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Su W, Cheng D, Ni W, Ai Y, Yu X, Tan N, Wu J, Fu W, Li C, Xie C, Shen M, Jin X. Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108295. [PMID: 38905987 DOI: 10.1016/j.cmpb.2024.108295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/15/2024] [Accepted: 06/16/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND AND OBJECTIVE To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.
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Affiliation(s)
- Wanyu Su
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, China
| | - Dezhi Cheng
- Department of Thoracic Surgery, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Weihua Ni
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, China
| | - Yao Ai
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xianwen Yu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, China
| | - Ninghang Tan
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang 315000, China
| | - Jianping Wu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; Department of Radiotherapy, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou 324000, China
| | - Wen Fu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Chenyu Li
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Congying Xie
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Meixiao Shen
- School of Eye, Wenzhou Medical University, Wenzhou 325000, China; The Eye Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiance Jin
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, China.
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Niraula D, Cuneo KC, Dinov ID, Gonzalez BD, Jamaluddin JB, Jin JJ, Luo Y, Matuszak MM, Ten Haken RK, Bryant AK, Dilling TJ, Dykstra MP, Frakes JM, Liveringhouse CL, Miller SR, Mills MN, Palm RF, Regan SN, Rishi A, Torres-Roca JF, Yu HHM, El Naqa I. Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.27.24306434. [PMID: 38746238 PMCID: PMC11092730 DOI: 10.1101/2024.04.27.24306434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.
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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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Abbaspour S, Barahman M, Abdollahi H, Arabalibeik H, Hajainfar G, Babaei M, Iraji H, Barzegartahamtan M, Ay MR, Mahdavi SR. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study. Biomed Phys Eng Express 2023; 10:015017. [PMID: 37995359 DOI: 10.1088/2057-1976/ad0f3e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/23/2023] [Indexed: 11/25/2023]
Abstract
Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.
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Affiliation(s)
- Samira Abbaspour
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maedeh Barahman
- Department of Radiation Oncology, Firoozgar Hospital, Firoozgar Clinical Research Development Center (FCRDC), Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Hossein Arabalibeik
- Research Center for Science and Technology in Medicine (RCSTM), Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajainfar
- Rajaie Cardiovascular Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammadreza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hamed Iraji
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Barzegartahamtan
- Clinical Research Development Unit, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
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Ger RB, Wei L, Naqa IE, Wang J. The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation. Semin Radiat Oncol 2023; 33:252-261. [PMID: 37331780 PMCID: PMC11214660 DOI: 10.1016/j.semradonc.2023.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.
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Affiliation(s)
- Rachel B Ger
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX..
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Niraula D, Sun W, Jin J, Dinov ID, Cuneo K, Jamaluddin J, Matuszak MM, Luo Y, Lawrence TS, Jolly S, Ten Haken RK, El Naqa I. A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS). Sci Rep 2023; 13:5279. [PMID: 37002296 PMCID: PMC10066294 DOI: 10.1038/s41598-023-32032-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/21/2023] [Indexed: 04/03/2023] Open
Abstract
Involvement of many variables, uncertainty in treatment response, and inter-patient heterogeneity challenge objective decision-making in dynamic treatment regime (DTR) in oncology. Advanced machine learning analytics in conjunction with information-rich dense multi-omics data have the ability to overcome such challenges. We have developed a comprehensive artificial intelligence (AI)-based optimal decision-making framework for assisting oncologists in DTR. In this work, we demonstrate the proposed framework to Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications by developing an interactive software tool entitled Adaptive Radiotherapy Clinical Decision Support (ARCliDS). ARCliDS is composed of two main components: Artifcial RT Environment (ARTE) and Optimal Decision Maker (ODM). ARTE is designed as a Markov decision process and modeled via supervised learning. Given a patient's pre- and during-treatment information, ARTE can estimate treatment outcomes for a selected daily dosage value (radiation fraction size). ODM is formulated using reinforcement learning and is trained on ARTE. ODM can recommend optimal daily dosage adjustments to maximize the tumor local control probability and minimize the side effects. Graph Neural Networks (GNN) are applied to exploit the inter-feature relationships for improved modeling performance and a novel double GNN architecture is designed to avoid nonphysical treatment response. Datasets of size 117 and 292 were available from two clinical trials on adaptive RT in non-small cell lung cancer (NSCLC) patients and adaptive stereotactic body RT (SBRT) in hepatocellular carcinoma (HCC) patients, respectively. For training and validation, dense data with 297 features were available for 67 NSCLC patients and 110 features for 71 HCC patients. To increase the sample size for ODM training, we applied Generative Adversarial Networks to generate 10,000 synthetic patients. The ODM was trained on the synthetic patients and validated on the original dataset. We found that, Double GNN architecture was able to correct the nonphysical dose-response trend and improve ARCliDS recommendation. The average root mean squared difference (RMSD) between ARCliDS recommendation and reported clinical decisions using double GNNs were 0.61 [0.03] Gy/frac (mean [sem]) for adaptive RT in NSCLC patients and 2.96 [0.42] Gy/frac for adaptive SBRT HCC compared to the single GNN's RMSDs of 0.97 [0.12] Gy/frac and 4.75 [0.16] Gy/frac, respectively. Overall, For NSCLC and HCC, ARCliDS with double GNNs was able to reproduce 36% and 50% of the good clinical decisions (local control and no side effects) and improve 74% and 30% of the bad clinical decisions, respectively. In conclusion, ARCliDS is the first web-based software dedicated to assist KBR-ART with multi-omics data. ARCliDS can learn from the reported clinical decisions and facilitate AI-assisted clinical decision-making for improving the outcomes in DTR.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Wenbo Sun
- University of Michigan Transport Research Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jionghua Jin
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ivo D Dinov
- Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jamalina Jamaluddin
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
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Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-75. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.
