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Suzuki H, Kokabu T, Yamada K, Ishikawa Y, Yabu A, Yanagihashi Y, Hyakumachi T, Tachi H, Shimizu T, Endo T, Ohnishi T, Ukeba D, Nagahama K, Takahata M, Sudo H, Iwasaki N. Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks. Spine J 2024:S1529-9430(24)00299-7. [PMID: 38909909 DOI: 10.1016/j.spinee.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND CONTEXT Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonance imaging (MRI) is useful in the diagnosis of LSCS, but the equipment is often not available or difficult to read. LSCS patients with progressive neurologic deficits have difficulty with recovery if surgical treatment is delayed. So, early diagnosis and determination of appropriate surgical indications are crucial in the treatment of LSCS. Convolutional neural networks (CNNs), a type of deep learning, offers significant advantages for image recognition and classification, and work well with radiographs, which can be easily taken at any facility. PURPOSE Our purpose was to develop an algorithm to diagnose the presence or absence of LSCS requiring surgery from plain radiographs using CNNs. STUDY DESIGN Retrospective analysis of consecutive, nonrandomized series of patients at a single institution. PATIENT SAMPLE Data of 150 patients who underwent surgery for LSCS, including degenerative spondylolisthesis, at a single institution from January 2022 to August 2022, were collected. Additionally, 25 patients who underwent surgery at 2 other hospitals were included for extra external validation. OUTCOME MEASURES In annotation 1, the area under the curve (AUC) computed from the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. In annotation 2, correlation coefficients were used. METHODS Four intervertebral levels from L1/2 to L4/5 were extracted as region of interest from lateral plain lumbar spine radiographs totaling 600 images were obtained. Based on the date of surgery, 500 images derived from the first 125 cases were used for internal validation, and 100 images from the subsequent 25 cases used for external validation. Additionally, 100 images from other hospitals were used for extra external validation. In annotation 1, binary classification of operative and nonoperative levels was used, and in annotation 2, the spinal canal area measured on axial MRI was labeled as the output layer. For internal validation, the 500 images were divided into each 5 dataset on per-patient basis and 5-fold cross-validation was performed. Five trained models were registered in the external validation prediction performance. Grad-CAM was used to visualize area with the high features extracted by CNNs. RESULTS In internal validation, the AUC and accuracy for annotation 1 ranged between 0.85-0.89 and 79-83%, respectively, and the correlation coefficients for annotation 2 ranged between 0.53 and 0.64 (all p<.01). In external validation, the AUC and accuracy for annotation 1 were 0.90 and 82%, respectively, and the correlation coefficient for annotation 2 was 0.69, using 5 trained CNN models. In the extra external validation, the AUC and accuracy for annotation 1 were 0.89 and 84%, respectively, and the correlation coefficient for annotation 2 was 0.56. Grad-CAM showed high feature density in the intervertebral joints and posterior intervertebral discs. CONCLUSIONS This technology automatically detects LSCS from plain lumbar spine radiographs, making it possible for medical facilities without MRI or nonspecialists to diagnose LSCS, suggesting the possibility of eliminating delays in the diagnosis and treatment of LSCS that require early treatment.
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Affiliation(s)
- Hisataka Suzuki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan; Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Terufumi Kokabu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan; Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Katsuhisa Yamada
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan.
| | - Yoko Ishikawa
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Akito Yabu
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Yasushi Yanagihashi
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Takahiko Hyakumachi
- Department of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane Chuo, Eniwa, Hokkaido 061-1449, Japan
| | - Hiroyuki Tachi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Tomohiro Shimizu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Tsutomu Endo
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Takashi Ohnishi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Daisuke Ukeba
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Ken Nagahama
- Department of Orthopaedic Surgery, Sapporo Endoscopic Spine Surgery, N16E16, Sapporo, Hokkaido 065-0016, Japan
| | - Masahiko Takahata
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Hideki Sudo
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan
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Zeinali N, Youn N, Albashayreh A, Fan W, Gilbertson White S. Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review. JMIR Cancer 2024; 10:e52322. [PMID: 38502171 PMCID: PMC10988375 DOI: 10.2196/52322] [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: 09/12/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. OBJECTIVE This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. METHODS We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. RESULTS A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods-deep learning, ensemble classifiers, and unsupervised ML-constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. CONCLUSIONS This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.
