The related studies usually compute a large number of handcrafted imaging features to decode the different tumor phenotypes (6, 12–14). doi: 10.1007/s00330-019-06533-w, 29. doi: 10.1016/j.media.2017.06.014, 24. However, under common medical diagnosis conditions, collecting, and building a large uniform image dataset is very difficult because of the inconformity of CT screening standard and lacking surgical pathological confirmed GGNs. RPS 1011b - Radiomics and deep learning in neuroimaging. The persistent presence of ground-glass nodules (GGN) in computed tomography (CT) image usually serves as an indicator of the presence of lung adenocarcinoma or its precursors (1). Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. To build the DL and radiomics feature based scheme, we applied some publicly available Python packages, i.e., SimpleITK, pyradiomics (26), Pytorch, scikit-learn, scikit-feature, scipy. 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. 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). To address this issue, we proposed a RRCNN model to segment GGNs, and then used a transfer learning method to fine-tune the segmentation DL model. The study presents a novel analysis by integrating traditional radiomics features through multi-task learning, applying a time-based survival analysis, and incorporating new deep learning methods … Fourth, we built a radiomics feature analysis model to classify between non-IA and IA GGNs. MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Radiology. The kappa value for inter-radiologist agreement is 0.6. RDL framework reached accuracy of 0.966 in the verifying of an independent dataset. Figure 6 shows performance comparisons of three models and radiologists. (2020) 30:1847–55. deep learning/radiomics approach is more accurate than using only one feature type, or image mode. In this talk I will discuss the development work in CCIPD on new radiomic and pathomic and deep learning approaches for capturing intra-tumoral heterogeneity and modeling tumor appearance. 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. Computational radiomics system to decode the radiographic phenotype. Med Phys. (2018) 8:491–9. We use cookies to help provide and enhance our service and tailor content and ads. In order to evaluate the performance of our scheme, we compared the scheme prediction scores with two radiologists by testing on an independent dataset. We used the default configuration of performance evaluation functions. doi: 10.1016/j.jtho.2018.09.026, 5. The architecture of our segmentation DL model were showed in Figure 2. It demonstrates (1) fusing DL and radiomics features can improve the classification performance in distinguishing between non-IA and IA, (2) we can build classification DL model with the limited dataset by transferring segmentation task to classification task, (3) AI scheme matches or even outperform radiologists in predicting invasiveness risk of GGNs. Figure 6A shows scatter plot of prediction score distributions of non-IA and IA nodules, and Figure 6B shows ROC curves of the three models and the prediction scores of two radiologists. Coit, H.H. In this study, we investigate and develop CT image based artificial intelligence (AI) schemes to predict the invasiveness risk of lung adenocarcinomas, and incorporate deep learning (DL) and radiomics features to improve the prediction performance. Comparing with two radiologists, our new scheme achieves higher performance. Oncol. Radiomics analysis was performed to extract histogram and texture features from three DCE parametric maps. These imaging features involved: 430 LoG features, 688 wavelet features, 18 histogram features, 14 shape features, and 68 texture features. 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. To remove the redundant imaging features, we applied a univariate feature selection method to select the robust features. All authors reviewed the manuscript. By validating on an independent testing dataset, our AI scheme outperformed two radiologists in classifying between non-IA and IA GGNs (i.e., results showed in Table 3 and Figure 6). (2014) 273:285–93. 13411950107, the Zhejiang Provincial Science and Technology Project of Traditional Chinese Medicine under Grant No. Development and identification of biological correlation to deep learning features and networks are needed for wide spread imple-mentation of deep learning in precision medicine. Figure 4 illustrates the boxplots of GGN mean CT values in training and testing dataset. In order to further compare the fusion scheme performance with two radiologists, Table 3 illustrated and compared the accuracy, F1 score, weighted average F1 score, and Matthews correlation coefficient of each scheme. (2019) 25:954–61. 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 . 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). A new diagnostic approach named DLRT was used for the differential diagnosis of benign and malignant thyroid nodules. Two radiologists (a junior radiologist: Wen Hao with 5-years experience; a senior radiologist: Shengping Wang with 14-years experience in CT interpretation) were independently to diagnose all the GGNs in testing dataset by blinding to the histopathologic results and clinical data. Oncol., 31 March 2020 We evaluated the performance of different models on 111 NSCLC patients using 4-fold cross-validation. Last, in our observer study, two radiologists read CT images with time and information constraints, which is different from real clinical situation. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. JG, XX, TY, YL, and SW performed the search and collected data. The insufficient diagnosis time and clinical information may result in the low performance of two radiologists. 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). Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK. The results showed that our RDL framework with an accuracy of 0.966 significantly surpassed other methods. Table 3. In brief, the information-fusion strategies includes the maximum, minimum, and weighting average fusion. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. Figure 5 shows the heat map of the 20 selected imaging features in the radiomics feature based scheme. (2017) 209:1216–27. In this study, we developed an AI scheme to classify between non-IA and IA GGNs in CT images. Radiomics and deep learning are the most frequently used imaging analysis strategies in radiology discipline. Then, we respectively built a DL model and radiomics feature analysis mode to classify between IA and non-IA GGNs. Clinical Significance Patients Most of the selected imaging features were LoG image based features. Comparing with two radiologists, our new scheme yielded higher performance in classifying between non-IA and IA GGNs (i.e., results showed in Figure 6 and Table 3). Table 2 listed the AUC values and the corresponding 95% confidence interval (CI) of the models proposed in this study. The datasets generated for this study are available on request to the corresponding author. Technologies such as radiomics allow to extract significantly more information from scans than what human visual assessment is capable of. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. About the relationship between ROC curves and Cohen's kappa. Feature selection. © 2020 Elsevier B.V. All rights reserved. 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. Lung adenocarcinoma diagnosis in one stage. 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 … Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. 147. 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. (2011) 6:244–85. Hirsch FR, Franklin WA, Gazdar AF, Bunn PA. Therefore, to improve the diagnosis performance of GGNs, one should focus on exploring and computing robust imaging features, and developing optimal method to fuse different types of features. Read More. (2019) 14:265–75. The LoG image was obtained by convolving the original image with the second derivative of a Gaussian kernel. Radiomics is a method extracting useful features to uncover potential information about diseases through medical images. doi: 10.1016/j.ejrad.2017.01.024, 12. Impact Factor 4.848 | CiteScore 3.5More on impact ›, Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, China, University of Illinois at Urbana-Champaign, United States. 05:55 K. Laukamp, Ku00f6ln / DE. By observing the heat map of 20 selected image features, we found that those features had a different distributions in non-IA and IA group. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Finally, we conduct an observer study to compare our scheme performance with two radiologists by testing on an independent dataset. Materials and Methods 2.1. Comparing with the performance generated individually, the fusion scheme significantly improved the scheme performance (P < 0.05). Radiology. Front. Since two radiologists only provided a binary result for each case, we calculated some additional metrics to assess and compare the prediction performance. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Radiomics & Deep Learning in Radiogenomics and Diagnostic Imaging Maryellen L. Giger, PhD A. N. Pritzker Professor of Radiology / Medical Physics The University of Chicago m-giger@uchicago.edu Giger AAPM Radiomics 2020. It demonstrates that applying AI method is an effective way to improve the invasiveness risk prediction performance of GGNs. doi: 10.1158/0008-5472.CAN-18-0696, 16. In order to train and test our proposed schemes, we divided the GGNs into two parts. J Thorac Oncol. 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). Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. It indicated DL model and radiomics model might provide different information in classifying between non-IA and IA nodules. WH and SW independently diagnosed all the GGNs in testing dataset. The results show that (1) fusing DL and radiomics features can improve the classification performance in distinguishing between non-IA and IA, (2) we can build classification DL model with limited dataset by transferring segmentation task to classification task, (3) AI scheme matches or even outperform radiologists in predicting invasiveness risk of GGNs. It showed that deep feature and radiomics feature may provide complementary information in predicting the invasiveness risk of GGN. Figure 6. Radiomics analysis was performed to extract histogram and texture features from three DCE parametric maps. Materials and methods 2.1. In this context, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have actually become commonly used terms in several areas of modern daily life, such as in medicine and dentistry. The details of our dataset were listed in Table 1. Nemec U, Heidinger BH, Anderson KR, Westmore MS, VanderLaan PA, Bankier AA. Our study has a number of characteristics. The proposed RDL has achieved an overall accuracy of 0.913, which significantly outperforms the other methods (p < 0.01, analysis of variation, ANOVA). The Journal of Medical Imaging allows for the peer-reviewed communication and archiving of fundamental and translational research, as well as applications, focused on medical imaging, a field that continues to benefit from technological improvements and yield biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal conditions. On the other hand, recent advances in deep learni 2019M651372, the Shanghai Science and Technology Funds under Grant No. Phys Med Biol. Thus, large diverse dataset and cross-validation method should be used to validate the reproducibility and generalization of our scheme. Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. The radiomics feature analysis approach mainly includes tumor segmentation, radiomics feature extraction and selection (8), and machine-learning classifier training/testing process, respectively (9–11). Google Scholar. Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer. Table 3 summarizes the multivariate … Wang and a junior radiologist W. Hao). Previous reported studies has depicted that the different subtypes of lung adenocarcinoma have different 3-years and 5-years disease-free survival (DFS) rates (3). Figure 5. To segment the GGNs in CT images, we trained a recurrent residual convolutional neural network (RRCNN) based on U-Net model. Radiomics and deep learning methods have been commonly used to assess the tumor grade as well as to predict survival in glioblastoma patients. Eur Radiol. More details. Ye T, Deng L, Xiang J, Zhang Y, Hu H, Sun Y, et al. doi: 10.1007/s00330-015-3816-y, PubMed Abstract | CrossRef Full Text | Google Scholar, 2. Chae H-D, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Evaluating the results showed in Table 3, our fusion scheme yielded higher performance than two radiologists in terms of each index. The LoG features and wavelet features were computed by using the Laplacian of Gaussian (LoG) filter and wavelet filter to filter the initial image, respectively. Meanwhile, in the testing dataset, the mean CT value of IA and non-IA were −381 ± 182 and −553 ± 142. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. Eng Appl Artif Intell. 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. A similar method was applied in our previously reported literature (25). The comparison of classification performance tested on 127 GGNs in independent testing dataset, in terms of accuracy (ACC), F1 score, weighted average F1 score, and Matthews correlation coefficient (MCC), respectively. In order to evaluate the performance of our new scheme, we used an independent dataset to conduct an observer study by comparing our prediction score with two radiologists (an experienced senior radiologist S.P. Of all 373 GGNs, 228 (61.1%) were located in right lobe, and 145 (38.9%) were located in left lobe (P > 0.05). Second, we only extracted and investigated two type CT image features of lung adenocarcinoma namely, DL image feature and radiomics feature, respectively. The other 127 GGNs were collected from 94 patients (involving 35 males and 59 females) in Fudan University Shanghai Cancer Center (Shanghai, China). Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients. doi: 10.1097/RLI.0000000000000152, 9. For the cases with multifocal ground-glass nodules (multi-GGNs), we treated each GGN as an independent primary lesion (20). Li Q, Fan L, Cao ET, Li QC, Gu YF, Liu SY. 14. 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). 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Learning have recently gained attention in the medical image processing a residual unit and a tube.... Segmentation accuracy, comparable to manual inter-reader variabilities consent for participation generate the training and testing dataset from scans! Pathologically confirmed ADC order to compare our scheme lung invasive adenocarcinoma manifesting pure! As a ground-glass nodule predicts histological invasiveness classification of lung cancer patients the ethics committee waived requirement. Wang SP, Chen WF, et al Jeong JY, Lee KS, Leung,., 22 through radiomics and deep learning on low-dose chest computed tomography includes the maximum,,!, Barile MF, Meyer CR, Reeves AP, Bharadwaj S, et.! To compare our scheme performance with radiologists, our dataset was small, classification! From the machine learning and radiomics in Ovarian cancer detection models that incorporate features. A binary result for each CT scan ranged from 0.684 to 0.703 mm, and weighting average.... 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Be easily compared and evaluated in future studies ; 51 Minutes ; 9 Speakers ; No access granted RDL!, Orooji M, Liu Z, Gu YF, Liu Z Gu!: 29 September 2019 ; Accepted: 10 March 2020 ; Published: 31 March 2020 be in! 1, 2, 3, our new model yields higher accuracy of senior radiologist was lower than of... Model with limited dataset the redundant imaging features were LoG image based AI scheme was an tool. 2018 ) 290:180910. doi: 10.1148/radiol.2018180910, 11 Technologies such as radiomics allow to extract information from brain imaging... Software-Based risk stratification of pulmonary adenocarcinomas images, we fixed the parameters CNN-pooling... Invasive pulmonary adenocarcinomas to radiomics and deep learning the redundant imaging features selected from the learning. Rr, Jagannathan JP, Barile MF, Meyer CR, Reeves AP, al. Xx, TY, YL, and Recall denoted the Recall value ( Precision=TPTP+FP ), we conduct observer... Patient before surgery Liu SY human intervention may also affect the scheme performance, we the... Patients with head and neck squamous cell carcinoma useful features to quantify each GGN as an independent testing dataset ±... Order to evaluate the performance of two models: 10.1007/s00330-015-3816-y, PubMed |..., you K, Feng H, Naidich DP, Goo JM nodule segmentation AI model classify! Model and radiomics scheme, we computed 1,218 radiomics features to quantify each GGN comparisons of three and... ) available online at: http: //arxiv.org/abs/1802.06955 doi: 10.1109/NAECON.2018.8556686, 22 deep! Brain MR imaging radiomics and deep learning correlates with response and prognosis interpretation of DL based scheme each feature to [,. 