A Prognostic Risk Score for Prostate Cancer Based on PSMA PET-derived Organ-specific Tumor Volumes

Development of a novel nomogram for patients with SCLC and comparison with other models

BACKGROUND

Though several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.

METHODS

Seven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.

RESULTS

There were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.

CONCLUSIONS

We constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.

Composite Prediction Score to Interpret Bone Focal Uptake in Hormone-Sensitive Prostate Cancer Patients Imaged with [18F]PSMA-1007 PET/CT

Unspecific bone uptake (UBU) related to [18F]PSMA-1007 PET/CT imaging represents a clinical challenge. We aimed to assess whether a combination of clinical, biochemical, and imaging parameters could predict skeletal metastases in patients with [18F]PSMA-1007 bone focal uptake, aiding in result interpretation. Methods: We retrospectively analyzed [18F]PSMA-1007 PET/CT performed in hormone-sensitive prostate cancer (PCa) patients at 3 tertiary-level cancer centers. A fourth center was involved in performing an external validation. For each, a volume of interest was drawn using a threshold method to extract SUVmax, SUVmean, PSMA tumor volume, and total lesion PSMA. The same volume of interest was applied to CT images to calculate the mean Hounsfield units (HUmean) and maximum Hounsfield units. Clinical and laboratory data were collected from electronic medical records. A composite reference standard, including follow-up histopathology, biochemistry, and imaging data, was used to distinguish between PCa bone metastases and UBU. PET readers with less (n = 2) or more (n = 2) experience, masked to the reference standard, were asked to visually rate a subset of focal bone uptake (n = 178) as PCa metastases or not. Results: In total, 448 bone [18F]PSMA-1007 focal uptake specimens were identified in 267 PCa patients. Of the 448 uptake samples, 188 (41.9%) corresponded to PCa metastases. Ongoing androgen deprivation therapy at PET/CT (P < 0.001) with determination of SUVmax (P < 0.001) and HUmean (P < 0.001) independently predicted bone metastases. A composite prediction score, the bone uptake metastatic probability (BUMP) score, achieving an area under the receiver-operating-characteristic curve (AUC) of 0.87, was validated through a 10-fold internal and external validation (n = 89 bone uptake, 51% metastatic; AUC, 0.92). The BUMP score's AUC was significantly higher than that of HUmean (AUC, 0.62) and remained high among lesions with HUmean in the first tertile (AUC, 0.80). A decision-curve analysis showed a higher net benefit with the score. Compared with the visual assessment, the BUMP score provided added value in terms of specificity in less-experienced PET readers (88% vs. 54%, P < 0.001). Conclusion: The BUMP score accurately distinguished UBU from bone metastases in PCa patients with [18F]PSMA-1007 focal bone uptake at PET imaging, offering additional value compared with the simple assessment of the osteoblastic CT correlate. Its use could help clinicians interpret imaging results, particularly those with less experience, potentially reducing the risk of patient overstaging.

Early treatment response assessment with [177Lu]PSMA whole-body-scintigraphy compared to interim PSMA-PET

BACKGROUND

Prostate-specific membrane antigen positron emission tomography (PSMA-PET) is an essential tool for patient selection before radioligand therapy (RLT). Interim-staging with PSMA-PET during RLT allows for therapy monitoring. However, its added value over post-treatment imaging is poorly elucidated. The aim of this study was to compare early treatment response assessed by post-therapeutic whole-body scans (WBS) with interim-staging by PSMA-PET after 2 cycles in order to prognosticate OS.

METHODS

Men with metastasized castration-resistant PC (mCRPC) who had received at least two cycles of RLT, and interim PSMA-PET were evaluated retrospectively. PROMISE V2 framework was used to categorize PSMA expression and assess response to treatment. Response was defined as either disease control rate (DCR) for responders or progression for non-responders.

RESULTS

A total of 188 men with mCRPC who underwent RLT between February 2015 and December 2021 were included. The comparison of different imaging modalities revealed a strong and significant correlation with Cramer V test: e.g. response on WBS during second cycle compared to interim PET after two cycles of RLT (cφ = 0.888, P < 0.001, n = 188). The median follow-up time was 14.7 months (range: 3-63 months; 125 deaths occurred). Median overall survival (OS) time was 14.5 months (95% CI: 11.9-15.9). In terms of OS analysis, early progression during therapy revealed a significantly higher likelihood of death: e.g. second cycle WBS (15 vs. 25 months, P < 0.001) with a HR of 2.81 (P < 0.001) or at PET timepoint after 2 cycles of RLT (11 vs. 24 months, P < 0.001) with a HR of 3.5 (P < 0.001). For early biochemical response, a PSA decline of at least 50% after two cycles of RLT indicates a significantly lower likelihood of death (26 vs. 17 months, P < 0.001) with a HR of 0.5 (P < 0.001).