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Affiliation(s)
- Nahid Zeinali
- Department of Computer Science and Informatics, University of Iowa, Iowa City, IA, United States
| | - Nayung Youn
- College of Nursing, University of Iowa, Iowa City, IA, United States
| | - Alaa Albashayreh
- College of Nursing, University of Iowa, Iowa City, IA, United States
| | - Weiguo Fan
- Department of Business Analytics, University of Iowa, Iowa City, IA, United States
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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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Mansouri Z, Salimi Y, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study. Radiat Oncol 2024; 19:12. [PMID: 38254203 PMCID: PMC10804728 DOI: 10.1186/s13014-024-02409-6] [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: 04/11/2023] [Accepted: 01/17/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. METHODS A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. RESULTS The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. CONCLUSION Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Yang SS, Peng QH, Wu AQ, Zhang BY, Liu ZQ, Chen EN, Xie FY, OuYang PY, Chen CY. Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy. iScience 2023; 26:108394. [PMID: 38047064 PMCID: PMC10690639 DOI: 10.1016/j.isci.2023.108394] [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: 04/03/2023] [Revised: 06/28/2023] [Accepted: 11/01/2023] [Indexed: 12/05/2023] Open
Abstract
To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.
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Affiliation(s)
- Shan-Shan Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Qing-He Peng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - Ai-Qian Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China
| | - Bao-Yu Zhang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - Zhi-Qiao Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - En-Ni Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - Fang-Yun Xie
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - Pu-Yun OuYang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
| | - Chun-Yan Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong 510060, China
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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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Wu Q, Wang J, Sun Z, Xiao L, Ying W, Shi J. Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks. IEEE J Biomed Health Inform 2023; 27:5564-5575. [PMID: 37643107 DOI: 10.1109/jbhi.2023.3309840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Immunotherapy is an effective way to treat non-small cell lung cancer (NSCLC). The efficacy of immunotherapy differs from person to person and may cause side effects, making it important to predict the efficacy of immunotherapy before surgery. Radiomics based on machine learning has been successfully used to predict the efficacy of NSCLC immunotherapy. However, most studies only considered the radiomic features of the individual patient, ignoring the inter-patient correlations. Besides, they usually concatenated different features as the input of a single-view model, failing to consider the complex correlation among features of multiple types. To this end, we propose a multi-view adaptive weighted graph convolutional network (MVAW-GCN) for the prediction of NSCLC immunotherapy efficacy. Specifically, we group the radiomic features into several views according to the type of the fitered images they extracted from. We construct a graph in each view based on the radiomic features and phenotypic information. An attention mechanism is introduced to automatically assign weights to each view. Considering the view-shared and view-specific knowledge of radiomic features, we propose separable graph convolution that decomposes the output of the last convolution layer into two components, i.e., the view-shared and view-specific outputs. We maximize the consistency and enhance the diversity among different views in the learning procedure. The proposed MVAW-GCN is evaluated on 107 NSCLC patients, including 52 patients with valid efficacy and 55 patients with invalid efficacy. Our method achieved an accuracy of 77.27% and an area under the curve (AUC) of 0.7780, indicating its effectiveness in NSCLC immunotherapy efficacy prediction.
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Ma Z, Liang B, Wei R, Liu Y, Bao Y, Yuan M, Men Y, Wang J, Deng L, Zhai Y, Bi N, Wang L, Dai J, Hui Z. Enhanced prediction of postoperative radiotherapy-induced esophagitis in non-small cell lung cancer: Dosiomic model development in a real-world cohort and validation in the PORT-C randomized controlled trial. Thorac Cancer 2023; 14:2839-2845. [PMID: 37596813 PMCID: PMC10542460 DOI: 10.1111/1759-7714.15068] [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: 06/28/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Radiotherapy-induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non-small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE. METHODS Models were trained with a real-world cohort and validated with PORT-C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade ≥2 RE. Esophageal three-dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy-based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision-recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison. RESULTS A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN-extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN-extracted features, respectively. Precision-recall curves revealed that CNN-extracted features outperformed dosimetric and handcrafted features. CONCLUSIONS Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric-feature model in predicting RE. CNN-extracted features were more predictive but less interpretable than handcrafted features.
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Affiliation(s)
- Zeliang Ma
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Bin Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Ran Wei
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yunsong Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yongxing Bao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Meng Yuan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yu Men
- Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianyang Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Lei Deng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yirui Zhai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Nan Bi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Luhua Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhouguang Hui
- Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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9
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Zhuang L, Bai X, Chen Y, Zhang D, Sheng L, Du X. Analysis of the risk factors of radiation pneumonitis and the predictive ability of dosiomics in non-small-cell lung cancer. Future Oncol 2023; 19:2157-2169. [PMID: 37887073 DOI: 10.2217/fon-2023-0316] [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] [Indexed: 10/28/2023] Open
Abstract
Purpose: This prospective study investigated the incidence of radiation pneumonitis (RP) after immunotherapy followed by radiotherapy in non-small-cell lung cancer, analyzed the risk factors for RP, and explored the predictive performance of dosimetry and dosiomics. Methods & materials: Risk factors for grade ≥2 RP were calculated by using a logistic regression model. Predictive performance was compared on the basis of area under the curve values. Results: Grade ≥2 RP occurred in 16 cases (26.7%). The AUC values of V5 Gy, gray-level dependence matrix-small dependence high gray-level emphasis (GLDM-SDHGLE) and combined features were 0.685, 0.724 and 0.734, respectively. Conclusion: Smoking history, bilateral lung V5 Gy and GLDM-SDHGLE were independent risk factors for RP. Dosiomics can effectively predict RP.
