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Rabe M, Kurz C, Thummerer A, Landry G. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlenther Onkol 2024:10.1007/s00066-024-02277-9. [PMID: 39138806 DOI: 10.1007/s00066-024-02277-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 07/07/2024] [Indexed: 08/15/2024]
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- Bavarian Cancer Research Center (BZKF), Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
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Li J, Lu S, Tian Y, Jiang L, Li L, Xie SH, Li Q. Neoadjuvant low-dose radiotherapy plus durvalumab and chemotherapy for potentially resectable stage III NSCLC: A phase Ib dose-escalation study. Radiother Oncol 2024; 196:110316. [PMID: 38679201 DOI: 10.1016/j.radonc.2024.110316] [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: 11/09/2023] [Revised: 04/17/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND PURPOSE This phase Ib study was designed to assess the safety/tolerability and preliminary antitumor activity of neoadjuvant low-dose radiotherapy (LDRT) plus durvalumab and chemotherapy for potentially resectable stage III non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS Eligible patients received dose-escalated radiotherapy (10 Gy in 5 fractions [cohort 1], 20 Gy in 10 fractions [cohort 2], and 30 Gy in 15 fractions [cohort 3]) according to a 3 + 3 design, with concurrent durvalumab plus standard chemotherapy for two cycles. Primary objective was safety/tolerability. Secondary objectives included major pathological response (MPR), pathological complete response (pCR), event-free survival (EFS), and exploratory biomarker analysis. RESULTS Nine patients were enrolled and completed the planned neoadjuvant therapy. No dose-limiting toxicity was recorded. Grade 3-4 treatment-related adverse events were observed in three (33.3 %) patients. Seven (77.8 %) patients successfully converted to resectable cases with R0 resection. No treatment-related surgical delay or death was reported. The MPR and pCR rates were both 33.3 % % (1/3) for cohort 1, 66.7 % (2/3) and 0.0 % for cohort 2, and 100.0 % (3/3), and 66.7 % (2/3) for cohort 3. At data cutoff, the 12 month-EFS rates were 33.3 %, 66.7 %, and 100 % for three cohorts, respectively. By biomarker analysis, TMB values were higher in either pathologically or radiologically responders than in others (all p > 0.05). CONCLUSION Neoadjuvant LDRT plus durvalumab and chemotherapy was well-tolerated in potentially resectable stage III NSCLC. The preliminary efficacy supports this combined regimen's potential, the optimal radiotherapy dosage was determined to be 30 Gy in 15 fractions, warranting further clinical investigation.
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Affiliation(s)
- Juan Li
- Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Shun Lu
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yuke Tian
- Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Lan Jiang
- Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Lu Li
- Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Shao-Hua Xie
- Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Qiang Li
- Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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van Timmeren JE, Bussink J, Koopmans P, Smeenk RJ, Monshouwer R. Longitudinal Image Data for Outcome Modeling. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00277-2. [PMID: 39003124 DOI: 10.1016/j.clon.2024.06.053] [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: 10/23/2023] [Revised: 04/15/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
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Affiliation(s)
- J E van Timmeren
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - P Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R J Smeenk
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
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Feng A, Huang Y, Zeng Y, Shao Y, Wang H, Chen H, Gu H, Duan Y, Shen Z, Xu Z. Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features. Clin Lung Cancer 2024; 25:e173-e180.e2. [PMID: 38402120 DOI: 10.1016/j.cllc.2024.01.006] [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: 04/17/2023] [Revised: 12/22/2023] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
INTRODUCTION Patients with early non-small-cell lung cancer (NSCLC) have a relatively long survival time after stereotactic body radiation therapy (SBRT). Predicting radiation-induced pneumonia (RP) has important clinical and social implications for improving the quality of life of such patients. This study developed an RP prediction model by using 3-dimensional (3D) dosiomic features. The model can be used to guide radiation therapy to reduce toxicity. METHODS Radiomic features were extracted from pre-treatment CT, dose-volume histogram (DVH) parameters and dosiomic features were extracted from the 3D dose distribution of 140 lung cancer patients. Four predictive models: (1) CT; (2) CT + DVH; (3) CT + Rtdose; and (4) Hybrid, CT + DVH + Rtdose, were trained to predict symptomatic RP by extremely randomized trees. Accuracy, sensitivity, specificity, and area under the receiver operator characteristic curve were evaluated. RESULT Results showed that the fraction regimen was correlated with symptomatic RP (P < .001). The proposed model achieved promising prediction results. The performance metrics for CT, CT + DVH, CT + Rtdose, and Hybrid were as follows: accuracy: 0.786, 0.821, 0.821, and 0.857; sensitivity: 0.625, 1, 0.875, and 1; specificity: 0.8, 0.565, 0.5, and 0.875; and area under the receiver operator characteristic curve: 0.791, 0.809, 0.907, and 0.920, respectively. CONCLUSION Dosiomic features can improve the performance of the predictive model for symptomatic RP compared with that obtained with the CT + DVH model. The model proposed in this study can help radiation oncologists individually predict the incidence rate of RP.
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Affiliation(s)
- AiHui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China; Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, China
| | - Ying Huang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China; Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, China
| | - Ya Zeng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - HengLe Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - YanHua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China; Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, China
| | - ZhenJiong Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - ZhiYong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Kong Y, Su M, Zhu Y, Li X, Zhang J, Gu W, Yang F, Zhou J, Ni J, Yang X, Zhu Z, Huang J. Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study. Strahlenther Onkol 2024:10.1007/s00066-024-02221-x. [PMID: 38498173 DOI: 10.1007/s00066-024-02221-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/25/2024] [Indexed: 03/20/2024]
Abstract
OBJECTIVE This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND METHODS The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value. RESULTS The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05). CONCLUSION Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
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Affiliation(s)
- Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Mingming Su
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Yan Zhu
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Xuan Li
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Jinmeng Zhang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, 305-8577, Ibaraki, Japan
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, 33136, Miami, FL, USA
| | - Jialiang Zhou
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Xi Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
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Brown KH, Ghita-Pettigrew M, Kerr BN, Mohamed-Smith L, Walls GM, McGarry CK, Butterworth KT. Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics. Radiother Oncol 2024; 192:110106. [PMID: 38253201 DOI: 10.1016/j.radonc.2024.110106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/10/2024] [Accepted: 01/17/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND PURPOSE Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation. MATERIALS AND METHODS Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model. RESULTS Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96). CONCLUSIONS This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.
