Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer

Chen, Liyuan; Zhou, Zhiguo; Sher, David J.; Zhang, Qiongwen; Shah, Jennifer; Pham, Nhat-Long; Jiang, Steve B; Wang, Jing

doi:10.1088/1361-6560/ab083a

Cited by 87 publications

(67 citation statements)

References 37 publications

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“…In our previous study, we identified several advantages of our multi-objective, multi-modality, and multi-classifier radiomics model for some outcome prediction applications, such as predicting DM in HNSCC, non-small cell lung cancer, and cervical cancer. 12,29,43,44 However, the previous approaches focused on only one or two of the aspects in the current mCOM, such as a multi-objective model trained with features from a single modality, 44 or a multi-modality model built with a single classifier. 29 In addition, these approaches also required that the best solution be selected manually from the Pareto-optimal solution set for the final prediction.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics-based methods have shown promising performance in HNSCC-related tasks, such as treatment outcome prediction, tumor segmentation, and pathologic classification. [7][8][9][10][11][12] These methods extract handcrafted quantitative features from radiological images, then use machine learning tools to analyze these features and build predictive models. Parmar et al extracted radiomics features from pretreatment X-ray computed tomography (CT) images and built a Cox and logistic regression model for predicting local tumor control (LC) after chemoradiotherapy of HNSCC.…”

Section: Introductionmentioning

confidence: 99%

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A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer

et al. 2020

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Purpose: Locoregional recurrence (LRR) is the predominant pattern of relapse after nonsurgical treatment of head and neck squamous cell cancer (HNSCC). Therefore, accurately identifying patients with HNSCC who are at high risk for LRR is important for optimizing personalized treatment plans. In this work, we developed a multi-classifier, multi-objective, and multi-modality (mCOM) radiomics-based outcome prediction model for HNSCC LRR. Methods: In mCOM, we considered sensitivity and specificity simultaneously as the objectives to guide the model optimization. We used multiple classifiers, comprising support vector machine (SVM), discriminant analysis (DA), and logistic regression (LR), to build the model. We used features from multiple modalities as model inputs, comprising clinical parameters and radiomics feature extracted from X-ray computed tomography (CT) images and positron emission tomography (PET) images. We proposed a multi-task multi-objective immune algorithm (mTO) to train the mCOM model and used an evidential reasoning (ER)-based method to fuse the output probabilities from different classifiers and modalities in mCOM. We evaluated the effectiveness of the developed method using a retrospective public pretreatment HNSCC dataset downloaded from The Cancer Imaging Archive (TCIA). The input for our model included radiomics features extracted from pretreatment PET and CT using an open source radiomics software and clinical characteristics such as sex, age, stage, primary disease site, human papillomavirus (HPV) status, and treatment paradigm. In our experiment, 190 patients from two institutions were used for model training while the remaining 87 patients from the other two institutions were used for testing. Results: When we built the predictive model using features from single modality, the multi-classifier (MC) models achieved better performance over the models built with the three base-classifiers individually. When we built the model using features from multiple modalities, the proposed method achieved area under the receiver operating characteristic curve (AUC) values of 0.76 for the radiomics-only model, and 0.77 for the model built with radiomics and clinical features, which is significantly higher than the AUCs of models built with single-modality features. The statistical analysis was performed using MATLAB software. Conclusions: Comparisons with other methods demonstrated the efficiency of the mTO algorithm and the superior performance of the proposed mCOM model for predicting HNSCC LRR.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer

et al. 2020

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“…In Reference [ 58 ], Nie et al proposed a multi-channel structure of 3D CNN for survival time prediction of Glioblastoma patients using multi-modal head images (T1 weighted MRI and diffusion tensor imaging, DTI). Recently, in Reference [ 82 ], the author presented a hybrid model for the classification and prediction of lymph node metastasis (LNM) in head and neck cancer. They combined the outputs of MaO-radiomics and 3D CNN architecture by using an evidential reasoning (ER) fusion strategy.…”

Section: Applications In 3d Medical Imagingmentioning

confidence: 99%

3D Deep Learning on Medical Images: A Review

Singh

Wang

Gupta

et al. 2020

Sensors

388

150

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The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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“…Future prospective studies will show whether these results can also be achieved in people, but the approach seems promising. Another group achieved promising results in the detection of micrometastases in lymph nodes in head and neck cancers by combining radiomics analysis of CT data and 3-dimensional CNN analysis of 18 F-FDG PET data through evidential reasoning (55).…”

Section: Reportingmentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine

2019

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Despite the great media attention for artificial intelligence (AI), for many health care professionals the term and the functioning of AI remain a "black box," leading to exaggerated expectations on the one hand and unfounded fears on the other. In this review, we provide a conceptual classification and a brief summary of the technical fundamentals of AI. Possible applications are discussed on the basis of a typical work flow in medical imaging, grouped by planning, scanning, interpretation, and reporting. The main limitations of current AI techniques, such as issues with interpretability or the need for large amounts of annotated data, are briefly addressed. Finally, we highlight the possible impact of AI on the nuclear medicine profession, the associated challenges and, last but not least, the opportunities.

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Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer

Cited by 87 publications

References 37 publications

A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer

A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer

3D Deep Learning on Medical Images: A Review

Artificial Intelligence in Nuclear Medicine

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