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Fang T, Jiang Z, Zhou Y, Jia S, Zhao J, Nie S. Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning. Med Phys 2024. [PMID: 38687043 DOI: 10.1002/mp.17101] [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: 09/15/2023] [Revised: 04/04/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative method for early ischemic changes in acute ischemic stroke. PURPOSE However, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion-weighted imaging (DWI) mode to help clinicians make accurate treatment plans. METHODS Eighty-two patients with stroke were included in the study. First, we designed a new deep learning network for segmenting ASPECTS scoring brain regions. The network is improved based on U-net, which integrates multiple modules. Second, we proposed using hybrid classifiers to classify brain regions. For brain regions with larger areas, we used brain grayscale comparison algorithm to train machine learning classifiers, while using hybrid feature training for brain regions with smaller areas. RESULTS The average DICE coefficient of the segmented hindbrain area can reach 0.864. With the proposed hybrid classifier, our method performs significantly on both region-level ASPECTS and dichotomous ASPECTS. The sensitivity and accuracy on the test set are 95.51% and 93.43%, respectively. For dichotomous ASPECTS, the intraclass correlation coefficient (ICC) between our automated ASPECTS score and the expert reading was 0.87. CONCLUSIONS This study proposed an automated model for ASPECTS scoring of patients with acute ischemic stroke based on DWI images. Experimental results show that the method of segmentation first and then classification is feasible. Our method has the potential to assist physicians in the Alberta Stroke Program with early CT scoring and clinical stroke diagnosis.
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Affiliation(s)
- Ting Fang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhuoyun Jiang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuxi Zhou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Shouqiang Jia
- Department of Imaging, Jinan People's Hospital affiliated to Shandong First Medical University, Shandong, China
| | - Jiaqi Zhao
- Department of Ultrasound, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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2
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Huma C, Hawon L, Sarisha J, Erdal T, Kevin C, Valentina KA. Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2024; 9:3-16. [PMID: 38550554 PMCID: PMC10972602 DOI: 10.1080/23808993.2024.2325936] [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: 07/20/2023] [Accepted: 02/28/2024] [Indexed: 04/01/2024]
Abstract
Introduction Patient selection remains challenging as the clinical use of re-irradiation (re-RT) increases. Re-RT data is limited to retrospective studies and small prospective single-institution reports, resulting in small, heterogenous data sets. Validated prognostic and predictive biomarkers are derived from large-volume studies with long-term follow-up. This review aims to examine existing re-RT publications and available data sets and discuss strategies using artificial intelligence (AI) to approach small data sets to optimize the use of re-RT data. Methods Re-RT publications were identified where associated public data was present. The existing literature on small data sets to identify biomarkers was also explored. Results Publications with associated public data were identified, with glioma and nasopharyngeal cancers emerging as the most common tumor sites where the use of re-RT was the primary management approach. Existing and emerging AI strategies have been used to approach small data sets including data generation, augmentation, discovery, and transfer learning. Conclusions Further data is needed to generate adaptive frameworks, improve the collection of specimens for molecular analysis, and improve the interpretability of results in re-RT data.
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Affiliation(s)
- Chaudhry Huma
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
| | - Lee Hawon
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
| | - Jagasia Sarisha
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
| | - Tasci Erdal
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
| | - Camphausen Kevin
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
| | - Krauze Andra Valentina
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD, 20892, United States
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3
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Nemoto H, Saito M, Satoh Y, Komiyama T, Marino K, Aoki S, Suzuki H, Sano N, Nonaka H, Watanabe H, Funayama S, Onishi H. Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study. J Appl Clin Med Phys 2024:e14322. [PMID: 38436611 DOI: 10.1002/acm2.14322] [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: 11/15/2023] [Revised: 01/14/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
PURPOSE Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. METHODS Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. RESULTS The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. CONCLUSIONS We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.