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Affiliation(s)
- Kathryn H Brown
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK.
| | - Mihaela Ghita-Pettigrew
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Brianna N Kerr
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Letitia Mohamed-Smith
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Gerard M Walls
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK; Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK
| | - Conor K McGarry
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK
| | - Karl T Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
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Yang P, Shan J, Ge X, Zhou Q, Ding M, Niu T, Du J. Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study. Phys Eng Sci Med 2024; 47:295-307. [PMID: 38165634 DOI: 10.1007/s13246-023-01366-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/06/2023] [Indexed: 01/04/2024]
Abstract
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Affiliation(s)
- Pengfei Yang
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jingjing Shan
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xin Ge
- School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Qinxuan Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
| | - Tianye Niu
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
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Ye F, Xu L, Ren Y, Xia B, Chen X, Ma S, Deng Q, Li X. Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients. Front Oncol 2024; 14:1343170. [PMID: 38357195 PMCID: PMC10864532 DOI: 10.3389/fonc.2024.1343170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). Methods We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. Results The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. Discussions The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.
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Affiliation(s)
- Fengsong Ye
- Department of Tumor Radiotherapy and Chemotherapy, Lishui People’s Hospital, Lishui, China
| | - Lixia Xu
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Yao Ren
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Bing Xia
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Xueqin Chen
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Shenlin Ma
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Qinghua Deng
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
| | - Xiadong Li
- Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Zhejiang, Hangzhou, China
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Li J, Li L, Tang S, Yu Q, Liu W, Liu N, Yang F, Zhang D, Yuan S. Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study. BMC Cancer 2024; 24:78. [PMID: 38225543 PMCID: PMC10789008 DOI: 10.1186/s12885-023-11809-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/28/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Chemoradiotherapy is a critical treatment for patients with locally advanced and unresectable non-small cell lung cancer (NSCLC), and it is essential to identify high-risk patients as early as possible owing to the high incidence of radiation pneumonitis (RP). Increasing attention is being paid to the effects of endogenous factors for RP. This study aimed to investigate the value of computed tomography (CT)-based radiomics combined with genomics in analyzing the risk of grade ≥ 2 RP in unresectable stage III NSCLC. METHODS In this retrospective multi-center observational study, 100 patients with unresectable stage III NSCLC who were treated with chemoradiotherapy were analyzed. Radiomics features of the entire lung were extracted from pre-radiotherapy CT images. The least absolute shrinkage and selection operator algorithm was used for optimal feature selection to calculate the Rad-score for predicting grade ≥ 2 RP. Genomic DNA was extracted from formalin-fixed paraffin-embedded pretreatment biopsy tissues. Univariate and multivariate logistic regression analyses were performed to identify predictors of RP for model development. The area under the receiver operating characteristic curve was used to evaluate the predictive capacity of the model. Statistical comparisons of the area under the curve values between different models were performed using the DeLong test. Calibration and decision curves were used to demonstrate discriminatory and clinical benefit ratios, respectively. RESULTS The Rad-score was constructed from nine radiomic features to predict grade ≥ 2 RP. Multivariate analysis demonstrated that histology, Rad-score, and XRCC1 (rs25487) allele mutation were independent high-risk factors correlated with RP. The area under the curve of the integrated model combining clinical factors, radiomics, and genomics was significantly higher than that of any single model (0.827 versus 0.594, 0.738, or 0.641). Calibration and decision curve analyses confirmed the satisfactory clinical feasibility and utility of the nomogram. CONCLUSION Histology, Rad-score, and XRCC1 (rs25487) allele mutation could predict grade ≥ 2 RP in patients with locally advanced unresectable NSCLC after chemoradiotherapy, and the integrated model combining clinical factors, radiomics, and genomics demonstrated the best predictive efficacy.
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Affiliation(s)
- Jiaran Li
- Shandong University Cancer Center, Jinan, Shandong, China
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Li Li
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shanshan Tang
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qingxi Yu
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wenju Liu
- Department of Radiation Oncology, Liaocheng Pepole's Hospital, Liaocheng, Shandong, China
| | - Ning Liu
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Fengchang Yang
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Dexian Zhang
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shuanghu Yuan
- Shandong University Cancer Center, Jinan, Shandong, China.
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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Nie T, Chen Z, Cai J, Ai S, Xue X, Yuan M, Li C, Shi L, Liu Y, Verma V, Bi J, Han G, Yuan Z. Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy. Radiother Oncol 2024; 190:110047. [PMID: 38070685 DOI: 10.1016/j.radonc.2023.110047] [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: 05/10/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023]
Abstract
PURPOSE This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy. MATERIALS AND METHODS This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting. RESULTS HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts. CONCLUSIONS In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.
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Affiliation(s)
- Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zien Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China; School of Biomedical Engineering, South-Central Minzu University, Wuhan, PR China
| | - Jun Cai
- Department of Oncology, First Affiliated Hospital of Yangtze University, Nanhuan Road, Jingzhou, Hubei, PR China
| | - Shuangquan Ai
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China; School of Biomedical Engineering, South-Central Minzu University, Wuhan, PR China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Mengting Yuan
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Chao Li
- Department of Oncology, First Affiliated Hospital of Yangtze University, Nanhuan Road, Jingzhou, Hubei, PR China
| | - Liting Shi
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Vivek Verma
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Jianping Bi
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Guang Han
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
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11
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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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Zhou J, Yu B, Guo P, Wang S. The predictive value of the change of the number of pixels under different CT value intervals in the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. BMC Pulm Med 2023; 23:426. [PMID: 37924039 PMCID: PMC10623708 DOI: 10.1186/s12890-023-02732-w] [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: 08/12/2023] [Accepted: 10/24/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND Due to the fact that the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. (CT-occult CLSCC and SEPL) cannot be detected by lung CT screening, early and timely diagnosis of central lung cancer becomes very difficult, which directly affects the prognosis of patients. METHODS We retrospectively review medical records of patients at the Zhejiang Cancer Hospital and enrolled 41 patients with the CT-occult CLSCC and SEPL and 48 patients without the CT-occult CLSCC and SEPL. We compare the clinical characteristics, imaging features and Changes in the number of pixels under different CT value intervals of patients with and without the CT-occult CLSCC and SEPL and we perform univariate and multivariate logistic regression analysis to explore independent factors for the CT-occult CLSCC and SEPL in the patients. RESULTS We demonstrate that pack-years ≥ 20 (OR: 3.848, 95% CI: 1.086 ~ 13.633), the number of pixels change of CT value in interval [-850 ~ -750HU] (OR: 5.302, 95% CI: 1.122 ~ 25.057) and in interval [-900 ~ -850HU] (OR: 3.478, 95% CI: 1.167 ~ 10.365) are independently associated with the CT-occult CLSCC and SEPL in the patients. Ultimately, the logistic model obtained is statistically significant (p < 0.05) and an area under the ROC curve is 0.776 (95% CI: 0.682-0.870). The sensitivity of this model is 90.2% and the specificity is 52.1%. CONCLUSION The results of this study indicate that in the CT value range [-950 ~ -750HU], when the total number of lung pixels tend to increase towards the region with high CT value, the probability of the occurrence of CT-occult CLSCC and SEPL lesions also increases. Meanwhile, these results have guiding significance for the further study of radiomic.
