Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

OBJECTIVE

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

METHODS

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

RESULTS

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

CONCLUSIONS

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

The role of interventional radiology and molecular imaging for near infrared photoimmunotherapy

Near infrared photoimmunotherapy (NIR-PIT) is a recently approved cancer therapy for recurrent head and neck cancer. It involves the intravenous administration of an antibody-photoabsorber (IRDye700DX: IR700) conjugate (APC) to target cancer cells, followed 24 h later by exposure to near infrared light to activate cell-specific cytotoxicity. NIR-PIT selectively targets cancer cells for destruction and activates a strong anticancer host immunity. The fluorescent signal emitted by IR700 enables the visualization of the APC in vivo using fluorescence imaging. Similarly, the activation of IR700 during therapy can be monitored by loss of fluorescence. NIR-PIT can be used with a variety of antibodies and therefore, a variety of cancer types. However, in most cases, NIR-PIT requires direct light exposure only achieved with interstitial diffuser light fibers that are placed with image-guided interventional needle insertion. In addition, the unique nature of NIR-PIT cell death, means that metabolic molecular imaging techniques such as PET and diffusion MRI can be used to assess therapeutic outcomes. This mini-review focuses on the potential implications of NIR-PIT for interventional radiology and therapeutic monitoring.

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients

OBJECTIVE

This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE.

METHODS

We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making.

RESULTS

We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r =  - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965.

CONCLUSIONS

Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.