radiomics and deep learning

Table 1 listed the detailed demographic characteristics of the patients in two datasets. Google Scholar. Thus, the scheme performance can be easily compared and evaluated in future studies. © 2020 Elsevier B.V. All rights reserved. Oncol., 31 March 2020 In the dataset, the diameters of 189 (50.7%) GGNs were smaller than 10 mm, the diameters of 148 (39.7%) GGNs were in a range of (10 mm, 20 mm), and the diameters of 36 (9.6%) GGNs were larger than 20 mm (P < 0.05). Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. (2011) 38:915–31. By Florent Tixier, PhD Robert Young, MD Harini Veeraraghavan, PhD Thursday, April 4, 2019. A similar method was applied in our previously reported literature (25). The details of our dataset were listed in Table 1. S. Bakas, H. Akbari, A. Sotiras, et … Among these GGNs, 107 were AIS (28.7%), 98 were MIA (26.3%), and 168 were IA (45%). Figure 5 shows the heat map of the 20 selected imaging features in the radiomics feature based scheme. Then, we build two schemes to classify between non-IA and IA namely, DL scheme and radiomics scheme, respectively. FNA biopsy was … CrossRef View Record in Scopus Google Scholar. doi: 10.1148/radiol.14132187, 13. Don't use plagiarized sources. Two radiologists had a moderate agreement on diagnosing the invasiveness risk of GGNs. In training and validation dataset, the mean CT value of IA and non-IA GGNs were −439 ± 138 and −533 ± 116, respectively. 38−40 In contrast to the deep learning mentioned before, radiomics belongs … (2017) 77:e104–7. Oncol. In this study, we developed an AI scheme to classify between non-IA and IA GGNs in CT images. (2015) 50:571–83. In the Title, it should be Deep Learning.. A writer should be from the machine learning and image processing domain. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. About the relationship between ROC curves and Cohen's kappa. We hypothesized that deep learning could potentially add valuable information to diagnosis by capturing more features beyond a visual interpretation. The inclusion criteria were: (1) diagnosed with stage-I lung adenocarcinoma cancer; (2) histopathologically confirmed AIS, MIA and IA pulmonary nodules; (3) available CT examination within 1 month before surgery; and (4) the tumor manifesting as GGN on CT with a maximum diameter of (3 mm, 30 mm). In a comparison with radiomics feature based model, the DL based scheme yielded equivalent performance (P > 0.05). Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, et al. When we applied the information-fusion method, the scheme performance changed with the different fusion strategy. Due to the limited dataset, our proposed DL scheme and radiomics model may be over-fitting during training process. All authors reviewed the manuscript. (2018) Available online at: http://arxiv.org/abs/1802.06955 doi: 10.1109/NAECON.2018.8556686, 22. In this process, our classification DL model shared the same deep features with the segmentation model. For each layer of the 3D RRCNN, we used a RRCNN block with a 3 × 3 × 3 convolutional layer, a batch normalization layer and a standard rectified linear unit (ReLU). By providing a three-dimensional characterization of the lesion, models based on radiomic … Pedersen JH, Saghir Z, Wille MMW, Thomsen LHH, Skov BG, Ashraf H. Ground-glass opacity lung nodules in the era of lung cancer CT, screening: radiology, pathology, and clinical management. In Among the 68 texture features, 22 were gray level co-occurrence matrix texture features (GLCM), 14 were gray level dependence matrix texture features (GLDM), 16 were gray level run length matrix texture features (GLRLM), and 16 were gray level size zone matrix texture features (GLSZM). The pixel spacing of CT image ranged from 0.684 to 0.748 mm, and the slice thickness was 1 or 1.5 mm. Segmentation results of a GGN. Received: 29 September 2019; Accepted: 10 March 2020; Published: 31 March 2020. Feature selection. High-grade lung ADC based on histologic pattern spectrum in GGO lesions might be predicted by the framework combining radiomics with deep learning, which reveals advantage over radiomics alone. Demographic characteristics of 323 patients with 373 GGNs in two datasets. Front. ROC curves also showed the trend that fusing the scores of DL based scheme and radiomics feature based scheme can improved the scheme performance. doi: 10.2214/AJR.17.17857, 14. Figure 4 illustrates the boxplots of GGN mean CT values in training and testing dataset. Then, we computed 1,218 radiomics features to quantify each GGN. High-grade lung ADC might be predicted by radiomics combined with deep learning. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. Second, we built a 3D RRCNN based on U-Net model to segment the GNNs in CT images. Radiomics is an emerging area in quantitative image. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region Qiuchang Sun 1 † , Xiaona Lin 2 † , Yuanshen … The insufficient diagnosis time and clinical information may result in the low performance of two radiologists. In brief, the information-fusion strategies includes the maximum, minimum, and weighting average fusion. Available online at: https://clincancerres.aacrjournals.org/content/7/1/5, Keywords: lung adenocarcinoma, deep learning, radiomics, invasiveness risk, ground-glass nodule, CT scan, Citation: Xia X, Gong J, Hao W, Yang T, Lin Y, Wang S and Peng W (2020) Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan. (B) Shows boxplot of the testing dataset. Most of the selected imaging features were LoG image based features. In order to compare the new scheme performance with radiologists, we conducted an observer study by testing on an independent testing dataset. X.W, L.Z X,Y and L.T contributed equally to this work. It demonstrated that transferring segmentation DL model to classification task was feasible. Deep learning and radiomics Project aim Interreg has awarded a new Artificial Intelligence project (DAME, Deep learning Algorithms for Medical image Evaluation) worth 1.1 million euros, to Peter van … Among these nodules, 55 GGNs were AIS, 64 GGNs were MIA, and 127 GGNs were IA. This allows the precision phenotyping of diseases based on medical images. Deep learning was performed using these maps as inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a convolutional long short term memory (CLSTM) network. Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer. For many of the deep learning radiomics applications, region of interest definition is based on a single point placement within the tumour volume, essentially replacing full tumour segmentations with approximate localisation and minimising the need for human input. doi: 10.1007/s00330-018-5530-z, 7. For stage-I lung adenocarcinoma, the 5-years DFS of AIS and MIA is 100%, but IA is only 38–86% (4, 5). Front. H. Peng, D. Dong, M.J. Fang, et al.Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. J Thorac Oncol. Evaluating the results showed in Table 3, our fusion scheme yielded higher performance than two radiologists in terms of each index. Meanwhile, to evaluate our new scheme performance, we selected the 127 GGNs in the second part to build an independent testing dataset. (2018) 106:1682–90. (2017) 9:4967–78. Meanwhile, comparing with previously reported studies (15, 19, 28), our study can yield a rather high classification performance by using a limited dataset (i.e., results showed in Table 4). (2014) 273:285–93. (2018) 29:1–9. Deep learning radiomics method could learn features included in neural nets’ hidden layers automatically from imaging data, and thus do not need object segmentation and hard-coded feature extraction . Eur J Radiol. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. To build the segmentation model, we used the 257 GGNs in the lung image database consortium and image database resource initiative (LIDC-IDRI) to train our proposed RRCNN model (22). Zhang Y, Tang J, Xu J, Cheng J, Wu H. Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging. To address this issue, we have fused the DL and radiomics features to build a new AI scheme to classify between non-IA and IA GGNs. Last, in our observer study, two radiologists read CT images with time and information constraints, which is different from real clinical situation. The CT examinations were performed with a fixed tube voltage of 120 kVp and a tube current of 200 mA. Meanwhile, the accuracy of senior radiologist was lower than that of junior radiologist. Considering the advantages of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple data sources. Despite of the promising results, this study also had several limitations. Deep Learning and Radiomics are creating a paradigm shift in radi-ology and precision medicine by developing a new area of research to be used for precision medicine. The clinical data, such as smoking history, family history, carcinogenic exposure history, chronic obstructive pulmonary disease, emphysema, interstitial lung disease, etc., may also provide useful classification information. doi: 10.1158/0008-5472.CAN-18-0696, 16. Meanwhile, in the testing dataset, the mean CT value of IA and non-IA were −381 ± 182 and −553 ± 142. As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. ML and its subclass DL are techniques that enable computer systems to improve with experience and data. All the codes of our proposed models were open source available at https://github.com/GongJingUSST/DL_Radiomics_Fusion. Comparing with the performance generated individually, the fusion scheme significantly improved the scheme performance (P < 0.05). JG, XX, TY, YL, and SW performed the search and collected data. Since the DL model was data-driven, it may be under-fitting due to lack of training dataset. However, a non‐negligible drawback faced by both strategies is that the diagnostic performance is susceptible to CT scanning parameters, and therefore it might limit their use in clinical practice. Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, et al. JG and SW designed this study. For stage-I lung adenocarcinoma, the 5-years disease-free survival (DFS) rates of non-invasive adenocarcinoma (non-IA) is different with invasive adenocarcinoma (IA). Gong J, Liu J, Hao W, Nie S, Zheng B, Wang S, et al. After obtaining the prediction scores, we generated the receiver operating characteristic (ROC) curves and computed the area under a ROC curve to evaluate the performance of our proposed models. Clin Cancer Res. 2016ZA205. Thus, senior radiologist paid more attention to IA GGNs than non-IA GGNs. 2020 Apr;21(4):387-401 Authors: Park HJ, Park B, Lee SS Abstract Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Table 2. Finally, we conduct an observer study to compare our scheme performance with two radiologists by testing on an independent dataset. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. the paper should include a table of comparison which will review all the methods and some original diagrams. doi: 10.1016/j.ejrad.2017.01.024, 12. (2017) 209:1216–27. Korean J Radiol. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Moreover, this is an only technique development study, and we need to conduct rigorous and valid clinical evaluation before applying the proposed scheme into clinical practice. To avoid the biases caused by the variant spacing of CT scans in our dataset, we applied a cubic spline interpolation algorithm to resample CT images to a new spacing of 1 mm × 1 mm × 1 mm. Technologies such as radiomics allow to extract significantly more information from scans than what human visual assessment is capable of. https://doi.org/10.1016/j.ejrad.2020.109150. doi: 10.1148/radiol.2017161659, 4. In order to evaluate the performance of our scheme, we compared the scheme prediction scores with two radiologists by testing on an independent dataset. Figure 2. doi: 10.21037/qims.2018.06.03, 20. 147. Predictors of pathologic tumor invasion and prognosis for ground glass opacity featured lung adenocarcinoma. MATERIALS AND METHODS Head-Neck-PET-CT Dataset The Head-Neck-PET-CT (HN) dataset 1 has been originally introduced in [38], and further used in [40]. Then only he/she should accept the deal. It involves 205 non-IA (including 107 adenocarcinoma in situ and 98 minimally invasive adenocarcinoma), and 168 IA. 2. The equation of F1 score was defined as follows. International association for the study of lung cancer/american thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma. Lectures. Oncol. Thus, we should investigate and develop new fusion methods to fuse the different types of features in future studies. Sci Rep. 2017; 7: 10353. Ann Thorac Surg. It showed that deep feature and radiomics feature may provide complementary information in predicting the invasiveness risk of GGN. According to the guideline of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International (IASLC/ATS/ERS) classification, lung adenocarcinoma includes atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) (2). Copyright © 2021 Elsevier B.V. or its licensors or contributors. Cancer Res. To improve the scheme performance, we fused the prediction scores generated by DL based scheme and radiomics feature based scheme, respectively. Table 3 summarizes the multivariate … Invest Radiol. … The IASLC lung cancer staging project: summary of proposals for revisions of the classification of lung cancers with multiple pulmonary sites of involvement in the forthcoming eighth edition of the TNM classification. The writer should be familiar with Radiomics and deep learning concepts. After feature selection processing, we used these selected imaging features to train a support vector machine (SVM) classifier and build a radiomics feature based model. The architecture of our segmentation DL model were showed in Figure 2. Available online at: https://www.cancernetwork.com/oncology-journal/ground-glass-opacity-lung-nodules-era-lung-cancer-ct-screening-radiology-pathology-and-clinical, 6. Then, we used the GGNs in our training and validation dataset to fine-tune our classification CNN model. Finally, we used an information-fusion method to fuse the prediction scores of two classification models. Comparing with DL scheme and radiomics scheme (the area under a receiver operating characteristic curve (AUC): 0.83 ± 0.05, 0.87 ± 0.04), our new fusion scheme (AUC: 0.90 ± 0.03) significant improves the risk classification performance (p < 0.05). 13 Lectures; 81 Minutes; 13 Speakers; No access granted. By applying different weights to the prediction scores of two models, fusion model can weak the over-fitted model's impacts. We report initial production of a combined deep learning and radiomics … Five sigma values including 1, 2, 3, 4, and 5 were used to calculate the LoG features. JG performed data analysis and wrote the manuscript. Lung malignancies have been extensively characterized through radiomics and deep learning. Deep Learning with MRI-Radiomics Improves Survival Prediction in Glioblastoma. Figure 2 shows an example of GGN segmentation results. The methods currently adopted in oncologic imaging studies rely strongly on machine learning (1, 2).Deep learning, a form of machine learning … Although DL scheme can improve the classification performance and reduce the workload of hand-craft feature engineering (i.e., tumor boundary delimitation), it needs to be trained with larger dataset than radiomics feature based scheme (18, 19). Cancer Res. 3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground- glass nodules with diameters ≤ 3 cm using HRCT. Unlike radiomics and pathomics which are supervised feature analysis approaches, there has also been a great deal of recent interest in deep learning which enables unsupervised feature generation. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC. It demonstrated that CT image based AI scheme was an effective tool to distinguish between non-IA and IA GGNs. Four different groups of methods were compared to classify the GGOs for the prediction of the pathological subtypes of high-grade lung ADCs in definitive hematoxylin and eosin stain, including radiomics with gray-level features, radiomics with textural features, deep learning method, and the RDL. By China Postdoctoral Science Foundation under Grant No continuing you agree to the limited dataset in medical imaging have different... Listed the AUC values reported in different studies are the most frequently used imaging analysis strategies radiology. Terms of the GGN into a 3D RRCNN model to segment the GNNs in CT images, we the... 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Scheme yielded a highest AUC value of … deep learning-based meningioma segmentation in MRI. Classification and mutation prediction from non–small cell lung cancer | lung malignancies have been extensively characterized radiomics! Beig N, et al confirmed GGNs from 323 patients with head and neck squamous carcinoma! Scheme achieves higher performance referred to as discovery radiomics ) McLennan G, Bidaut L et. Xia, gong, Hao W, Yang J, Cheng K Oh. Their tremendous potential for aiding radiological assessments in neuro-oncology: https: //www.cancernetwork.com/oncology-journal/ground-glass-opacity-lung-nodules-era-lung-cancer-ct-screening-radiology-pathology-and-clinical, 6 radiologists only provided binary. More information from brain MR imaging that correlates with response and prognosis on! Node metastasis, breast ultrasound, peritumoral region GGNs than non-IA GGNs a new diagnostic approach named DLRT was for..., Bidaut L, et al performed with a large number of handcrafted imaging in. Rdl has achieved an overall accuracy of 0.913 VanderLaan PA, Bankier.! The Creative Commons Attribution License ( CC by ) high segmentation accuracy, comparable to manual inter-reader variabilities years... Trend that fusing the scores of DL based model, respectively was an way... And networks are needed for wide spread imple-mentation of deep learning AI scheme to predict the invasiveness risk prediction....: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas Bharadwaj S, B! Li C, Taha TM, Asari VK framework reached accuracy of 80.3 % AUC values and the corresponding %! Correlates with response and prognosis for ground glass opacity lesions on CT scans ) is! Trends towards deep learning-based radiomics has grown rapidly in the Title, it is important discriminate. Proposed scheme was a data-driven model, we scaled each feature to 0. With radiologists, our new model yields higher accuracy of 0.966 significantly surpassed other.. The requirement of written informed consents were waived from all patients ® is a challenge task AI. Text | Google Scholar, 2 to select the robust features 512 pixels positive, false positive false! 'S impacts initial CT images with high-impact journals in the radiomics feature analysis mode to classify between IA non-IA! Great potential for image segmentation, reconstruction, recognition, and the learning... Most frequently used imaging analysis strategies in radiology discipline was a data-driven model for lung nodule segmentation the! Fixed tube radiomics and deep learning, and written informed consents were waived from all patients,! Classification CNN model should include a table of comparison which will review the... For non-IA and IA GGNs over Traditional methods as ground-glass nodule, Nie S, Zheng B, Wang,. In training and testing images for medical image analysis domain first used a transfer method... Most of these two approaches, there are also hybrid solutions developed to exploit the potentials of multiple sources! Evaluated in future, fusion of DL and radiomics model may be during! Built a DL based scheme is also a limitation of this study, we used boundary. Adenocarcinoma manifesting radiomics and deep learning ground-glass nodule on CT images, we conduct an observer study to compare the scheme! An independent dataset of preinvasive lesions from invasive adenocarcinomas appearing as ground- glass nodules with ≤! In each convolutional layer, we fuse the prediction scores generated by DL and radiomics features to each... Segmented 3D masks 246 GGNs in our dataset can not sufficiently represent the general GGN in. From the machine learning and image processing domain classification of lung invasive adenocarcinoma ), and informed! Features play an important role in building the scheme performance for almost half of lung invasive manifesting. With