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Yuan G, Cai L, Qu W, Zhou Z, Liang P, Chen J, Xu C, Zhang J, Wang S, Chu Q, Li Z. Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning. Bioengineering (Basel) 2024; 11:662. [PMID: 39061744 PMCID: PMC11274102 DOI: 10.3390/bioengineering11070662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/28/2024] Open
Abstract
Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.
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Affiliation(s)
- Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Lingli Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ziling Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jun Chen
- Bayer Healthcare, Wuhan 430000, China;
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
| | - Jiaqiao Zhang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (G.Y.); (L.C.); (W.Q.); (Z.Z.); (P.L.); (Z.L.)
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Zhu N, Meng X, Wang Z, Hu Y, Zhao T, Fan H, Niu F, Han J. Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00193-4. [PMID: 38772802 DOI: 10.1016/j.acra.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance. METHODS In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test. RESULTS Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment. CONCLUSION Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials. CLINICAL RELEVANCE STATEMENT Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.
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Affiliation(s)
- Nana Zhu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Zhi Wang
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Tingting Zhao
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
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Gaye B, Vignac M, Gådin JR, Ladouceur M, Caidahl K, Olsson C, Franco-Cereceda A, Eriksson P, Björck HM. Predictive machine learning models for ascending aortic dilatation in patients with bicuspid and tricuspid aortic valves undergoing cardiothoracic surgery: a prospective, single-centre and observational study. BMJ Open 2024; 14:e067977. [PMID: 38508639 PMCID: PMC10961501 DOI: 10.1136/bmjopen-2022-067977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
OBJECTIVES The objective of this study was to develop clinical classifiers aiming to identify prevalent ascending aortic dilatation in patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV). DESIGN AND SETTING A prospective, single-centre and observational cohort. PARTICIPANTS The study involved 543 BAV and 491 TAV patients with aortic valve disease and/or ascending aortic dilatation, excluding those with coronary artery disease, undergoing cardiothoracic surgery at the Karolinska University Hospital (Sweden). MAIN OUTCOME MEASURES Predictors of high risk of ascending aortic dilatation (defined as ascending aorta with a diameter above 40 mm) were identified through the application of machine learning algorithms and classic logistic regression models. EXPOSURES Comprehensive multidimensional data, including valve morphology, clinical information, family history of cardiovascular diseases, prevalent diseases, demographic details, lifestyle factors, and medication. RESULTS BAV patients, with an average age of 60.4±12.4 years, showed a higher frequency of aortic dilatation (45.3%) compared with TAV patients, who had an average age of 70.4±9.1 years (28.9% dilatation, p <0.001). Aneurysm prediction models for TAV patients exhibited mean area under the receiver-operating-characteristic curve (AUC) values above 0.8, with the absence of aortic stenosis being the primary predictor, followed by diabetes and high-sensitivity C reactive protein. Conversely, prediction models for BAV patients resulted in AUC values between 0.5 and 0.55, indicating low usefulness for predicting aortic dilatation. Classification results remained consistent across all machine learning algorithms and classic logistic regression models. CONCLUSION AND RECOMMENDATION Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.
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Affiliation(s)
- Bamba Gaye
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Maxime Vignac
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Jesper R Gådin
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Kenneth Caidahl
- Clinical Physiology Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Christian Olsson
- Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anders Franco-Cereceda
- Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Per Eriksson
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Hanna M Björck
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
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Li Z, Zhang X, Ding L, Jing J, Gu HQ, Jiang Y, Meng X, Du C, Wang C, Wang M, Xu M, Zhang Y, Hu M, Li H, Gong X, Dong K, Zhao X, Wang Y, Liu L, Xian Y, Peterson E, Fonarow GC, Schwamm LH, Wang Y. Rationale and design of the GOLDEN BRIDGE II: a cluster-randomised multifaceted intervention trial of an artificial intelligence-based cerebrovascular disease clinical decision support system to improve stroke outcomes and care quality in China. Stroke Vasc Neurol 2024:svn-2023-002411. [PMID: 37699726 DOI: 10.1136/svn-2023-002411] [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: 02/20/2023] [Accepted: 08/11/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Given the swift advancements in artificial intelligence (AI), the utilisation of AI-based clinical decision support systems (AI-CDSSs) has become increasingly prevalent in the medical domain, particularly in the management of cerebrovascular disease. AIMS To describe the design, rationale and methods of a cluster-randomised multifaceted intervention trial aimed at investigating the effect of cerebrovascular disease AI-CDSS on the clinical outcomes of patients who had a stroke and on stroke care quality. DESIGN The GOLDEN BRIDGE II trial is a multicentre, open-label, cluster-randomised multifaceted intervention study. A total of 80 hospitals in China were randomly assigned to the AI-CDSS intervention group or the control group. For eligible participants with acute ischaemic stroke in the AI-CDSS intervention group, cerebrovascular disease AI-CDSS will provide AI-assisted imaging analysis, auxiliary stroke aetiology and pathogenesis analysis, and guideline-based treatment recommendations. In the control group, patients will receive the usual care. The primary outcome is the occurrence of new vascular events (composite of ischaemic stroke, haemorrhagic stroke, myocardial infarction or vascular death) at 3 months after stroke onset. The sample size was estimated to be 21 689 with a 26% relative reduction in the incidence of new composite vascular events at 3 months by using multiple quality-improving interventions provided by AI-CDSS. All analyses will be performed according to the intention-to-treat principle and accounted for clustering using generalised estimating equations. CONCLUSIONS Once the effectiveness is verified, the cerebrovascular disease AI-CDSS could improve stroke care and outcomes in China. TRIAL REGISTRATION NUMBER NCT04524624.