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Affiliation(s)
- Lei Zhuang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Xue Bai
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Ying Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Danhong Zhang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Liming Sheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Xianghui Du
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine & Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
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10
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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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Li C, Deng M, Zhong X, Ren J, Chen X, Chen J, Xiao F, Xu H. Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI. Front Oncol 2023; 13:1198899. [PMID: 37448515 PMCID: PMC10338012 DOI: 10.3389/fonc.2023.1198899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction This study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. Methods A total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison. Results The optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model's predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics. Discussion The constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.
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Affiliation(s)
- Chunyu Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ming Deng
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaohui Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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12
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Bohannon D, Janopaul-Naylor J, Rudra S, Yang X, Chang CW, Wang Y, Ma C, Patel SA, McDonald MW, Zhou J. Prediction of plan adaptation in head and neck cancer proton therapy using clinical, radiographic, and dosimetric features. Acta Oncol 2023:1-8. [PMID: 37335043 DOI: 10.1080/0284186x.2023.2224050] [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: 01/23/2023] [Accepted: 06/01/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE Because proton head and neck (HN) treatments are sensitive to anatomical changes, plan adaptation (re-plan) during the treatment course is needed for a significant portion of patients. We aim to predict re-plan at plan review stage for HN proton therapy with a neural network (NN) model trained with patients' dosimetric and clinical features. The model can serve as a valuable tool for planners to assess the probability of needing to revise the current plan. METHODS AND MATERIALS Mean beam dose heterogeneity index (BHI), defined as the ratio of the maximum beam dose to the prescription dose, plan robustness features (clinical target volume (CTV), V100 changes, and V100 > 95% passing rates in 21 robust evaluation scenarios), as well as clinical features (e.g., age, tumor site, and surgery/chemotherapy status) were gathered from 171 patients treated at our proton center in 2020, with a median age of 64 and stages from I-IVc across 13 HN sites. Statistical analyses of dosimetric parameters and clinical features were conducted between re-plan and no-replan groups. A NN was trained and tested using these features. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of the prediction model. A sensitivity analysis was done to determine feature importance. RESULTS Mean BHI in the re-plan group was significantly higher than the no-replan group (p < .01). Tumor site (p < .01), chemotherapy status (p < .01), and surgery status (p < .01) were significantly correlated to re-plan. The model had sensitivities/specificities of 75.0%/77.4%, respectively, and an area under the ROC curve of .855. CONCLUSION There are several dosimetric and clinical features that correlate to re-plans, and NNs trained with these features can be used to predict HN re-plans, which can be used to reduce re-plan rate by improving plan quality.
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Affiliation(s)
- D Bohannon
- Department of Nuclear and Radiological Engineering, Georgia institute of Technology, Atlanta, GA, USA
| | - J Janopaul-Naylor
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S Rudra
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - X Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C W Chang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Y Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C Ma
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S A Patel
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - M W McDonald
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - J Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
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13
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Zheng X, Guo W, Wang Y, Zhang J, Zhang Y, Cheng C, Teng X, Lam S, Zhou T, Ma Z, Liu R, Wu H, Ge H, Cai J, Li B. Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy. Eur J Med Res 2023; 28:126. [PMID: 36935504 PMCID: PMC10024847 DOI: 10.1186/s40001-023-01041-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/03/2023] [Indexed: 03/21/2023] Open
Abstract
PURPOSE The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. METHODS 161 patients with stage IIIA-IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose-volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training-testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy. RESULTS Among all patients, 51 developed ARE grade ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively. CONCLUSIONS In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.
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Affiliation(s)
- Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yunhan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ruining Liu
- Department of Interventional Therapy, Henan Provincial People's Hospital, Zhengzhou, China
| | - Hui Wu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
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14
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Wang B, Liu J, Zhang X, Wang Z, Cao Z, Lu L, Lv W, Wang A, Li S, Wu X, Dong X. Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. EJNMMI Res 2023; 13:14. [PMID: 36779997 PMCID: PMC9925656 DOI: 10.1186/s13550-023-00959-6] [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: 10/26/2022] [Accepted: 01/26/2023] [Indexed: 02/14/2023] Open
Abstract
OBJECTIVES By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. MATERIALS AND METHODS A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models. RESULTS The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). CONCLUSION Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.
