Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [⁶⁸Ga]Ga-PSMA-11 PET/MRI

Prostate cancer is the second most common cancer in men worldwide, with 1.3 million new cases diagnosed in 2018 [1, 2]. The worldwide incidence rates significantly increased during the last decade, most likely due to the wider application of prostate-specific antigen (PSA) screening [2]. While the 10-year survival rate of prostate cancer is approximately 90%, advanced or late-stage prostate cancer may be life-threatening, in particular, in metastasized stages of the disease [3].

The 5-year risk stratification in patients with primary prostate cancer is mainly built on clinical stage, PSA, and Gleason scores, derived from invasive biopsy samples [4]. Despite having profound effects on treatment planning and, thus, patient’s quality of life, this approach has a number of limitations [3, 5]. First, Gleason scoring relies on biopsy sampling, hence, can neither help assess the entire prostate nor fully characterize the heterogeneity of any pertinent tumor [6]. In addition, transrectal biopsy sampling has been associated with side-effects, such as haematospermia or haematuria [3]. Second, previously published risk classification systems were reported to have the tendency of incorrectly grading primary prostate cancer [3]. In patients with a high risk score and absent metastatic disease, radical prostatectomy is the treatment-of-choice [7] despite the risk of potential overtreatment [8] and at the same time, a 20–40% chance of biochemical recurrence (BCR) [9, 10].

Combined positron emission tomography/computed tomography (PET/CT) or PET/magnetic resonance imaging (PET/MRI) using radiotracers targeting prostate-specific membrane antigen (PSMA) can help to localize suspicious lesions in the prostate [11, 12]. PSMA-PET in combination with CT has been reported to improve primary tumor localization [13] and the diagnosis of recurrent prostate cancer [14, 15] in patients after radical prostatectomy even at low PSA levels [16]. In contrast, PSMA-PET/MRI was shown to support the diagnosis of intermediate and high-risk patients as well as to detect tumor recurrence [13]. Nevertheless, the diagnosis of primary prostate cancer is still based on core-needle biopsy, with non-invasive imaging playing a role in the visual identification of lesions and/or in image-guidance for biopsy sampling [17, 18].

Recently, radiomics have been argued to add value to the diagnostic pathways and patient management [19]. Various studies have been investigating the correlation of PSMA expression and clinical end-points in prostate cancer patients [14, 15]. Furthermore, radiomics combined with machine learning in MRI [20, 21] as well as in PET/CT [22‐24] demonstrated the potential feasibility to establish novel in vivo prediction models for prostate cancer risk assessment.

In light of the potential of combining PET/MR imaging, radiomics and machine learning (ML), the objectives of this study were as follows: (a) to establish and cross-validate prostate lesion low-vs-high risk in vivo ML predictive models built on PET/MRI radiomics, (b) to establish and validate biochemical recurrence and overall patient risk (OPR) models that utilize in vivo ML scores instead of biopsy grades together with PSA and clinical stage, and (c) to compare the above patient risk models to the standard risk stratification.

In this study, we investigated the feasibility of predicting prostate lesion-specific low-vs-high risk built on PET/MRI radiomics and patient-specific biochemical recurrence as well as overall patient risk. We demonstrated excellent cross-validation performances for M_LH (AUC 0.86) as well as for M_BCR (AUC 0.90) and M_OPR (AUC 0.94). Based on the above approaches and our achieved model performances, we consider that our findings have important clinical implications in the field of primary prostate cancer risk assessment as they point towards the feasibility to estimate lesion and patient risks in vivo.

Next to establishing the above models with radiomics and machine learning, conventional [⁶⁸Ga]Ga-PSMA-11 SUV and volume analysis were also conducted. This analysis revealed that SUV_max had the highest predictive power (AUC 0.80) to classify low-vs-high prostate lesions followed by SUV_peak, and SUV_TLG, while lesion volume had no significant predictive power (AUC 0.53). These findings are in line with previous analyses performed in PET/CT [24].

Feature ranking across our Monte Carlo folds demonstrated that [⁶⁸Ga]Ga-PSMA-11 is the most important in vivo feature source to establish lesion risk prediction models compared to ADC and T2w MRI features. The highest-ranking [⁶⁸Ga]Ga-PSMA-11 features were either simple statistical values such as the coefficient of variation and SUV_max or simple second-order textural ones such as information correlation from the GLCM feature category. Information correlation is a first-order GLCM feature reflecting on the information content (a.k.a. entropy) of voxel neighborhood connectivity occurrences; thus, it is a basic heterogeneity descriptor. This feature was previously also identified as highly robust across various PET imaging centers [36]. The feature ranking across MC folds identified SUV_peak, SUV_TLG, and volume as low-ranking; however, SUV_max was among the highest ranking ones. While the potential of PSMA SUV_max in characterizing prostate cancer had been presented [37, 38], Cysouw et al. concluded in a recent study that prostate risk in PSMA can be better characterized by textural parameters compared to SUVmax [24]. They utilized [¹⁸F]-DCFPyL PET/CT and reported 0.81 AUC to differentiate high (GS > = 8) and low-risk prostate cases. Our findings on the other hand demonstrate that conventional SUV parameters in combination with simple textural features can yield high-performing models in [⁶⁸Ga]Ga-PSMA-11 PET/MRI to characterize prostate risk.

