Treffer: Accurate Lung Cancer Prediction From CT Scans Using Advanced Deep Learning Methods.
Original Publication: New York, N.Y. : Masson Pub. USA, c1982-
Wankhade S, Vigneshwari S. A novel hybrid deep learning method for early detection of lung cancer using neural networks. Healthcare Analytics. 2023;3:100195.
Huang S, Arpaci I, Al-Emran M, et al. A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability. Multimedia Tool Appl. 2023;82:34183–34198.
Nafea AA, Ibrahim MS, Shwaysh MM, et al. A Deep Learning Algorithm for lung cancer detection using EfficientNet-B3. Wasit J Comput Math Sci. 2023;2:68–76.
Mikhael PG, Wohlwend J, Yala A, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol. 2023;41:2191–2200.
Zhang H, Xi Q, Zhang F, et al. Application of deep learning in cancer prognosis prediction model. Technol Cancer Res Treat. 2023;22:15330338231199287.
Oh S, Kang SR, Oh IJ, et al. Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients. BMC Bioinform. 2023;24:39.
Lavanya C, Pooja S, Kashyap AH, et al. Novel biomarker prediction for lung cancer using random forest classifiers. Cancer Inform. 2023;22:11769351231167992.
Quanyang W, Yao H, Sicong W, et al. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med. 2024;13:7140.
Cheng N, Liu J, Chen C, et al. Prediction of lung cancer metastasis by gene expression. Comput Biol Med. 2023;153:106490.
Jan N, Sofi S, Qayoom H, et al. Targeting breast cancer stem cells through retinoids: A new hope for treatment. Crit Rev Oncol Hematol. 2023;192:104156.
Zhu L, Shi H, Wei H, et al. An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images. EBioMedicine. 2023;87:104426.
Ellen JG, Jacob E, Nikolaou N, et al. Autoencoder-based multimodal prediction of non-small cell lung cancer survival. Sci Rep. 2023;13:15761.
Qayoom H, Alkhanani M, Almilaibary A, et al. A network pharmacology-based investigation of brugine reveals its multi-target molecular mechanism against breast cancer. Med Oncol. 2023;40:202.
Lou N, Cui X, Lin X, et al. Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data. Transl Lung Cancer Res. 2024;13:706.
Sakthi A, Rani P. Detection of movement disorders using multi-SVM. Global J Comput Sci Technol. 2023;13:23–25.
Lam S, Wynes MW, Connolly C, et al. The International Association for the Study of Lung Cancer early lung imaging confederation open-source deep learning and quantitative measurement initiative. J Thorac Oncol. 2024;19:94–105.
Li B, Yang L, Zhang H, et al. Outcome-supervised deep learning on pathologic whole slide images for survival prediction of immunotherapy in patients with non–small cell lung cancer. Mod Pathol. 2023;36:100208.
Singh J. Automated lung cancer detection using 3D CNN architecture for CT imaging. J Digit Imaging. 2024;37:280–290.
Zhao W. Multi-scale feature extraction for enhanced lung cancer detection using deep learning. Med Image Anal. 2024;88:102496.
Gupta S. Ensemble learning for automated lung nodule detection and classification from CT scans. J Healthc Eng. 2023:572346.
Ali M. Predictive modelling for lung cancer risk assessment from CT images. BMC Med Imaging. 2024;24:12.
Huang L. Combining deep learning and radiomics for improved lung cancer diagnosis. Front Oncol. 2023;13:987654.
Venkatesh C, Chinna Babu J, Kiran A, et al. A hybrid model for lung cancer prediction using patch processing and deep learning on CT images. Multimedia Tool Appl. 2024;83:43931–43952.
Tong Y, Arimura H, Yoshitake T, et al. Prediction of consolidation tumor ratio on planning CT images of lung cancer patients treated with radiotherapy based on deep learning. Appl Sci. 2024;14:3275.
Deshpande P, Bhatt MW, Shinde SK, et al. Combining handcrafted features and deep learning for automatic classification of lung cancer on CT scans. J Artif Intell Technol. 2024;4:102–113.
Park J, Rho MJ, Moon MH. Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS ONE. 2024;19:0300442.
Kumar V, Prabha C, Sharma P, et al. Unified deep learning models for enhanced lung cancer prediction with ResNet-50–101 and EfficientNet-B3 using DICOM images. BMC Med Imaging. 2024;24:63.
Hassan A, Khan H, Ali A, et al. An enhanced lung cancer identification and classification based on Advanced Deep Learning and Convolutional Neural Network. Bull Business Economics. 2024;13:136–141.
Kim S, Lim JH, Kim CH, et al. Deep learning–radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients. Sci Rep. 2024;14:922.
Aslani S, Alluri P, Gudmundsson E, et al. Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning. Comput Med Imaging Graph. 2024;116:102399.
Qu W, Chen C, Cai C, et al. Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study. Front Immunol. 2024;15:1327779.
Zhang X, Zhang G, Qiu X, et al. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res. 2024;12:12.
Weitere Informationen
Objectives: Accurate lung cancer prediction from CT scans using advanced deep learning methods is crucial for improving early diagnosis and treatment outcomes, as it harnesses innovative algorithms to enhance the detection and classification of malignant lesions in imaging data. The comprehensive approach for accurate lung cancer prediction from CT scans using advanced deep learning methods. Lung cancer remains one of the leading causes of cancer-related deaths globally, emphasizing the need for early and precise diagnosis.
Methods: They propose a multistage framework that integrates state-of-the-art techniques, including hybrid Graph Convolutional Networks (GCNs) and Conditional Random Fields (CRFs) for image segmentation, followed by an innovative feature extraction pipeline utilizing Capsule Networks (CapsNets), Siamese Neural Networks, and Hybrid Deep Autoencoders. This combination allows for the effective identification of lung regions and the detection of potential lesions, ensuring high segmentation accuracy and robustness against noise.
Results: The feature extraction implements a refined classification strategy that merges a Hybrid CNN-Transformer Model with Graph Neural Networks (GNNs). This dual approach leverages CNNs for capturing local patterns and transformers for modelling long-range dependencies, enhancing the ability to recognize subtle features indicative of malignancies. GNNs further contribute by modelling spatial and relational information among extracted features, facilitating a deeper understanding of the lung's complex anatomic structures.
Conclusions: The proposed technique also leads with 91%, compared with LSTM's 80%, FNN's 70%, and RNN's 70%, highlighting its ability to minimize false positives, implemented using Python software. The future scope for accurate lung cancer prediction from CT scans using advanced deep learning methods includes the development of more sophisticated algorithms that integrate multimodal imaging data, enhancing diagnostic precision, and personalization of treatment plans.
(Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
The authors declare no conflicts of interest.