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Affiliation(s)
- Bingzhen Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Jinghua Liu
- Department of Nursing, Chengde Central Hospital, Chengde, Hebei China ,grid.11142.370000 0001 2231 800XDepartment of Nursing, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
| | - Xiaolei Zhang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhongxiao Wang
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Zhendong Cao
- grid.413851.a0000 0000 8977 8425Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Lijun Lu
- grid.284723.80000 0000 8877 7471School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong China
| | - Wenbing Lv
- grid.440773.30000 0000 9342 2456Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan China
| | - Aihui Wang
- grid.413851.a0000 0000 8977 8425Department of Nuclear Medicine, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei China
| | - Shuyan Li
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xiaotian Wu
- grid.413851.a0000 0000 8977 8425Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China. .,Hebei International Research Center of Medical-Engineering, Chengde Medical University, Chengde, Hebei, China.
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Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network. Int J Radiat Oncol Biol Phys 2023:S0360-3016(23)00002-0. [PMID: 36641040 DOI: 10.1016/j.ijrobp.2022.12.050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 12/26/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
PURPOSE This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. METHODS AND MATERIALS Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. RESULTS Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853). CONCLUSIONS Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.
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Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis. J Clin Med 2023; 12:jcm12020499. [PMID: 36675427 PMCID: PMC9867485 DOI: 10.3390/jcm12020499] [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: 11/20/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Early detection of deformity and timely intervention, such as brace treatment, can help inhibit progressive changes. A three-dimensional (3D) depth-sensor imaging system with a convolutional neural network was previously developed to predict the Cobb angle. The purpose of the present study was to (1) evaluate the performance of the deep learning algorithm (DLA) in predicting the Cobb angle and (2) assess the predictive ability depending on the presence or absence of clothing in a prospective analysis. We included 100 subjects with suspected AIS. The correlation coefficient between the actual and predicted Cobb angles was 0.87, and the mean absolute error and root mean square error were 4.7° and 6.0°, respectively, for Adam's forward bending without underwear. There were no significant differences in the correlation coefficients between the groups with and without underwear in the forward-bending posture. The performance of the DLA with a 3D depth sensor was validated using an independent external validation dataset. Because the psychological burden of children and adolescents on naked body imaging is an unignorable problem, scoliosis examination with underwear is a valuable alternative in clinics or schools.
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Zhang H, Liao M, Guo Q, Chen J, Wang S, Liu S, Xiao F. Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method. Med Phys 2022; 50:2049-2060. [PMID: 36563341 DOI: 10.1002/mp.16177] [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: 06/14/2022] [Revised: 11/07/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. PURPOSE To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method. METHODS In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively. RESULTS Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. CONCLUSION This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.
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Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Jun Chen
- Wuhan GE Healthcare, Wuhan, China
| | - Shan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Songmei Liu
- Department of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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18
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Qin Y, Zhu LH, Zhao W, Wang JJ, Wang H. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer. Front Oncol 2022; 12:913683. [PMID: 36016617 PMCID: PMC9395725 DOI: 10.3389/fonc.2022.913683] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
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Affiliation(s)
- Yun Qin
- School of Physics, Beihang University, Beijing, China
| | - Li-Hua Zhu
- School of Physics, Beihang University, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Jun-Jie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
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19
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Yang J, Wang L, Qin J, Du J, Ding M, Niu T, Li R. Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac515b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/02/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Purpose. This study aims to develop and validate a multi-view learning method by the combination of primary tumor radiomics and lymph node (LN) radiomics for the preoperative prediction of LN status in gastric cancer (GC). Methods. A total of 170 contrast-enhanced abdominal CT images from GC patients were enrolled in this retrospective study. After data preprocessing, two-step feature selection approach including Pearson correlation analysis and supervised feature selection method based on test-time budget (FSBudget) was performed to remove redundance of tumor and LN radiomics features respectively. Two types of discriminative features were then learned by an unsupervised multi-view partial least squares (UMvPLS) for a latent common space on which a logistic regression classifier is trained. Five repeated random hold-out experiments were employed. Results. On 20-dimensional latent common space, area under receiver operating characteristic curve (AUC), precision, accuracy, recall and F1-score are 0.9531 ± 0.0183, 0.9260 ± 0.0184, 0.9136 ± 0.0174, 0.9468 ± 0.0106 and 0.9362 ± 0.0125 for the training cohort respectively, and 0.8984 ± 0.0536, 0.8671 ± 0.0489, 0.8500 ± 0.0599, 0.9118 ± 0.0550 and 0.8882 ± 0.0440 for the validation cohort respectively (reported as mean ± standard deviation). It shows a better discrimination capability than single-view methods, our previous method, and eight baseline methods. When the dimension was reduced to 2, the model not only has effective prediction performance, but also is convenient for data visualization. Conclusions. Our proposed method by integrating radiomics features of primary tumor and LN can be helpful in predicting lymph node metastasis in patients of GC. It shows multi-view learning has great potential for guiding the prognosis and treatment decision-making in GC.