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Affiliation(s)
- Hikaru Nemoto
- Department of Advanced Biomedical Imaging, University of Yamanashi, Chuo, Yamanashi, Japan
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Masahide Saito
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Yoko Satoh
- Imaging Center, Fujita Medical Innovation Center Tokyo, Tokyo, Japan
| | - Takafumi Komiyama
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Kan Marino
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Shinichi Aoki
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Hidekazu Suzuki
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Naoki Sano
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Hotaka Nonaka
- Department of Radiology, Fuji City General Hospital, Fuji, Shizuoka, Japan
| | - Hiroaki Watanabe
- Department of Radiology, Yamanashi Central Hospital, Kofu, Yamanashi, Japan
| | - Satoshi Funayama
- Department of Radiology, Hamamatsu University school of medicine, Hamamatsu, Shizuoka, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
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Hu Y, Jiang T, Wang H, Song J, Yang Z, Wang Y, Su J, Jin M, Chang S, Deng K, Jiang W. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC. Phys Med 2024; 117:103200. [PMID: 38160516 DOI: 10.1016/j.ejmp.2023.103200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/18/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.
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Affiliation(s)
- Yue Hu
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Tao Jiang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Huan Wang
- Radiation Oncology Department Of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Jiangdian Song
- School of Medical Informatics, China Medical University, Liaoning 110122, PR China
| | - Zhiguang Yang
- Department of Radiology, Shengjing Hospital, Shenyang 110004, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Meiqi Jin
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China.
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui 230036, PR China.
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China.
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Fang T, Liu N, Nie S, Jia S, Ye X. A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:17-30. [PMID: 37980594 DOI: 10.3233/xst-230119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. OBJECTIVE We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS. METHODS Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region. RESULTS The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96). CONCLUSIONS This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.
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Affiliation(s)
- Ting Fang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Naijia Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Shouqiang Jia
- Jinan People's Hospital affiliated to Shandong First Medical University, Shandong, China
| | - Xiaodan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
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Miao S, Jia H, Huang W, Cheng K, Zhou W, Wang R. Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model. Cancer Biomark 2024; 39:171-185. [PMID: 38043007 PMCID: PMC11091603 DOI: 10.3233/cbm-230219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/24/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVES This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. METHODS CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. RESULTS The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). CONCLUSION Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.
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Affiliation(s)
- Shidi Miao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Haobo Jia
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Wenjuan Huang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, China
| | - Ke Cheng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Wenjin Zhou
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Ruitao Wang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, China
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7
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Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
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Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
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8
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Fiandra C, Rosati S, Arcadipane F, Dinapoli N, Fato M, Franco P, Gallio E, Scaffidi Gennarino D, Silvetti P, Zara S, Ricardi U, Balestra G. Active bone marrow segmentation based on computed tomography imaging in anal cancer patients: A machine-learning-based proof of concept. Phys Med 2023; 113:102657. [PMID: 37567068 DOI: 10.1016/j.ejmp.2023.102657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/30/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
PURPOSE Different methods are available to identify haematopoietically active bone marrow (ActBM). However, their use can be challenging for radiotherapy routine treatments, since they require specific equipment and dedicated time. A machine learning (ML) approach, based on radiomic features as inputs to three different classifiers, was applied to computed tomography (CT) images to identify haematopoietically active bone marrow in anal cancer patients. METHODS A total of 40 patients was assigned to the construction set (training set + test set). Fluorine-18-Fluorodeoxyglucose Positron Emission Tomography (18FDG-PET) images were used to detect the active part of the pelvic bone marrow (ActPBM) and stored as ground-truth for three subregions: iliac, lower pelvis and lumbosacral bone marrow (ActIBM, ActLPBM, ActLSBM). Three parameters were used for the correspondence analyses between 18FDG-PET and ML classifiers: DICE index, Precision and Recall. RESULTS For the 40-patient cohort, median values [min; max] of the Dice index were 0.69 [0.20; 0.84], 0.76 [0.25; 0.89], and 0.36 [0.15; 0.67] for ActIBM, ActLSBM, and ActLPBM, respectively. The Precision/Recall (P/R) ratio median value for the ActLPBM structure was 0.59 [0.20; 1.84] (over segmentation), while for the other two subregions the P/R ratio median has values of 1.249 [0.43; 4.15] for ActIBM and 1.093 [0.24; 1.91] for ActLSBM (under segmentation). CONCLUSION A satisfactory degree of overlap compared to 18FDG-PET was found for 2 out of the 3 subregions within pelvic bones. Further optimization and generalization of the process is required before clinical implementation.
