The Triple-Tracer strategy against Metastatic PrOstate cancer (3TMPO) study protocol

Dual 177 Lu-Prostate-Specific Membrane Antigen and 177 Lu-DOTATATE Therapy in Metastatic Castration-Resistant Prostate Cancer With Neuroendocrine Differentiation

ABSTRACT

We present an 87-year-old man diagnosed with prostate cancer and neuroendocrine differentiation, posttherapy results to consecutive 177 Lu-prostate-specific membrane antigen and 177 Lu-DOTATATE. Despite hormonal therapy and chemoradiotherapy, the patient progressed rapidly, and multiple liver and bone metastases showed regression after 177 Lu-prostate-specific membrane antigen and 177 Lu-DOTATATE treatment. Prostate cancer with neuroendocrine differentiation is resistant to treatments; however, treatment with the combination of 177 Lu-DOTATATE therapy may be promising.

PSMA PET/CT in Castration-Resistant Prostate Cancer: Myth or Reality?

BACKGROUND

prostate-specific membrane antigen (PSMA) ligand PET has been recently incorporated into international guidelines for several different indications in prostate cancer (PCa) patients. However, there are still some open questions regarding the role of PSMA ligand PET in castration-resistant prostate cancer (CRPC). The aim of this work is to assess the clinical value of PSMA ligand PET/CT in patients with CRPC.

RESULTS

PSMA ligand PET has demonstrated higher detection rates in comparison to conventional imaging and allows for a significant reduction in the number of M0 CRPC patients. However, its real impact on patients' prognosis is still an open question. Moreover, in CRPC patients, PSMA ligand PET presents some sensitivity and specificity limitations. Due to its heterogeneity, CRPC may present a mosaic of neoplastic clones, some of which could be PSMA-/FDG+, or vice versa. Likewise, unspecific bone uptake (UBU) and second primary neoplasms (SNPs) overexpressing PSMA in the neoangiogenic vessels represent potential specificity issues. Integrated multi-tracer imaging (PSMA ligand and [18F]FDG PET) together with a multidisciplinary discussion could allow for reaching the most accurate evaluation of each patient from a precision medicine point of view.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation.

Deep learning-based methods have been proposed to substitute CT-based PET attenuation and scatter correction to achieve CT-free PET imaging. Here, the authors present a simple way to integrate domain knowledge in deep learning for CT-free PET imaging.