Synergistic role of molecular imaging and genomics in thyroid cancer management: from diagnosis to personalized treatment.

Abstract

The management of thyroid cancer has advanced through the integration of molecular imaging, genomic profiling, and bioinformatics, supporting precision oncology across diverse subtypes. Although radioiodine imaging and therapy remain standard for differentiated thyroid cancers, efficacy can be limited in refractory cases due to heterogeneous iodine uptake. Advances including dosimetry-guided radioiodine therapy, hybrid imaging modalities (SPECT/CT, PET/CT, PET/MRI), and novel radiotracers (FDG, NaF) may improve diagnostic accuracy and therapeutic decision-making, particularly for aggressive tumors. Genomic profiling has reshaped tumor classification, prognosis, and therapy selection by identifying key alterations in BRAF, RAS, RET/PTC, PAX8/PPARγ, TERT, TP53, ALK, and NTRK. Targeted inhibitors against BRAF, RET, and NTRK demonstrate clinical benefit, while synergistic mutations such as BRAF V600E with TERT promoter highlight tumor complexity and may support investigation of combination strategies. Recent studies have utilized bioinformatics and multi-omics analyses for high-throughput mutation mapping, pathway analysis, and biomarker discovery, leveraging patient genomic data from TCGA and cBioPortal with visualization via R packages such as ggplot2, dplyr, ggrepel, and tidyr. Pathway analyses using GO, KEGG, and Reactome databases revealed involvement in extracellular matrix organization, cell junction assembly, PI3K-Akt signaling, and collagen formation. Integrating insights from molecular imaging, genomics, and computational biology enhances understanding of tumor biology, supports risk stratification, and informs the design of personalized therapies. Future directions include machine learning-driven data integration and expanded clinical application of next-generation sequencing and hybrid imaging to improve patient stratification and management.