Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

Wang, Chengdi; Shao, Jun; Lv, Junwei; Cao, Yidi; Zhu, Chaonan; Li, Jingwei; Shen, Wei; Shi, Lei; Liú, Dan; Liu, Weimin

doi:10.1016/j.tranon.2021.101141

Cited by 33 publications

(19 citation statements)

References 35 publications

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“…In clinical practice, CT scans are routinely available. Frontier studies combined radiological images and deep learning technology and have become trend in screening, diagnosis, gene prediction, and prognosis of lung cancer (15,25,26). Previous studies proposed deep learning models trained on CT images to predict high PD-L1 expression or EGFR mutated status of NSCLC (17,18).…”

Section: Discussionmentioning

confidence: 99%

Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images

Wang

Ma²,

Shao

et al. 2022

Front. Immunol.

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BackgroundEpidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images.MethodsA multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs).ResultsOur multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825–0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779–0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762–0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775–0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%–49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909–0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886–0.930) for discriminating PD-L1 expression status.ConclusionOur AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention.

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Section: Discussionmentioning

confidence: 99%

Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images

Wang

Ma²,

Shao

et al. 2022

Front. Immunol.

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“…Our study achieved a state-of-the-art performance on nodule detection with recall of 0.9507 and risk classification with ACC of 0.8696 in prospective large populations. Furthermore, it was possible to determine adenocarcinoma subtypes, gene mutation status and prognosis based on noninvasive CT images, reforming the selection of treatment strategies [30][31][32]. Beyond gains in consistency and accuracy, the capacity of deep learning to leverage diverse information has become of prime importance in improving efficiency of lung cancer management.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites

Shao¹,

Wang²,

Liu³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. Methods: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). Results: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. Conclusions: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.

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“…The use of these methods has been proven to be an efficient way to overcome the shortcomings of traditional image analysis on many sub-specialty applications. DL methods in medical image analysis have been applied in MRI tumor grading [8][9][10], thyroid nodule ultrasound classification [11][12][13] and CT pulmonary nodule detection [14][15][16]. However, only a limited number of studies have been performed to analyze the musculoskeletal imaging associated with the lesion.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

Wang

Li²,

Zhu

et al. 2022

Front. Phys.

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Screening of osteochondral lesions of the talus (OLTs) from MR imags usually requires time and efforts, and in most case lesions with small size are often missed in clinical practice. Thereore, it puts forward higher requirements for a more efficient OLTs screening method. To develop an automatic screening system for osteochondral lesions of the talus (OLTs), we collected 92 MRI images of patients with ankle pain from Qilu Hospital of Shandong University and proposed an AI (artificial intelligence) aided lesion screening system, which is automatic and based on deep learning method. A two-stage detection method based on the cascade R-CNN model was proposed to significantly improve the detection performance by taking advantage of multiple intersection-over-union thresholds. The backbone network was based on ResNet50, which was a state-of-art convolutional neural network model in image classification task. Multiple regression using cascaded detection heads was applied to further improve the detection precision. The mean average precision (mAP) that is adopted as major metrics in the paper and mean average recall (mAR) was selected to evaluate the performance of the model. Our proposed method has an average precision of 0.950, 0.975, and 0.550 for detecting the talus, gaps and lesions, respectively, and the mAP, mAR was 0.825, 0.930. Visualization of our network performance demonstrated the effectiveness of the model, which implied that accurate detection performance on these tasks could be further used in real clinical practice.

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Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

Cited by 33 publications

References 35 publications

Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images

Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

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