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Affiliation(s)
- C Fiandra
- Department of Oncology, University of Turin, Turin, Italy.
| | - S Rosati
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - F Arcadipane
- Department of Oncology, University of Turin, Turin, Italy
| | - N Dinapoli
- UOC Radioterapia Oncologica, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - M Fato
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy
| | - P Franco
- Department of Oncology, University of Turin, Turin, Italy
| | - E Gallio
- Medical Physics Unit, A.O.U. Città della Salute e della Scienza, Turin, Italy
| | - D Scaffidi Gennarino
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - P Silvetti
- Department of Oncology, University of Turin, Turin, Italy
| | - S Zara
- Tecnologie Avanzate, Torino, Italy
| | - U Ricardi
- Department of Oncology, University of Turin, Turin, Italy
| | - G Balestra
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
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Zhang N, Zhang X, Li J, Ren J, Li L, Dong W, Liu Y. CT-derived radiomic analysis for predicting the survival rate of patients with non-small cell lung cancer receiving radiotherapy. Phys Med 2023; 107:102546. [PMID: 36796178 DOI: 10.1016/j.ejmp.2023.102546] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 12/09/2022] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Radiomics provides an opportunity to minimize adverse effects and optimize the efficacy of treatments noninvasively. This study aims to develop a computed tomography (CT) derived radiomic signature to predict radiological response for the patients with non-small cell lung cancer (NSCLC) receiving radiotherapy. METHODS Total 815 NSCLC patients receiving radiotherapy were sourced from public datasets. Using CT images of 281 NSCLC patients, we adopted genetic algorithm to establish a predictive radiomic signature for radiotherapy that had optimal C-index value by Cox model. Survival analysis and receiver operating characteristic curve were performed to estimate the predictive performance of the radiomic signature. Furthermore, radiogenomics analysis was performed in a dataset with matched images and transcriptome data. RESULTS Radiomic signature consisting of three features was established and then validated in the validation dataset (log-rank P = 0.0047) including 140 patient, and showed a significant predictive power in two independent datasets totaling 395 NSCLC patients with binary 2-year survival endpoint. Furthermore, the novel proposed radiomic nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors. Radiogenomics analysis linked our signature with important tumor biological processes (e.g. Mismatch repair, Cell adhesion molecules and DNA replication) associated with clinical outcomes. CONCLUSIONS The radiomic signature, reflecting tumor biological processes, could noninvasively predict therapeutic efficacy of NSCLC patients receiving radiotherapy and demonstrate unique advantage for clinical application.
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Affiliation(s)
- Nannan Zhang
- Modern Educational Technology and Experiment Center, Harbin Normal University, Harbin, China
| | - Xinxin Zhang
- College of Life Science and Technology, Harbin Normal University, Harbin, China
| | - Junheng Li
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Jie Ren
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Luyang Li
- Basic Medicine College, Harbin Medical University, Harbin, China
| | - Wenlei Dong
- Department of Radiotherapy Technology Center, Harbin Medical University Cancer Hospital, Harbin, China.
| | - Yixin Liu
- Basic Medicine College, Harbin Medical University, Harbin, China.