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Affiliation(s)
- Jiaming Zhou
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bijun Yu
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Peng Guo
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shi Wang
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China.
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13
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Huang Q, Yang C, Pang J, Zeng B, Yang P, Zhou R, Wu H, Shen L, Zhang R, Lou F, Jin Y, Abdilim A, Jin H, Zhang Z, Xie X. CT-based dosiomics and radiomics model predicts radiation-induced lymphopenia in nasopharyngeal carcinoma patients. Front Oncol 2023; 13:1168995. [PMID: 37954080 PMCID: PMC10634512 DOI: 10.3389/fonc.2023.1168995] [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: 02/18/2023] [Accepted: 09/12/2023] [Indexed: 11/14/2023] Open
Abstract
Purpose This study aims to develop and validate a model predictive for the incidence of grade 4 radiation-induced lymphopenia (G4RIL), based on dosiomics features and radiomics features from the planning CT of nasopharyngeal carcinoma (NPC) treated by radiation therapy. Methods The dataset of 125 NPC patients treated with radiotherapy from August 2018 to March 2019 was randomly divided into two sets-an 85-sample training set and a 40-sample test set. Dosiomics features and radiomics features of the CT image within the skull bone and cervical vertebrae were extracted. A feature selection process of multiple steps was employed to identify the features that most accurately forecast the data and eliminate superfluous or insignificant ones. A support vector machine learning classifier with correction for imbalanced data was trained on the patient dataset for prediction of RIL (positive classifier for G4RIL, negative otherwise). The model's predictive capability was gauged by gauging its sensitivity (the likelihood of a positive test being administered to patients with G4RIL) and specificity in the test set. The area beneath the ROC curve (AUC) was utilized to explore the association of characteristics with the occurrence of G4RIL. Results Three clinical features, three dosiomics features, and three radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction. The combination model, based on nine robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with three dosiomics features, had an ACC value of 0.82, the radiomics model, with three radiomics features, had an ACC value of 0.82, and the clinical model, with its initial features, had an ACC value of 0.6 for prediction performance. Conclusion The findings show that radiomics and dosiomics features are correlated with the G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients.
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Affiliation(s)
- Qingfang Huang
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Chao Yang
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- College of Physics and Electronic Science, Shandong Normal University, Jinan, China
| | - Jinmeng Pang
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Biao Zeng
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Pei Yang
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Rongrong Zhou
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Haijun Wu
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Liangfang Shen
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Rong Zhang
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Fan Lou
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Yi Jin
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Albert Abdilim
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Hekun Jin
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Zijian Zhang
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoxue Xie
- Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China
- Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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Tan D, Mohamad Salleh SA, Manan HA, Yahya N. Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis. J Med Imaging Radiat Oncol 2023; 67:564-579. [PMID: 37309680 DOI: 10.1111/1754-9485.13546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. METHODS A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. RESULTS Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. CONCLUSION Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
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Affiliation(s)
- Daryl Tan
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Zhang Z, Wang Z, Yan M, Yu J, Dekker A, Zhao L, Wee L. Radiomics and Dosiomics Signature From Whole Lung Predicts Radiation Pneumonitis: A Model Development Study With Prospective External Validation and Decision-curve Analysis. Int J Radiat Oncol Biol Phys 2023; 115:746-758. [PMID: 36031028 DOI: 10.1016/j.ijrobp.2022.08.047] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/10/2022] [Accepted: 08/20/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction. METHODS AND MATERIALS Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models. RESULTS A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram. CONCLUSIONS Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.
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Affiliation(s)
- Zhen Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zhixiang Wang
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jiaqi Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
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Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis. JOURNAL OF ONCOLOGY 2023; 2023:5328927. [PMID: 36852328 PMCID: PMC9966572 DOI: 10.1155/2023/5328927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/18/2023] [Accepted: 02/01/2023] [Indexed: 02/20/2023]
Abstract
Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.
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Thakur P, Olson JD, Dugan GO, Daniel Bourland J, Kock ND, Mark Cline J. Quantitative Assessment and Comparative Analysis of Longitudinal Lung CT Scans of Chest-Irradiated Nonhuman Primates. Radiat Res 2023; 199:39-47. [PMID: 36394559 PMCID: PMC9987082 DOI: 10.1667/rade-21-00225.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Computed tomography (CT) imaging has been used to diagnose radiation-induced lung injury for decades. However, histogram-based quantitative tools have rarely been applied to assess lung abnormality due to radiation-induced lung injury (RILI). Here, we used first-order summary statistics to derive and assess threshold measures extracted from whole lung histograms of CT radiodensity in rhesus macaques. For the present study, CT scans of animals exposed to 10 Gy of whole thorax irradiation were utilized from a previous study spanning 2-9 months postirradiation. These animals were grouped into survivors and non-survivors based on their clinical and experimental endpoints. We quantified the change in lung attenuation after irradiation relative to baseline using three density parameters; average lung density (ALD), percent change in hyper-dense lung volume (PCHV), hyperdense volume as a percent of total volume (PCHV/TV) at 2-month intervals and compared each parameter between the two irradiated groups (non-survivors and survivors). We also correlated our results with histological findings. All the three indices (ALD, PCHV, PCHV/TV) obtained from density histograms showed a significant increase in lung injury in non-survivors relative to survivors, with PCHV relatively more sensitive to detect early RILI changes. We observed a significant positive correlation between histologic pneumonitis scores and each of the three CT measurements, indicating that CT density is useful as a surrogate for histologic disease severity in RILI. CT-based three density parameters, ALD, PCHV, PCHV/TV, may serve as surrogates for likely histopathology patterns in future studies of RILI disease progression.
