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Li C, Zhang J, Ning B, Xu J, Lin Z, Zhang J, Tan N, Yu X, Su W, Ni W, Yu W, Wu J, Cao G, Cao Z, Xie C, Jin X. Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy. Radiat Oncol 2024; 19:72. [PMID: 38851718 PMCID: PMC11161999 DOI: 10.1186/s13014-024-02462-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/28/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
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Affiliation(s)
- Chenyu Li
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Ji Zhang
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Boda Ning
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Jiayi Xu
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhixi Lin
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Jicheng Zhang
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Ninghang Tan
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Xianwen Yu
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Wanyu Su
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Weihua Ni
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Wenliang Yu
- Department of Radiation Oncology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou, 324000, China
| | - Jianping Wu
- Department of Radiation Oncology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou, 324000, China
| | - Guoquan Cao
- Radiological Department, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhuo Cao
- Department of Respiratory, Lishui People's Hospital, Lishui, 323000, China.
| | - Congying Xie
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xiance Jin
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, 325000, China.
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Saadatmand P, Mahdavi SR, Nikoofar A, Jazaeri SZ, Ramandi FL, Esmaili G, Vejdani S. A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac. Eur J Med Res 2024; 29:282. [PMID: 38735974 PMCID: PMC11089719 DOI: 10.1186/s40001-024-01855-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. METHODS Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations. RESULTS Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. CONCLUSIONS This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.
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Affiliation(s)
- Pegah Saadatmand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Nikoofar
- Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seyede Zohreh Jazaeri
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Division of NeuroscienceCellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | | | - Soheil Vejdani
- Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Department of Radiation Oncology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
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Tan D, Mohd Nasir NF, Abdul Manan H, Yahya N. Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review. Cancer Radiother 2023; 27:398-406. [PMID: 37482464 DOI: 10.1016/j.canrad.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity. MATERIALS AND METHODS A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria. RESULTS Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed. CONCLUSION Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.
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Affiliation(s)
- D Tan
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - N F Mohd Nasir
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - H Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia
| | - N Yahya
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia.
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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] [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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Morelli L, Parrella G, Molinelli S, Magro G, Annunziata S, Mairani A, Chalaszczyk A, Fiore MR, Ciocca M, Paganelli C, Orlandi E, Baroni G. A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy. Cancers (Basel) 2022; 15:cancers15010033. [PMID: 36612029 PMCID: PMC9817801 DOI: 10.3390/cancers15010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan-Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LETd maps, with DRBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LETd maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Giovanni Parrella
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Giuseppe Magro
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Simone Annunziata
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Andrea Mairani
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
- Heidelberg Ion Beam Therapy Center (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
| | - Agnieszka Chalaszczyk
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Maria Rosaria Fiore
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Mario Ciocca
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Ester Orlandi
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Zhang F, Liao L, Wei S, Lu Y. Risk Factors of Acute Radiation-Induced Lung Injury Induced by Radiotherapy for Esophageal Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2416196. [PMID: 35872959 PMCID: PMC9300318 DOI: 10.1155/2022/2416196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022]
Abstract
Objective To investigate the risk factors of acute radiation-induced lung injury (acute RILI) induced by radiotherapy for esophageal cancer. Methods A total of 206 patients with esophageal cancer who received radiotherapy in our hospital from January 2017 to March 2020 were selected. The general data such as gender, age, and comorbidities of the patients were collected, as well as the levels of cytokines (TNF-α, TNF-β, and IL-6) in peripheral blood before radiotherapy; radiotherapy dose-related parameters were recorded during radiotherapy. Follow-up was 12 months after radiotherapy. The patients with induced acute RILI after radiotherapy were set as the observation group (n = 75). Patients without acute RILI after radiotherapy were set as the control group (n = 131). Univariate and multivariate logistic regression analysis was performed on the risk factors of acute RILI induced by radiotherapy for esophageal cancer. Results Univariate analysis and multivariate logistic regression analysis showed that the combined diabetes, total radiation dose, combined lung disease, physical factors (V30, Dmean), and preradiotherapy cytokine (TNF-α, TNF-β, and IL-6) elevated level was an independent risk factor for radiotherapy-induced acute RILI in esophageal cancer (P < 0.05). Conclusion Concomitant diabetes, total radiation dose, lung disease, physical factors (V30, Dmean), and levels of cytokines (TNF-α, TNF-β, and IL-6) before radiation therapy are risk factors for acute RILI induced by radiation therapy in esophageal cancer. The possibility of acute RILI should be comprehensively assessed according to the patient's condition, and the radiotherapy regimen should be adjusted to reduce and avoid the induction of acute radiation-induced lung injury.
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Affiliation(s)
- Faen Zhang
- Department of Oncology, The People's Hospital of Hechi, Guangxi 547000, China
| | - Lihua Liao
- Department of Oncology, The People's Hospital of Hechi, Guangxi 547000, China
| | - Song Wei
- Department of Oncology, The People's Hospital of Hechi, Guangxi 547000, China
| | - Yuqing Lu
- Department of Oncology, The People's Hospital of Hechi, Guangxi 547000, China
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