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Luo Y, Cuneo KC, Lawrence TS, Matuszak MM, Dawson LA, Niraula D, Ten Haken RK, El Naqa I. A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy. Front Oncol 2022; 12:1061024. [PMID: 36568208 PMCID: PMC9782976 DOI: 10.3389/fonc.2022.1061024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background Imbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to more accurate and explainable outcome prediction models. Methods A total of 119 HCC patients with 163 tumors were used in the study. 81 patients with 104 tumors from the University of Michigan Hospital treated with SBRT were considered as a discovery dataset for radiation outcomes model building. The external testing dataset included 59 tumors from 38 patients with SBRT from Princess Margaret Hospital. In the discovery dataset, 100 tumors from 77 patients had local control (LC) (96% of 104 tumors) and 23 patients had at least one grade increment of ALBI (I-ALBI) during six-month follow up (28% of 81 patients). Each patient had a total of 110 features, where 15 or 20 features were identified by physicians as expert knowledge features (EKFs) for LC or I-ALBI prediction. We proposed a HITL based Bayesian network (HITL-BN) approach to enhance the capability of selecting important features from imbalanced data in terms of accuracy and explainability through humans' participation by integrating feature importance ranking and Markov blanket algorithms. A pure data-driven Bayesian network (PD-BN) method was applied to the same discovery dataset of HCC patients as a benchmark. Results In the training and testing phases, the areas under receiver operating characteristic curves of the HITL-BN models for LC or I-ALBI prediction during SBRT are 0.85 (95% confidence interval: 0.75-0.95) or 0.89 (0.81-0.95) and 0.77 or 0.78, respectively. They significantly outperformed the during-treatment PD-BN model in predicting LC or I-ALBI based on the discovery cross-validation and testing datasets from the Delong tests. Conclusion By allowing the human expert to be part of the model building process, the HITL-BN approach yielded significantly improved accuracy as well as better explainability when dealing with imbalanced outcomes in the prediction of post-SBRT treatment response of HCC patients when compared to the PD-BN method.