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Brancato V, Brancati N, Esposito G, La Rosa M, Cavaliere C, Allarà C, Romeo V, De Pietro G, Salvatore M, Aiello M, Sangiovanni M. A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:1552. [PMID: 36772592 PMCID: PMC9921618 DOI: 10.3390/s23031552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/13/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
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Affiliation(s)
- Valentina Brancato
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Nadia Brancati
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Giusy Esposito
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Massimo La Rosa
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Ciro Allarà
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Giuseppe De Pietro
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Mara Sangiovanni
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
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11
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Wang Q, Xu J, Wang A, Chen Y, Wang T, Chen D, Zhang J, Brismar TB. Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer. LA RADIOLOGIA MEDICA 2023; 128:136-148. [PMID: 36648615 PMCID: PMC9938810 DOI: 10.1007/s11547-023-01593-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/04/2023] [Indexed: 01/18/2023]
Abstract
This study aimed to systematically summarize the performance of the machine learning-based radiomics models in the prediction of microsatellite instability (MSI) in patients with colorectal cancer (CRC). It was conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guideline and was registered at the PROSPERO website with an identifier CRD42022295787. Systematic literature searching was conducted in databases of PubMed, Embase, Web of Science, and Cochrane Library up to November 10, 2022. Research which applied radiomics analysis on preoperative CT/MRI/PET-CT images for predicting the MSI status in CRC patients with no history of anti-tumor therapies was eligible. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) were applied to evaluate the research quality (full score 100%). Twelve studies with 4,320 patients were included. All studies were retrospective, and only four had an external validation cohort. The median incidence of MSI was 19% (range 8-34%). The area under the receiver operator curve of the models ranged from 0.78 to 0.96 (median 0.83) in the external validation cohort. The median sensitivity was 0.76 (range 0.32-1.00), and the median specificity was 0.87 (range 0.69-1.00). The median RQS score was 38% (range 14-50%), and half of the studies showed high risk in patient selection as evaluated by QUADAS-2. In conclusion, while radiomics based on pretreatment imaging modalities had a high performance in the prediction of MSI status in CRC, so far it does not appear to be ready for clinical use due to insufficient methodological quality.
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Affiliation(s)
- Qiang Wang
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden. .,Department of Radiology, Karolinska University Hospital Huddinge, Room 601, Novum PI 6, Hiss F, Hälsovägen 7, 141 86, Huddinge, Stockholm, Sweden.
| | - Jianhua Xu
- Department of General Surgery, Songshan Hospital, Chongqing, China
| | - Anrong Wang
- grid.452206.70000 0004 1758 417XDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China ,Department of Interventional Therapy, People’s Hospital of Dianjiang County, Chongqing, China
| | - Yi Chen
- grid.4714.60000 0004 1937 0626Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Tian Wang
- grid.517910.bDepartment of Gastroenterology, Chongqing General Hospital, Chongqing, China
| | - Danyu Chen
- grid.412536.70000 0004 1791 7851Department of Gastroenterology and Hepatology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiaxing Zhang
- grid.459540.90000 0004 1791 4503Department of Pharmacy, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Torkel B. Brismar
- grid.4714.60000 0004 1937 0626Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden ,grid.24381.3c0000 0000 9241 5705Department of Radiology, Karolinska University Hospital Huddinge, Room 601, Novum PI 6, Hiss F, Hälsovägen 7, 141 86 Huddinge, Stockholm, Sweden
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Miao S, Jia H, Cheng K, Hu X, Li J, Huang W, Wang R. Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients. Brief Bioinform 2022; 23:6748489. [PMID: 36198668 DOI: 10.1093/bib/bbac432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022] Open
Abstract
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.
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Affiliation(s)
- Shidi Miao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Haobo Jia
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Ke Cheng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaohui Hu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Jing Li
- Department of Geriatrics, the Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Wenjuan Huang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Ruitao Wang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
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Dong Y, Zhang Q, Chen H, Jin Y, Ji Z, Han H, Wang W. Radiomics of Multi-modality Ultrasound in Rabbit VX2 Liver Tumors: Differentiating Residual Tumors from Hyperemic Rim After Ablation. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00763-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Li YZ, Wang Y, Fang KB, Zheng HZ, Lai QQ, Xia YF, Chen JY, Dai ZS. Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN. Eur J Med Res 2022; 27:247. [DOI: 10.1186/s40001-022-00883-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/01/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Background
The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis.
Purpose
This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears.
Materials and methods
Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis.
Results
Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results.
