Lung cancer histology classification from CT images based on radiomics and deep learning models

Marentakis, Panagiotis; Karaiskos, P.; Kouloulias, Vassilis; Kelekis, Nikolaos; Argentos, Stylianos; Oikonomopoulos, Nikolaos; Loukas, Constantinos

doi:10.1007/s11517-020-02302-w

Cited by 97 publications

(40 citation statements)

References 33 publications

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“…We also compared the proposed GGR method with state-ofthe-art methods. [26,28] achieved better performance than handcrafted radiomics-based methods [12,14,17,28] The proposed GGR method improves the performance significantly and achieves an accuracy of 83.28%. The results demonstrated that the use of the genotypeguidance in the training phase is important to learn more useful features and enhance the CT-based recurrence prediction accuracy.…”

Section: Comparison With State-of-art Methodsmentioning

confidence: 91%

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Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer

et al. 2021

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Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after surgery. Recently, many machine learning methods have been proposed for recurrence prediction. The methods using gene expression data achieve high accuracy rates but expensive. While, the radiomics features using computer tomography (CT) image is a cost-effective method, but their accuracy is not competitive. In this paper, we propose a genotype-guided radiomics method (GGR) for obtaining high prediction accuracy at a low cost. We used a public radiogenomics dataset of NSCLC, which includes CT images and gene expression data. Our proposed method is two steps method that uses two models. The first model is a gene estimation model, which is used to estimate the gene expression from radiomics features and deep features extracted from CT images. The second model is used to predict the recurrence using the estimated gene. The proposed GGR method is designed based on hybrid features which is the fusion of handcrafted-and deep learning-based features. The experiments demonstrated that the prediction accuracy can be improved significantly from 78.61% (existing radiomics method) and 79.09% (ResNet50) to 83.28% by the proposed GGR.

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Section: Comparison With State-of-art Methodsmentioning

confidence: 91%

“…In 2018, Gao, et al also proposed the model named densely connected convolution networks, known as DenseNet [24]. Many researchers in the medical field also proved that the deep learning models are excellent at working with medical imaging [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer

et al. 2021

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“…There have been many studies on the imaging-based deep learning model for predicting the histology of lung cancer (i.e., adenocarcinoma, squamous cell carcinoma, and small cell lung cancer), and it has shown relatively good accuracy [27][28][29][30]. However, there are few studies on radiomics-based machine learning or CT-based deep learning models predicting high grade histologic patterns of lung ADC (i.e., micropapillary or solid pattern, MPSol).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Choi

Aum²,

Lee

et al. 2021

Cancers

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We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal–training and internal–validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16–2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.

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“…Most of the studies utilized DL architectures for the analysis of imaging and genomic data with respect to risk prediction and stratification. Indicatively, in [64] , [65] , [66] , [67] , [68] , [69] DL models were trained to classify and detect disease subtypes based on images and genetic data. These data-driven approaches demonstrated the superiority of ML-based frameworks towards exploiting heterogeneous datasets with respect to improved diagnosis and treatment.…”

Section: Literature Reviewmentioning

confidence: 99%

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

Κούρου

Exarchos

Papaloukas

et al. 2021

Computational and Structural Biotechnology Journal

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Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

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Lung cancer histology classification from CT images based on radiomics and deep learning models

Cited by 97 publications

References 33 publications

Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer

Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

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