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20
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Chen G, Cui J, Qian J, Zhu J, Zhao L, Luo B, Cui T, Zhong L, Yang F, Yang G, Zhao X, Zhou Y, Geng M, Sun J. Rapid Progress in Intelligent Radiotherapy and Future Implementation. Cancer Invest 2022; 40:425-436. [PMID: 35225723 DOI: 10.1080/07357907.2022.2044842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.
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Affiliation(s)
- Guangpeng Chen
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Jianxiong Cui
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China.,Department of Oncology, Sichuan Provincial Crops Hospital of Chinese People's Armed Police Forces, Leshan 614000, Sichuan, P.R. China
| | - Jindong Qian
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Jianbo Zhu
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Lirong Zhao
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Bangyu Luo
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Tianxiang Cui
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Liangzhi Zhong
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Fan Yang
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Guangrong Yang
- Qijiang District People's Hospital, Chongqing 401420, P.R. China
| | - Xianlan Zhao
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Yibing Zhou
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
| | - Mingying Geng
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing 400042, P.R. China
| | - Jianguo Sun
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, P.R. China
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21
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Lam SK, Zhang Y, Zhang J, Li B, Sun JC, Liu CYT, Chou PH, Teng X, Ma ZR, Ni RY, Zhou T, Peng T, Xiao HN, Li T, Ren G, Cheung ALY, Lee FKH, Yip CWY, Au KH, Lee VHF, Chang ATY, Chan LWC, Cai J. Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy. Front Oncol 2022; 11:792024. [PMID: 35174068 PMCID: PMC8842229 DOI: 10.3389/fonc.2021.792024] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/01/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). Methods and Materials Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. Results The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. Conclusions Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.
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Affiliation(s)
- Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jia-Chen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Carol Yee-Tung Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Pak-Hei Chou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Rui-Yan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tao Peng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Hao-Nan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong5Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Amy Tien-Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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22
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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: 37] [Impact Index Per Article: 18.5] [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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23
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Wang D, Lee SH, Geng H, Zhong H, Plastaras J, Wojcieszynski A, Caruana R, Xiao Y. Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. Front Artif Intell 2022; 5:1059033. [PMID: 36568580 PMCID: PMC9771385 DOI: 10.3389/frai.2022.1059033] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose Pathologic complete response (pCR) is a critical factor in determining whether patients with rectal cancer (RC) should have surgery after neoadjuvant chemoradiotherapy (nCRT). Currently, a pathologist's histological analysis of surgical specimens is necessary for a reliable assessment of pCR. Machine learning (ML) algorithms have the potential to be a non-invasive way for identifying appropriate candidates for non-operative therapy. However, these ML models' interpretability remains challenging. We propose using explainable boosting machine (EBM) to predict the pCR of RC patients following nCRT. Methods A total of 296 features were extracted, including clinical parameters (CPs), dose-volume histogram (DVH) parameters from gross tumor volume (GTV) and organs-at-risk, and radiomics (R) and dosiomics (D) features from GTV. R and D features were subcategorized into shape (S), first-order (L1), second-order (L2), and higher-order (L3) local texture features. Multi-view analysis was employed to determine the best set of input feature categories. Boruta was used to select all-relevant features for each input dataset. ML models were trained on 180 cases from our institution, with 37 cases from RTOG 0822 clinical trial serving as the independent dataset for model validation. The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations. Results The best input feature categories were CP+DVH+S+R_L1+R_L2 for all models, from which Boruta-selected features enabled the EBM, XGB, RF, and SVM models to attain the AUCs of 0.820, 0.828, 0.828, and 0.774, respectively. Although EBM did not achieve the best performance, it provided the best capability for identifying critical turning points in response scores at distinct feature values, revealing that the bladder with maximum dose >50 Gy, and the tumor with maximum2DDiameterColumn >80 mm, elongation <0.55, leastAxisLength >50 mm and lower variance of CT intensities were associated with unfavorable outcomes. Conclusions EBM has the potential to enhance the physician's ability to evaluate an ML-based prediction of pCR and has implications for selecting patients for a "watchful waiting" strategy to RC therapy.
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Affiliation(s)
- Du Wang
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sang Ho Lee
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Huaizhi Geng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Haoyu Zhong
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - John Plastaras
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Andrzej Wojcieszynski
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
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24
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On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies. Phys Med 2021; 90:108-114. [PMID: 34600351 DOI: 10.1016/j.ejmp.2021.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 09/06/2021] [Accepted: 09/17/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Dosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study. MATERIAL AND METHODS Based on 17 clinical delivered dose distributions (Pn), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm3 and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC). RESULTS The highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P3 showed the highest percentage of CV >1 (1.14%) followed by P15 (0.41%), P1 (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans. CONCLUSION Considering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing.