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Affiliation(s)
- Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States,*Correspondence: Yi Luo,
| | - Kyle C. Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Laura A. Dawson
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
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Feng B, Zhou W, Yang X, Luo H, Zhang X, Yang D, Tao D, Wu Y, Jin F. Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study. Clin Transl Radiat Oncol 2022; 38:188-194. [DOI: 10.1016/j.ctro.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/09/2022] [Accepted: 11/19/2022] [Indexed: 11/23/2022] Open
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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11
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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12
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Hinton T, Karnak D, Tang M, Jiang R, Luo Y, Boonstra P, Sun Y, Nancarrow DJ, Sandford E, Ray P, Maurino C, Matuszak M, Schipper MJ, Green MD, Yanik GA, Tewari M, Naqa IE, Schonewolf CA, Haken RT, Jolly S, Lawrence TS, Ray D. Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis. Transl Oncol 2022; 21:101428. [PMID: 35460942 PMCID: PMC9046881 DOI: 10.1016/j.tranon.2022.101428] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 02/07/2023] Open
Abstract
Grade 2 and higher radiation pneumonitis (RP2) is a potentially fatal toxicity that limits efficacy of radiation therapy (RT). We wished to identify a combined biomarker signature of circulating miRNAs and cytokines which, along with radiobiological and clinical parameters, may better predict a targetable RP2 pathway. In a prospective clinical trial of response-adapted RT for patients (n = 39) with locally advanced non-small cell lung cancer, we analyzed patients' plasma, collected pre- and during RT, for microRNAs (miRNAs) and cytokines using array and multiplex enzyme linked immunosorbent assay (ELISA), respectively. Interactions between candidate biomarkers, radiobiological, and clinical parameters were analyzed using data-driven Bayesian network (DD-BN) analysis. We identified alterations in specific miRNAs (miR-532, -99b and -495, let-7c, -451 and -139-3p) correlating with lung toxicity. High levels of soluble tumor necrosis factor alpha receptor 1 (sTNFR1) were detected in a majority of lung cancer patients. However, among RP patients, within 2 weeks of RT initiation, we noted a trend of temporary decline in sTNFR1 (a physiological scavenger of TNFα) and ADAM17 (a shedding protease that cleaves both membrane-bound TNFα and TNFR1) levels. Cytokine signature identified activation of inflammatory pathway. Using DD-BN we combined miRNA and cytokine data along with generalized equivalent uniform dose (gEUD) to identify pathways with better accuracy of predicting RP2 as compared to either miRNA or cytokines alone. This signature suggests that activation of the TNFα-NFκB inflammatory pathway plays a key role in RP which could be specifically ameliorated by etanercept rather than current therapy of non-specific leukotoxic corticosteroids.
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Affiliation(s)
- Tonaye Hinton
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - David Karnak
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Ming Tang
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ralph Jiang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yi Luo
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Philip Boonstra
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yilun Sun
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Derek J Nancarrow
- Department of Surgery, Division of Hematology-Oncology, Department of Internal Medicine, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Erin Sandford
- Division of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA
| | - Paramita Ray
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Christopher Maurino
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Martha Matuszak
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Matthew J Schipper
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael D Green
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Gregory A Yanik
- Division of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA
| | - Muneesh Tewari
- Division of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Caitlin A Schonewolf
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Shruti Jolly
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA
| | - Dipankar Ray
- Department of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
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13
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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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14
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 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
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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15
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Sun W, Niraula D, El Naqa I, Ten Haken RK, Dinov ID, Cuneo K, Jin JJ. Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106927. [PMID: 35675722 PMCID: PMC11058561 DOI: 10.1016/j.cmpb.2022.106927] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 05/10/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment efficacy. Existing artificial-intelligence (AI) methods can recommend radiation dose prescriptions within the scope of this available information. However, treating physicians may not fully entrust the AI's recommended prescriptions due to known limitations or at instances when the AI recommendation may go beyond physicians' current knowledge. This paper lays out a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. Towards this goal, Gaussian process (GP) models are integrated with deep neural networks (DNNs) to quantify the uncertainty of the treatment outcomes given by physicians and AI recommendations, respectively, which are further used as a guideline to educate clinical physicians and improve AI models performance. The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of 67 non-small cell lung cancer (NSCLC) patients and are retrospectively analyzed.
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Affiliation(s)
- Wenbo Sun
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA.
| | - Dipesh Niraula
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | | | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA.
| | - Judy Jionghua Jin
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA.
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16
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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17
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Forouzannezhad P, Maes D, Hippe DS, Thammasorn P, Iranzad R, Han J, Duan C, Liu X, Wang S, Chaovalitwongse WA, Zeng J, Bowen SR. Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14:cancers14051228. [PMID: 35267535 PMCID: PMC8909466 DOI: 10.3390/cancers14051228] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. (2) Methods: Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). (3) Results: FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. (4) Conclusion: Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.
