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Carcinoma, Non-Small-Cell Lung

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Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

Frontiers in immunology
OBJECTIVE: Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunothera...

A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.

Radiation oncology (London, England)
PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of...

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Scientific reports
Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their ...

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

BMJ open respiratory research
BACKGROUND: The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undetermined. This study aimed to elucidate the impact of the number of dissected lymph nodes on the risk o...

Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

Annals of medicine
INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters ...

Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.

Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.

British journal of cancer
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have emerged as a crucial treatment option for patients with advanced NSCLC. However, only a subset of pati...

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF), despite advanced RT. This study integrates patient-specific information to develop learning-based m...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

BioFactors (Oxford, England)
Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph n...