Nuclear imaging for bone metastases in prostate cancer: The Emergence of Modern Techniques Using Novel Radiotracers

Accurate staging of prostate cancer (PCa) at initial diagnosis and at biochemical recurrence is important to determine prognosis and the optimal treatment strategy. To date, treatment of metastatic PCa has mostly been based on the results of conventional imaging with abdominopelvic computed tomography (CT) and bone scintigraphy. However, these investigations have limited sensitivity and specificity which impairs their ability to accurately identify and quantify the true extent of active disease. Modern imaging modalities, such as those based on the detection of radioactively labeled tracers with combined positron emission tomography/computed tomography (PET/CT) scanning have been developed specifically for the detection of PCa. Novel radiotracers include18F-sodium fluoride (NaF),11C-/18F-fluorocholine (FCH),18F-fluordihydrotestosterone (FDHT),68Gallium and18F-radiolabeled prostate-specific membrane antigen (e.g.,68Ga-PSMA-11,18F-DCFPyL). PET/CT with these tracers outperforms conventional imaging. As a result of this, although their impact on outcome needs to be better defined in appropriate clinical trials, techniques like prostate-specific membrane antigen (PSMA) PET/CT have been rapidly adopted into clinical practice for (re)staging PCa. This review focuses on nuclear imaging for PCa bone metastases, summarizing the literature on conventional imaging (focusing on CT and bone scintigraphy—magnetic resonance imaging is not addressed in this review), highlighting the prognostic importance of high and low volume metastatic disease which serves as a driver for the development of better imaging techniques, and finally discussing modern nuclear imaging with novel radiotracers

Targeting PSMA Revolutionizes the Role of Nuclear Medicine in Diagnosis and Treatment of Prostate Cancer

Targeting the prostate-specific membrane antigen (PSMA) protein has become of great clinical value in prostate cancer (PCa) care. PSMA positron emission tomography/computed tomography (PET/CT) is increasingly used in initial staging and restaging at biochemical recurrence in patients with PCa, where it has shown superior detection rates compared to previous imaging modalities. Apart from targeting PSMA for diagnostic purposes, there is a growing interest in developing ligands to target the PSMA-protein for radioligand therapy (RLT). PSMA-based RLT is a novel treatment that couples a PSMA-antibody to (alpha or beta-emitting) radionuclide, such as Lutetium-177 (177Lu), to deliver high radiation doses to tumor cells locally. Treatment with 177Lu-PSMA RLT has demonstrated a superior overall survival rate within randomized clinical trials as compared to routine clinical care in patients with metastatic castration-resistant prostate cancer (mCRPC). The current review provides an overview of the literature regarding recent developments in nuclear medicine related to PSMA-targeted PET imaging and Theranostics

Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer.

IntroductionRadiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients.MethodsPatients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC).ResultsThe CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, pConclusionIn internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability

Preoperative characteristics of all included patients per dataset.

Preoperative characteristics of all included patients per dataset.</p

The optimal machine learning configuration based on the cross-validated AUCs of the training dataset.

The optimal machine learning configuration based on the cross-validated AUCs of the training dataset.</p

Final histopathological results of all included patients who underwent radical prostatectomy and extended pelvic lymph node dissection per dataset.

Final histopathological results of all included patients who underwent radical prostatectomy and extended pelvic lymph node dissection per dataset.</p

The effect of Combat harmonization in the external validation dataset on model performance per machine learning classifier for lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS) prediction.

The effect of Combat harmonization in the external validation dataset on model performance per machine learning classifier for lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS) prediction.</p

The AUC, sensitivity and specificity of the radiomics-based models per outcome.

The AUC, sensitivity and specificity of the radiomics-based models per outcome.</p

ROC-curves of the baseline models in the training dataset and the combined validation dataset.

The pre-radical prostatectomy MSKCC-nomogram for lymph node involvement (LNI; A) and extracapsular extension (ECE; B) prediction and the biopsy baseline model for postoperative Gleason score (GS; C) prediction.</p

Selected features after dimension reduction for the prediction of lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS).

Derived from a Random Forest and Logistic Regression using different machine-learning configurations (Table 1). (DOCX)</p