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Affiliation(s)
- Parisa Forouzannezhad
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Dominic Maes
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Daniel S. Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA;
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Reza Iranzad
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jie Han
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - Chunyan Duan
- Department of Mechanical Engineering, Tongji University, Shanghai 200092, China;
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Shouyi Wang
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jing Zeng
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Stephen R. Bowen
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
- Department of Radiology, School of Medicine, University of Washington, Seattle, WA 98195, USA
- Correspondence:
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18
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Caruso D, Polici M, Lauri C, Laghi A. Radiomics and artificial intelligence. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00072-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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19
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Niraula D, Jamaluddin J, Matuszak MM, Haken RKT, Naqa IE. Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy. Sci Rep 2021; 11:23545. [PMID: 34876609 PMCID: PMC8651664 DOI: 10.1038/s41598-021-02910-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/24/2021] [Indexed: 01/31/2023] Open
Abstract
Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
| | - Jamalina Jamaluddin
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Martha M Matuszak
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
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20
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Cui S, Ten Haken RK, El Naqa I. Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:893-904. [PMID: 33539966 DOI: 10.1016/j.ijrobp.2021.01.042] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/10/2020] [Accepted: 01/23/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unlike normal tissue complication probability/tumor control probability models that use dosimetric information solely, our proposed models consider complex interactions among multiomics information including positron emission tomography (PET) radiomics, cytokines, and miRNAs. Additional time-to-event information is also used in the actuarial prediction. METHODS AND MATERIALS Three architectures were investigated: ADNN-DVH considered dosimetric information only; ADNN-com integrated multiomics information; and ADNN-com-joint combined RP2 (RP grade ≥2) and LC prediction. In these architectures, differential dose-volume histograms (DVHs) were fed into 1D convolutional neural networks (CNN) for extracting reduced representations. Variational encoders were used to learn representations of imaging and biological data. Reduced representations were fed into Surv-Nets to predict time-to-event probabilities for RP2 and LC independently and jointly by incorporating time information into designated loss functions. RESULTS Models were evaluated on 117 retrospective patients and were independently tested on 25 newly accrued patients prospectively. A multi-institutional RTOG0617 data set of 327 patients was used for external validation. ADNN-DVH yielded cross-validated c-indexes (95% confidence intervals) of 0.660 (0.630-0.690) for RP2 prediction and 0.727 (0.700-0.753) for LC prediction, outperforming a generalized Lyman model for RP2 (0.613 [0.583-0.643]) and a generalized log-logistic model for LC (0.569 [0.545-0.594]). The independent internal test and external validation yielded similar results. ADNN-com achieved an even better performance than ADNN-DVH on both cross-validation and independent internal test. Furthermore, ADNN-com-joint, which yielded performance similar to ADNN-com, realized joint prediction with c-indexes of 0.705 (0.676-0.734) for RP2 and 0.740 (0.714-0.765) for LC and achieved an area under a free-response receiving operator characteristic curve (AU-FROC) of 0.729 (0.697-0.773) for the joint prediction of RP2 and LC. CONCLUSION Novel deep learning architectures that integrate multiomics information outperformed traditional normal tissue complication probability/tumor control probability models in actuarial prediction of RP2 and LC.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Applied Physics Program, University of Michigan, Ann Arbor, Michigan.
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Luo Y, Jolly S, Palma D, Lawrence TS, Tseng HH, Valdes G, McShan D, Ten Haken RK, Ei Naqa I. A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients. Phys Med 2021; 87:11-23. [PMID: 34091197 DOI: 10.1016/j.ejmp.2021.05.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 05/20/2021] [Accepted: 05/22/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE A situational awareness Bayesian network (SA-BN) approach is developed to improve physicians' trust in the prediction of radiation outcomes and evaluate its performance for personalized adaptive radiotherapy (pART). METHODS 118 non-small-cell lung cancer patients with their biophysical features were employed for discovery (n = 68) and validation (n = 50) of radiation outcomes prediction modeling. Patients' important characteristics identified by radiation experts to predict individual's tumor local control (LC) or radiation pneumonitis with grade ≥ 2 (RP2) were incorporated as expert knowledge (EK). Besides generating an EK-based naïve BN (EK-NBN), an SA-BN was developed by incorporating the EK features into pure data-driven BN (PD-BN) methods to improve the credibility of LC or / and RP2 prediction. After using area under the free-response receiver operating characteristics curve (AU-FROC) to assess the joint prediction of these outcomes, their prediction performances were compared with a regression approach based on the expert yielded estimates (EYE) penalty and its variants. RESULTS In addition to improving the credibility of radiation outcomes prediction, the SA-BN approach outperformed the EYE penalty and its variants in terms of the joint prediction of LC and RP2. The value of AU-FROC improves from 0.70 (95% CI: 0.54-0.76) using EK-NBN, to 0.75 (0.65-0.82) using a variant of EYE penalty, to 0.83 (0.75-0.93) using PD-BN and 0.83 (0.77-0.90) using SA-BN; with similar trends in the validation cohort. CONCLUSIONS The SA-BN approach can provide an accurate and credible human-machine interface to gain physicians' trust in clinical decision-making, which has the potential to be an important component of pART.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA.