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Affiliation(s)
- Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinmiao Zhang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lingling Ding
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Hong-Qiu Gu
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chunying Du
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chunjuan Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Meng Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Man Xu
- Hanalytics Artificial Intelligence Research Centre for Neurological Disorders, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yanxu Zhang
- Hanalytics Artificial Intelligence Research Centre for Neurological Disorders, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Meera Hu
- Hanalytics Artificial Intelligence Research Centre for Neurological Disorders, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiping Gong
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Kehui Dong
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Liping Liu
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Ying Xian
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Eric Peterson
- Division of Cardiology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Gregg C Fonarow
- Cardiology, Ronald Reagan UCLA Medical Center, Los Angeles, California, USA
| | - Lee H Schwamm
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
- Clinical Center for Precision Medicine in Stroke, Capital Medical Universit, Beijing, China
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Zhang H, Deng Y, Xiaojie M, Zou Q, Liu H, Tang N, Luo Y, Xiang X. CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy. Heliyon 2024; 10:e23923. [PMID: 38223741 PMCID: PMC10787243 DOI: 10.1016/j.heliyon.2023.e23923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024] Open
Abstract
Objective Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). Methods In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. Results A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. Conclusion CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.
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Affiliation(s)
- Hanjing Zhang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yu Deng
- The Affiliated Cancer Hospital of Guizhou Medical University, GuiYang, Guizhou Province, 550000, China
| | - M.A. Xiaojie
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Qian Zou
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Huanhui Liu
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Ni Tang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yuanyuan Luo
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Xuejing Xiang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
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Liu C, Liu Z, Holmes J, Zhang L, Zhang L, Ding Y, Shu P, Wu Z, Dai H, Li Y, Shen D, Liu N, Li Q, Li X, Zhu D, Liu T, Liu W. Artificial general intelligence for radiation oncology. META-RADIOLOGY 2023; 1:100045. [PMID: 38344271 PMCID: PMC10857824 DOI: 10.1016/j.metrad.2023.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.
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Affiliation(s)
- Chenbin Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | | | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | - Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Peng Shu
- School of Computing, University of Georgia, USA
| | - Zihao Wu
- School of Computing, University of Georgia, USA
| | - Haixing Dai
- School of Computing, University of Georgia, USA
| | - Yiwei Li
- School of Computing, University of Georgia, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, China
- Shanghai United Imaging Intelligence Co., Ltd, China
- Shanghai Clinical Research and Trial Center, China
| | - Ninghao Liu
- School of Computing, University of Georgia, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | | | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, USA
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Ashrafinia S, Dalaie P, Schindler TH, Pomper MG, Rahmim A. Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification. Cureus 2023; 15:e43343. [PMID: 37700937 PMCID: PMC10493172 DOI: 10.7759/cureus.43343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
PURPOSE Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score. METHODS We collected data from 428 patients with normal (non-ischemic) MPSS (99mTc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuation-corrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-to-outcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests. RESULTS Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the significance of the prediction model across all cardiac segments. Conclusions: Our standardized and statistically robust multivariate analysis demonstrated significant prediction of the CAC score for all cardiac segments when combining MPSS radiomic features with clinical features, suggesting radiomics analysis can add diagnostic or prognostic value to standard MPSS for wide clinical usage.
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Affiliation(s)
- Saeed Ashrafinia
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Pejman Dalaie
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | | | - Martin G Pomper
- Radiology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Arman Rahmim
- Physics and Astronomy, University of British Columbia, Vancouver, CAN
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Hami R, Apeke S, Redou P, Gaubert L, Dubois LJ, Lambin P, Visvikis D, Boussion N. Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images. J Imaging 2023; 9:124. [PMID: 37367472 DOI: 10.3390/jimaging9060124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023] Open
Abstract
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.
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Affiliation(s)
- Rihab Hami
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
| | - Sena Apeke
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Pascal Redou
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Laurent Gaubert
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Ludwig J Dubois
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Dimitris Visvikis
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CHRU BREST, 29200 Brest, France
| | - Nicolas Boussion
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CHRU BREST, 29200 Brest, France
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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10
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Wu Z, Li Y, Xu Z, Liu H, Liu K, Qiu P, Chen T, Lu X. Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study. BMJ Open 2023; 13:e066782. [PMID: 37012019 PMCID: PMC10083797 DOI: 10.1136/bmjopen-2022-066782] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
OBJECTIVES To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques. DESIGN Retrospective cohort study. SETTING Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018. PARTICIPANTS 380 inpatients diagnosed with acute AD were included in the study. PRIMARY OUTCOME Preoperative in-hospital mortality rate. RESULTS A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level. CONCLUSION In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database. TRIAL REGISTRATION NUMBER ChiCTR1900025818.
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Affiliation(s)
- Zhaoyu Wu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
| | - Yixuan Li
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Department of Economics, University of Waterloo, Waterloo, Ontario, Canada
| | - Zhijue Xu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
| | - Haichun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Kai Liu
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Peng Qiu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Department of Economics, University of Waterloo, Waterloo, Ontario, Canada
| | - Xinwu Lu
- Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China
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11
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Liu X, Li Z, Yin Y. Clinical application of MR-Linac in tumor radiotherapy: a systematic review. Radiat Oncol 2023; 18:52. [PMID: 36918884 PMCID: PMC10015924 DOI: 10.1186/s13014-023-02221-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/01/2023] [Indexed: 03/15/2023] Open
Abstract
Recent years have seen both a fresh knowledge of cancer and impressive advancements in its treatment. However, the clinical treatment paradigm of cancer is still difficult to implement in the twenty-first century due to the rise in its prevalence. Radiotherapy (RT) is a crucial component of cancer treatment that is helpful for almost all cancer types. The accuracy of RT dosage delivery is increasing as a result of the quick development of computer and imaging technology. The use of image-guided radiation (IGRT) has improved cancer outcomes and decreased toxicity. Online adaptive radiotherapy will be made possible by magnetic resonance imaging-guided radiotherapy (MRgRT) using a magnetic resonance linear accelerator (MR-Linac), which will enhance the visibility of malignancies. This review's objectives are to examine the benefits of MR-Linac as a treatment approach from the perspective of various cancer patients' prognoses and to suggest prospective development areas for additional study.
