Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

Abstract

Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer

“…Recently, 68 Ga-PSMA PET/CT showed satisfactory results to aid decision-making by con rming or eliminating the further need for biopsies [12][13][14]. However, according to the literature, approximately 10% of primary PCa are negative on PSMA-PET (exhibit no or minimal uptake) despite high PSA levels [15][16][17][18][19], which may be due to small lesion size or lesion con guration [15]. In literature by Zamboglou et al [15], their result indicated that the incidence of invisible PCa lesions was not correlated with patients' basic clinical parameters or basic SUV parameters derived from gross tumor volume PET(GTV-PET), and 40% of invisible lesions were clinically signi cant (ISUP>1) PCa.…”

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

“…Prostate-specific membrane antigen positron emission tomography (PSMA-PET) is another novel PCa visualization technology, which is characterized by high-precision visualization of primary PCa masses and provides superior accuracy at initial staging. At the same time, in a small-scale cohort, researchers reported the high accuracy of PSMA-PET-derived radiomics features for the diagnosis of visually unknown PCa (47). A newly reported study proposed a nomogram combining clinical information and PSMA-PET information.…”

Molecular Imaging in Primary Staging of Prostate Cancer Patients: Current Aspects and Future Trends

“…Interestingly, some studies have shown higher accuracy for hybrid PET/MRI compared to mpMRI [ 67 , 194 , 195 , 196 ]. Additionally, the novel field of radiomics has recently been exploited in PCa [ 215 ] and is expected to increase the accuracy of interpretation and prediction of the stage and outcome. Noteworthy, invasive biopsy has a significant false-negative rate [ 82 ], which can be reduced by the guidance of imaging.…”

“…Machine-learning models have been shown to correlate with human readers in detecting primary PCa [ 213 , 214 ]. They also have improved diagnostic accuracy [ 215 ]. Also, the analysis of radiomics features could discriminate lesions with Gleason score 7 and ≥8, as well as predict LN positivity with AUC of >0.84 [ 213 ].…”