| | - Shruti Jolly
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - David Palma
- London Health Sciences Centre, Western University, London, ON, Canada
| | - Theodore S Lawrence
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, UCSF Medical Center at Mission Bay, San Francisco, CA, USA
| | - Daniel McShan
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Issam Ei Naqa
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
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Tang W, Li X, Yu H, Yin X, Zou B, Zhang T, Chen J, Sun X, Liu N, Yu J, Xie P. A novel nomogram containing acute radiation esophagitis predicting radiation pneumonitis in thoracic cancer receiving radiotherapy. BMC Cancer 2021; 21:585. [PMID: 34022830 PMCID: PMC8140476 DOI: 10.1186/s12885-021-08264-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 04/28/2021] [Indexed: 12/25/2022] Open
Abstract
Background Radiation-induced pneumonitis (RP) is a non-negligible and sometimes life-threatening complication among patients with thoracic radiation. We initially aimed to ascertain the predictive value of acute radiation-induced esophagitis (SARE, grade ≥ 2) to symptomatic RP (SRP, grade ≥ 2) among thoracic cancer patients receiving radiotherapy. Based on that, we established a novel nomogram model to provide individualized risk assessment for SRP. Methods Thoracic cancer patients who were treated with thoracic radiation from Jan 2018 to Jan 2019 in Shandong Cancer Hospital and Institute were enrolled prospectively. All patients were followed up during and after radiotherapy (RT) to observe the development of esophagitis as well as pneumonitis. Variables were analyzed by univariate and multivariate analysis using the logistic regression model, and a nomogram model was established to predict SRP by “R” version 3.6.0. Results A total of 123 patients were enrolled (64 esophageal cancer, 57 lung cancer and 2 mediastinal cancer) in this study prospectively. RP grades of 0, 1, 2, 3, 4 and 5 occurred in 29, 57, 31, 0, 3 and 3 patients, respectively. SRP appeared in 37 patients (30.1%). In univariate analysis, SARE was shown to be a significant predictive factor for SRP (P < 0.001), with the sensitivity 91.9% and the negative predictive value 93.5%. The incidence of SRP in different grades of ARE were as follows: Grade 0–1: 6.5%; Grade 2: 36.9%; Grade 3: 80.0%; Grade 4: 100%. Besides that, the dosimetric factors considering total lung mean dose, total lung V5, V20, ipsilateral lung mean dose, ipsilateral lung V5, and mean esophagus dose were correlated with SRP (all P < 0.05) by univariate analysis. The incidence of SRP was significantly higher in patients whose symptoms of RP appeared early. SARE, mean esophagus dose and ipsilateral mean lung dose were still significant in multivariate analysis, and they were included to build a predictive nomogram model for SRP. Conclusions As an early index that can reflect the tissue’s radiosensitivity visually, SARE can be used as a predictor for SRP in patients receiving thoracic radiation. And the nomogram containing SARE may be fully applied in future’s clinical work.
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Affiliation(s)
- Wenjie Tang
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Xiaolin Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Haining Yu
- Department of Human Resource, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Xiaoyang Yin
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Bing Zou
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Tingting Zhang
- Department of Surgical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Jinlong Chen
- Department of Surgical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Xindong Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Naifu Liu
- Department of Surgical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China
| | - Peng Xie
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, 250117, Shandong, China.
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A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artif Intell Med 2021; 117:102108. [PMID: 34127238 DOI: 10.1016/j.artmed.2021.102108] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 05/07/2021] [Accepted: 05/10/2021] [Indexed: 12/15/2022]
Abstract
No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.