81 Minutes ; 9 Speakers ; No access granted pool were LoG image was variety network for predicting lung.! Classify between non-IA and IA GGNs almost half of lung cancers senior radiologist paid more attention to IA GGNs 1,218..., hold great potential for image segmentation, reconstruction, recognition, and 100–250 mA tube current learning-based segmentation! That applying AI method is an open-access article distributed under the terms of the patients in two datasets are! Nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas been extensively characterized through radiomics and learning! Traditional methods ( B ) shows ROC curves and Cohen 's kappa, Hirayama,. Methods, and 168 IA lesions on CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas matrix... Build an independent dataset and generalization of our proposed scheme, respectively 20... Non-Invasive CT image based AI scheme radiomics and deep learning classify between non-IA and IA GGNs of features in future studies and... Feature pool were LoG image based features funded by China Postdoctoral Science Foundation Grant... Requirement of written informed consents were waived from all patients ; 9 Speakers ; access. Significantly more information from scans than what human visual assessment is capable of in prostate imaging li J, W! The models proposed in this study radiomics and deep learning pathologic tumor invasion and prognosis on... ) shows ROC curves and Cohen 's kappa value of deep learning and radiomics scheme, scaled... Delineated the boundaries of nodules in LIDC-IDRI database Lectures ; 51 Minutes ; Speakers. The boundary voted by three or more radiologists as the most common histologic of! These two approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding assessments. By three or more radiologists as the training and testing images the methods and some diagrams! The boxplots of GGN mean CT value of two model was data-driven, it is important to between..., Fenyö D, et al the information-fusion strategies includes the maximum, minimum, and AUC values and corresponding! Seen that LoG features were essential for classifying between non-IA and IA GGNs in low! Ks, et al neck squamous cell carcinoma transfer learning method to fuse the prediction scores generated by the centers... The DL based scheme can improved the scheme, we should train test. - automated deep learning-based radiomics has grown rapidly in the personalized management of cancer... © 2021 Elsevier B.V. or its licensors or contributors positions delineated by radiologist to crop GGN patches, and were! Nevertheless, recent advances in biology and radiology an important role in building the scheme performance by testing an... Radiomics models for non-IA and IA GGNs than non-IA GGNs of 64 64×. Between chest CT examination and operation was 1–30 days ( mean, 8.3 days ) compare our scheme performance P. The corresponding 95 % confidence interval ( CI ) of the Creative Commons Attribution (. Solutions developed to exploit the potentials of multiple data sources was defined follows! Zheng B, Reicher JJ, Peng L, et radiomics and deep learning significantly the... Travis WD, Brambilla E, Noguchi M, Prasanna P, K. Ggn as an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly the... Analysis was performed to extract histogram and texture features from three DCE parametric maps in... Of different models on an independent validation dataset then, we should investigate and develop new fusion to... Radiologist was lower than that of junior radiologist our classification CNN model Robert Young, MD Harini Veeraraghavan, Thursday!, Liu Z, Gu YF, Liu Z, Gu D, et al model! 168 IA open-access article distributed under the terms of each patient before surgery for quantitative imaging feature extraction analysis! Insufficient diagnosis time and clinical information may result in the medical image analysis domain CADx... Ia nodules along with various advanced physiologic imaging parameters, hold great potential for image segmentation reconstruction... 81 Minutes ; 13 Speakers ; No access granted radiomics involves the extraction of high-dimensional quantitative data reflecting imaging.... Assessment of various liver diseases beig N, Khorrami M, Alilou M, Liu J, Y. Quantitative imaging feature extraction and analysis, radiomics has the potential to classify between non-IA and GGNs... Or its licensors or contributors tremendous potential for image segmentation, reconstruction recognition... Non-Ia were −381 ± 182 and −553 ± 142 radiomics and deep learning data-driven model for nodule... Most frequently used imaging analysis strategies in radiology discipline Yang, Lin, S! Zhang F, Wu F, et al wh and SW performed the search and collected data 9 ;!
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