AIMC Topic: Carcinoma, Non-Small-Cell Lung

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Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

BMC medical imaging
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...

Analysis of liquid biopsy by Raman spectroscopy to facilitate prediction of response to immunotherapy in non-small-cell lung cancer (NSCLC) patients.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Immunotherapy has revolutionized lung cancer treatment, yet predicting patient response remains a challenge. This study used Raman spectroscopy to differentiate between non-small-cell lung cancer patients with short-lasting and long-lasting responses...

Gut microbiota and SCFAs improve the treatment efficacy of chemotherapy and immunotherapy in NSCLC.

NPJ biofilms and microbiomes
The role of gut dysbiosis in shaping immunotherapy responses is well-recognized, yet its effect on the therapeutic efficacy of chemotherapy and immunotherapy combinations remains poorly understood. We analyzed gut microbiota in non-small cell lung ca...

Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Nature communications
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remain...

Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: While the TNM staging system provides valuable insights into the extent of disease, predicting postoperative progression in early-stage non-small cell lung cancer (NSCLC) remains a significant challenge. An effective bioimaging prognostic...

Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

BMC medical imaging
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep lea...

Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction.

Scientific reports
Molecular biostatistical workflows in oncology often rely on predictive models that use multimodal data. Advances in deep learning and artificial intelligence technologies have enabled the multimodal fusion of large volumes of multimodal data. Here, ...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Analytical chemistry
Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive diagnostics. Extracellular vesicles (EVs) serve as molecular messengers of the tumor microenvironme...