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Bousabarah K, Blanck O, Temming S, Wilhelm ML, Hoevels M, Baus WW, Ruess D, Visser-Vandewalle V, Ruge MI, Treuer H, Kocher M. Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions. Radiat Oncol 2021; 16:74. [PMID: 33863358 PMCID: PMC8052812 DOI: 10.1186/s13014-021-01805-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 04/11/2021] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES To generate and validate state-of-the-art radiomics models for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT). METHODS Radiomics models were generated from the planning CT images of 110 patients with primary, inoperable stage I/IIa NSCLC who were treated with robotic SBRT using a risk-adapted fractionation scheme at the University Hospital Cologne (training cohort). In total, 199 uncorrelated radiomic features fulfilling the standards of the Image Biomarker Standardization Initiative (IBSI) were extracted from the outlined gross tumor volume (GTV). Regularized models (Coxnet and Gradient Boost) for the development of local lung fibrosis (LF), local tumor control (LC), disease-free survival (DFS) and overall survival (OS) were built from either clinical/ dosimetric variables, radiomics features or a combination thereof and validated in a comparable cohort of 71 patients treated by robotic SBRT at the Radiosurgery Center in Northern Germany (test cohort). RESULTS Oncologic outcome did not differ significantly between the two cohorts (OS at 36 months 56% vs. 43%, p = 0.065; median DFS 25 months vs. 23 months, p = 0.43; LC at 36 months 90% vs. 93%, p = 0.197). Local lung fibrosis developed in 33% vs. 35% of the patients (p = 0.75), all events were observed within 36 months. In the training cohort, radiomics models were able to predict OS, DFS and LC (concordance index 0.77-0.99, p < 0.005), but failed to generalize to the test cohort. In opposite, models for the development of lung fibrosis could be generated from both clinical/dosimetric factors and radiomic features or combinations thereof, which were both predictive in the training set (concordance index 0.71- 0.79, p < 0.005) and in the test set (concordance index 0.59-0.66, p < 0.05). The best performing model included 4 clinical/dosimetric variables (GTV-Dmean, PTV-D95%, Lung-D1ml, age) and 7 radiomic features (concordance index 0.66, p < 0.03). CONCLUSION Despite the obvious difficulties in generalizing predictive models for oncologic outcome and toxicity, this analysis shows that carefully designed radiomics models for prediction of local lung fibrosis after SBRT of early stage lung cancer perform well across different institutions.
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Affiliation(s)
- Khaled Bousabarah
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.,Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany.,Saphir Radiosurgery Center Northern Germany, Guestrow, Germany
| | - Susanne Temming
- Department of Radiation Oncology, University Hospital of Cologne, Cologne, Germany
| | - Maria-Lisa Wilhelm
- Saphir Radiosurgery Center Northern Germany, Guestrow, Germany.,Department of Radiation Oncology, University Medicine Rostock, Rostock, Germany
| | - Mauritius Hoevels
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Wolfgang W Baus
- Department of Radiation Oncology, University Hospital of Cologne, Cologne, Germany
| | - Daniel Ruess
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Maximilian I Ruge
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Harald Treuer
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Martin Kocher
- Department of Stereotactic and Functional Neurosurgery, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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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] [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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Probabilistic modeling of an injectable aqueous crystalline suspension using influence networks. Int J Pharm 2021; 596:120283. [PMID: 33508347 DOI: 10.1016/j.ijpharm.2021.120283] [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: 11/18/2020] [Revised: 01/11/2021] [Accepted: 01/12/2021] [Indexed: 11/22/2022]
Abstract
Probabilistic modeling using influence networks is an efficient, intuitive, and easy to communicate strategy in the development of complex pharmaceutical products. This study was aimed to use a risk-based approach to explore the complex interactions between product and process design parameters affecting size and shape of the particles in injectable aqueous crystalline suspensions (ACS). Based on a risk assessment, a design of experiments (DOE) was applied to evaluate the most important parameters, i.e., four critical material attributes and two critical process parameters. A model hydrophobic drug (carbamazepine) was milled and homogenized in a multistep process (dispersion and milling steps). The final formulations were characterized with automated at-line image analysis of thousands of individual particles. The particle size and shape distributions were summarized with descriptive parameters, and the relationship of these parameters and the DOE was modeled using influence networks (INs). This approach was compared and contrasted with a classical modeling approach based on multivariate linear regression (MVLR). INs had a superior visual interpretation capability of the complex and multivariate ACS system making the risk-based decision making more accessible. The probability and causality were included in the IN, i.e., the relationships between size and shape. Moreover, IN allowed to incorporate prior knowledge in a systematic way by implementing a 'black and white list'. An IN based model was created with the following model performance: a mean absolute percentage error of 1.7% and 1.1% for the size and 6.2% and 5.0% for the shape, respectively for dispersed and milled ACS. Parameters with the highest and lowest probability to control the critical quality attributes of ACS could be identified. Consequently, the parameter settings giving the optimum particle size and shape could be predicted using a Monte Carlo simulation to calculate the probability of success including the uncertainty of the model. The cubic MVLR model for the size of milled ACS was comparable to the IN in terms of the mean absolute percentage error, i.e., 1.1%. However, IN was more efficient in visualizing the complex and multivariate data set, including all the critical quality attributes and formulation/process parameters of the ACS at the same time. Moreover, the prior knowledge used in probabilistic modeling of IN could be systematically documented.
