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Survival of NSCLC patients and Venn diagram summarizing the multimodal cohort A OS and PFS Kaplan-Meier survival curve (solid lines) for the whole NSCLC cohort (n = 311 for OS and n = 316 for PFS) with a 95% confidence interval (shaded areas). Patients are stratified with respect to their first-line therapy, either pembrolizumab alone or pembrolizumab + chemotherapy. Log-rank p-values are reported to characterize the separation of the survival curves. B OS and PFS Kaplan-Meier survival curves (solid lines) with 95% confidence interval (shaded areas) and log-rank p-values for the patients with available PD-L1 expression (n = 295 for OS and n = 300 for PFS). Patients are stratified with respect to their PD-L1 status (positive vs negative). C OS Kaplan-Meier survival curves (solid lines) with 95% confidence interval (shaded areas) and log-rank p-values for the 43 patients with available TMB and the 174 patients with available TILs status. For the TMB, patients are stratified with a threshold of 15 mutations per megabase (see Methods). For TILs, patients are stratified with respect to their positive vs negative TILs status. D Overview of the multimodal cohort with a Venn diagram. The four data modalities and their intersections are represented (i.e., PET/CT images, clinical data, pathological slides, and bulk RNA-seq profiles). Source data are provided as a Source Data file.
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Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and int...
Citations
Bronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complement to traditional lung biopsies. However, the similarity in morphology and function of cells in BALF, combined with the diversity of sample processing and analysis methods, can lead to confusion in recognizing and distinguishing these cellular features. This study presents an improved Yolov10 method for the detection and classification of BALF cells, specifically targeting macrophages, lymphocytes, neutrophils, and eosinophils. The backbone network incorporates the PLWA module in place of the PSA module to enhance the acquisition of useful information, and the C2f-DC module replaces the C2f module to improve image feature extraction capabilities. Furthermore, the head network employs the Cross-Attention Fusion module (CAP) to enhance the retrieval of image information. Experimental results demonstrate that the model achieves a mean Average Precision (mAP) of 86.5% and a recall rate of 79.1%, confirming the model’s effectiveness.
