The Role of Positron Emission Tomography and Computed Tomographic (PET/CT) Imaging for Radiation Therapy Planning: A Literature Review

Utility of Targeted Positron Emission Tomography Imaging to Predict Schwannoma Growth in a Murine Tumor Model

OBJECTIVE

To identify if targeted positron emission tomography (PET) imaging with radiolabeled antibodies can predict tumor growth rate and ultimate tumor size in a murine flank schwannoma model.

STUDY DESIGN

Animal research study.

METHODS

Rat schwannoma cells were cultured and implanted into 40 athymic nude mice. Once tumors reached 5 mm in diameter, 30 mice were injected with zirconium-89 labeled antibodies (HER2/Neu, vascular endothelial growth factor receptor 2 (VEGFR2), or IgG isotype). PET/CT was performed, and standardized uptake values (SUV) were recorded. Tumors were serially measured until mice were sacrificed per IACUC protocol. Statistical analysis was performed to measure correlations between SUV values, tumor size, and growth.

RESULTS

Mean tumor sizes in mm3 on Day 0 were 144 ± 162 for anti-HER2/Neu, 212 ± 247 for anti-VEGFR2, and 172 ± 204 for IgG isotype groups respectively. Mean growth rates in mm3 /day were 531 ± 250 for HER2, 584 ± 188 for VEGFR2, and 416 ± 163 for the IgG isotype group. For both initial tumor size and growth rates, there was no significant difference between groups. There were significant correlations between maximum tumor volume and both the SUV max in the HER2 group (p = 0.0218, R2  = 0.5020), and we observed significant correlations between growth rate, and SUV values (p = 0.0156, R2  = 0.5394). Respectively, in the anti-VEGFR2 group, there were no significant correlations.

CONCLUSION

In a murine schwannoma model, immunotargeted PET imaging with anti-HER2/Neu antibodies predicted tumor growth rate and final tumor size. Laryngoscope, 134:1372-1380, 2024.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations

OBJECTIVE

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC).

METHODS

In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets.

RESULTS

This study included 37 women (mean age 56.3 years; age range 36-83 years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging.

CONCLUSIONS

The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Lung Cancer Staging: Imaging and Potential Pitfalls

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.