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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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Ren W, Liang B, Sun C, Wu R, Men K, Chen H, Feng X, Hou L, Han F, Yi J, Dai J. A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma. Phys Med 2024; 121:103362. [PMID: 38653120 DOI: 10.1016/j.ejmp.2024.103362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/27/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI). MATERIALS AND METHODS Spatial features of dose distribution within the temporal lobe were extracted using both the three-dimensional convolution (C3D) network and the dosiomics method. The Minimal Redundancy-Maximal-Relevance (mRMR) method was employed to rank the extracted features and select the most relevant ones. Four machine learning (ML) classifiers, including logistic regression (LR), k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF), were used to establish prediction models. Nested sampling and hyperparameter tuning methods were applied to train and validate the prediction models. For comparison, a prediction model base on the conventional D0.5cc of the temporal lobe obtained from dose volume (DV) histogram was established. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the predictive performance of the different models. RESULTS A total of 127 nasopharyngeal carcinoma (NPC) patients were included in the study. In the model based on C3D deep learning features, the highest AUC value of 0.843 was achieved with 5 features. For the dosiomics features model, the highest AUC value of 0.715 was attained with 1 feature. Both of these models demonstrated superior performance compared to the prediction model based on DV parameters, which yielded an AUC of 0.695. CONCLUSION The prediction model utilizing C3D deep learning features outperformed models based on dosiomics features or traditional parameters in predicting the onset of TLI. This approach holds promise for predicting radiation-induced toxicities and guide individualized radiotherapy.
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Affiliation(s)
- Wenting Ren
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bin Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Runye Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Huan Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xin Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Fei Han
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Ubeira-Gabellini MG, Mori M, Palazzo G, Cicchetti A, Mangili P, Pavarini M, Rancati T, Fodor A, Del Vecchio A, Di Muzio NG, Fiorino C. Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches. Cancers (Basel) 2024; 16:934. [PMID: 38473296 DOI: 10.3390/cancers16050934] [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: 01/17/2024] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). METHODS The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. RESULTS The model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). CONCLUSIONS No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.
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Affiliation(s)
| | - Martina Mori
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Gabriele Palazzo
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Alessandro Cicchetti
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Paola Mangili
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Maddalena Pavarini
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Andrei Fodor
- Radiotherapy, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Radiotherapy, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Department of Radiotherapy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Claudio Fiorino
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
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Murakami Y, Kawahara D, Soyano T, Kozuka T, Takahashi Y, Miyake K, Kashihara K, Kashihara T, Kamima T, Oguchi M, Murakami Y, Yoshioka Y, Nagata Y. Dosiomics for intensity-modulated radiotherapy in patients with prostate cancer: survival analysis stratified by baseline prostate-specific antigen and Gleason grade group in a 2-institutional retrospective study. Br J Radiol 2024; 97:142-149. [PMID: 38263831 PMCID: PMC11008500 DOI: 10.1093/bjr/tqad004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 05/25/2023] [Accepted: 10/12/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group. METHODS A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan-Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined. RESULTS The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P < .01), GG 4 (92.2% vs 76.9%, P < .01), and GG 5 (83.1% vs 77.8%, P = .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P < .001). CONCLUSION The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs. ADVANCES IN KNOWLEDGE The effects of planned dose distribution on prognosis differ depending on the patient's clinical background.
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Affiliation(s)
- Yu Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
- Department of Physics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Takashi Soyano
- Department of Radiology, Japan Self-Defense Forces Central Hospital, 1-2-24 Ikejiri, Setagaya-ku, Tokyo 154-8532, Japan
| | - Takuyo Kozuka
- Department of Radiology, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuka Takahashi
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Konatsu Miyake
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Kenichi Kashihara
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Tairo Kashihara
- Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Tatsuya Kamima
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Masahiko Oguchi
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Yasuo Yoshioka
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
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Ishizaka N, Kinoshita T, Sakai M, Tanabe S, Nakano H, Tanabe S, Nakamura S, Mayumi K, Akamatsu S, Nishikata T, Takizawa T, Yamada T, Sakai H, Kaidu M, Sasamoto R, Ishikawa H, Utsunomiya S. Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution. J Appl Clin Med Phys 2024; 25:e14215. [PMID: 37987544 PMCID: PMC10795425 DOI: 10.1002/acm2.14215] [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: 03/09/2023] [Revised: 09/29/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
PURPOSE We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. METHODS A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. RESULTS The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. CONCLUSIONS The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.