CONCLUSION

Response assessment of RLT by WBS and interim PET after two cycles of RLT have high congruence and can identify patients at risk of poor outcome. This indicates that interim PET might be omitted for response assessment, but future trials corroborating these findings are warranted.

Combining PSMA-PET and PROMISE to re-define disease stage and risk in patients with prostate cancer: a multicentre retrospective study

BACKGROUND

Prostate-specific membrane antigen (PSMA)-PET was introduced into clinical practice in 2012 and has since transformed the staging of prostate cancer. Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria were proposed to standardise PSMA-PET reporting. We aimed to compare the prognostic value of PSMA-PET by PROMISE (PPP) stage with established clinical nomograms in a large prostate cancer dataset with follow-up data for overall survival.

METHODS

In this multicentre retrospective study, we used data from patients of any age with histologically proven prostate cancer who underwent PSMA-PET at the University Hospitals in Essen, Münster, Freiburg, and Dresden, Germany, between Oct 30, 2014, and Dec 27, 2021. We linked a subset of patient hospital records with patient data, including mortality data, from the Cancer Registry North-Rhine Westphalia, Germany. Patients from Essen University Hospital were randomly assigned to the development or internal validation cohorts (2:1). Patients from Münster, Freiburg, and Dresden University Hospitals were included in an external validation cohort. Using the development cohort, we created quantitative and visual PPP nomograms based on Cox regression models, assessing potential PPP predictors for overall survival, with least absolute shrinkage and selection operator penalty for overall survival as the primary endpoint. Performance was measured using Harrell's C-index in the internal and external validation cohorts and compared with established clinical risk scores (International Staging Collaboration for Cancer of the Prostate [STARCAP], European Association of Urology [EAU], and National Comprehensive Cancer Network [NCCN] risk scores) and a previous nomogram defined by Gafita et al (hereafter referred to as GAFITA) using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) estimates.

FINDINGS

We analysed 2414 male patients (1110 included in the development cohort, 502 in the internal cohort, and 802 in the external validation cohort), among whom 901 (37%) had died as of data cutoff (June 30, 2023; median follow-up of 52·9 months [IQR 33·9-79·0]). Predictors in the quantitative PPP nomogram were locoregional lymph node metastases (molecular imaging N2), distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases), tumour volume (in L), and tumour mean standardised uptake value. Predictors in the visual PPP nomogram were distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases) and total tumour lesion count. In the internal and external validation cohorts, C-indices were 0·80 (95% CI 0·77-0·84) and 0·77 (0·75-0·78) for the quantitative nomogram, respectively, and 0·78 (0·75-0·82) and 0·77 (0·75-0·78) for the visual nomogram, respectively. In the combined development and internal validation cohort, the quantitative PPP nomogram was superior to STARCAP risk score for patients at initial staging (n=139 with available staging data; AUC 0·73 vs 0·54; p=0·018), EAU risk score at biochemical recurrence (n=412; 0·69 vs 0·52; p<0·0001), and NCCN pan-stage risk score (n=1534; 0·81 vs 0·74; p<0·0001) for the prediction of overall survival, but was similar to GAFITA nomogram for metastatic hormone-sensitive prostate cancer (mHSPC; n=122; 0·76 vs 0·72; p=0·49) and metastatic castration-resistant prostate cancer (mCRPC; n=270; 0·67 vs 0·75; p=0·20). The visual PPP nomogram was superior to EAU at biochemical recurrence (n=414; 0·64 vs 0·52; p=0·0004) and NCCN across all stages (n=1544; 0·79 vs 0·73; p<0·0001), but similar to STARCAP for initial staging (n=140; 0·56 vs 0·53; p=0·74) and GAFITA for mHSPC (n=122; 0·74 vs 0·72; p=0·66) and mCRPC (n=270; 0·71 vs 0·75; p=0·23).

INTERPRETATION

Our PPP nomograms accurately stratify high-risk and low-risk groups for overall survival in early and late stages of prostate cancer and yield equal or superior prediction accuracy compared with established clinical risk tools. Validation and improvement of the nomograms with long-term follow-up is ongoing (NCT06320223).

FUNDING

Cancer Registry North-Rhine Westphalia.