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Affiliation(s)
- Xin Liu
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.,Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Zhenjiang Li
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
| | - Yong Yin
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China. .,Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
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12
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Baroudi H, Brock KK, Cao W, Chen X, Chung C, Court LE, El Basha MD, Farhat M, Gay S, Gronberg MP, Gupta AC, Hernandez S, Huang K, Jaffray DA, Lim R, Marquez B, Nealon K, Netherton TJ, Nguyen CM, Reber B, Rhee DJ, Salazar RM, Shanker MD, Sjogreen C, Woodland M, Yang J, Yu C, Zhao Y. Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'? Diagnostics (Basel) 2023; 13:diagnostics13040667. [PMID: 36832155 PMCID: PMC9955359 DOI: 10.3390/diagnostics13040667] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/21/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
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Affiliation(s)
- Hana Baroudi
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kristy K. Brock
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenhua Cao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xinru Chen
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Mohammad D. El Basha
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Maguy Farhat
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Skylar Gay
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Aashish Chandra Gupta
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soleil Hernandez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kai Huang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - David A. Jaffray
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca Lim
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Barbara Marquez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kelly Nealon
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Callistus M. Nguyen
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Brandon Reber
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ramon M. Salazar
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mihir D. Shanker
- The University of Queensland, Saint Lucia 4072, Australia
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos Sjogreen
- Department of Physics, University of Houston, Houston, TX 77004, USA
| | - McKell Woodland
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Jinzhong Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cenji Yu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Zhao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer. Radiother Oncol 2023; 182:109524. [PMID: 36764459 DOI: 10.1016/j.radonc.2023.109524] [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: 11/18/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023]
Abstract
PURPOSE To develop and implement a software that enables centers, treating patients with state-of-the-art radiation oncology, to compare their patient, treatment, and outcome data to a reference cohort, and to assess the quality of their treatment approach. MATERIALS AND METHODS A comprehensive data dashboard was designed, which al- lowed holistic assessment of institutional treatment approaches. The software was tested in the ongoing EMBRACE-II study for locally advanced cervical cancer. The tool created individualized dashboards and automatic analysis scripts, verified pro- tocol compliance and checked data for inconsistencies. Identified quality assurance (QA) events were analysed. A survey among users was conducted to assess usability. RESULTS The survey indicated favourable feedback to the prototype and highlighted its value for internal monitoring. Overall, 2302 QA events were identified (0.4% of all collected data). 54% were due to missing or incomplete data, and 46% originated from other causes. At least one QA event was found in 519/1001 (52%) of patients. QA events related to primary study endpoints were found in 16% of patients. Sta- tistical methods demonstrated good performance in detecting anomalies, with precisions ranging from 71% to 100%. Most frequent QA event categories were Treatment Technique (27%), Patient Characteristics (22%), Dose Reporting (17%), Outcome 156 (15%), Outliers (12%), and RT Structures (8%). CONCLUSION A software tool was developed and tested within a clinical trial in radia- tion oncology. It enabled the quantitative and qualitative comparison of institutional patient and treatment parameters with a large multi-center reference cohort. We demonstrated the value of using statistical methods to automatically detect implau- sible data points and highlighted common pitfalls and uncertainties in radiotherapy for cervical cancer.
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Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset. Sci Rep 2023; 13:1438. [PMID: 36697456 PMCID: PMC9876907 DOI: 10.1038/s41598-023-28394-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
Efforts have been made to improve the risk stratification model for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using machine learning models with iterated cross validation (CV) method. A total of 122 patients with pathologically confirmed DLBCL and receiving rituximab-containing chemotherapy were enrolled. Contributions of clinical, laboratory, and metabolic imaging parameters from fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scans to the prognosis were evaluated using five regression models, namely logistic regression, random forest, support vector classifier (SVC), deep neural network (DNN), and fuzzy neural network models. Binary classification predictions for 3-year progression free survival (PFS) and 3-year overall survival (OS) were conducted. The 10-iterated fivefold CV with shuffling process was conducted to predict the capability of learning machines. The median PFS and OS were 41.0 and 43.6 months, respectively. Two indicators were found to be independent predictors for prognosis: international prognostic index and total metabolic tumor volume (MTVsum) from FDG PET/CT. For PFS, SVC and DNN (both with accuracy 71%) have the best predictive results, of which outperformed other algorithms. For OS, the DNN has the best predictive result (accuracy 76%). Using clinical and metabolic parameters as input variables, the machine learning methods with iterated CV method add the predictive values for PFS and OS evaluation in DLBCL patients.
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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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Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model. J Pers Med 2022; 12:jpm12091491. [PMID: 36143276 PMCID: PMC9505150 DOI: 10.3390/jpm12091491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 12/22/2022] Open
Abstract
The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation.
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Yang B, Liu C, Wu R, Zhong J, Li A, Ma L, Zhong J, Yin S, Zhou C, Ge Y, Tao X, Zhang L, Lu G. Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer. Front Oncol 2022; 12:895014. [PMID: 35814402 PMCID: PMC9260694 DOI: 10.3389/fonc.2022.895014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/02/2022] [Indexed: 11/22/2022] Open
Abstract
Objective To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC). Patients and Methods This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups. Results The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P<0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D’Agostino test showed good calibration performance (P=0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis. Conclusions The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.
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Affiliation(s)
- Bin Yang
- Medical Imaging Center, Calmette Hospital and The First Hospital of Kunming (Affiliated Calmette Hospital of Kunming Medical University), Kunming, China
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Chengxing Liu
- Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ren Wu
- Department of Medical Imaging, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ang Li
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lu Ma
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jian Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Saisai Yin
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Changsheng Zhou
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | | | - Xinwei Tao
- Siemens Healthineers Ltd., Shanghai, China
| | - Longjiang Zhang
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- *Correspondence: Guangming Lu, ; Longjiang Zhang,
| | - Guangming Lu
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Medical Imaging, Jinling Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Guangming Lu, ; Longjiang Zhang,
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Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-378. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, Dinapoli N, Peng Q, Casà C, Gambacorta MA, Valentini V, Lang J. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol 2022; 17:78. [PMID: 35428267 PMCID: PMC9013126 DOI: 10.1186/s13014-022-02048-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Predicting pathological complete response (pCR) in patients affected by locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (nCRT) is a challenging field of investigation, but many of the published models are burdened by a lack of reliable external validation. Aim of this study was to evaluate the applicability of a magnetic resonance imaging (MRI) radiomic-based pCR model developed and validated in Europe, to a different cohort of patients from an intercontinental cancer center. METHODS The original model was based on two clinical and two radiomics features extracted from T2-weighted 1.5 T MRI of 161 LARC patients acquired before nCRT, considered as training set. Such model is here validated using the T2-w 1.5 and 3 T staging MRI of 59 LARC patients with different clinical characteristics consecutively treated in mainland Chinese cancer center from March 2017 to January 2018. Model performance were evaluated in terms of area under the receiver operator characteristics curve (AUC) and relative parameters, such as accuracy, specificity, negative and positive predictive value (NPV and PPV). RESULTS An AUC of 0.83 (CI 95%, 0.71-0.96) was achieved for the intercontinental cohort versus a value of 0.75 (CI 95%, 0.61-0.88) at the external validation step reported in the original experience. Considering the best cut-off threshold identified in the first experience (0.26), the following predictive performance were obtained: 0.65 as accuracy, 0.64 as specificity, 0.70 as sensitivity, 0.91 as NPV and 0.28 as PPV. CONCLUSIONS Despite the introduction of significant different factors, the proposed model appeared to be replicable on a real-world data extra-European patients' cohort, achieving a TRIPOD 4 level.