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Affiliation(s)
- Natsuki Ishizaka
- Department of RadiologyNiigata Prefectural Shibata HospitalShibata CityNiigataJapan
| | - Tomotaka Kinoshita
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Madoka Sakai
- Department of RadiologyNagaoka Chuo General HospitalNagaokaNiigataJapan
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Shunpei Tanabe
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Hisashi Nakano
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Satoshi Tanabe
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Sae Nakamura
- Department of Radiation OncologyNiigata Neurosurgical HospitalNiigata CityNiigataJapan
| | - Kazuki Mayumi
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Shinya Akamatsu
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
- Department of RadiologyTakeda General HospitalAizuwakamatsu CityFukushimaJapan
| | - Takayuki Nishikata
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
- Division of RadiologyNagaoka Red Cross HospitalNagaoka‐shiNiigataJapan
| | - Takeshi Takizawa
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
- Department of Radiation OncologyNiigata Neurosurgical HospitalNiigata CityNiigataJapan
| | - Takumi Yamada
- Section of Radiology, Department of Clinical SupportNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Hironori Sakai
- Section of Radiology, Department of Clinical SupportNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Motoki Kaidu
- Department of Radiology and Radiation OncologyNiigata University Graduate School of Medical and Dental SciencesNiigata CityNiigataJapan
| | - Ryuta Sasamoto
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Hiroyuki Ishikawa
- Department of Radiology and Radiation OncologyNiigata University Graduate School of Medical and Dental SciencesNiigata CityNiigataJapan
| | - Satoru Utsunomiya
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
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Yang T, Wang L, Zhong S, Peng L, Li N, Gui Y, Deng Q, Wang Y, Yuan Q, Li X. Prediction of radiation pneumonia after radiotherapy for esophageal cancer using a unified fractional dosiomics combined model. Br J Radiol 2023; 96:20230495. [PMID: 37750834 PMCID: PMC10646633 DOI: 10.1259/bjr.20230495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE This study aimed to construct an optimal model to predict radiation pneumonia (RP) after radiotherapy for esophageal cancer using unified fractional dosiomics and to investigate the improvements in the prediction efficiency of each model for RP. METHODS The clinical data, DVH, pre-treatment CT, and dose distribution of 182 patients were retrospectively analyzed.The independent risk factors were screened using univariate and multivariate logistic regression. The mutual information (MI),least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) methods were used to screen the omics features. The AUC values of ROC, calibration curves, and clinical decision curves were calculated to evaluate the efficacy and trends of each model. RESULTS The AUC of dosiomics model were 0.783 and 0.760 in the training and test cohorts, higher than 0.585 and 0.579 in the training and test cohorts of the DVH model. The AUC value of the R + D combination was the highest, reaching 0.833. The combined R + D model had a better calibration degree than the other models (mean absolute error = 0.018) and better net benefit in clinical decision-making. CONCLUSIONS The radiomics combined dosiomics model was the best combined model to predict RP after radiotherapy for esophageal cancer. The dosiomics model could cover the efficiency of the DVH model and significantly improve the efficiency of the combined model.In the future, we will include other centers for further verification. ADVANCES IN KNOWLEDGE For the first time, this study used CT images combined dose distribution to predict the occurrence of radiation pneumonitis after radiotherapy for esophageal cancer.
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Affiliation(s)
- Tianyue Yang
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Liu Wang
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Shuting Zhong
- Department of Medical Imaging, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Lei Peng
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Ningfu Li
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Yan Gui
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Qiao Deng
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Yujia Wang
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Qiang Yuan
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
| | - Xianfu Li
- Department of Radiation Oncology, Affiliated Hospital of North Sichuan Medical College, Shunqing District, Sichuan, China
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Sheen H, Shin HB, Kim H, Kim C, Kim J, Kim JS, Hong CS. Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors. Sci Rep 2023; 13:11027. [PMID: 37419940 PMCID: PMC10328946 DOI: 10.1038/s41598-023-35570-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 05/20/2023] [Indexed: 07/09/2023] Open
Abstract
This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose-volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.
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Affiliation(s)
- Heesoon Sheen
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - Han-Back Shin
- Department of Radiation Oncology, Gachon University Gil Medical Center, Incheon, South Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Hojae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Seoul, South Korea
| | - Changhwan Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jihun Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
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9
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Chounta S, Allodji R, Vakalopoulou M, Bentriou M, Do DT, De Vathaire F, Diallo I, Fresneau B, Charrier T, Zossou V, Christodoulidis S, Lemler S, Letort Le Chevalier V. Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer. Cancers (Basel) 2023; 15:3107. [PMID: 37370717 DOI: 10.3390/cancers15123107] [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: 03/01/2023] [Revised: 05/16/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors' Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart-dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines.