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Affiliation(s)
- Priyanka Thakur
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
| | - John D. Olson
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
| | - Gregory O Dugan
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
| | - J. Daniel Bourland
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
| | - Nancy D. Kock
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
| | - J. Mark Cline
- Department of Pathology, Section on Comparative Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157-1040
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Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
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Yang S, Huang S, Ye X, Xiong K, Zeng B, Shi Y. Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study. BMC Pulm Med 2022; 22:402. [PMID: 36344945 PMCID: PMC9639320 DOI: 10.1186/s12890-022-02211-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Background Radiotherapy is an important treatment for patients with stage III/IV non-small cell lung cancer (NSCLC), and due to its high incidence of radiation pneumonitis, it is essential to identify high-risk people as early as possible. The present work investigates the value of the application of different phase data throughout the radiotherapy process in analyzing risk of grade ≥ 2 radiation pneumonitis in stage III/IV NSCLC. Furthermore, the phase data fusion was gradually performed with the radiotherapy timeline to develop a risk assessment model. Methods This study retrospectively collected data from 91 stage III/IV NSCLC cases treated with Volumetric modulated arc therapy (VMAT). Patient data were collected according to the radiotherapy timeline for four phases: clinical characteristics, radiomics features, radiation dosimetry parameters, and hematological indexes during treatment. Risk assessment models for single-phase and stepwise fusion phases were established according to logistic regression. In addition, a nomogram of the final fusion phase model and risk classification system was generated. Receiver operating characteristic (ROC), decision curve, and calibration curve analysis were conducted to internally validate the nomogram to analyze its discrimination. Results Smoking status, PTV and lung radiomics feature, lung and esophageal dosimetry parameters, and platelets at the third week of radiotherapy were independent risk factors for the four single-phase models. The ROC result analysis of the risk assessment models created by stepwise phase fusion were: (area under curve [AUC]: 0.67,95% confidence interval [CI]: 0.52–0.81), (AUC: 0.82,95%CI: 0.70–0.94), (AUC: 0.90,95%CI: 0.80–1.00), and (AUC:0.90,95%CI: 0.80–1.00), respectively. The nomogram based on the final fusion phase model was validated using calibration curve analysis and decision curve analysis, demonstrating good consistency and clinical utility. The nomogram-based risk classification system could correctly classify cases into three diverse risk groups: low-(ratio:3.6%; 0 < score < 135), intermediate-(ratio:30.7%, 135 < score < 160) and high-risk group (ratio:80.0%, score > 160). Conclusions In our study, the risk assessment model makes it easy for physicians to assess the risk of grade ≥ 2 radiation pneumonitis at various phases in the radiotherapy process, and the risk classification system and nomogram identify the patient’s risk level after completion of radiation therapy.
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What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology. J Pers Med 2022; 12:jpm12111834. [PMID: 36579551 PMCID: PMC9694457 DOI: 10.3390/jpm12111834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
The constant evolution of technology has dramatically changed the history of radiation oncology, allowing clinicians to deliver increasingly accurate and precise treatments, moving from 2D radiotherapy to 3D conformal radiotherapy, leading to intensity-modulated image-guided (IMRT-IGRT) and stereotactic body radiotherapy treatments [...].
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Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study. Healthcare (Basel) 2022; 10:healthcare10112150. [DOI: 10.3390/healthcare10112150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/16/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.
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Gu J, Li B, Shu H, Zhu J, Qiu Q, Bai T. Development and verification of radiomics framework for computed tomography image segmentation. Med Phys 2022; 49:6527-6537. [PMID: 35917213 PMCID: PMC9805121 DOI: 10.1002/mp.15904] [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: 03/31/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Radiomics has been considered an imaging marker for capturing quantitative image information (QII). The introduction of radiomics to image segmentation is desirable but challenging. PURPOSE This study aims to develop and validate a radiomics-based framework for image segmentation (RFIS). METHODS RFIS is designed using features extracted from volume (svfeatures) created by sliding window (swvolume). The 53 svfeatures are extracted from 11 phantom series. Outliers in the svfeature datasets are detected by isolation forest (iForest) and specified as the mean value. The percentage coefficient of variation (%COV) is calculated to evaluate the reproducibility of svfeatures. RFIS is constructed and applied to the gross target volume (GTV) segmentation from the peritumoral region (GTV with a 10 mm margin) to assess its feasibility. The 127 lung cancer images are enrolled. The test-retest method, correlation matrix, and Mann-Whitney U test (p < 0.05) are used to select non-redundant svfeatures of statistical significance from the reproducible svfeatures. The synthetic minority over-sampling technique is utilized to balance the minority group in the training sets. The support vector machine is employed for RFIS construction, which is tuned in the training set using 10-fold stratified cross-validation and then evaluated in the test sets. The swvolumes with the consistent classification results are grouped and merged. Mode filtering is performed to remove very small subvolumes and create relatively large regions of completely uniform character. In addition, RFIS performance is evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and Dice similarity coefficient (DSC). RESULTS 30249 phantom and 145008 patient image swvolumes were analyzed. Forty-nine (92.45% of 53) svfeatures represented excellent reproducibility(%COV<15). Forty-five features (91.84% of 49) included five categories that passed test-retest analysis. Thirteen svfeatures (28.89% of 45) svfeatures were selected for RFIS construction. RFIS showed an average (95% confidence interval) sensitivity of 0.848 (95% CI:0.844-0.883), a specificity of 0.821 (95% CI: 0.818-0.825), an accuracy of 83.48% (95% CI: 83.27%-83.70%), and an AUC of 0.906 (95% CI: 0.904-0.908) with cross-validation. The sensitivity, specificity, accuracy, and AUC were equal to 0.762 (95% CI: 0.754-0.770), 0.840 (95% CI: 0.837-0.844), 82.29% (95% CI: 81.90%-82.60%), and 0.877 (95% CI: 0.873-0.881) in the test set, respectively. GTV was segmented by grouping and merging swvolume with identical classification results. The mean DSC after mode filtering was 0.707 ± 0.093 in the training sets and 0.688 ± 0.072 in the test sets. CONCLUSION Reproducible svfeatures can capture the differences in QII among swvolumes. RFIS can be applied to swvolume classification, which achieves image segmentation by grouping and merging the swvolume with similar QII.
