101625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Hao X, Bao Y, Guo Y, Yu M, Zhang D, Risacher SL, Saykin AJ, Yao X, Shen L. Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease. Med Image Anal 2020; 60:101625. [PMID: 31841947 PMCID: PMC6980345 DOI: 10.1016/j.media.2019.101625] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 11/25/2019] [Accepted: 11/25/2019] [Indexed: 12/12/2022]
Abstract
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the proposed method has better classification performance than the start-of-the-art multimodality-based methods. Specifically, we achieved higher accuracy and area under the curve (AUC) for AD versus normal controls (NC), MCI versus NC, and MCI converters (MCI-C) versus MCI non-converters (MCI-NC) on ADNI datasets. Therefore, the proposed model not only outperforms the traditional method in terms of AD/MCI classification, but also discovers the characteristics associated with the disease, demonstrating its promise for improving disease-related mechanistic understanding.
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Affiliation(s)
- Xiaoke Hao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Yongjin Bao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Yingchun Guo
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.
| | - Ming Yu
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Daoqiang Zhang
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis 46202, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
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9
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Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models. REMOTE SENSING 2019. [DOI: 10.3390/rs11101231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.
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10
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Peeken JC, Goldberg T, Knie C, Komboz B, Bernhofer M, Pasa F, Kessel KA, Tafti PD, Rost B, Nüsslin F, Braun AE, Combs SE. Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients. Strahlenther Onkol 2018; 194:824-834. [PMID: 29557486 DOI: 10.1007/s00066-018-1294-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 03/05/2018] [Indexed: 12/01/2022]
Abstract
BACKGROUND AND PURPOSE Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. MATERIALS AND METHODS A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared. RESULTS The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. CONCLUSIONS A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany. .,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany.
| | | | - Christoph Knie
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Basil Komboz
- Allianz SE, Königinstraße 28, 80802, Munich, Germany
| | - Michael Bernhofer
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
| | - Francesco Pasa
- Department of Computer Science, Informatik 9, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany.,Chair of Biomedical Physics, Department of Physics, Technical University of Munich (TUM), James-Franck-Straße 1, 85748, Garching, Germany
| | - Kerstin A Kessel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.,Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany
| | - Pouya D Tafti
- Allianz SE, Königinstraße 28, 80802, Munich, Germany
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | | | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.,Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.,Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany
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