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Affiliation(s)
- Luca Boldrini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Lucia Clara Orlandini
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Davide Cusumano
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Nicola Dinapoli
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Qian Peng
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Calogero Casà
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Vincenzo Valentini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jinyi Lang
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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20
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O’Connor JD, Overton IM, McMahon SJ. RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity. Brief Bioinform 2022; 23:bbab561. [PMID: 35066588 PMCID: PMC8921666 DOI: 10.1093/bib/bbab561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 11/20/2022] Open
Abstract
Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R2 for the published models on test data was very poor at 0.01 (range: -0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: -0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.
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Affiliation(s)
- John D O’Connor
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, BT9 7AE, United Kingdom
| | - Ian M Overton
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, BT9 7AE, United Kingdom
| | - Stephen J McMahon
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, BT9 7AE, United Kingdom
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21
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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; 14:1228. [PMID: 35267535 PMCID: PMC8909466 DOI: 10.3390/cancers14051228] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [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
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. 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). 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. 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
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22
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Craddock M, Crockett C, McWilliam A, Price G, Sperrin M, van der Veer SN, Faivre-Finn C. Evaluation of Prognostic and Predictive Models in the Oncology Clinic. Clin Oncol (R Coll Radiol) 2022; 34:102-113. [PMID: 34922799 DOI: 10.1016/j.clon.2021.11.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 12/13/2022]
Abstract
Predictive and prognostic models hold great potential to support clinical decision making in oncology and could ultimately facilitate a paradigm shift to a more personalised form of treatment. While a large number of models relevant to the field of oncology have been developed, few have been translated into clinical use and assessment of clinical utility is not currently considered a routine part of model development. In this narrative review of the clinical evaluation of prediction models in oncology, we propose a high-level process diagram for the life cycle of a clinical model, encompassing model commissioning, clinical implementation and ongoing quality assurance, which aims to bridge the gap between model development and clinical implementation.
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Affiliation(s)
- M Craddock
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK.
| | - C Crockett
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - A McWilliam
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK
| | - G Price
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK
| | - M Sperrin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - S N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - C Faivre-Finn
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
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23
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Radiomic Phenotypes for Improving Early Prediction of Survival in Stage III Non-Small Cell Lung Cancer Adenocarcinoma after Chemoradiation. Cancers (Basel) 2022; 14:cancers14030700. [PMID: 35158971 PMCID: PMC8833400 DOI: 10.3390/cancers14030700] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/23/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.
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24
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Overgaard J, Aznar MC, Bacchus C, Coppes RP, Deutsch E, Georg D, Haustermans K, Hoskin P, Krause M, Lartigau EF, Lee AWM, Löck S, Offersen BV, Thwaites DI, van der Kogel AJ, van der Heide UA, Valentini V, Baumann M. Personalised radiation therapy taking both the tumour and patient into consideration. Radiother Oncol 2022; 166:A1-A5. [PMID: 35051440 DOI: 10.1016/j.radonc.2022.01.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark.
| | - Marianne C Aznar
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, United Kingdom
| | - Carol Bacchus
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rob P Coppes
- Departments of Radiation Oncology and Biomedical Sciences of Cells & Systems, Section Molecular Cell Biology, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Eric Deutsch
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, France
| | - Dietmar Georg
- Division Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna/AKH Wien, Austria
| | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals Leuven, Belgium
| | - Peter Hoskin
- Mount Vernon Cancer Centre and University of Manchester, United Kingdom
| | - Mechthild Krause
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany
| | - Eric F Lartigau
- Academic Department of Radiotherapy, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - Anne W M Lee
- Department of Clinical Oncology, University of Hong Kong - Shenzhen Hospital and University of Hong Kong, China
| | - Steffen Löck
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany
| | - Birgitte V Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, The University of Sydney, Australia; Medical Physics Group, Leeds Institute of Medical Research, School of Medicine, University of Leeds, United Kingdom
| | - Albert J van der Kogel
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, USA
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
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25
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Kobayashi K, Hamamoto R. [5. The Potential of Medical Image Analysis Technology in the Field of Cancer Treatment]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:101-106. [PMID: 35046214 DOI: 10.6009/jjrt.780110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligent Project
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligent Project
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26
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The european particle therapy network (EPTN) consensus on the follow-up of adult patients with brain and skull base tumours treated with photon or proton irradiation. Radiother Oncol 2022; 168:241-249. [DOI: 10.1016/j.radonc.2022.01.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/19/2022] [Indexed: 12/25/2022]
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27
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Varon E, Blumrosen G, Shefi O. A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment. Front Oncol 2022; 12:1037419. [PMID: 36911792 PMCID: PMC9999042 DOI: 10.3389/fonc.2022.1037419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 11/21/2022] [Indexed: 01/06/2023] Open
Abstract
A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.
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Affiliation(s)
- Eli Varon
- Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.,Bar-Ilan Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel
| | - Gaddi Blumrosen
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel.,Department of Computer Science, Holon Institute of Technology, Holon, Israel
| | - Orit Shefi
- Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.,Bar-Ilan Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel.,Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
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28
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Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P. Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med 2022; 140:105111. [PMID: 34891095 DOI: 10.1016/j.compbiomed.2021.105111] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023]
Abstract
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown the same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair, and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision-making process. Therefore, there is a need to ensure the interpretability of deep neural networks before they can be incorporated into the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally, we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.
