Prognostic recurrence analysis method for non-small cell lung cancer based on CT imaging

Xu, Weihua; Duan, Huihong; Nie, Shengdong

doi:10.1117/12.2539428

Cited by 8 publications

(6 citation statements)

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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%

“…In 2019, Wang et al used the radiomics technique to make the preoperative recurrence prediction of NSCLC using principal component analysis (PCA) [11] for feature selection then used several machine learning methods such as decision tree, random forest, etc. to make the classification [12]. In 2020, Lee, et al applied the radiomics technique using the Relief-F method [13] for feature selection and performed classification using machine learning methods [14].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Our results are comparable with those obtained by Wang et al who analyzed CT images from a cohort of 157 NSCLC patients using only handcrafted-radiomic features, which are however operator dependent. In their study, they reached an Accuracy equals to 0.85 [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region

et al. 2023

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Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.

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“…Several machine learning methods have been proposed to develop computer-aided diagnosis. Wang et al (2019) proposed a prognostic recurrence analysis method for non-small cell lung cancer (NSCLC) based on CT image features. The method is to semi-automatically segment the tumor region, extract CT image features such as grayscale, shape and texture, and use principal component analysis (PCA) to reduce the dimension of the extracted feature data and reduce errors caused by redundant information.…”

Section: Introductionmentioning

confidence: 99%

DADFN: dynamic adaptive deep fusion network based on imaging genomics for prediction recurrence of lung cancer

Jia¹,

Wu²,

Hou³

et al. 2023

Phys. Med. Biol.

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Objective. Recently, imaging genomics has increasingly shown great potential for predicting postoperative recurrence of lung cancer patients. However, prediction methods based on imaging genomics have some disadvantages such as small sample size, high-dimensional information redundancy and poor multimodal fusion efficiency. This study aim to develop a new fusion model to overcome these challenges. Approach. In this study, a dynamic adaptive deep fusion network (DADFN) model based on imaging genomics is proposed for predicting recurrence of lung cancer. In this model, the 3D spiral transformation is used to augment the dataset, which better retains the 3D spatial information of the tumor for deep feature extraction. The intersection of genes screened by LASSO, F-test and CHI-2 selection methods is used to eliminate redundant data and retain the most relevant gene features for the gene feature extraction. A dynamic adaptive fusion mechanism based on the cascade idea is proposed, and multiple different types of base classifiers are integrated in each layer, which can fully utilize the correlation and diversity between multimodal information to better fuse deep features, handcrafted features and gene features. Main results. The experimental results show that the DADFN model achieves good performance, and its accuracy and AUC are 0.884 and 0.863, respectively. This indicates that the model is effective in predicting lung cancer recurrence. Significance. The proposed model has the potential to help physicians to stratify the risk of lung cancer patients and can be used to identify patients who may benefit from a personalized treatment option.

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Prognostic recurrence analysis method for non-small cell lung cancer based on CT imaging

Cited by 8 publications

References 0 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

A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region

DADFN: dynamic adaptive deep fusion network based on imaging genomics for prediction recurrence of lung cancer

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