Conclusions
3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.
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15
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What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology. J Pers Med 2022; 12:jpm12111834. [PMID: 36579551 PMCID: PMC9694457 DOI: 10.3390/jpm12111834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
The constant evolution of technology has dramatically changed the history of radiation oncology, allowing clinicians to deliver increasingly accurate and precise treatments, moving from 2D radiotherapy to 3D conformal radiotherapy, leading to intensity-modulated image-guided (IMRT-IGRT) and stereotactic body radiotherapy treatments [...].
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[Automatic identification algorithm of meniscus tear based on radiomics of knee MRI]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2022; 36:1395-1399. [PMID: 36382458 PMCID: PMC9681581 DOI: 10.7507/1002-1892.202206016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE To establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury. METHODS A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity. RESULTS A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95% CI (0.845, 0.942), P<0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95% CI (0.875, 0.984), P<0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively. CONCLUSION The model established by the radiomics method has good automatic identification performance of meniscus tear.
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Braghetto A, Marturano F, Paiusco M, Baiesi M, Bettinelli A. Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset. Sci Rep 2022; 12:14132. [PMID: 35986072 PMCID: PMC9391464 DOI: 10.1038/s41598-022-18085-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/04/2022] [Indexed: 11/08/2022] Open
Abstract
In this study, we tested and compared radiomics and deep learning-based approaches on the public LUNG1 dataset, for the prediction of 2-year overall survival (OS) in non-small cell lung cancer patients. Radiomic features were extracted from the gross tumor volume using Pyradiomics, while deep features were extracted from bi-dimensional tumor slices by convolutional autoencoder. Both radiomic and deep features were fed to 24 different pipelines formed by the combination of four feature selection/reduction methods and six classifiers. Direct classification through convolutional neural networks (CNNs) was also performed. Each approach was investigated with and without the inclusion of clinical parameters. The maximum area under the receiver operating characteristic on the test set improved from 0.59, obtained for the baseline clinical model, to 0.67 ± 0.03, 0.63 ± 0.03 and 0.67 ± 0.02 for models based on radiomic features, deep features, and their combination, and to 0.64 ± 0.04 for direct CNN classification. Despite the high number of pipelines and approaches tested, results were comparable and in line with previous works, hence confirming that it is challenging to extract further imaging-based information from the LUNG1 dataset for the prediction of 2-year OS.
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Affiliation(s)
- Anna Braghetto
- Physics and Astronomy Department "Galileo Galilei", University of Padova, Via Marzolo 8, 35131, Padua, Italy.
- INFN, Sezione di Padova, Via Marzolo 8, 35131, Padua, Italy.
| | - Francesca Marturano
- Medical Physics Department, Veneto Institute of Oncology-IOV IRCCS, Padua, Italy
| | - Marta Paiusco
- Medical Physics Department, Veneto Institute of Oncology-IOV IRCCS, Padua, Italy
| | - Marco Baiesi
- Physics and Astronomy Department "Galileo Galilei", University of Padova, Via Marzolo 8, 35131, Padua, Italy
- INFN, Sezione di Padova, Via Marzolo 8, 35131, Padua, Italy
| | - Andrea Bettinelli
- Medical Physics Department, Veneto Institute of Oncology-IOV IRCCS, Padua, Italy
- Department of Information Engineering, University of Padova, Padua, Italy
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18
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Ieko Y, Kadoya N, Sugai Y, Mouri S, Umeda M, Tanaka S, Kanai T, Ichiji K, Yamamoto T, Ariga H, Jingu K. Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients. Phys Med 2022; 101:28-35. [PMID: 35872396 DOI: 10.1016/j.ejmp.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV1/FVC]) using data extracted from chest computed tomography (CT) images. METHODS This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV1/FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional). RESULTS For the estimated FEV1/FVC, the Pearson's r were 0.21 (P =.06), 0.69 (P <.01), and 0.73 (P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV1/FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively. CONCLUSIONS The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.