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Affiliation(s)
- Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands.
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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29
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Ravera F, Cirmena G, Dameri M, Gallo M, Vellone VG, Fregatti P, Friedman D, Calabrese M, Ballestrero A, Tagliafico A, Ferrando L, Zoppoli G. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol. BMJ Open 2021; 11:e054256. [PMID: 34972769 PMCID: PMC8720992 DOI: 10.1136/bmjopen-2021-054256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
INTRODUCTION Standard procedures aimed at the early diagnosis of breast cancer (BC) present suboptimal accuracy and imply the execution of invasive and sometimes unnecessary tissue biopsies. The assessment of circulating biomarkers for diagnostic purposes, together with radiomics, is of great potential in BC management. METHODS AND ANALYSIS This is a prospective translational study investigating the accuracy of the combined assessment of multiple circulating analytes together with radiomic variables for early BC diagnosis. Up to 750 patients will be recruited at their presentation at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT) for the execution of a diagnostic biopsy after the detection of a suspect breast lesion (t0). Each recruited patient will be asked to donate peripheral blood and urine before undergoing breast biopsy. Blood and urine samples will also be collected from a cohort of 100 patients with negative mammography. For cases with histological diagnosis of invasive BC, a second sample of blood and urine will be collected after breast surgery. Circulating tumour DNA, cell-free methylated DNA and circulating proteins will be assessed in samples collected at t0 from patients with stage I-IIA BC at surgery together with those collected from patients with histologically confirmed benign lesions of similar size and from healthy controls with negative mammography. These analyses will be combined with radiomic variables extracted with freeware algorithms applied to cases and matched controls for which digital mammography is available. The overall goal of the present study is to develop a horizontal data integration classifier for the early diagnosis of BC. ETHICS AND DISSEMINATION This research protocol has been approved by Regione Liguria Ethics Committee (reference number: 2019/75, study ID: 4452). Patients will be required to provide written informed consent. Results will be published in international peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER NCT04781062.
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Affiliation(s)
- Francesco Ravera
- Department of Internal Medicine, Università degli Studi di Genova, Genova, Italy
| | - Gabriella Cirmena
- Department of Internal Medicine, Università degli Studi di Genova, Genova, Italy
| | - Martina Dameri
- Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico per l'Oncologia, Genova, Italy
| | - Maurizio Gallo
- Department of Internal Medicine, Università degli Studi di Genova, Genova, Italy
| | - Valerio Gaetano Vellone
- Department of Surgical Sciences and Integrated Diagnostic, Università degli Studi di Genova, Genova, Italy
| | - Piero Fregatti
- Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico per l'Oncologia, Genova, Italy
| | - Daniele Friedman
- Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico per l'Oncologia, Genova, Italy
| | - Massimo Calabrese
- Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico per l'Oncologia, Genova, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine, Università degli Studi di Genova, Genova, Italy
| | - Alberto Tagliafico
- Department of Health Sciences, Università degli Studi di Genova, Genova, Italy
| | - Lorenzo Ferrando
- Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico per l'Oncologia, Genova, Italy
| | - Gabriele Zoppoli
- Department of Internal Medicine, Università degli Studi di Genova, Genova, Italy
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30
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Thijssen SV, Jacobs MJ, Swart RR, Heising L, Ou CX, Roumen C. The barriers and facilitators of radical innovation implementation in secondary healthcare: a systematic review. J Health Organ Manag 2021; ahead-of-print:289-312. [PMID: 34910413 PMCID: PMC10430798 DOI: 10.1108/jhom-12-2020-0493] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/17/2021] [Accepted: 08/09/2021] [Indexed: 11/17/2022]
Abstract
PURPOSE This study aimed to identify the barriers and facilitators related to the implementation of radical innovations in secondary healthcare. DESIGN/METHODOLOGY/APPROACH A systematic review was conducted and presented in accordance with a PRISMA flowchart. The databases PubMed and Web of Science were searched for original publications in English between the 1st of January 2010 and 6th of November 2020. The level of radicalness was determined based on five characteristics of radical innovations. The level of evidence was classified according to the level of evidence scale of the University of Oxford. The Consolidated Framework for Implementation Research was used as a framework to classify the barriers and facilitators. FINDINGS Based on the inclusion and exclusion criteria, nine publications were included, concerning six technological, two organizational and one treatment innovation. The main barriers for radical innovation implementation in secondary healthcare were lack of human, material and financial resources, and lack of integration and organizational readiness. The main facilitators included a supportive culture, sufficient training, education and knowledge, and recognition of the expected added value. ORIGINALITY/VALUE To our knowledge, this is the first systematic review examining the barriers and facilitators of radical innovation implementation in secondary healthcare. To ease radical innovation implementation, alternative performance systems may be helpful, including the following prerequisites: (1) Money, (2) Added value, (3) Timely knowledge and integration, (4) Culture, and (5) Human resources (MATCH). This study highlights the need for more high-level evidence studies in this area.
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Affiliation(s)
- Salina V. Thijssen
- Department of Radiation Oncology (Maastro), GROW School for Oncology,
Maastricht University Medical Centre+
, Maastricht,
The Netherlands
| | - Maria J.G. Jacobs
- Tilburg School of Economics and Management
,
Tilburg University
, Tilburg,
Netherlands
| | - Rachelle R. Swart
- Department of Radiation Oncology (Maastro), GROW School for Oncology,
Maastricht University Medical Centre+
, Maastricht,
The Netherlands
| | - Luca Heising
- Tilburg School of Economics and Management
,
Tilburg University
, Tilburg,
Netherlands
| | - Carol X.J. Ou
- Tilburg School of Economics and Management
,
Tilburg University
, Tilburg,
Netherlands
| | - Cheryl Roumen
- Department of Radiation Oncology (Maastro), GROW School for Oncology,
Maastricht University Medical Centre+
, Maastricht,
The Netherlands
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Gao A, Yang H, Wang Y, Zhao G, Wang C, Wang H, Zhang X, Zhang Y, Cheng J, Yang G, Bai J. Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma. Front Oncol 2021; 11:725926. [PMID: 34881174 PMCID: PMC8645689 DOI: 10.3389/fonc.2021.725926] [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: 06/16/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Objective This study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE. Methods This retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical-radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong's test was used to compare the performance of the models. A two-sided t-test and Fisher's exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance. Results The classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical-radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819-0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707-0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively. Conclusion Radiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical-radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.
