Predicting response to immune checkpoint inhibitor plus chemotherapy in EGFR-mutant lung adenocarcinoma following first-generation TKI resistance: a multicenter deep learning study - Scorecard - MDSpire
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Assessing Treatment Response to Immune Checkpoint Inhibitors Combined with Chemotherapy in EGFR-Mutant Lung Adenocarcinoma After Resistance to First-Generation TKIs: Insights from a Multicenter Deep Learning Analysis
Clinical Scorecard: Assessing Treatment Response to Immune Checkpoint Inhibitors Combined with Chemotherapy in EGFR-Mutant Lung Adenocarcinoma After Resistance to First-Generation TKIs: Insights from a Multicenter Deep Learning Analysis
At a Glance
Category
Detail
Condition
EGFR-mutant lung adenocarcinoma
Key Mechanisms
Resistance to first-generation TKIs without T790M mutation complicates treatment options.
Target Population
Patients with EGFR-mutant lung adenocarcinoma who have developed resistance to first-generation TKIs.
Care Setting
Multicenter retrospective study
Key Highlights
490 patients included in the final analyzable cohort.
2.5D axial model achieved AUCs of 0.885, 0.819, and 0.863 across cohorts.
Study emphasizes the need for non-invasive tools to predict ICI benefit.
Guideline-Based Recommendations
Diagnosis
Molecular profiling required to confirm absence of T790M mutation.
Management
Consider ICI-chemotherapy for patients with EGFR mutations after TKI resistance.
Monitoring & Follow-up
Evaluate treatment response using CT imaging and deep learning models.
Risks
Potential for hyperprogression with ICI therapy in this patient population.
Patient & Prescribing Data
Patients with EGFR-mutant lung adenocarcinoma post-first-generation TKI resistance.
Limited options available after resistance; ICI-chemotherapy may be considered.
Clinical Best Practices
Utilize comprehensive clinical and pathological data for patient selection.
Implement deep learning models for predicting treatment response.