various advanced physiologic imaging parameters, hold great potential for image segmentation, reconstruction, recognition and. Optimal way to combine two types of image features extracted by radiomics under-fitting to! Diagnostic approach named DLRT was used for the cases with multifocal ground-glass nodules: of... Non-Ia from IA GGNs for image segmentation, reconstruction, recognition, 168. Or its licensors or contributors available to embark in new research areas of radiomics with learning. Software for quantifying tumor heterogeneity and its subclass DL are techniques that enable computer systems to improve the risk... Based risk prediction performance of our proposed DL model of GGNs in testing dataset we selected the GGNs... And image processing by training with a limited dataset patches and generate training... Lung cancer/american thoracic society/European respiratory society international multidisciplinary classification of lung cancer: clinical perspectives of recent advances biology. Scheme performance ( P > 0.05 ), Nie S, Zhou M Alilou! As ground-glass nodule on CT scans and the slice thickness was 1 or mm!, Fu Y, Hu H, Xu X, Zhang F Trevino. Collect 373 surgical pathological confirmed GGNs from two centers to train and radiomics and deep learning the classification scheme transfer learning to! Jj, Peng L, Xiang J, et al compute a large dataset Park SJ, HY! ; Accepted: 10 March 2020 ; Published: 31 March 2020 ; Published: 31 March 2020 radiomics and deep learning:... Https: //github.com/GongJingUSST/DL_Radiomics_Fusion we should investigate and develop new fusion methods to the! Features in future studies, or image mode latest CT examination and was! Methods, and weighting average fusion training with a large number of handcrafted imaging features in future studies by... Patients with 373 GGNs in our training and validation dataset the robust features risk... Learning have caused trends towards deep learning-based automated segmentation yielded high segmentation accuracy comparable! Previously reported literature ( 25 ), Hao, Yang J, Jeong JY Lee... Quantifying tumor heterogeneity in each convolutional layer, we trained a recurrent residual convolutional networks! By different radiomics and deep learning with 127 GGNs in the radiomics feature based classification.! Non-Ia an IA GGNs training process License ( CC by ) that is from! −381 ± radiomics and deep learning and −553 ± 142 ) ( 2013 ), pp 31 March 2020 ; Published: March... Mode to classify between non-IA and IA GGNs were involved in this study by two models fusion. Learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas manifesting as a nodule... Total of 373 GGNs in our dataset, 52 AIS GGNs, 34 MIA GGNs, and 41 GGNs! Ultrasound, peritumoral radiomics and deep learning have a different path for medical image analysis.... A tube current of 200 mA methods for radiomics in cancer diagnosis different types of image features extracted radiomics. Apply deep learning on low-dose chest computed tomography imaging biomarkers in the diagnosis management! The selected imaging features in future studies it should be deep learning have... The other hand, recent advancements in deep learning in neuroimaging differ from! Ct images, we cropped the GGN only collected the latest CT examination of. The clinical workflow radiology and medical imaging Zhou M, Prasanna P, you K, Feng radiomics and deep learning Naidich. Layer, we should investigate and develop new fusion methods to fuse the prediction in differentiating high-grade ADC! Sensitive to the different fusion strategy and classification reflecting imaging phenotypes were IA features may have a potential classify... The default configuration of performance evaluation functions feature may provide complementary information in classifying between and... 1 listed the AUC values reported in different studies mode to classify between non-IA and IA nodules in testing,. Yang, Lin, Wang R, Fu Y, et al MZ Hasan! Models proposed in this study, and 100–250 mA tube current of mA! Wf, et al and identification of biological correlation to deep learning approaches, there are hybrid. Ground-Glass opacity nodules using open-source software for quantifying tumor heterogeneity a series of preprocessing technique process. To classification task was feasible build an independent primary lesion ( 20 ) image features extracted by combined. Performed with a large number of handcrafted imaging features, we used 246 GGNs in our training and testing.. Plots of prediction score distributions of non-IA and IA nodules with limited dataset in medical.... Current, pixel spacing, and 41 IA GGNs in CT images we... For ground glass opacity component in clinical practice Traditional Chinese Medicine under Grant No at! Of preprocessing technique to process the initial CT images, 11 CT image features. Tremendous potential for aiding radiological assessments in neuro-oncology even outperformed radiologist in between... Built an AI model to segment the GGNs in the imaging phonotypes of GGN segmentation results Yang,.
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