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A Summary of concordance for biomarkers classification between the AI classifiers and the conventional reference methods. B Receiver operating characteristic (ROC) curves, and the calculated Area under the curve (AUC) for EGFR , ALK , BRAF , and MET biomarker classifiers.

Journal: NPJ Precision Oncology

Article Title: Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers

doi: 10.1038/s41698-025-01267-z

Figure Lengend Snippet: A Summary of concordance for biomarkers classification between the AI classifiers and the conventional reference methods. B Receiver operating characteristic (ROC) curves, and the calculated Area under the curve (AUC) for EGFR , ALK , BRAF , and MET biomarker classifiers.

Article Snippet: In some samples, the EGFR mutations were assessed by the real-time PCR IdyllaTM EGFR Mutation Test (Biocartis, Mechelen, Belgium).

Techniques: Biomarker Discovery

A Rate of reference method negatives in the EGFR AI negative group. Negative predictive value (NPV) adjusted based on an assumed prevalence of 15% in the population, using a 2-tier classification of a negative group and a likely positive group combined with intermediate samples. B Occurrences of reference method positivity across the EGFR AI groups. The proportion of reference method positive cases was calculated for each AI group, demonstrating a significant decrease in reference method positives, from a relatively high percentage in the AI likely-positive group to a very low percentage in the AI negative group. Sensitivity was assessed by calculating the Positive Percent Agreement (PPA) using a 2-tier classification, including a negative group and a likely positive group combined with intermediate samples. C Distribution of the test set samples according to their AI scores percentile ranks. Samples were plotted according to the reference method, positive and negative groups for all test set samples, and divided according to the source sites. Samples were plotted according to their AI scores and percentile distribution (see “Methods” for details). Box plots display the median, interquartile range (IQR), and whiskers (extending 1.5 times the IQR). Dashed lines indicate the defined thresholds, dividing the samples into the three-tier classification system. P value was calculated using the Mann–Whitney U test. Sheba test set: negatives (N) = 366, positives (P) = 131; Hoag test set: negatives (N) = 345, positives (P) = 113. D Risk for positivity as a function of the prediction score. Dashed curves represent the 95% confidence interval, while the rug plot above the x -axis depicts individual cases, with pink indicating reference method-negative and green indicating reference method-positive for the alteration. Risk and scores are shown on a logarithmic scale. Vertical lines represent the lower and upper thresholds, dividing the curves into negative, intermediate, and likely positive score categories. For each class, the percentage of the population included in the group and the mean risk of carrying the gene alteration are indicated.

Journal: NPJ Precision Oncology

Article Title: Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers

doi: 10.1038/s41698-025-01267-z

Figure Lengend Snippet: A Rate of reference method negatives in the EGFR AI negative group. Negative predictive value (NPV) adjusted based on an assumed prevalence of 15% in the population, using a 2-tier classification of a negative group and a likely positive group combined with intermediate samples. B Occurrences of reference method positivity across the EGFR AI groups. The proportion of reference method positive cases was calculated for each AI group, demonstrating a significant decrease in reference method positives, from a relatively high percentage in the AI likely-positive group to a very low percentage in the AI negative group. Sensitivity was assessed by calculating the Positive Percent Agreement (PPA) using a 2-tier classification, including a negative group and a likely positive group combined with intermediate samples. C Distribution of the test set samples according to their AI scores percentile ranks. Samples were plotted according to the reference method, positive and negative groups for all test set samples, and divided according to the source sites. Samples were plotted according to their AI scores and percentile distribution (see “Methods” for details). Box plots display the median, interquartile range (IQR), and whiskers (extending 1.5 times the IQR). Dashed lines indicate the defined thresholds, dividing the samples into the three-tier classification system. P value was calculated using the Mann–Whitney U test. Sheba test set: negatives (N) = 366, positives (P) = 131; Hoag test set: negatives (N) = 345, positives (P) = 113. D Risk for positivity as a function of the prediction score. Dashed curves represent the 95% confidence interval, while the rug plot above the x -axis depicts individual cases, with pink indicating reference method-negative and green indicating reference method-positive for the alteration. Risk and scores are shown on a logarithmic scale. Vertical lines represent the lower and upper thresholds, dividing the curves into negative, intermediate, and likely positive score categories. For each class, the percentage of the population included in the group and the mean risk of carrying the gene alteration are indicated.

Article Snippet: In some samples, the EGFR mutations were assessed by the real-time PCR IdyllaTM EGFR Mutation Test (Biocartis, Mechelen, Belgium).

Techniques: MANN-WHITNEY