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Affiliation(s)
- Jiabing Gu
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China,Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Baosheng Li
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China,Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Huazhong Shu
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China
| | - Jian Zhu
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina,Shandong Key Laboratory of Digital Medicine and Computer Assisted SurgeryThe Affiliated Hospital of Qingdao UniversityQingdaoP. R. China
| | - Qingtao Qiu
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Tong Bai
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
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Radiomic Analysis for Pretreatment Prediction of Recurrence Post-Radiotherapy in Cervical Squamous Cell Carcinoma Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102346. [PMID: 36292034 PMCID: PMC9600567 DOI: 10.3390/diagnostics12102346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/30/2022] Open
Abstract
Background: The current study aims to predict the recurrence of cervical cancer patients treated with radiotherapy from radiomics features on pretreatment T1- and T2-weighted MR images. Methods: A total of 89 patients were split into model training (63 patients) and model testing (26 patients). The predictors of recurrence were selected using the least absolute shrinkage and selection operator (LASSO) regression. The machine learning used neural network classifiers. Results: Using LASSO analysis of radiomics, we found 25 features from the T1-weighted and 4 features from T2-weighted MR images, respectively. The accuracy was highest with the combination of T1- and T2-weighted MR images. The model performances with T1- or T2-weighted MR images were 86.4% or 89.4% accuracy, 74.9% or 38.1% sensitivity, 81.8% or 72.2% specificity, and 0.89 or 0.69 of the area under the curve (AUC). The model performance with the combination of T1- and T2-weighted MR images was 93.1% accuracy, 81.6% sensitivity, 88.7% specificity, and 0.94 of AUC. Conclusions: The radiomics analysis with T1- and T2-weighted MR images could highly predict the recurrence of cervix cancer after radiotherapy. The variation of the distribution and the difference in the pixel number at the peripheral and the center were important predictors.
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Shiri I, Mostafaei S, Haddadi Avval A, Salimi Y, Sanaat A, Akhavanallaf A, Arabi H, Rahmim A, Zaidi H. High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms. Sci Rep 2022; 12:14817. [PMID: 36050434 PMCID: PMC9437017 DOI: 10.1038/s41598-022-18994-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/23/2022] [Indexed: 12/11/2022] Open
Abstract
We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Thomas HMT, Hippe DS, Forouzannezhad P, Sasidharan BK, Kinahan PE, Miyaoka RS, Vesselle HJ, Rengan R, Zeng J, Bowen SR. Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics? Discov Oncol 2022; 13:85. [PMID: 36048266 PMCID: PMC9437196 DOI: 10.1007/s12672-022-00548-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC. METHODS Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout. RESULTS Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69-12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59-0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65-0.88]) or shape feature classes (c-index 0.79 [0.66-0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59-0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R2 = 0.84-1.00), representing surrogates rather than independent predictors of pneumonitis risk. CONCLUSIONS In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted.
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Affiliation(s)
- Hannah M T Thomas
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Parisa Forouzannezhad
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Balu Krishna Sasidharan
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | - Paul E Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Robert S Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA.
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
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Dmytriw AA, Ortega C, Anconina R, Metser U, Liu ZA, Liu Z, Li X, Sananmuang T, Yu E, Joshi S, Waldron J, Huang SH, Bratman S, Hope A, Veit-Haibach P. Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up. Cancers (Basel) 2022; 14:cancers14133105. [PMID: 35804877 PMCID: PMC9264840 DOI: 10.3390/cancers14133105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nasopharyngeal carcinoma (NPC) is a frequent head and neck cancer, especially in Asian countries. Our studies investigated the value of minable data derived from standard of care PET/CT imaging in patients with NPC. The here presented evaluation found that certain specific imaging features in this patient population can be potentially used to predict overall survival and progression free survival at different time points in those patients. Abstract Purpose: We aim determine the value of PET and CT radiomic parameters on survival with serial follow-up PET/CT in patients with nasopharyngeal carcinoma (NPC) for which curative intent therapy is undertaken. Methods: Patients with NPC and available pre-treatment as well as follow up PET/CT were included from 2005 to 2006 and were followed to 2021. Baseline demographic, radiological and outcome data were collected. Univariable Cox proportional hazard models were used to evaluate features from baseline and follow-up time points, and landmark analyses were performed for each time point. Results: Sixty patients were enrolled, and two-hundred and seventy-eight (278) PET/CT were at baseline and during follow-up. Thirty-eight percent (38%) were female, and sixty-two patients were male. All patients underwent curative radiation or chemoradiation therapy. The median follow-up was 11.72 years (1.26–14.86). Five-year and ten-year overall survivals (OSs) were 80.0% and 66.2%, and progression-free survival (PFS) was 90.0% and 74.4%. Time-dependent modelling suggested that, among others, PET gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) (HR 2.74 95% CI 1.06, 7.05) was significantly associated with OS. Landmark analyses suggested that CT parameters were most predictive at 15 month, whereas PET parameters were most predictive at time points 3, 6, 9 and 15 month. Conclusions: This study with long-term follow up data on NPC suggests that mainly PET-derived radiomic features are predictive for OS but not PFS in a time-dependent evaluation. Furthermore, CT radiomic measures may predict OS and PFS best at initial and long-term follow-up time points and PET measures may be more predictive in the interval. These modalities are commonly used in NPC surveillance, and prospective validation should be considered.
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Affiliation(s)
- Adam A. Dmytriw
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Claudia Ortega
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
| | - Ur Metser
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Zhihui A. Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Zijin Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Xuan Li
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Thiparom Sananmuang
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University,270 Rama VI Road, Ratchathewi, Bangkok 10400, Thailand
| | - Eugene Yu
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Sayali Joshi
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - John Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Scott Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Andrew Hope
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Correspondence: ; Tel.: +416-340-4800 (ext. 6085); Fax: 416-340-3900
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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30
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Yang C, Jiang Z, Cheng T, Zhou R, Wang G, Jing D, Bo L, Huang P, Wang J, Zhang D, Jiang J, Wang X, Lu H, Zhang Z, Li D. Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:893103. [PMID: 35600395 PMCID: PMC9121398 DOI: 10.3389/fonc.2022.893103] [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: 03/10/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation. Methods Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk. Results A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957–0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71–0.95) and 0.82 (95% CI: 0.68–0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88–0.93). Conclusion Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.
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Affiliation(s)
- Chao Yang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Tingting Cheng
- Department of General Practice, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Rongrong Zhou
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Guangcan Wang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Di Jing
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Jianbo Wang
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Daizhou Zhang
- Shandong Provincial Key Laboratory of Mucosal and Transdermal Drug Delivery Technologies, Shandong Academy of Pharmaceutical Sciences, Jinan, China
| | - Jianwei Jiang
- Optical and Digital Image Processing Division, Qingdao NovelBeam Technology Co., Ltd., Qingdao, China
| | - Xing Wang
- Software Research and Development Center, Shangdong AccurDx Diagnosis of Biotech Co., Ltd., Jinan, China
| | - Hua Lu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zijian Zhang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
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31
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Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A. Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 2022; 43:483-493. [PMID: 35131965 DOI: 10.1097/mnm.0000000000001543] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Science, New Delhi, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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33
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Szmul A, Chandy E, Veiga C, Jacob J, Stavropoulou A, Landau D, Hiley CT, McClelland JR. A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans. Cancers (Basel) 2022; 14:1341. [PMID: 35267649 PMCID: PMC8909378 DOI: 10.3390/cancers14051341] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 02/01/2023] Open
Abstract
Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.
