Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

Abstract

Purpose To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). Material and methods Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. Results Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. Conclusion This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

“…Table 4 provides a summary of the seven studies predicting PC recurrence following RP (n = 2, 28.6%) [2,30] and following radiation (n = 5, 71.4) [3,[32][33][34][35]. Of these seven studies, six (86.0%) used PSMA PET/CT-derived radiomics to primarily predict biochemical recurrence (BCR), treatment response, and overall survival.…”

“…Three studies (42.9%) reported on BCR [2,30,32], three (42.9%) reported on treatment response [3,12,33], and three (42.9%) reported on overall survival (OS) [32,34,35]. Assadi et al used both BCR and OS as primary endpoints [32].…”

“…Prostate cancer (PC) continues to be the most common non-cutaneous malignancy among men [1][2][3], demanding continuous advancements in diagnostic and prognostic methodologies. Over the past few decades, significant strides have been made in the realm of medical imaging techniques, aiding early detection, precise localization, and improvement in risk stratification for PC patients.…”

“…Not only can these radiomic features (i.e., textural, morphological, functional, or statistical) be used to characterize subvisual patterns in tumor morphology and the microenvironment [10], but they can also be aggregated into models to predict long-term treatment outcomes [11,12]. Within the context of PC, similar models utilizing multiparametric magnetic resonance imaging (mpMRI) [1,2] and ultrasound (U/S) [13] have also demonstrated potential to facilitate disease-tailored treatment planning. In this regard, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in the diagnosis, staging, and treatment of localized PC.…”

Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy

“…This enables professionals to analyze additional information that is not visible to the human eye and allows the creation of predictive mathematical models 8 . Such radiomics models can be used for multiple tasks such as tumor characterization, prediction of treatment response, and prognostic risk assessment [9][10][11][12][13] . Some radiomic features are sensitive to acquisition modes and reconstruction parameters 14 .…”