Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

18F-FDG PET/CT plays a central role in staging, treatment planning, and response assessment for head and neck cancer by providing functional information that complements anatomical CT imaging. However, PET acquisition requires radiotracer administration, specialized infrastructure, and additional cost, limiting its availability for repeated imaging. We present a proof of concept deep learning framework for synthesizing PET like images directly from routine CT scans with the goal of providing complementary metabolic information that may support imaging triage and clinical decision support rather than replace diagnostic PET. Forty-four patients from the publicly available QIN-HEADNECK dataset were retrospectively analyzed using five fold cross-validation. We propose a fully three dimensional dual path architecture consisting of (i) a regression U-Net optimized for voxel-wise quantitative SUV estimation and (ii) a conditional generative adversarial network optimized for realistic PET texture. Their outputs are integrated using hotspot guided Laplacian pyramid blending, allowing quantitative information from the regression pathway to be preserved within metabolically active regions while leveraging adversarial texture synthesis elsewhere. The proposed framework achieved a mean absolute error of 0.00395, PSNR of 39.19 dB, and SSIM of 0.9634 on reconstructed three dimensional PET volumes. Qualitative evaluation demonstrated accurate localization of many FDG-avid lesions while producing anatomically realistic background texture. Consistent with previous CT to PET synthesis studies, the principal limitation was systematic underestimation of SUV within highly metabolically active tumor regions.

The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic imaging. However, existing deep learning methods for PET/CT remain largely task-specific, are often trained on single-center cohorts, or adopt dual-branch fusion schemes that delay cross-modal interaction and underutilize early spatial correspondence between PET and CT. To address these limitations, we present an open-source, multi-center, whole-body FDG PET/CT foundation model utilizing 4,997 harmonized scans from four public datasets. Our framework employs hierarchical UNet-shaped backbones with early channel-wise concatenation, enabling anatomical and metabolic features to interact from the first embedding layer onward. We further introduce a masked autoencoding objective based on zero-mean imputation, combined with a weighted global reconstruction loss. This design avoids non-physical intensity discontinuities at masked-region boundaries that arise from learnable mask tokens. On downstream AutoPET lesion segmentation, the proposed models demonstrate strong label efficiency: with only 10% of the labeled training data, they achieve performance comparable to models trained from scratch on the full dataset. Under extreme 5-shot linear probing, joint PET/CT pretraining also achieves higher Dice scores than separated-modality pretraining. This multi-center foundation model demonstrates label efficiency and cross-modality representation learning for PET/CT tumor segmentation. It provides a robust, open-source basis for advancing automated oncologic imaging, significantly reducing the need for large-scale manual annotations in clinical practice.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival outcomes critically dependent on early and accurate detection. When low-dose computed tomography (LDCT) findings are indeterminate, clinicians typically defer diagnosis pending follow-up CT imaging obtained up to 12 months later, inevitably delaying treatment for patients with malignant nodules. To address this clinical gap, we propose Nodule-Aligned Multimodal (Latent) Diffusion (NAMD), a novel generative framework that synthesizes one-year follow-up nodule CT images conditioned on the baseline CT scan, quantitative nodule biomarkers, and patient-level Electronic Health Records (EHR), enabling timely prediction of nodule malignant progression without requiring actual follow-up scans. NAMD introduces two key contributions: (i) a nodule-aligned latent space regularized so that embedding distances reflect clinically meaningful biomarker changes, and (ii) an LLM-driven multimodal conditioning mechanism encoding heterogeneous EHR data into the diffusion backbone. Evaluated on the National Lung Screening Trial (NLST), our method's synthetic follow-up images achieve an AUROC of 0.805 and an AUPRC of 0.346 for lung nodule malignancy prediction, outperforming both the baseline LDCT performance without virtual follow-up generation, and existing state-of-the-art conditional generation methods, while maintaining competitive image quality. These findings suggest that NAMD enables earlier and more accurate lung cancer diagnosis by capturing clinically meaningful features of nodule progression.