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Affiliation(s)
- Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - Edward Chandy
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
- Sussex Cancer Centre, Royal Sussex County Hospital, Brighton BN2 5BE, UK
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
- UCL Respiratory Department, University College London Hospital, London NW1 2PG, UK
| | - Alkisti Stavropoulou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - David Landau
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
| | - Crispin T. Hiley
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
- University College Hospital, University College London, London NW1 2BU, UK
| | - Jamie R. McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
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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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Chandy E, Szmul A, Stavropoulou A, Jacob J, Veiga C, Landau D, Wilson J, Gulliford S, Fenwick JD, Hawkins MA, Hiley C, McClelland JR. Quantitative Analysis of Radiation-Associated Parenchymal Lung Change. Cancers (Basel) 2022; 14:946. [PMID: 35205693 PMCID: PMC8870325 DOI: 10.3390/cancers14040946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 02/01/2023] Open
Abstract
We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.
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Affiliation(s)
- Edward Chandy
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
- Sussex Cancer Centre, Royal Sussex County Hospital, Brighton BN2 5BE, UK
| | - Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Alkisti Stavropoulou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Respiratory Department, University College London Hospital, London NW1 2PG, UK
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - David Landau
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - James Wilson
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Sarah Gulliford
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - John D. Fenwick
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK;
| | - Maria A. Hawkins
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Crispin Hiley
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - Jamie R. McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
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Ninomiya K, Arimura H, Yoshitake T, Hirose TA, Shioyama Y. Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis. PLoS One 2022; 17:e0263292. [PMID: 35100322 PMCID: PMC8803154 DOI: 10.1371/journal.pone.0263292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 01/18/2022] [Indexed: 12/25/2022] Open
Abstract
Objectives We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer. Methods A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models. Results The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively. Conclusion This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.
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Affiliation(s)
- Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Hidetaka Arimura
- Faculty of Medical Sciences, Division of Medical Quantum Science, Department of Health Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
- * E-mail: (HA); (TY)
| | - Tadamasa Yoshitake
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
- * E-mail: (HA); (TY)
| | - Taka-aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Higashi-ku, Fukuoka, Japan
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Qu H, Shi R, Li S, Che F, Wu J, Li H, Chen W, Zhang H, Li Z, Cui X. Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03053-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Pang S, Field M, Dowling J, Vinod S, Holloway L, Sowmya A. Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings. Artif Intell Med 2022; 123:102230. [PMID: 34998514 DOI: 10.1016/j.artmed.2021.102230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/20/2021] [Accepted: 11/30/2021] [Indexed: 12/24/2022]
Abstract
Radiological images play a central role in radiotherapy, especially in target volume delineation. Radiomic feature extraction has demonstrated its potential for predicting patient outcome and cancer risk assessment prior to treatment. However, inherent methodological challenges such as severe class imbalance, small training sample size, multi-centre data and weak correlation of image representations to outcomes are yet to be addressed adequately. Current radiomic analysis relies on segmented images (e.g., of tumours) for feature extraction, leading to loss of important context information in surrounding tissue. In this work, we examine the correlation between radiomics and clinical outcomes by combining two data modalities: pre-treatment computerized tomography (CT) imaging data and contours of segmented gross tumour volumes (GTVs). We focus on a clinical head & neck cancer dataset and design an efficient convolutional neural network (CNN) architecture together with appropriate machine learning strategies to cope with the challenges. During the training process on two cohorts, our algorithm learns to produce clinical outcome predictions by automatically extracting radiomic features. Test results on two other cohorts show state-of-the-art performance in predicting different clinical endpoints (i.e., distant metastasis: AUC = 0.91; loco-regional failure: AUC = 0.78; overall survival: AUC = 0.70 on segmented CT data) compared to prior studies. Furthermore, we also conduct extensive experiments both on the whole CT dataset and a combination of CT and GTV contours to investigate different learning strategies for this task. For example, further experiments indicate that overall survival prediction significantly improves to 0.83 AUC by combining CT and GTV contours as inputs, and the combination provides more intuitive visual explanations for patient outcome predictions.
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Affiliation(s)
- Shuchao Pang
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia; Medical Physics, Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
| | - Matthew Field
- Medical Physics, Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, Australia.
| | - Jason Dowling
- Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia; The Australian e-Health and Research Centre, CSIRO Health and Biosecurity, Herston, QLD, Australia.
| | - Shalini Vinod
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, Australia; Liverpool & Macarthur Cancer Therapy Centres, Liverpool Hospital, Liverpool, NSW, Australia.
| | - Lois Holloway
- Medical Physics, Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW, Australia; Liverpool & Macarthur Cancer Therapy Centres, Liverpool Hospital, Liverpool, NSW, Australia.
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
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Zhang R, Cai Z, Luo Y, Wang Z, Wang W. Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features. Eur J Radiol Open 2022; 9:100391. [PMID: 34977279 PMCID: PMC8688890 DOI: 10.1016/j.ejro.2021.100391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose Explore the longitudinal CT-based radiomics to demonstrate the changing trend of radiotherapy response and to determine at which point after the onset of treatment radiomics exhibit the greatest change for stage III NSCLC patients. Methods and materials Ten stage III NSCLC patients in line with inclusion criteria were enrolled retrospectively, each of whom received radiotherapy or concurrent chemo-radiotherapy and performed eight series of follow-up CT imaging. Longitudinal radiomics were extracted on region of interest from the eight registered images, then two steps were conducted to select significant features as indicators of tumor change: 1) stable features were selected by Kendall rank correlation; 2) texture feature types with a steadily changing trend were retained and intensity features with stable change trends were selected to represent the large number of them. Next, the trend and rate of tumor change were analyzed using the Delta method and Curve-fitting method. Finally, the statistics in the distribution of stable features in patients were calculated. Results 675 stable features were selected from a total number of 1371 radiomics features, then 12 texture features types were retained and three intensity features were chosen to represent their own category. Among the final selected feature types, it was found that the two time points were weeks 1 and 3 with the higher rate of change. One patient had very few stable tumor features out of a total of 101 features, and the rate of change of features of another patient was conspicuously higher than the average level with number of 301 features. Conclusion The longitudinal CT radiomics could demonstrate the change trend of tumor and at which point exhibit the greatest change during radiotherapy, and potentially be used for treatment decisions concerning adaptive radiotherapy.
