AIMC Topic: Lung Neoplasms

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Automatic lung dose painting for functional lung avoidance radiotherapy through multi-modality-guided dose prediction.

Physics in medicine and biology
This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.The proposed auto-planning algorithm consists of a no...

A deep learning model to enhance lung cancer detection using 'Dual-Branch' model classification approach.

PloS one
Cancer remains a life-threatening global challenge, with lung cancer ranking among the most devastating forms, impacting millions annually. Early detection and accurate classification are essential for improving patient survival rates, and computed t...

AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.

Journal of cancer research and clinical oncology
PURPOSE: Multidisciplinary team (MDT) consultations are crucial for managing pulmonary nodules, yet face challenges in efficiency, evidence-based decision support, and data utilization within the MDT process. We present an integrated artificial intel...

PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer.

Military Medical Research
BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients t...

Emerging Anti-Cancer and Repurposed Therapies for Overcoming Multidrug Resistance in Lung Cancer.

Medical oncology (Northwood, London, England)
Multidrug resistance (MDR) still constitutes a significant barrier to the effective treatment of lung cancer and makes a significant contribution to the poor clinical results. MDR is explained by a set of mechanisms; increase of drug efflux, metaboli...

Unraveling tissue-specific molecular targets of dihydroartemisinin in non-small cell lung cancer: an integrative machine learning and network pharmacology approach.

Medical oncology (Northwood, London, England)
Non-small cell lung cancer (NSCLC) presents significant therapeutic challenges due to resistance and immune evasion. Dihydroartemisinin (DHA), a derivative of artemisinin, exhibits broad anti-tumor activity, but its molecular targets and mechanisms i...

A teacherless lightweight classification framework for benign and malignant pulmonary nodules based on GAS.

Biomedical physics & engineering express
Deep learning methods have been widely adopted for classifying benign and malignant pulmonary nodules. However, existing models often suffer from high memory usage, computational cost, and large parameter counts. As a result, the development of light...

A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning.

PloS one
Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognos...

Flexible state space modelling for accurate and efficient 3D lung nodule detection.

Biomedical physics & engineering express
Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is critical for reducing lung cancer mortality. While convolutional neural networks (CNNs) and Transformer-based architectures have been widely used for this task, th...