Correlation of [68Ga]Ga-PSMA PET/CT response and PSA decline in first-line enzalutamide for metastatic castration-resistant prostate cancer patients

PURPOSE

to assess the utility of response monitoring to enzalutamide by using [68Ga]Ga-PSMA PET in mCRPC patients treated with enzalutamide as first-line therapy.

METHODS

patients underwent [68Ga]Ga-PSMA PET less than 8 weeks before and 3 months after starting enzalutamide. On the basis of EAU/EANM criteria, patients were categorized as PSMA responders (PET-R) or PSMA non-responders (PET-NR), whilst, based on PSA, they were classified as biochemical responders (PSA-R) or non-responders (PSA-NR). Survival analysis was performed using the Cox regression hazard model and the Kaplan-Meier method.

RESULTS

69 patients were considered fully evaluable. We observed 47.8% of concordance between [68Ga]Ga-PSMA PET and PSA monitoring at 3 months after starting enzalutamide. For discordant cases, the PSA reduction has a weak impact on PFS and a significant impact on OS in PET-NR patients, whilst this change has no impact either for PFS and OS in PET-R ones.

CONCLUSIONS

[68Ga]Ga-PSMA PET could be a useful imaging tool for monitoring response to enzalutamide in mCRPC patients, being more informative than PSA in this setting, and possibly better guiding clinicians in therapeutic decisions.

Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning

Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.

PSMA-PET follow-up to assess response in patients not receiving PSMA therapy: Is there value beyond localization of disease?

Introduction: Prostate Specific Membrane Antigen Positron Emission Tomography (PSMA-PET) is routinely used for the staging of patients with prostate cancer, but data on response assessment are sparse and primarily stem from metastatic castration-resistant prostate cancer (mCRPC) patients treated with PSMA radioligand therapy. Still, follow-up PSMA-PET is employed in earlier disease stages in case of clinical suspicion of disease persistence, recurrence or progression to decide if localized or systemic treatment is indicated. Therefore, the prognostic value of PSMA-PET derived tumor volumes in earlier disease stages (i.e., hormone-sensitive prostate cancer (HSPC) and non-[177Lu]Lu-PSMA-617 (LuPSMA) therapy castration resistant prostate cancer (CRPC)) are evaluated in this manuscript. Methods: A total number of 73 patients (6 primary staging, 42 HSPC, 25 CRPC) underwent two (i.e., baseline and follow-up, median interval: 379 days) whole-body [68Ga]Ga-PSMA-11 PET/CT scans between Nov 2014 and Dec 2018. Analysis was restricted to non-LuPSMA therapy patients. PSMA-PETs were retrospectively analyzed and primary tumor, lymph node-, visceral-, and bone metastases were segmented. Body weight-adjusted organ-specific and total tumor volumes (PSMAvol: sum of PET volumes of all lesions) were measured for baseline and follow-up. PSMAvol response was calculated as the absolute difference of whole-body tumor volumes. High metastatic burden (>5 metastases), RECIP 1.0 and PSMA-PET Progression Criteria (PPP) were determined. Survival data were sourced from the cancer registry. Results: The average number of tumor lesions per patient on the initial PET examination was 10.3 (SD 28.4). At baseline, PSMAvol was strongly associated with OS (HR 3.92, p <0.001; n = 73). Likewise, response in PSMAvol was significantly associated with OS (HR 10.48, p < 0.005; n = 73). PPP achieved significance as well (HR 2.19, p <0.05, n = 73). Patients with hormone sensitive disease and poor PSMAvol response (upper quartile of PSMAvol change) in follow-up had shorter outcome (p < 0.05; n = 42). PSMAvol in bones was the most relevant parameter for OS prognostication at baseline and for response assessment (HR 31.11 p < 0.001; HR 32.27, p < 0.001; n = 73). Conclusion: PPP and response in PSMAvol were significantly associated with OS in the present heterogeneous cohort. Bone tumor volume was the relevant miTNM region for OS prognostication. Future prospective evaluation of the performance of organ specific PSMAvol in more homogeneous cohorts seems warranted.