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Affiliation(s)
- Stefania Chounta
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Rodrigue Allodji
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Polytechnic School of Abomey-Calavi (EPAC), University of Abomey-Calavi, 01, Cotonou P.O. Box 2009, Benin
| | - Maria Vakalopoulou
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Mahmoud Bentriou
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Duyen Thi Do
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
| | - Florent De Vathaire
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
| | - Ibrahima Diallo
- Department of Radiation Oncology, Gustave Roussy, F-94800 Villejuif, France
- Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, Paris-Saclay University, F-94800 Villejuif, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Pediatric Oncology, F-94805 Villejuif, France
| | - Thibaud Charrier
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Institut Curie, PSL Research University, INSERM, U900, F-92210 Saint Cloud, France
| | - Vincent Zossou
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- INSERM, CESP, Cancer and Radiation Team, F-94805 Villejuif, France
- Gustave Roussy, Department of Clinical Research, Cancer and Radiation Team, F-94805 Villejuif, France
- Polytechnic School of Abomey-Calavi (EPAC), University of Abomey-Calavi, 01, Cotonou P.O. Box 2009, Benin
- Institut de Formation et de Recherche en Informatique, (IFRI-UAC), Cotonou P.O. Box 2009, Benin
| | - Stergios Christodoulidis
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Sarah Lemler
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
| | - Veronique Letort Le Chevalier
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, F-91190 Gif-sur-Yvette, France
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Mansour IR, Thomson RM. Haralick texture feature analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales. Phys Med Biol 2023; 68. [PMID: 36731130 DOI: 10.1088/1361-6560/acb885] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 02/02/2023] [Indexed: 02/04/2023]
Abstract
Objective.To investigate an approach for quantitative characterization of the spatial distribution of dosimetric data by introducing Haralick texture feature analysis in this context.Approach.Monte Carlo simulations are used to generate 3D arrays of dosimetric data for 2 scenarios: (1) cell-scale microdosimetry: specific energy (energy imparted per unit mass) in cell-scale targets irradiated by photon spectra (125I,192Ir, 6 MV); (2) tumour-scale dosimetry: absorbed dose in voxels for idealized models of125I permanent implant prostate brachytherapy, considering 'TG186' (realistic tissues including 0% to 5% intraprostatic calcifications; interseed attenuation) and 'TG43' (water model, no interseed attenuation) conditions. Five prominent Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are computed and trends are interpreted using fundamental radiation physics.Main results.In the cell-scale scenario, the Haralick measures quantify differences in 3D specific energy distributions due to source spectra. For example, contrast and entropy are highest for125I reflecting the large variations in specific energy in adjacent voxels (photoelectric interactions; relatively short range of electrons), while 6 MV has the highest homogeneity with smaller variations in specific energy between voxels (Compton scattering dominates; longer range of electrons). For the tumour-scale scenario, the Haralick measures quantify differences due to TG186/TG43 simulation conditions and the presence of calcifications. For example, as calcifications increase from 0% to 5%, contrast increases while correlation decreases, reflecting the large differences in absorbed dose in adjacent voxels (higher absorbed dose in voxels with calcification due to photoelectric interactions).Significance.Haralick texture analysis provides a quantitative method for the characterization of 3D dosimetric distributions across cellular to tumour length scales, with promising future applications including analyses of multiscale tissue models, patient-specific data, and comparison of treatment approaches.
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Affiliation(s)
- Iymad R Mansour
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, 1125 Colonel By Dr, Ottawa, K1S 5B6, Ontario, Canada
| | - Rowan M Thomson
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, 1125 Colonel By Dr, Ottawa, K1S 5B6, Ontario, Canada
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11
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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Zhang Z, Wang Z, Yan M, Yu J, Dekker A, Zhao L, Wee L. Radiomics and Dosiomics Signature From Whole Lung Predicts Radiation Pneumonitis: A Model Development Study With Prospective External Validation and Decision-curve Analysis. Int J Radiat Oncol Biol Phys 2023; 115:746-758. [PMID: 36031028 DOI: 10.1016/j.ijrobp.2022.08.047] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/10/2022] [Accepted: 08/20/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction. METHODS AND MATERIALS Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models. RESULTS A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram. CONCLUSIONS Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.