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Affiliation(s)
- Ankang Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hongxi Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Guohua Zhao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xiaonan Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jie Bai
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ, Combs SE. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol 2021; 164:73-82. [PMID: 34506832 DOI: 10.1016/j.radonc.2021.08.023] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/15/2021] [Accepted: 08/27/2021] [Indexed: 02/09/2023]
Abstract
PURPOSE In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany; Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands.
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Katja Specht
- Institute of Pathology, Technical University of Munich, Germany
| | - Eleanor Y Chen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States
| | - Olena Klymenko
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Victor Akinkuoroye
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Matthew B Spraker
- Department of Radiation Oncology, Washington University in St. Louis, United States
| | - Stephanie K Schaub
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Alexandra S Gersing
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Henry C Woodruff
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Radiology, University of Washington, Seattle, United States
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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Xu Y, Ji W, Hou L, Lin S, Shi Y, Zhou C, Meng Y, Wang W, Chen X, Wang M, Yang H. Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma. Front Oncol 2021; 11:704994. [PMID: 34513686 PMCID: PMC8429899 DOI: 10.3389/fonc.2021.704994] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/09/2021] [Indexed: 12/25/2022] Open
Abstract
Objective We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC. Methods 170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics. Results Using quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance. Conclusion The use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.
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Affiliation(s)
- Yunyu Xu
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Shuangxiang Lin
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Yangyang Shi
- Department of Radiation Oncology, University of Arizona, Tucson, AZ, United States
| | - Chao Zhou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Yinnan Meng
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Wei Wang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Xiaofeng Chen
- Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IU, United States
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haihua Yang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
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Han P, Lee SH, Noro K, Haller JW, Nakatsugawa M, Sugiyama S, Bowers M, Lakshminarayanan P, Hoff J, Friedes C, Hu C, McNutt TR, Voong KR, Lee J, Hales RK. Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy. JCO Clin Cancer Inform 2021; 5:944-952. [PMID: 34473547 DOI: 10.1200/cci.20.00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.
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Affiliation(s)
- Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | | | | | | | | | - Michael Bowers
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Jeffrey Hoff
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Cole Friedes
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Chen Hu
- Department of Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, MD
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - K Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
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Zerka F, Urovi V, Bottari F, Leijenaar RTH, Walsh S, Gabrani-Juma H, Gueuning M, Vaidyanathan A, Vos W, Occhipinti M, Woodruff HC, Dumontier M, Lambin P. Privacy preserving distributed learning classifiers - Sequential learning with small sets of data. Comput Biol Med 2021; 136:104716. [PMID: 34364262 DOI: 10.1016/j.compbiomed.2021.104716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/16/2021] [Accepted: 07/28/2021] [Indexed: 01/09/2023]
Abstract
BACKGROUND Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database. METHODS We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC). FINDINGS The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset. INTERPRETATION Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset.
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Affiliation(s)
- Fadila Zerka
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Radiomics (Oncoradiomics SA), Liège, Belgium.
| | - Visara Urovi
- Institute of Data Science (IDS), Maastricht University, the Netherlands
| | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Akshayaa Vaidyanathan
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Michel Dumontier
- Institute of Data Science (IDS), Maastricht University, the Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
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Tagliaferri L, Ciardo FG, Fionda B, Casà C, DI Stefani A, Lancellotta V, Placidi E, Macchia G, Capocchiano ND, Morganti AG, Kovács G, Bussu F, Peris K, Valentini V. Non-melanoma Skin Cancer Treated by Contact High-dose-rate Radiotherapy (Brachytherapy): A Mono-institutional Series and Literature Review. In Vivo 2021; 35:2313-2319. [PMID: 34182511 PMCID: PMC8286496 DOI: 10.21873/invivo.12505] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/11/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND/AIM Non-melanoma skin cancers (NMSC) are the most common neoplasms worldwide and their incidence has been proven to increase in recent years and their treatment should aim at cancer cure as well as cosmetic and functional results. The aim of the study was to report the results of our mono-institutional series of high-dose-rate radiotherapy (cHDR-RT) in NMSC, based on a homogenous technique and two different treatment schedules. PATIENTS AND METHODS All patients affected by NMSC who were consecutively evaluated and treated at our Interventional Oncology Center from October 2018 to August 2020, were included. Patients underwent cHDR-RT using flap applicators and remotely afterloaded Ir-192 sources. RESULTS Overall, 51 patients were treated for a total of 67 lesions. Local control (LC) and disease-specific survival (DSS) were 94.0% and 100%, respectively. Grade 1, grade 2, grade 3 and grade 4 acute toxicity rates were 24.6%, 3.5%, 3.5%, and 0.0%, respectively. The cosmetic results were graded as excellent/good, fair, and poor in 73.7%, 19.3%, and 7.0%. CONCLUSION cHDR-RT of NMSC is an effective alternative to surgery due to excellent outcomes both in terms of local control and aesthetic results especially in the face.