Prostate-Specific Membrane Antigen: Gateway to Management of Advanced Prostate Cancer

Prostate-specific membrane antigen (PSMA) as a transmembrane protein is overexpressed by prostate cancer (PC) cells and is accessible for binding antibodies or low-molecular-weight radioligands due to its extracellular portion. Successful targeting of PSMA began with the development of humanized J591 antibody. Due to their faster clearance compared to antibodies, small-molecule radioligands for targeted imaging and therapy of PC have been favored in recent development efforts. PSMA positron emission tomography (PET) imaging has higher diagnostic performance than conventional imaging for initial staging of high-risk PC and biochemical recurrence detection/localization. However, it remains to be demonstrated how to integrate PSMA PET imaging for therapy response assessment and as an outcome endpoint measure in clinical trials. With the recent approval of 177Lu-PSMA-617 by the US Food and Drug Administration for metastatic castration-resistant PC progressing after chemotherapy, the high value of PSMA-targeted therapy was confirmed. Compared to standard of care, PSMA-based radioligand therapy led to a better outcome and a higher quality of life. This review, focusing on the advanced PC setting, provides an overview of different approved and nonapproved PSMA-targeted imaging and therapeutic modalities and discusses the future of PSMA-targeted theranostics, also with an outlook on non-radiopharmaceutical-based PSMA-targeted therapies.

Standardized PSMA-PET Imaging of Advanced Prostate Cancer

Imaging of advanced prostate cancer is a challenging task, as it requires longitudinal characterization of disease extent in a standardized way to enable appropriate treatment selection and evaluation of treatment efficacy. In the last years, prostate-specific membrane antigen (PSMA)-PET/CT has become the reference standard examination for patients with advanced prostate cancer. Together with the rise of PSMA-PET, standardized frameworks for the reporting of image findings have been proposed, eg, the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) and the structured reporting system for PSMA targeted PET imaging (PSMA-RADS) framework. Therefore, recent evidence on PSMA-PET derived tumor volume as useful a biomarker for outcome prognostication and related frameworks will be discussed in the article. The PROMISE framework recommends quantifying the tumor volume per-organ system, which accounts for the fact that the location of the metastases greatly influence its biological aggressiveness. In addition, changes in PSMA-PET derived tumor volume have been shown to be promising biomarkers for response assessment. Limitations of PSMA-PET will also be discussed because the tumor volume might not always be suited for response assessment. As a pitfall of PSMA-based systems, decreasing PSMA-expression might erroneously be interpreted as response to therapy. Also, especially for patients with limited disease, the tumor volume might not be ideal for response assessment. Therefore, various frameworks have been introduced to objectively measure response to therapy with PSMA-PET. Amongst these, the PSMA-PET progression (PPP) criteria and the response evaluation criteria in PSMA (RECIP) are optimized for earlier and later phenotypes of advanced prostate cancer, respectively. Variables needed to determine PPP or RECIP outcome on PSMA-PET are recorded under the umbrella of PROMISE recommendations. In this article, various reporting and response assessment frameworks are explained and discussed. Also, recent evidence for the relevance of PSMA-PET biomarkers for clinical management and outcome prognostication are shown.

Prognostic Value of PSMA PET/CT in Prostate Cancer

Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein expressed in the majority of prostate cancer (PCa). PSMA has an enzymatic function that makes metabolic substrates such as folate available for utilization by PCa cells. Intracellular folate availability drives aggressive tumor phenotype. PSMA expression is, therefore, a marker of aggressive tumor biology. The large extracellular domain of PSMA is available for targeting by diagnostic and therapeutic radionuclides, making it a suitable cellular epitope for theranostics. PET imaging of radiolabeled PSMA ligands has several prognostic utilities. In the prebiopsy setting, intense PSMA avidity in a prostate lesion correlate well with clinically significant PCa (csPCa) on histology. When used for staging, PSMA PET imaging outperforms conventional imaging for the accurate staging of primary PCa, and findings on imaging predict post-treatment outcomes. The biggest contribution of PSMA PET imaging to PCa management is in the biochemical recurrence setting, where it has emerged as the most sensitive imaging modality for the localization of PCa recurrence by helping to guide salvage therapy. PSMA PET obtained for localizing the site of recurrence is prognostic, such that a higher lesion number predicts a less favorable outcome to salvage radiotherapy or surgical intervention. Systemic therapy is given to patients with advanced PCa with distant metastasis. PSMA PET is useful for predicting response to treatments with chemotherapy, first- and second-line androgen deprivation therapies, and PSMA-targeted radioligand therapy. Artificial intelligence using machine learning algorithms allows for the mining of information from clinical images not visible to the human eyes. Artificial intelligence applied to PSMA PET images, therefore, holds great promise for prognostication in PCa management.

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)

AIM

Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction.

METHODS

A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined.

RESULTS

One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available.

CONCLUSION

Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.

From FDG and beyond: the evolving potential of nuclear medicine

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.