PET Imaging Moves Beyond Diagnosis to Prognostic and Predictive Applications in Precision Oncology

Oncologic imaging is increasingly using positron emission tomography (搜索)搜索该药品 (PET) not only to detect, stage, and restage tumors, but also to characterize tumor biology, disease burden, target expression, and treatment-induced changes. However, important questions remain regarding which PET-derived biomarkers reliably predict treatment response or resistance and which provide meaningful prognostic information on progression, recurrence, survival, toxicity, and other clinically relevant outcomes.

A new Research Topic from Frontiers (搜索)搜索该词条, titled "Beyond Diagnosis: Prognostic and Predictive Applications of PET in Oncology," aims to examine the prognostic and predictive applications of PET across oncologic settings and to assess its contribution to individualized treatment planning and risk stratification.

Shifting From Detection to Prediction

Recent studies have demonstrated the potential of visual interpretation, semi-quantitative and quantitative parameters, radiomics, artificial intelligence, and integrated clinical-imaging models to improve patient stratification and response assessment. The Research Topic will explore how PET-derived measures can support patient selection, predict response or resistance to systemic and local therapies, monitor treatment-related changes, and identify patients at risk of progression, recurrence, toxicity, or adverse events.

Contributions may also address the comparative value of different tracers, imaging time points, quantitative parameters, radiomic features, artificial intelligence-based methods, and multimodal clinical-imaging models. The Research Topic further seeks to identify methodological and translational barriers to clinical adoption, including reproducibility, standardization, external validation, interpretability, and integration into prospective trials and clinical decision-making.

Scope and Clinical Focus

The Research Topic will consider studies spanning different malignancies, tracers, treatment contexts, and analytical approaches, while emphasizing clinical relevance, methodological rigor, and validation. Submissions focused exclusively on diagnostic detection without prognostic or predictive implications are outside its scope. Likewise, studies that solely measure or report treatment response without evaluating its prognostic or predictive value are also outside its scope.

Welcomed themes include PET biomarkers that predict treatment response, resistance, progression, recurrence, or survival; patient selection and treatment stratification using PET-derived measures; PET for monitoring systemic, radiation, surgical, and targeted therapies; prognostic and predictive value of quantitative and semi-quantitative PET parameters; radiomics, artificial intelligence, and machine learning for PET-based risk assessment; novel PET tracers and assessment of target expression or tumor biology; dynamic, multiparametric, and longitudinal PET imaging; integrated PET, clinical, pathological, molecular, and genomic models; PET-based prediction of treatment-related toxicity and adverse events; and standardization, reproducibility, harmonization, and external validation of PET biomarkers.

Manuscripts consisting solely of bioinformatics or computational analysis of public omics databases without relevant functional validation are out of scope, as are manuscripts that solely measure treatment response without assessing its prognostic or predictive value.

Growing Market and Clinical Demand

The shift toward predictive PET applications coincides with broader growth in molecular imaging. According to Kings Research (搜索)搜索该词条 analysis of the global positron emission tomography (搜索)搜索该药品 market, global revenue is projected to grow from USD 1,130.0 million in 2025 to USD 1,447.4 million by 2032, a compound annual growth rate (CAGR) of 3.61%.

This growth reflects broader changes across healthcare systems, where molecular imaging capabilities are becoming increasingly important for oncology programs, infrastructure planning, and long-term diagnostic capacity. The World Health Organization projects that global cancer cases will exceed 35 million by 2050, representing a 77% increase compared to 2022 levels. In the United States alone, researchers projected 2,041,910 new cancer cases and 618,120 cancer deaths for the year 2025.

Precision Oncology and Theranostics

PET imaging is becoming directly connected to treatment planning through the rise of PSMA (搜索)搜索该靶点-targeted imaging. The U.S. Food and Drug Administration (搜索)搜索该词条 has approved PSMA-targeted PET imaging agents for prostate cancer (搜索)搜索该疾病 diagnostics, accelerating the integration of molecular imaging into oncology decision-making. This is especially relevant in prostate cancer imaging, neuroendocrine tumor (搜索)搜索该疾病 management, and theranostics.

The most common tracer used in PET imaging is fluorodeoxyglucose (FDG) (搜索)搜索该药品, which behaves similarly to glucose inside the body. Cancer cells typically consume energy faster than normal cells, absorbing larger amounts of the tracer and allowing physicians to detect cancer activity before structural abnormalities become visible on conventional imaging systems such as CT or MRI.

Barriers to Clinical Adoption

Despite the promise of PET-derived biomarkers, variation in tracers, acquisition protocols, analytical methods, validation strategies, and clinical endpoints limits reproducibility and implementation. Further investigation is needed to establish robust, clinically actionable PET markers and clarify how they can be integrated into evidence-based precision oncology.

The complexity of PET interpretation also requires specialized expertise. Physicians review PET findings alongside CT scans, laboratory results, medical history, and clinical symptoms to distinguish cancerous activity from non-cancerous metabolic changes, as inflammation, infection, healing tissue, and certain physiological processes may also show increased tracer uptake. This complexity is increasing interest in AI-assisted PET imaging tools that improve diagnostic accuracy and reduce variability across imaging workflows.