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Affiliation(s)
- Zhen Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zhixiang Wang
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jiaqi Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
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13
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Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis. JOURNAL OF ONCOLOGY 2023; 2023:5328927. [PMID: 36852328 PMCID: PMC9966572 DOI: 10.1155/2023/5328927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/18/2023] [Accepted: 02/01/2023] [Indexed: 02/20/2023]
Abstract
Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.
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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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16
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Puttanawarut C, Sirirutbunkajorn N, Tawong N, Khachonkham S, Pattaranutaporn P, Wongsawat Y. Impact of Interfractional Error on Dosiomic Features. Front Oncol 2022; 12:726896. [PMID: 35756677 PMCID: PMC9231355 DOI: 10.3389/fonc.2022.726896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives The purpose of this study was to investigate the stability of dosiomic features under random interfractional error. We investigated the differences in the values of features with different fractions and the error in the values of dosiomic features under interfractional error. Material and Methods The isocenters of the treatment plans of 15 lung cancer patients were translated by a maximum of ±3 mm in each axis with a mean of (0, 0, 0) and a standard deviation of (1.2, 1.2, 1.2) mm in the x, y, and z directions for each fraction. A total of 81 dose distributions for each patient were then calculated considering four fraction number groups (2, 10, 20, and 30). A total of 93 dosiomic features were extracted from each dose distribution in four different regions of interest (ROIs): gross tumor volume (GTV), planning target volume (PTV), heart, and both lungs. The stability of dosiomic features was analyzed for each fraction number group by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The agreements in the means of dosiomic features among the four fraction number groups were tested by ICC. The percent differences (PD) between the dosiomic features extracted from the original dose distribution and the dosiomic features extracted from the dose distribution with interfractional error were calculated. Results Eleven out of 93 dosiomic features demonstrated a large CV (CV ≥ 20%). Overall CV values were highest in GTV ROIs and lowest in lung ROIs. The stability of dosiomic features decreased as the total number of fractions decreased. The ICC results showed that five out of 93 dosiomic features had an ICC lower than 0.75, which indicates intermediate or poor stability under interfractional error. The mean dosiomic feature values were shown to be consistent with different numbers of fractions (ICC ≥ 0.9). Some of the dosiomic features had PD greater than 50% and showed different PD values with different numbers of fractions. Conclusion Some dosiomic features have low stability under interfractional error. The stability and values of the dosiomic features were affected by the total number of fractions. The effect of interfractional error on dosiomic features should be considered in further studies regarding dosiomics for reproducible results.
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Affiliation(s)
- Chanon Puttanawarut
- Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samutprakarn, Thailand.,Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhorn Pathom, Thailand
| | - Nat Sirirutbunkajorn
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Narisara Tawong
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Suphalak Khachonkham
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Poompis Pattaranutaporn
- Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Yodchanan Wongsawat
- Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhorn Pathom, Thailand
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Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches. J Pers Med 2022; 12:jpm12060854. [PMID: 35743639 PMCID: PMC9224778 DOI: 10.3390/jpm12060854] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 02/07/2023] Open
Abstract
Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.
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Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer. Radiat Oncol 2022; 17:84. [PMID: 35484597 PMCID: PMC9052564 DOI: 10.1186/s13014-022-02053-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/11/2022] [Indexed: 02/08/2023] Open
Abstract
Background To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum. Methods Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.
Results Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).
Conclusions Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.
Supplementary Information The online version contains supplementary material available at 10.1186/s13014-022-02053-y.
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Chen J, Bermejo I, Dekker A, Wee L. Generative models improve radiomics performance in different tasks and different datasets: An experimental study. Phys Med 2022; 98:11-17. [PMID: 35468494 DOI: 10.1016/j.ejmp.2022.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/11/2022] [Accepted: 04/17/2022] [Indexed: 02/02/2023] Open
Abstract
PURPOSE Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. METHODS We used two datasets of low dose CT scans - NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers - a support vector machine (SVM) and a deep attention based multiple instance learning model - for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. RESULTS Denoising with the encoder-decoder network and the CGAN improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value < 0.01). On the other hand, the encoder-decoder network and the CGAN improved the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value < 0.01). Finally, there are no statistically significant improvements in AUC using encoder-decoder networks and CGAN (p-value = 0.34) when networks trained at 75 and 100 epochs. CONCLUSION Generative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs.
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Affiliation(s)
- Junhua Chen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht 6229 ET, Netherlands.
| | - Inigo Bermejo
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht 6229 ET, Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht 6229 ET, Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht 6229 ET, Netherlands
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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