Initial Institutional Experience with 18F-Fluciclovine PET-CT in Biochemical Recurrence of Prostate Cancer

Application of Metabolic Reprogramming to Cancer Imaging and Diagnosis

Cellular metabolism governs the signaling that supports physiological mechanisms and homeostasis in an individual, including neuronal transmission, wound healing, and circadian clock manipulation. Various factors have been linked to abnormal metabolic reprogramming, including gene mutations, epigenetic modifications, altered protein epitopes, and their involvement in the development of disease, including cancer. The presence of multiple distinct hallmarks and the resulting cellular reprogramming process have gradually revealed that these metabolism-related molecules may be able to be used to track or prevent the progression of cancer. Consequently, translational medicines have been developed using metabolic substrates, precursors, and other products depending on their biochemical mechanism of action. It is important to note that these metabolic analogs can also be used for imaging and therapeutic purposes in addition to competing for metabolic functions. In particular, due to their isotopic labeling, these compounds may also be used to localize and visualize tumor cells after uptake. In this review, the current development status, applicability, and limitations of compounds targeting metabolic reprogramming are described, as well as the imaging platforms that are most suitable for each compound and the types of cancer to which they are most appropriate.

Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study

Objective

This study was aimed at developing a model for predicting postoperative biochemical recurrence of prostate cancer (PCa) using clinical data-CEUS-MRI radiomics and at verifying its clinical effectiveness.

Methods

The clinical imaging data of 159 patients pathologically confirmed with PCa and who underwent radical prostatectomy in Xiangyang No. 1 People's Hospital and Jiangsu Hospital of Chinese Medicine from March 2016 to December 2020 were retrospectively analyzed. According to the 2-5-year follow-up results, the patients were divided into the biochemical recurrence (BCR) group (n = 59) and the control group (n = 100). The training set and test set were established in the proportion of 7/3; 4 prediction models were established based on the clinical imaging data. In training set, the area under the curve (AUC) and decision curve analysis (DCA) by R was conducted to compare the efficiency of 4 prediction models, and then, external validation was performed using the test set. Finally, a nomogram tool for predicting BCR was developed.

Results

Univariate regression analysis confirmed that the SmallAreaHighGrayLevelEmphasis, RunVariance, Contrast, tumor diameter, clinical T stage, lymph node metastasis, distant metastasis, Gleason score, preoperative PSA, treatment method, CEUS-peak intensity (PI), time to peak (TTP), arrival time (AT), and elastography grade were the influencing factors for predicting BCR. In the training set, the AUC of combinatorial model demonstrated the highest efficiency in predicting BCR [AUC: 0.914 (OR 0.0305, 95% CI: 0.854-0.974)] vs. the general clinical data model, the CEUS model, and the MRI radiomics model. The DCA confirmed the largest net benefits of the combinatorial model. The test set validation gave consistent results. The nomogram tool has been well applied clinically.

Conclusion

The previous clinical and imaging data alone did not perform well for predicting BCR. Our combinatorial model firstly using clinical data-CEUS-MRI radiomics provided an opportunity for clinical screening of BCR and help improve its prognosis.

[18F]Fluciclovine PET/CT Improves the Clinical Management of Early Recurrence Prostate Cancer Patients

Simple Summary

In the challenge between increasingly sensitive PET radiopharmaceuticals for the evaluation of prostate cancer patient in biochemical relapse, the choice of the most accurate PET tracer must be guided by literature data, but above all tailored to the patient’s profile. In describing our single-center experience, we aimed to identify biochemical and clinical–histological factors to be considered in patient selection and the semiquantitative parameters that can help the interpretation of malignant from benign lesions, in order to optimize the performance of this imaging method. These data in combination with a significant impact on therapeutic decision making can be useful to further validate the [¹⁸F]Fluciclovine PET/CT clinical application.

Abstract

We investigated the [¹⁸F]Fluciclovine PET/CT reliability in the early detection of recurrent prostate cancer (PCa) and its impact on therapeutic decision making. We retrospectively analyzed 58 [¹⁸F]Fluciclovine PET/CT scans performed to identify early PCa recurrence. Detection rate (DR) and semiquantitative analysis were evaluated in relation to biochemical and clinical–histological features. Clinical follow-up data were collected and considered as gold standard to evaluate sensitivity, specificity, accuracy, positive and negative predictive value (PPV, NPV). The impact of [¹⁸F]Fluciclovine PET/CT on clinical management was also assessed. Overall DR resulted as 66%, while DR was 53%, 28%, and 7% in prostate/bed, lymph nodes, and bone, respectively. DR significantly increased with higher PSA values (p = 0.009) and 0.45 ng/mL was identified as the optimal cut-off value. Moreover, SUVmax and SUVmean resulted significant parameters in interpreting malignant from benign findings. [¹⁸F]Fluciclovine PET/CT reached a sensitivity, specificity, PPV, NPV, and accuracy of 87.10%, 80.00%, 87.10%, 80.00%, and 84.31%, respectively. Therapeutic strategy was changed in 51% of patients. Our results support [¹⁸F]Fluciclovine PET/CT as a reliable tool for early restaging of PCa patients, especially for local recurrence detection, leading to a significant impact on clinical management. Semiquantitative analysis could improve specificity in interpreting malignant from benign lesions.