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Affiliation(s)
- Luca Tagliaferri
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | | | - Bruno Fionda
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy;
| | - Calogero Casà
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessandro DI Stefani
- U.O.C. Dermatologia, Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
- Istituto di Dermatologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Valentina Lancellotta
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Elisa Placidi
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Gabriella Macchia
- Radiotherapy Unit, Gemelli Molise Hospital, Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Nikola Dino Capocchiano
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - György Kovács
- Educational Program Director Gemelli-INTERACTS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Bussu
- Divisione di Otorinolaringoiatria, Azienda Ospedaliero Universitaria, Sassari, Italy
- Department of Otorinolaringoiatria, Università degli studi di Sassari, Sassari, Italy
| | - Ketty Peris
- U.O.C. Dermatologia, Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
- Istituto di Dermatologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- U.O.C. Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
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Tatin X, Muggiolu G, Sauvaigo S, Breton J. Evaluation of DNA double-strand break repair capacity in human cells: Critical overview of current functional methods. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2021; 788:108388. [PMID: 34893153 DOI: 10.1016/j.mrrev.2021.108388] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 06/17/2021] [Accepted: 06/23/2021] [Indexed: 02/05/2023]
Abstract
DNA double-strand breaks (DSBs) are highly deleterious lesions, responsible for mutagenesis, chromosomal translocation or cell death. DSB repair (DSBR) is therefore a critical part of the DNA damage response (DDR) to restore molecular and genomic integrity. In humans, this process is achieved through different pathways with various outcomes. The balance between DSB repair activities varies depending on cell types, tissues or individuals. Over the years, several methods have been developed to study variations in DSBR capacity. Here, we mainly focus on functional techniques, which provide dynamic information regarding global DSB repair proficiency or the activity of specific pathways. These methods rely on two kinds of approaches. Indirect techniques, such as pulse field gel electrophoresis (PFGE), the comet assay and immunofluorescence (IF), measure DSB repair capacity by quantifying the time-dependent decrease in DSB levels after exposure to a DNA-damaging agent. On the other hand, cell-free assays and reporter-based methods directly track the repair of an artificial DNA substrate. Each approach has intrinsic advantages and limitations and despite considerable efforts, there is currently no ideal method to quantify DSBR capacity. All techniques provide different information and can be regarded as complementary, but some studies report conflicting results. Parameters such as the type of biological material, the required equipment or the cost of analysis may also limit available options. Improving currently available methods measuring DSBR capacity would be a major step forward and we present direct applications in mechanistic studies, drug development, human biomonitoring and personalized medicine, where DSBR analysis may improve the identification of patients eligible for chemo- and radiotherapy.
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Affiliation(s)
- Xavier Tatin
- Univ. Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 38000 Grenoble, France; LXRepair, 5 Avenue du Grand Sablon, 38700 La Tronche, France
| | | | - Sylvie Sauvaigo
- LXRepair, 5 Avenue du Grand Sablon, 38700 La Tronche, France
| | - Jean Breton
- Univ. Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 38000 Grenoble, France.
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Yang B, Zhou L, Zhong J, Lv T, Li A, Ma L, Zhong J, Yin S, Huang L, Zhou C, Li X, Ge YQ, Tao X, Zhang L, Son Y, Lu G. Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer. Respir Res 2021; 22:189. [PMID: 34183009 PMCID: PMC8240400 DOI: 10.1186/s12931-021-01780-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 06/14/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). RESULTS The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. CONCLUSIONS CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.
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Affiliation(s)
- Bin Yang
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Li Zhou
- Department of Respiratory and Critical Care Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Sch Med, Nanjing, 210002, Jiangsu, China
| | - Jing Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Tangfeng Lv
- Department of Respiratory and Critical Care Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Sch Med, Nanjing, 210002, Jiangsu, China
| | - Ang Li
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Lu Ma
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Jian Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Saisai Yin
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Litang Huang
- Department of Respiratory and Critical Care Medicine, Affiliated Jinling Hospital, Southeast University, Sch Med, Nanjing, 210002, Nanjing, China
| | - Changsheng Zhou
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Xinyu Li
- Department of Medical Imaging, Affiliated Jinling Hospital, Nanjing Medical University, Nanjing, 210002, China
| | - Ying Qian Ge
- Siemens Healthineers Ltd., Shanghai, 200000, China
| | - Xinwei Tao
- Siemens Healthineers Ltd., Shanghai, 200000, China
| | - Longjiang Zhang
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
| | - Yong Son
- Department of Respiratory and Critical Care Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Sch Med, Nanjing, 210002, Jiangsu, China.
| | - Guangming Lu
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
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Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 19:13-24. [PMID: 34307915 PMCID: PMC8295850 DOI: 10.1016/j.phro.2021.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/23/2022]
Abstract
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Michael Jameson
- GenesisCare, Alexandria, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia
| | - Noel Aherne
- Mid North Coast Cancer Institute, NSW, Australia.,Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.,Cancer Therapy Centre, Liverpool Hospital, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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Ai Y, Zhang J, Jin J, Zhang J, Zhu H, Jin X. Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Front Oncol 2021; 11:610742. [PMID: 34178617 PMCID: PMC8222738 DOI: 10.3389/fonc.2021.610742] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 04/28/2021] [Indexed: 11/17/2022] Open
Abstract
Background There is urgent need for an accurate preoperative prediction of metastatic status to optimize treatment for patients with ovarian cancer (OC). The feasibility of predicting the metastatic status based on radiomics features from preoperative computed tomography (CT) images alone or combined with clinical factors were investigated. Methods A total of 101 OC patients who underwent primary debulking surgery were enrolled. Radiomics features were extracted from the tumor volumes contoured on CT images with LIFEx package. Mann-Whitney U tests, least absolute shrinkage selection operator (LASSO), and Ridge Regression were applied to select features and to build prediction models. Univariate and regression analysis were applied to select clinical factors for metastatic prediction. The performance of models generated with radiomics features alone, clinical factors, and combined factors were evaluated and compared. Results Nine radiomics features were screened out from 184 extracted features to classify patients with and without metastasis. Age and cancer antigen 125 (CA125) were the two clinical factors that were associated with metastasis. The area under curves (AUCs) for the radiomics signature, clinical factors model, and combined model were 0.82 (95% CI, 0.66-0.98; sensitivity = 0.90, specificity = 0.70), 0.83 (95% CI, 0.67-0.95; sensitivity = 0.71, specificity = 0.8), and 0.86 (95% CI, 0.72-0.99, sensitivity = 0.81, specificity = 0.8), respectively. Conclusions Radiomics features alone or radiomics features combined with clinical factors are feasible and accurate enough to predict the metastatic status for OC patients.
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Affiliation(s)
- Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jindi Zhang
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiyan Zhu
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiance Jin
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China
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van Wijk Y, Ramaekers B, Vanneste BGL, Halilaj I, Oberije C, Chatterjee A, Marcelissen T, Jochems A, Woodruff HC, Lambin P. Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study. Cancers (Basel) 2021; 13:cancers13112687. [PMID: 34072509 PMCID: PMC8198879 DOI: 10.3390/cancers13112687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. Abstract The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.