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25
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Han P, Lee SH, Noro K, Haller JW, Nakatsugawa M, Sugiyama S, Bowers M, Lakshminarayanan P, Hoff J, Friedes C, Hu C, McNutt TR, Voong KR, Lee J, Hales RK. Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy. JCO Clin Cancer Inform 2021; 5:944-952. [PMID: 34473547 DOI: 10.1200/cci.20.00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.
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Affiliation(s)
- Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | | | | | | | | | - Michael Bowers
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Jeffrey Hoff
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Cole Friedes
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Chen Hu
- Department of Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, MD
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - K Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
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26
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Ren W, Liang B, Sun C, Wu R, Men K, Xu Y, Han F, Yi J, Qu Y, Dai J. Dosiomics-based prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma patients. Phys Med 2021; 89:219-225. [PMID: 34425512 DOI: 10.1016/j.ejmp.2021.08.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/23/2021] [Accepted: 08/11/2021] [Indexed: 11/19/2022] Open
Abstract
PURPOSE To predict the incidence of radiation-induced hypothyroidism (RHT) in nasopharyngeal carcinoma (NPC) patients, dosiomics features based prediction models were established. MATERIALS AND METHODS A total of 145 NPC patients treated with radiotherapy from January 2012 to January 2015 were included. Dosiomics features of the dose distribution within thyroid gland were extracted. The minimal-redundancy-maximal-relevance (mRMR) criterion was used to rank the extracted features and selected the most relevant features. Machine learning (ML) algorithms including logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were utilized to establish prediction models, respectively. Nested sampling and hyper-tuning methods were adopted to train and validate the prediction models. The dosiomics-based (DO) prediction models were evaluated through comparing with the dose-volume factor-based (DV) models in terms of the area under the receiver operating characteristic (ROC) curve (AUC). The demographics factors (age and gender) were included in both DO model and DV model. RESULTS Age, V45 and 37 dosiomics features exhibited significant correlations with RHT in univariate analysis. For prediction performance, DO prediction models exhibited better results with the best AUC value 0.7 while DV prediction models 0.61. In DO prediction models, the AUC values displayed a trend from ascending to descending with the increasing of selected features. The highest AUC value was achieved when the number of selected features was 3. In DV prediction model, similar trend was not observed. CONCLUSION This study established a prediction model based on the dosiomics features with better performance than conventional dose-volume factors, leading to early predict the possible RHT among NPC patients who had received radiotherapy and take precaution measures for NPC patients.
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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
| | - Yingjie Xu
- 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
| | - Yuan Qu
- 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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Dose-based radiomic analysis (dosiomics) for intensity-modulated radiotherapy in patients with prostate cancer: Correlation between planned dose distribution and biochemical failure. Int J Radiat Oncol Biol Phys 2021; 112:247-259. [PMID: 34706278 DOI: 10.1016/j.ijrobp.2021.07.1714] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 06/18/2021] [Accepted: 07/27/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE Although radiotherapy is one of the most significant modalities for localized prostate cancer, the prognostic factors for biochemical recurrence (BCR) regarding the treatment plan are unclear. We aimed to develop a novel dosiomics-based prediction model for BCR in patients with prostate cancer and clarify the correlations between the dosimetric factors and BCR. METHODS AND MATERIALS This study included 489 patients with localized prostate cancer (BCR: 96, No-BCR: 393) who received intensity-modulated radiation therapy. A total of 2,475 dosiomic features were extracted from the dose distributions on the prostate, clinical target volume (CTV), and planning target volume. A prediction model for BCR was trained on a training cohort of 342 patients. The performance of this model was validated using the concordance index (C-index) in a validation cohort of 147 patients. Another model was constructed using clinical variables, dosimetric parameters, and radiomic features for comparisons. Kaplan-Meier curves with log-rank analysis were used to assess the univariate discrimination based on the predictive dosiomic features. RESULTS The dosiomic feature derived from the CTV was significantly associated with BCR (hazard ratio: 0.73; 95% confidence interval [CI]: 0.57-0.93; P = .01). Although the dosiomics model outperformed the dosimetric and radiomics models, it did not outperform the clinical model. The performance significantly improved by combining the clinical variables and dosiomic features (C-index: 0.67; 95% CI: 0.65-0.68; P < .0001). The predictive dosiomic features were used to distinguish high-risk and low-risk patients (P < .05). CONCLUSIONS The dosiomic feature extracted from the CTV was significantly correlated with BCR in patients with prostate cancer, and the dosiomics model outperformed the model with conventional dose indices. Hence, new metrics for evaluating the quality of a treatment plan are warranted. Moreover, further research should be conducted to determine whether dosiomics can be incorporated in a clinical workflow or clinical trial.