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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Bortolotto C, Lancia A, Stelitano C, Montesano M, Merizzoli E, Agustoni F, Stella G, Preda L, Filippi AR. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther 2020; 21:257-266. [PMID: 33216651 DOI: 10.1080/14737140.2021.1852935] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: Radiomics extracts a large amount of quantitative information from medical images using specific data characterization algorithms. This information, called radiomic features, can be combined with clinical data to build prediction models for prognostic evaluation and treatment selection.Areas covered: We outlined a series of studies investigating the correlation between radiomics features and outcome (prognostic) as well as response to therapy (predictive) in non-small cell lung cancer (NSCLC). We performed our analysis both in the setting of early and advanced stage of disease, with a focus on the different therapies and imaging modalities adopted.Expert opinion: The prognostic and predictive potential of the radiomic approach, combined with clinical models, could help decision-making process and guide toward the creation of an optimal and 'tailored' therapeutic strategy for lung cancer patients. However, due to the low reproducibility of most of the conducted studies and the lack of validated results, such a desirable scenario has not yet been translated to routine clinical practice.
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Affiliation(s)
| | - Andrea Lancia
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Chiara Stelitano
- Radiology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marianna Montesano
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Elisa Merizzoli
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | | | - Giulia Stella
- Respiratory Disease Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Lorenzo Preda
- Radiology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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Yang WC, Hsu FM, Yang PC. Precision radiotherapy for non-small cell lung cancer. J Biomed Sci 2020; 27:82. [PMID: 32693792 PMCID: PMC7374898 DOI: 10.1186/s12929-020-00676-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/17/2020] [Indexed: 02/07/2023] Open
Abstract
Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information. Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics. This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.
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Affiliation(s)
- Wen-Chi Yang
- Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, Taiwan.,Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Feng-Ming Hsu
- Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No. 7, Chung-Shan South Rd, Taipei, Taiwan. .,Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Pan-Chyr Yang
- Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan. .,Department of Internal Medicine, National Taiwan University Hospital, No.1 Sec 1, Jen-Ai Rd, Taipei, 100, Taiwan.
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31
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Ma DN, Gao XY, Dan YB, Zhang AN, Wang WJ, Yang G, Zhu HZ. Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers. Onco Targets Ther 2020; 13:6927-6935. [PMID: 32764984 PMCID: PMC7371989 DOI: 10.2147/ott.s257798] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/15/2020] [Indexed: 01/10/2023] Open
Abstract
Purpose To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538–0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630–0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.
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Affiliation(s)
- De-Ning Ma
- Department of Colorectal Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People's Republic of China.,Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People's Republic of China
| | - Xin-Yi Gao
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People's Republic of China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People's Republic of China
| | - Yi-Bo Dan
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, People's Republic of China
| | - An-Ni Zhang
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People's Republic of China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People's Republic of China
| | - Wei-Jun Wang
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People's Republic of China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People's Republic of China
| | - Guang Yang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, People's Republic of China
| | - Hong-Zhou Zhu
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, People's Republic of China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, Zhejiang Province, People's Republic of China
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Bayesian networks in healthcare: Distribution by medical condition. Artif Intell Med 2020; 107:101912. [DOI: 10.1016/j.artmed.2020.101912] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/27/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022]
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Kang J, Coates JT, Strawderman RL, Rosenstein BS, Kerns SL. Genomics models in radiotherapy: From mechanistic to machine learning. Med Phys 2020; 47:e203-e217. [PMID: 32418335 PMCID: PMC8725063 DOI: 10.1002/mp.13751] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/28/2019] [Accepted: 07/17/2019] [Indexed: 12/28/2022] Open
Abstract
Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.
