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Image Search Results


Immunohistochemical Staining (×200 Magnification). (a) PD-L1 Negative; (b) PD-L1 Positive; (c) VEGF Negative; (d) VEGF Positive.

Journal: Frontiers in Immunology

Article Title: Deep learning-based multimodal approach for non-invasive prediction and prognostic analysis of immune and angiogenic biomarkers in extrahepatic cholangiocarcinoma

doi: 10.3389/fimmu.2025.1658122

Figure Lengend Snippet: Immunohistochemical Staining (×200 Magnification). (a) PD-L1 Negative; (b) PD-L1 Positive; (c) VEGF Negative; (d) VEGF Positive.

Article Snippet: The primary antibodies used were the PD-L1 SP142 antibody (Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.) and the VEGF antibody (Luzhou Keyang Biotechnology Co., Ltd.).

Techniques: Immunohistochemical staining, Staining

Prediction of PD-L1 status. (a) Violin plot showing the distribution of predicted scores for PD-L1 positive (1) and negative (0) cases. (b–e) Performance evaluation of PD-L1 prediction in the test cohort: (b) ROC curve, (c) Confusion matrix, (d) Calibration curve, (e) Decision curve analysis.

Journal: Frontiers in Immunology

Article Title: Deep learning-based multimodal approach for non-invasive prediction and prognostic analysis of immune and angiogenic biomarkers in extrahepatic cholangiocarcinoma

doi: 10.3389/fimmu.2025.1658122

Figure Lengend Snippet: Prediction of PD-L1 status. (a) Violin plot showing the distribution of predicted scores for PD-L1 positive (1) and negative (0) cases. (b–e) Performance evaluation of PD-L1 prediction in the test cohort: (b) ROC curve, (c) Confusion matrix, (d) Calibration curve, (e) Decision curve analysis.

Article Snippet: The primary antibodies used were the PD-L1 SP142 antibody (Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.) and the VEGF antibody (Luzhou Keyang Biotechnology Co., Ltd.).

Techniques:

Prognostic analysis results. (a) Cox nomogram for individualized survival probability prediction. The “Points” axis assigns a score for a patient’s predicted PD-L1 and VEGF levels. The sum of these two scores (“Total Points”) is projected downward to obtain the “Linear Predictor” and further to the survival probability axes for 12, 24, and 36 months (Pro, probability). (b) Kaplan-Meier curve analysis of overall survival stratified by the risk groups derived from the multimodal deep learning framework. (c) Kaplan-Meier curve analysis of overall survival based on risk stratification using traditional immunohistochemistry methods.

Journal: Frontiers in Immunology

Article Title: Deep learning-based multimodal approach for non-invasive prediction and prognostic analysis of immune and angiogenic biomarkers in extrahepatic cholangiocarcinoma

doi: 10.3389/fimmu.2025.1658122

Figure Lengend Snippet: Prognostic analysis results. (a) Cox nomogram for individualized survival probability prediction. The “Points” axis assigns a score for a patient’s predicted PD-L1 and VEGF levels. The sum of these two scores (“Total Points”) is projected downward to obtain the “Linear Predictor” and further to the survival probability axes for 12, 24, and 36 months (Pro, probability). (b) Kaplan-Meier curve analysis of overall survival stratified by the risk groups derived from the multimodal deep learning framework. (c) Kaplan-Meier curve analysis of overall survival based on risk stratification using traditional immunohistochemistry methods.

Article Snippet: The primary antibodies used were the PD-L1 SP142 antibody (Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.) and the VEGF antibody (Luzhou Keyang Biotechnology Co., Ltd.).

Techniques: Derivative Assay, Immunohistochemistry