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Lee SH, Kao GD, Feigenberg SJ, Dorsey JF, Frick MA, Jean-Baptiste S, Uche CZ, Cengel KA, Levin WP, Berman AT, Aggarwal C, Fan Y, Xiao Y. Multiblock Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:1451-1465. [PMID: 33662459 PMCID: PMC8286285 DOI: 10.1016/j.ijrobp.2021.02.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/07/2021] [Accepted: 02/12/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE The main objective of the present study was to integrate 18F-FDG-PET/CT radiomics with multiblock discriminant analysis for predicting circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic body radiation therapy (SBRT). METHODS Fifty-six patients with stage I NSCLC treated with SBRT underwent 18F-FDG-PET/CT imaging pre-SBRT and post-SBRT (median, 5 months; range, 3-10 months). CTCs were assessed via a telomerase-based assay before and within 3 months after SBRT and dichotomized at 5 and 1.3 CTCs/mL. Pre-SBRT, post-SBRT, and delta PET/CT radiomics features (n = 1548 × 3/1562 × 3) were extracted from gross tumor volume. Seven feature blocks were constructed including clinical parameters (n = 12). Multiblock data integration was performed using block sparse partial least squares-discriminant analysis (sPLS-DA) referred to as Data Integration Analysis for Biomarker Discovery Using Latent Components (DIABLO) for identifying key signatures by maximizing common information between different feature blocks while discriminating CTC levels. Optimal input blocks were identified using a pairwise combination method. DIABLO performance for predicting pre-SBRT and post-SBRT CTCs was evaluated using combined AUC (area under the curve, averaged across different blocks) analysis with 20 × 5-fold cross-validation (CV) and compared with that of concatenation-based sPLS-DA that consisted of combining all features into 1 block. CV prediction scores between 1 class versus the other were compared using the Wilcoxon rank sum test. RESULTS For predicting pre-SBRT CTCs, DIABLO achieved the best performance with combined pre-SBRT PET radiomics and clinical feature blocks, showing CV AUC of 0.875 (P = .009). For predicting post-SBRT CTCs, DIABLO achieved the best performance with combined post-SBRT CT and delta CT radiomics feature blocks, showing CV AUCs of 0.883 (P = .001). In contrast, all single-block sPLS-DA models could not attain CV AUCs higher than 0.7. CONCLUSIONS Multiblock integration with discriminant analysis of 18F-FDG-PET/CT radiomics has the potential for predicting pre-SBRT and post-SBRT CTCs. Radiomics and CTC analysis may complement and together help guide the subsequent management of patients with ES-NSCLC.
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Affiliation(s)
- Sang Ho Lee
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Gary D Kao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Steven J Feigenberg
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jay F Dorsey
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa A Frick
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Samuel Jean-Baptiste
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Chibueze Z Uche
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Keith A Cengel
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - William P Levin
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail T Berman
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Charu Aggarwal
- Division of Hematology/Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
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29
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Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
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A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity. Cancers (Basel) 2021; 13:cancers13153835. [PMID: 34359737 PMCID: PMC8345157 DOI: 10.3390/cancers13153835] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Dosiomics is born directly as an extension of radiomics: it entails extracting features from the patients’ three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images to obtain specific spatial and statistical information. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. This study provides the first multicentre evaluation of the dosiomic features in terms of reproducibility, stability and sensitivity across various dose distributions obtained from multiple technologies and techniques and considering different dose calculation algorithms of TPS and two different resolutions of the dose grid. Harmonisation strategies to account for a possible variation in the dose distribution due to these confounding factors should be adopted when investigating a correlation between dosiomic features and clinical outcomes in multicentre studies. Abstract Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.
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Ebert MA, Gulliford S, Acosta O, de Crevoisier R, McNutt T, Heemsbergen WD, Witte M, Palma G, Rancati T, Fiorino C. Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations. Phys Med Biol 2021; 66:12TR01. [PMID: 34049304 DOI: 10.1088/1361-6560/ac0681] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/28/2021] [Indexed: 12/20/2022]
Abstract
For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.
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Affiliation(s)
- Martin A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- 5D Clinics, Claremont, Western Australia, Australia
| | - Sarah Gulliford
- Department of Radiotherapy Physics, University College Hospitals London, United Kingdom
- Department of Medical Physics and Bioengineering, University College London, United Kingdom
| | - Oscar Acosta
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI-UMR 1099, F-35000 Rennes, France
| | | | - Todd McNutt
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Marnix Witte
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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Kokabu T, Kanai S, Kawakami N, Uno K, Kotani T, Suzuki T, Tachi H, Abe Y, Iwasaki N, Sudo H. An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection. Spine J 2021; 21:980-987. [PMID: 33540125 DOI: 10.1016/j.spinee.2021.01.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. PURPOSE This study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS. STUDY DESIGN Retrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. PATIENT SAMPLE One hundred and-sixty human subjects suspected to have AIS were included. OUTCOME MEASURES Patient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study. METHODS One hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest. RESULTS The correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0° and 5.4°, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10° and 89% for that of ≥20°. CONCLUSIONS The three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools.