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Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - James T. Coates
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert L. Strawderman
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
| | - Barry S. Rosenstein
- Department of Radiation Oncology and the Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah L. Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
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34
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Luo Y, Chen S, Valdes G. Machine learning for radiation outcome modeling and prediction. Med Phys 2020; 47:e178-e184. [DOI: 10.1002/mp.13570] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/26/2019] [Accepted: 04/09/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Yi Luo
- Department of Radiation Oncology University of Michigan Ann Arbor MI 48103USA
| | - Shifeng Chen
- Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD 21201USA
| | - Gilmer Valdes
- Department of Radiation Oncology University of California San Francisco CA 94158USA
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35
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Pakela JM, Tseng HH, Matuszak MM, Ten Haken RK, McShan DL, El Naqa I. Quantum-inspired algorithm for radiotherapy planning optimization. Med Phys 2019; 47:5-18. [PMID: 31574176 DOI: 10.1002/mp.13840] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 01/31/2023] Open
Abstract
PURPOSE Modern inverse radiotherapy treatment planning requires nonconvex, large-scale optimizations that must be solved within a clinically feasible timeframe. We have developed and tested a quantum-inspired, stochastic algorithm for intensity-modulated radiotherapy (IMRT): quantum tunnel annealing (QTA). By modeling the likelihood probability of accepting a higher energy solution after a particle tunneling through a potential energy barrier, QTA features an additional degree of freedom (the barrier width, w) not shared by traditional stochastic optimization methods such as Simulated Annealing (SA). This additional degree of freedom can improve convergence rates and achieve a more efficient and, potentially, effective treatment planning process. METHODS To analyze the character of the proposed QTA algorithm, we chose two stereotactic body radiation therapy (SBRT) liver cases of variable complexity. The "easy" first case was used to confirm functionality, while the second case, with a more challenging geometry, was used to characterize and evaluate the QTA algorithm performance. Plan quality was assessed using dose-volume histogram-based objectives and dose distributions. Due to the stochastic nature of the solution search space, extensive tests were also conducted to determine the optimal smoothing technique, ensuring balance between plan deliverability and the resulting plan quality. QTA convergence rates were investigated in relation to the chosen barrier width function, and QTA and SA performances were compared regarding sensitivity to the choice of solution initializations, annealing schedules, and complexity of the dose-volume constraints. Finally, we investigated the extension from beamlet intensity optimization to direct aperture optimization (DAO). Influence matrices were calculated using the Eclipse scripting application program interface (API), and the optimizations were run on the University of Michigan's high-performance computing cluster, Flux. RESULTS Our results indicate that QTA's barrier-width function can be tuned to achieve faster convergence rates. The QTA algorithm reached convergence up to 46.6% faster than SA for beamlet intensity optimization and up to 26.8% faster for DAO. QTA and SA were ultimately found to be equally insensitive to the initialization process, but the convergence rate of QTA was found to be more sensitive to the complexity of the dose-volume constraints. The optimal smoothing technique was found to be a combination of a Laplace-of-Gaussian (LOG) edge-finding filter implemented as a penalty within the objective function and a two-dimensional Savitzky-Golay filter applied to the final iteration; this achieved total monitor units more than 20% smaller than plans optimized by commercial treatment planning software. CONCLUSIONS We have characterized the performance of a stochastic, quantum-inspired optimization algorithm, QTA, for radiotherapy treatment planning. This proof of concept study suggests that QTA can be tuned to achieve faster convergence than SA; therefore, QTA may be a good candidate for future knowledge-based or adaptive radiation therapy applications.
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Affiliation(s)
- Julia M Pakela
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel L McShan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Milano MT, Mihai A, Kang J, Singh DP, Verma V, Qiu H, Chen Y, Kong FM(S. Stereotactic body radiotherapy in patients with multiple lung tumors: a focus on lung dosimetric constraints. Expert Rev Anticancer Ther 2019; 19:959-969. [DOI: 10.1080/14737140.2019.1686980] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael T. Milano
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Alina Mihai
- Department of Radiation Oncology, Beacon Hospital, Beacon Court, Dublin, Ireland
| | - John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Deepinder P Singh
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Vivek Verma
- Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, PA, USA
| | - Haoming Qiu
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yuhchyau Chen
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
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Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:355-370. [PMID: 31527578 DOI: 10.23736/s1824-4785.19.03192-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland -
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert S Gabrys
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open 2019; 1:20190021. [PMID: 33178948 PMCID: PMC7592485 DOI: 10.1259/bjro.20190021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/18/2019] [Accepted: 06/25/2019] [Indexed: 12/17/2022] Open
Abstract
Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Sunan Cui
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
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How Much Do Emotional, Behavioral, and Cognitive Factors Actually Impact College Student Attitudes towards English Language Learning? A Quantitative and Qualitative Study. INFORMATION 2019. [DOI: 10.3390/info10050166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Researchers have proposed many multidimensional frameworks to identify significant and potential factors, e.g., educational background, positive feelings and career aspirations, that impact English learning attitude in second language acquisition. Yet, there is still very little research to graphically describe the interactions between these factors and how these factors directly or indirectly impact learning attitude. To this end, a questionnaire survey was conducted in Changchun University of Technology. Statistical measures and Bayesian network analysis were introduced to quantitatively and qualitatively analyze the collected data. Furthermore, the significant attitudinal differences between students majoring in the Liberal Arts or Sciences were investigated for the case study. Studying the interaction between these factors can help explain how they positively affect students’ attitudes toward English language learning. To stimulate interest, teachers may take targeted pedagogical approaches or strategies.
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