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Affiliation(s)
- Yvonka van Wijk
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
- Correspondence:
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Ben G. L. Vanneste
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
| | - Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Cary Oberije
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Tom Marcelissen
- Department of Urology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
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Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021; 85:175-191. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022] Open
Abstract
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
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Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Olga Green
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Görkem Güngör
- Acıbadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
| | - Nick Reynaert
- Department of Medical Physics, Institut Jules Bordet, Belgium
| | - Ruggero Ruggieri
- Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany
| | - Poonam Yadav
- Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA
| | - Yingli Yang
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Dirk Verellen
- Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
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Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
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Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T, Takeda K, Dobashi S, Ohashi H, Takeda K, Jingu K. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol 2021; 16:80. [PMID: 33931085 PMCID: PMC8086112 DOI: 10.1186/s13014-021-01810-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/21/2021] [Indexed: 02/08/2023] Open
Abstract
Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. Methods A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. Results In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Conclusions Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01810-9.
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Affiliation(s)
- Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Shunpei Tanabe
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Mariko Umeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Haruna Ohashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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Humbert-Vidan L, Patel V, Oksuz I, King AP, Guerrero Urbano T. Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer. Br J Radiol 2021; 94:20200026. [PMID: 33684314 PMCID: PMC8010531 DOI: 10.1259/bjr.20200026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 12/16/2020] [Accepted: 01/25/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. METHODS A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose-volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar's hypothesis test. RESULTS The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar's test applied to all model pair combinations, no statistically significant difference between the models was found. CONCLUSION Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. ADVANCES IN KNOWLEDGE This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.
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Affiliation(s)
| | - Vinod Patel
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | | | - Andrew Peter King
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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48
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Mahadevaiah G, Rv P, Bermejo I, Jaffray D, Dekker A, Wee L. Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance. Med Phys 2021; 47:e228-e235. [PMID: 32418341 PMCID: PMC7318221 DOI: 10.1002/mp.13562] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 04/27/2019] [Accepted: 04/27/2019] [Indexed: 01/16/2023] Open
Abstract
Background Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability. Aim In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance. Results A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well‐trained end‐users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved. Conclusion We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.
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Affiliation(s)
| | - Prasad Rv
- Philips Research India, Bangalore, 560045, India
| | - Inigo Bermejo
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, 6229 ET, Netherlands
| | - David Jaffray
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2M9, Canada
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, 6229 ET, Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, 6229 ET, Netherlands
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Chen Q, Xia T, Zhang M, Xia N, Liu J, Yang Y. Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges. Aging Dis 2021; 12:143-154. [PMID: 33532134 PMCID: PMC7801280 DOI: 10.14336/ad.2020.0421] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 04/21/2020] [Indexed: 12/11/2022] Open
Abstract
Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year. Neuroimaging, such as cranial computed tomography or magnetic resonance imaging, is the backbone of stroke management strategies, which can guide treatment decision-making (thrombolysis or hemostasis) at an early stage. With advances in computational technologies, particularly in machine learning, visual image information can now be converted into numerous quantitative features in an objective, repeatable, and high-throughput manner, in a process known as radiomics. Radiomics is mainly used in the field of oncology, which remains an area of active research. Over the past few years, investigators have attempted to apply radiomics to stroke in the hope of gaining benefits similar to those obtained in cancer management, i.e., in promoting the development of personalized precision medicine. Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis. In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies. We also discuss the potential challenges facing its widespread implementation in routine practice and the directions for future research.
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Affiliation(s)
- Qian Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Tianyi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Mingyue Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Nengzhi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Jinjin Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
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50
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Yang S, Han G, Chen Q, Yu L, Wang P, Zhang Q, Dong J, Zhang W, Huang J. Au-Pt Nanoparticle Formulation as a Radiosensitizer for Radiotherapy with Dual Effects. Int J Nanomedicine 2021; 16:239-248. [PMID: 33469284 PMCID: PMC7811476 DOI: 10.2147/ijn.s287523] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/18/2020] [Indexed: 12/20/2022] Open
Abstract
Background Radiotherapy occupies an essential position as one of the most significant approaches for the clinical treatment of cancer. However, we cannot overcome the shortcoming of X-rays which is the high value of the oxygen enhancement ratio (OER). Radiosensitizers with the ability to enhance the radiosensitivity of tumor cells provide an alternative to changing X-rays to protons and heavy ion radiotherapy. Materials and Methods We prepared the Au-Pt nanoparticles (Au-Pt NPs) using a one-step method. The characteristics of the Au-Pt NPs were determined using TEM, HAADF-STEM, elemental mapping images, and DLS. The enhanced radiotherapy was demonstrated in vitro using MTT assays, colony formation assays, fluorescence imaging, and flow cytometric analyses of the apoptosis. The biodistribution of the Au-Pt NPs was analyzed using ICP-OES, and thermal images. The enhanced radiotherapy was demonstrated in vitro using immunofluorescence images, tumor volume and weigh, and hematoxylin & eosin (H&E) staining. Results Polyethylene glycol (PEG) functionalized nanoparticles composed of the metallic elements Au and Pt were designed to increase synergistic radiosensitivity. The mechanism demonstrated that heavy metal NPs possess a high X-ray photon capture cross-section and Compton scattering effect which increased DNA damage. Furthermore, the Au-Pt NPs exhibited enzyme-mimicking activities by catalyzing the decomposition of endogenous H2O2 to O2 in the solid tumor microenvironment (TME). Conclusion Our work provides a systematically administered radiosensitizer that can selectively reside in a tumor via the EPR effect and enhances the efficiency of treating cancer with radiotherapy.
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Affiliation(s)
- Song Yang
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Gaohua Han
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Quan Chen
- Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China.,Department of Thoracic Surgery, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China
| | - Lei Yu
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Peng Wang
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Qi Zhang
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Jiang Dong
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
| | - Wei Zhang
- Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China.,Department of Infectious Disease, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China
| | - Junxing Huang
- Department of Oncology, Taizhou People's Hospital, Taizhou, Jiangsu, People's Republic of China.,Medical School of Nantong University, Nantong, Jiangsu, People's Republic of China
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