While no T2w feature was selected as high-ranking, ADC interquartile range (also referred to as “robust” value range) was selected as high-ranking. Prior studies focusing on ADC analysis to predict prostate lesion risk consistently identified ADC_min, ADC_mean as well as ADC_median [20, 39] as highly predictive (AUC range 0.72–0.90). We consider that the above findings and ours describe the same phenomenon, namely, the strong predictive ability of simple ADC values without the need of incorporating second or higher-order radiomic features in the analysis. The above findings in prior reports demonstrate the predictive performance of PSMA PET and ADC MR images individually. Hence, we hypothesize that the high performance of our M_LH model is due to the fact that it combines both [⁶⁸Ga]Ga-PSMA-11 PET and ADC MRI features in one model scheme.

Further to the above findings, we also established patient biochemical recurrence (M_BCR) and overall patient risk (M_OPR) models. In order to provide an in vivo score per patient in lieu of biopsy grades in these models, we created a CLH score which weighted each M_LH score per lesion with its respective volume in each patient. Since volume was identified as non-predictive to classify low-vs-high risk in prostate lesions (AUC 0.53), we assumed that the volume effect [40] in our high-ranking features was negligible, and thus, lesion volume was an independent value from our lesion M_LH scores. This assumption allowed us to utilize volume as a weight factor for each lesion M_LH score to compose the patient-specific CLH score. The resulted CLH score in combination with PSA and clinical stage values resulted in high-performing M_BCR and M_OPR models (0.90 and 0.94 cross-validation AUCs respectively). We assume that the accuracy performance increase of + 20% and + 21% in our M_BCR and M_OPR models compared to standard risk estimation are due to the following reasons: first, the clinical standard utilizes Gleason patterns from biopsy to describe lesion pattern risks in the prostate [41]. Biopsy is considered imperfect as it may not be able to describe the overall heterogeneity of the prostate lesions [19, 42]. In contrast, our CLH score could characterize whole prostate lesions in vivo. Second, the clinical standard categorizes the PSA, the Gleason, and the clinical stage values independently into three categories (low, medium, and high risk). In contrast, we incorporated PSA, clinical stage, and the CLH score without re-binning them, and thus, avoiding potential information loss. Third, the clinical standard score acts as a maximum filter across its pre-binned risk categories to estimate overall risk to the patient. In contrast, the random forest ensemble logics in our M_BCR and M_OPR models could describe more complex relationships among PSA, clinical stage, and our in vivo CLH score. Our results demonstrate that such relationships may be indeed present and that building on those relationships may lead to in vivo risk predictive models in prostate cancer patients with the potential to eliminate the need of biopsy sampling in the future.

This study had a number of limitations. First, it built on a single-center cohort; however, due to utilizing a pre-generated MC fold scheme for all training and validation processes, no training and validation samples were mixed in between the lesion and patient predictors. In addition, the utilized data preparation (redundancy reduction, feature ranking, and class imbalance correction) as well as training (mixed ensemble) and validation (1000-fold CV, sham data analysis) approaches minimized the chances of false discoveries. Second, due to the dual-tracer study design from which our images were taken, the [⁶⁸Ga]Ga-PSMA-11 scans were not entirely exempt of [¹⁸F]FMC uptake remnants. Nevertheless, [¹⁸F]FMC can be regarded an irreversible tracer [43] and, thus, the [¹⁸F]FMC uptake in terms of tissue to lesion ratio is expected not to change until the [⁶⁸Ga]Ga-PSMA-11 examination. Last, only patients with proven prostate cancer were included after radical prostatectomy. Nevertheless, this selection criterion was necessary to acquire stable ground truth for lesion labeling.

This study demonstrates the feasibility of [⁶⁸Ga]Ga-PSMA-11 PET/MRI in combination with radiomics and machine learning to non-invasively deliver both lesion characterization and risk prediction equally to preoperative invasive biopsy in patients with primary prostate cancer. Prospective multicentric studies are required to investigate the reproducibility and clinical utility of this approach.