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Affiliation(s)
- Terufumi Kokabu
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Satoshi Kanai
- Division of Systems Science and Informatics, Hokkaido University Graduate School of Information Science and Technology, Nishi 9 Chome Kita 13 Jo, Kita Ward, Sapporo, Hokkaido 060-0813, Japan
| | - Noriaki Kawakami
- Department of Orthopedic Surgery, Ichinomiyanishi Hospital, Ichinomiya, Kaimei, Aza Hira 1, 494-0001 Aichi, Japan
| | - Koki Uno
- Department of Orthopedic Surgery, National Hospital Organization, Kobe Medical Center, 3 Chome-1-1 Nishiochiai, Suma Ward, Kobe, Hyogo 654-0155, Japan
| | - Toshiaki Kotani
- Department of Orthopedic Surgery, Seirei Sakura Citizen Hospital, 2 Chome-36-2 Ebaradai, Sakura, Chiba 285-8765, Japan
| | - Teppei Suzuki
- Department of Orthopedic Surgery, National Hospital Organization, Kobe Medical Center, 3 Chome-1-1 Nishiochiai, Suma Ward, Kobe, Hyogo 654-0155, Japan
| | - Hiroyuki Tachi
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Yuichiro Abe
- Department of Orthopedic Surgery, Eniwa Hospital, Koganechuo 2-1-1, Eniwa, Hokkaido 061-1449, Japan
| | - Norimasa Iwasaki
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan
| | - Hideki Sudo
- Department of Orthopedic Surgery, Hokkaido University Hospital, Nishi 5 Chome Kita 14 Jo, Kita Ward, Sapporo, Hokkaido 060-8648, Japan; Department of Advanced Medicine for Spine and Spinal Cord Disorders, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15W7, Sapporo, Hokkaido 060-8638, Japan.
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Buizza G, Paganelli C, D’Ippolito E, Fontana G, Molinelli S, Preda L, Riva G, Iannalfi A, Valvo F, Orlandi E, Baroni G. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers (Basel) 2021; 13:339. [PMID: 33477723 PMCID: PMC7832399 DOI: 10.3390/cancers13020339] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/05/2021] [Accepted: 01/14/2021] [Indexed: 02/08/2023] Open
Abstract
Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained. MRI and CT-based radiomic, and dosiomic features were selected and fed to two survival models, singularly or by combining them with clinical factors. Adverse LC was given by in-field recurrence or tumour progression. The dataset was split in development and test sets and the models' performance evaluated using the concordance index (C-index). Patients were then assigned a low- or high-risk score. Survival curves were estimated, and risk groups compared through log-rank tests (after Bonferroni correction α = 0.0083). The best performing models were built on features describing tumour shape and dosiomic heterogeneity (median/interquartile range validation C-index: 0.80/024 and 0.79/0.26), followed by combined (0.73/0.30 and 0.75/0.27) and CT-based models (0.77/0.24 and 0.64/0.28). Dosiomic and combined models could consistently stratify patients in two significantly different groups. Dosiomic and multi-parametric radiomic features showed to be promising prognostic factors for LC in SBC treated with CIRT.
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Affiliation(s)
- Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
| | - Emma D’Ippolito
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Giulia Fontana
- Clinical Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
| | - Lorenzo Preda
- Radiology Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
- Unit of Radiology, Department of Intensive Medicine, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Giulia Riva
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Alberto Iannalfi
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Francesca Valvo
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Ester Orlandi
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
- Clinical Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
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Hernandez V, Hansen CR, Widesott L, Bäck A, Canters R, Fusella M, Götstedt J, Jurado-Bruggeman D, Mukumoto N, Kaplan LP, Koniarová I, Piotrowski T, Placidi L, Vaniqui A, Jornet N. What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiother Oncol 2020; 153:26-33. [PMID: 32987045 DOI: 10.1016/j.radonc.2020.09.038] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 12/25/2022]
Abstract
Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.
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Affiliation(s)
- Victor Hernandez
- Department of Medical Physics, Hospital Sant Joan de Reus, IISPV, Spain.
| | - Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Denmark; Institute of Clinical Research, University of Southern Denmark, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark
| | | | - Anna Bäck
- Department of Therapeutic Radiation Physics, Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Sweden
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Julia Götstedt
- Department of Radiation Physics, University of Gothenburg, Göteborg, Sweden
| | - Diego Jurado-Bruggeman
- Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain
| | - Nobutaka Mukumoto
- Department of Radiation Oncology and Image-applied Therapy, Graduate, School of Medicine, Kyoto University, Japan
| | | | - Irena Koniarová
- National Radiation Protection Institute, Prague, Czech Republic
| | - Tomasz Piotrowski
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland; Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy
| | - Ana Vaniqui
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Nuria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
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