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Key Words
- CBCT, Cone-beam Computed Tomography
- CT, Computed Tomography
- Computed tomography
- GLCM25/GLCM3, Gray Level Co-occurrence Matrix25/Gray Level Co-occurrence Matrix3
- GLRLM25, Gray Level Run Length Matrix25
- GTV, Gross Tumor Volume
- HU, Hounsfield Units
- IBEX, Imaging Biomarker Explorer
- LASSO, Least Absolute Shrinkage and Selection Operator
- Longitudinal radiomics features
- NID25/NID3, Neighborhood Intensity Difference25/Neighborhood Intensity Difference3
- NSCLC, Non-small cell lung carcinoma
- Non-small cell lung cancer
- PCA, Principle Component Analysis
- ROI, Region of Interest
- Radiation therapy
- VMAT, Volumetric Modulated Arc Therapy
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Affiliation(s)
- Ruiping Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
| | - Zhengting Cai
- School of Automation (Artificial Intelligence), Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang Province 310018, China
| | - Yan'an Luo
- Department of Physics, Nankai University, Weijin Road, Nankai District, Tianjin 300071, China
| | - Zhizhen Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
| | - Wei Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
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Pulmonary Toxicities of Immunotherapy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1342:357-375. [PMID: 34972974 DOI: 10.1007/978-3-030-79308-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Immune checkpoint inhibitors are a form of immunotherapy that are increasingly being used in a wide variety of cancers. Immune-related adverse events (irAEs) pose a major challenge in the treatment of cancer patients. Pneumonitis, the most common lung irAE, can cause significant disruptions in the treatment of cancer and may be life-threatening. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.
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Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [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: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
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Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
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Development of a novel detection method for changes in lung conditions during radiotherapy using a temporal subtraction technique. Phys Eng Sci Med 2021; 44:1341-1350. [PMID: 34704221 DOI: 10.1007/s13246-021-01070-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/19/2021] [Indexed: 01/04/2023]
Abstract
We aimed to develop a novel method of detecting changes in lung conditions during radiotherapy using temporal subtraction technique. Twenty patients who underwent radiotherapy were retrospectively assessed by calculating optimal direct similarity error (ODSE) between initial and mid-treatment registered images. Patients were grouped according to region in tumor size and atelectasis for lung of < 20 or ≥ 20 cm3, which analyzed two field regions (1024 × 768 pixels, 512 × 512 pixels). Correlations between ODSE and changes in lung conditions were analyzed based on effect of radiation dose; receiver operating characteristic (ROC) analysis was performed to evaluate whether changes can be detected during treatment period. The ODSE of 1024 × 768 pixels was changed to 1.00 (0.28-3.48) for lung lesion size of < 20 cm3 and 1.86 (0.55-6.58) for the ≥ 20 cm3 lung lesion size. ODSE of 512 × 512 pixels was 1.03 (0.40-2.12) for the region in tumor size and atelectasis of < 20 cm3 and 1.90 (0.39-27.8) for the ≥ 20 cm3 lung lesion size. The region under the curve values from ROC analysis were 0.796 (1024 × 768 pixels) and 0.983 (512 × 512 pixels). A novel method can visually and numerically help to detect changes in lung condition at early treatment stages. Using this method, difference between plan and actual positional relationship for target and risk organs that cannot be predicted at the time of planning can be avoided, ensuring high safety and accuracy in lung radiotherapy.
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Chen X, Zhou M, Wang Z, Lu S, Chang S, Zhou Z. Immunotherapy treatment outcome prediction in metastatic melanoma through an automated multi-objective delta-radiomics model. Comput Biol Med 2021; 138:104916. [PMID: 34656867 DOI: 10.1016/j.compbiomed.2021.104916] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 01/18/2023]
Abstract
Based on recent studies, immunotherapy led by immune checkpoint inhibitors has significantly improved the patient survival rate and effectively reduced the recurrence risk. However, immunotherapy has different therapeutic effects for different patients, leading to difficulties in predicting the treatment response. Conversely, delta-radiomic features, which measure the difference between pre- and post-treatment through quantitative image features, have proven to be promising descriptors for treatment outcome prediction. Consequently, we developed an effective model termed as the automated multi-objective delta-radiomics (Auto-MODR) model for the prediction of immunotherapy response in metastatic melanoma. In Auto-MODR, delta-radiomic features and traditional radiomic features were used as inputs. Furthermore, a novel automated multi-objective model was developed to obtain more reliable and balanced results between sensitivity and specificity. We conducted extensive comparisons with existing studies on treatment outcome prediction. Our method achieved an area under the curve (AUC) of 0.86 in a cross-validation study and an AUC of 0.73 in an independent study. Compared with the model using conventional radiomic features (pre- and post-treatment) only, better performance can be obtained when conventional radiomic and delta-radiomic features are combined. Furthermore, Auto-MODR outperformed the currently available radiomic strategies.
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Affiliation(s)
- Xi Chen
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Meijuan Zhou
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhilong Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Si Lu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Renal Cancer and Melanoma, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shaojie Chang
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhiguo Zhou
- School of Computer Science and Mathematics, University of Central Missouri, Warrensburg, MO, USA.