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Affiliation(s)
- Yoshiro Ieko
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiation Oncology, Iwate Medical University School of Medicine, Yahaba, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shiina Mouri
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Mariko Umeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Kei Ichiji
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hisanori Ariga
- Department of Radiation Oncology, Iwate Medical University School of Medicine, Yahaba, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Li J, Zhu Y, Dong Z, He X, Xu M, Liu J, Zhang M, Tao X, Du H, Chen D, Huang L, Shang R, Zhang L, Luo R, Zhou W, Deng Y, Huang X, Li Y, Chen B, Gong R, Zhang C, Li X, Wu L, Yu H. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study. EClinicalMedicine 2022; 46:101366. [PMID: 35521066 PMCID: PMC9061989 DOI: 10.1016/j.eclinm.2022.101366] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient survival. However, most previous computer-aided-diagnosis (CAD) systems did not concretize or explain diagnostic theories. We aimed to develop a logical anthropomorphic artificial intelligence (AI) diagnostic system named ENDOANGEL-LA (logical anthropomorphic) for EGCs under magnifying image enhanced endoscopy (M-IEE). METHODS We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared. FINDINGS Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model. INTERPRETATION We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs. FUNDING This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Nursing Department of Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, PR China
| | - Mengjiao Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Hongliu Du
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Di Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Renquan Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yunchao Deng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Rongrong Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xun Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Correspondeing authors at: Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China.
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Correspondeing authors at: Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China.
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Wang Y, Li Y, Huang M, Lai Q, Huang J, Chen J. Feasibility of Constructing an Automatic Meniscus Injury Detection Model Based on Dual-Mode Magnetic Resonance Imaging (MRI) Radiomics of the Knee Joint. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2155132. [PMID: 35392588 PMCID: PMC8983204 DOI: 10.1155/2022/2155132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/09/2022] [Accepted: 03/07/2022] [Indexed: 02/08/2023]
Abstract
Objective To explore the feasibility of automatically detecting the degree of meniscus injury by radiomics fusion of dual-mode magnetic resonance imaging (MRI) features of sagittal and coronal planes of the knee joint. Methods This retrospective study included 164 arthroscopically confirmed meniscus injuries in 152 patients admitted to the Department of Orthopaedics of our hospital from July 2018 to March 2021. A total of 1316-dimensional radiomics signatures were extracted from single-mode sagittal and coronal plane images of menisci, respectively. Then, the sagittal and coronal plane features were fused to form a dual-mode joint feature group with a total of 2632-dimensional radiomics signatures. The minimum redundancy maximum relevance (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate optimal radiomics signatures. The single-mode sagittal plane feature model (Model 1), single-mode coronal plane feature model (Model 2), and the combined sagittal and coronal plane feature model (Model 3) performance were tested by receiver operating characteristic (ROC) curves and Delong test. The calibration curve test was used to verify the reliability of radiomics signatures of the three models. Results The average intra- and interobserver intraclass correlation coefficients (ICCs) of the most significant 8-dimensional radiomics signatures of Model 1 and Model 2 were 0.935 (range 0.832-0.998) and 0.928 (range 0.845-0.998), respectively. All the three models had good detection performance; Model 3 had the most significant performance (the areas under the curve (AUCs) of training, and validation sets were 0.947 and 0.923, respectively), which was superior to Model 1 (AUCs of training set and validation set were 0.889 and 0.876, respectively) and Model 2 (AUCs of training set and validation set were 0.831 and 0.851, respectively). The detection probability of training and validation sets in the three models was highly consistent with the actual clinical probability. Conclusions It is feasible to establish a model for automatic detection of meniscus damage by means of radiomics. The detection performance of the dual-mode knee MRI model is better than that of any single-mode model, showing potent feature analysis ability and outstanding detection performance.
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Affiliation(s)
- Yi Wang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yuanzhe Li
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Meiling Huang
- Radiology Department, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Qingquan Lai
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jing Huang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jiayang Chen
- Radiology Department, Anxi Hospital of Traditional Chinese Medicine, Quanzhou 362400, China
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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