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Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021; 11:17633. [PMID: 34480036 PMCID: PMC8417253 DOI: 10.1038/s41598-021-96600-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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Affiliation(s)
- James C. Korte
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XDepartment of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Carlos Cardenas
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicholas Hardcastle
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1007.60000 0004 0486 528XCentre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Tomas Kron
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XSir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Jihong Wang
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Houda Bahig
- grid.410559.c0000 0001 0743 2111Radiation Oncology Department, Centre Hospitalier de l’Université de Montréal, Montreal, Canada
| | - Baher Elgohari
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.10251.370000000103426662Clinical Oncology & Nuclear Medicine Department, Mansoura University, Mansoura, Egypt
| | - Rachel Ger
- grid.470142.40000 0004 0443 9766Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ USA
| | - Laurence Court
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D. Fuller
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sweet Ping Ng
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.1055.10000000403978434Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia ,grid.482637.cDepartment of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Melbourne, Australia
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Su CW, Lee JC, Chang YF, Su NW, Lee PH, Dai KY, Tai HC, Leu YS, Chen YJ. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. TUMORI JOURNAL 2021; 108:450-460. [PMID: 34423708 DOI: 10.1177/03008916211039018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) is recommended for larynx-preserving treatment of locally advanced hypopharyngeal cancer (LAHC). However, the conventional evaluation of response is not robust enough to predict the outcome of subsequent treatments. This study aimed to develop an imaging biomarker using changes in radiomic features in invasive tumor front (ITF) by IC to predict treatment outcome of subsequent CCRT in LAHC. METHODS From 2006 to 2018, 59 computed tomography (CT) scan images before and after IC in patients with LAHC were used to contour the gross tumor volumes (GTVs). A total of 48 delta-volume radiomics features were acquired from the absolute spatial difference of GTVs (delta-GTV) before and after IC, conceptually representing a consistent portion of ITF. Least absolute shrinkage and selection operator regression (LASSO) was used to select features for establishing the model generating radiomic score (R score). RESULTS A model including 5 radiomic features from delta-GTV to predict better progression-free survival (PFS) of patients receiving subsequent CCRT was established. The R score was validated with all datasets (area under the curve 0.77). Low R score (<-0.16) was associated with improved PFS (p < 0.05). CONCLUSIONS The established radiomic model for ITF from radiomic features of delta-GTV after IC might be a potential imaging biomarker for predicting clinical outcome of subsequent CCRT in LAHC.
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Affiliation(s)
- Che-Wei Su
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Jehn-Chuan Lee
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yi-Fang Chang
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Nai-Wen Su
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Pei-Hsuan Lee
- Department of International Business, National Chengchi University, Taipei
| | - Kun-Yao Dai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Hung-Chi Tai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Yi-Shing Leu
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yu-Jen Chen
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei.,Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, Taipei.,Department of Medical Research, China Medical University Hospital, Taichung
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Bagher-Ebadian H, Zhu S, Siddiqui F, Lu M, Movsas B, Chetty IJ. Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas. Med Phys 2021; 48:7552-7562. [PMID: 34390003 DOI: 10.1002/mp.15159] [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: 05/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS AND MATERIALS One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance. RESULTS Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively. CONCLUSION Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Mei Lu
- Department of Public Health, Henry Ford Health System, Michigan, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
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Jiang W, Song Y, Sun Z, Qiu J, Shi L. Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis. Int J Radiat Oncol Biol Phys 2021; 110:1161-1170. [PMID: 33548340 DOI: 10.1016/j.ijrobp.2021.01.049] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 11/21/2020] [Accepted: 01/24/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE This study aimed to establish machine learning models using dosimetric factors and radiomics features within 5 regions of interest (ROIs) in treatment planning computed tomography images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥2). METHODS AND MATERIALS This study retrospectively collected data on 79 patients with lung cancer (25 RP ≥2) who underwent chemoradiotherapy between 2015 and 2018. We defined 5 ROIs in planning computed tomography images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV and TL-PTV and the mean dose within the other ROIs. A total of 1924 radiomics features were extracted from all 5 ROIs. We selected the best predictors for classifying 2 groups of patients using a sequential backward elimination support vector machine model. A permutation test was used to assess its statistical significance (P < .05). RESULTS The best predictors for symptomatic RP were the combination of 11 radiomics features, 5 dosimetric factors, age, and T stage, achieving an area under the curve (AUC) of 0.94 (95% confidence interval [CI], 0.85-1) (accuracy, 90%; sensitivity, 80% [95% CI, 44%-96%]; specificity, 95% [95% CI, 73%-100%]; P = 8 × 10-4). The clinical characteristics, dosimetric factors, and their combination showed limited predictive power (accuracy, 63.3%, 70%, and 70%; AUC [95% CI]: 0.73 [0.54-0.92], 0.53 [0.31-0.75], and 0.72 [0.51-0.92], respectively). The radiomics features of PTV-GTV and TL-PTV outperformed those of the other ROIs (accuracy, 76.7% and 76.7%; AUC [95% CI]: 0.82 [0.65-0.99] and 0.80 [0.59-1], respectively). CONCLUSIONS Combining dosimetric factors and radiomics features within different ROIs can improve the prediction of symptomatic RP. Our results can help physicians adjust the radiation dose distribution of the dose-sensitive lungs and target volumes based on personalized RP estimates.
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Affiliation(s)
- Wei Jiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Radiotherapy, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, China
| | - Yipeng Song
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, China
| | - Zhe Sun
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
| | - Liting Shi
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
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48
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Chen X, Sheikh K, Nakajima E, Lin CT, Lee J, Hu C, Hales RK, Forde PM, Naidoo J, Voong KR. Radiation Versus Immune Checkpoint Inhibitor Associated Pneumonitis: Distinct Radiologic Morphologies. Oncologist 2021; 26:e1822-e1832. [PMID: 34251728 PMCID: PMC8488797 DOI: 10.1002/onco.13900] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies. MATERIALS AND METHODS We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology. RESULTS Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus. CONCLUSION RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making. IMPLICATIONS FOR PRACTICE Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.
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Affiliation(s)
- Xuguang Chen
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Erica Nakajima
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cheng Ting Lin
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chen Hu
- Division of Biostatistics, Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick M Forde
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jarushka Naidoo
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Khinh Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 19:13-24. [PMID: 34307915 PMCID: PMC8295850 DOI: 10.1016/j.phro.2021.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/23/2022]
Abstract
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Michael Jameson
- GenesisCare, Alexandria, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia
| | - Noel Aherne
- Mid North Coast Cancer Institute, NSW, Australia.,Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.,Cancer Therapy Centre, Liverpool Hospital, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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50
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Hope A, Verduin M, Dilling TJ, Choudhury A, Fijten R, Wee L, Aerts HJWL, El Naqa I, Mitchell R, Vooijs M, Dekker A, de Ruysscher D, Traverso A. Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers. Cancers (Basel) 2021; 13:2382. [PMID: 34069307 PMCID: PMC8156328 DOI: 10.3390/cancers13102382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/21/2021] [Accepted: 05/03/2021] [Indexed: 11/16/2022] Open
Abstract
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
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Affiliation(s)
- Andrew Hope
- Department of Radiation Oncology, University of Toronto, Toronto, ON 5MT 1P5, Canada;
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON 5MT 1P5, Canada
| | - Maikel Verduin
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Thomas J Dilling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Ananya Choudhury
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Leonard Wee
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Hugo JWL Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA;
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, 6228 ET Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Ross Mitchell
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Marc Vooijs
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Andre Dekker
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
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