Background
Prostate cancer (PCa) is one of the most common cancers and the second leading cause of cancer-related deaths among men worldwide [
1]. Transrectal ultrasound-guided biopsy is currently a standard method for making a definitive diagnosis in patients with suspected PCa based on an elevated prostate-specific antigen level and/or an abnormal digital rectal examination [
2]. However, traditional 10-core or 12-core systematic biopsy could fail to detect some cases of PCa and may incorrectly grade the tumor because of down-staging [
2,
3]. In addition, prostate biopsy may be associated with notable side effects, including bleeding, pain, and infection. A non-invasive imaging approach for detecting PCa is thus an attractive prospect, to spare patients from unnecessary biopsies and overtreatment.
Prostate-specific membrane antigen (PSMA) is a highly specific prostatic epithelial cell transmembrane protein that is highly expressed in most primary PCa [
4,
5].
68Ga-labeled PSMA inhibitors have been explored and translated successfully for the clinical diagnosis of PCa in the last decade [
6,
7].
68Ga-PSMA-11 has been proposed for use in positron emission tomography/computed tomography (PET/CT) examinations among patients with primary PCa, and has demonstrated higher sensitivity and specificity than magnetic resonance imaging (MRI) for the detection of both intraprostatic tumor focal lesions and metastasis [
8‐
10]. However, PSMA PET/CT imaging data are usually analyzed manually by nuclear medicine specialists, based on experience, which is challenging. Notably, significant numbers of intraprostatic lesions might be missed by visual PET-image interpretation due to their small size or configuration [
11]. Quantitative measures of PSMA expression are therefore necessary to allow risk stratification of patients with primary PCa.
Radiomics is an attractive approach that converts medical images into mineable high-dimensional data via the high-throughput extraction of abundant imaging features [
12,
13]. These features include a variety of gene expression types that provide a more comprehensive description of the tumor characteristics, thus enabling researchers to obtain an effective signature to inform objective clinical decisions [
14‐
17]. However, the predictive value of
68Ga-PSMA-11 PET/CT radiomics in patients with PCa has not been widely investigated.
We therefore aimed to perform a comprehensive analysis and develop a radiomics model based on 68Ga-PSMA PET/CT, and evaluate its diagnostic performance for the non-invasive prediction of PCa.
Discussion
In the present study, we developed a radiomics model based on 68Ga-PSMA-11 PET/CT for the non-invasive discrimination of patients with PCa from those with BPD. The model was successfully validated in independent test set (AUC, 0.85; sensitivity, 0.84; specificity, 77%; PPV, 0.88, NPV, 0.71) and outperformed visual assessments by nuclear medicine radiologists (AUC, 0.63; P = 0.036).
The non-invasive identification of patients with PCa is an important issue. Multi-parametric MRI (mp-MRI) has been an important diagnostic tool for detecting primary PCa for several years. Furthermore, the use of radiomics tools has improved radiologists’ assessments. Ginsburg et al. [
23] evaluated features related to cancer detection in a transition zone and a peripheral zone in a cross-institutional setting and found that the radiomics features considered useful for cancer detection differed between the two zones. Cameron et al. [
24] proposed a model consisting of an initial tumor candidate identification schema followed by the MAPS system (morphology, asymmetry, physiology, size) to score the candidate regions. The goal of the proposed model was to incorporate high-level features using candidate tumor regions through mp-MRI and region morphology to construct a high-dimensional feature space that could be mined for different purposes, such as cancer detection or prognosis prediction. However, these studies based on MRI do not reflect tumor heterogeneity as well as PSMA PET/CT, which targets a transmembrane glycoprotein substantially overexpressed in PCa cells [
4,
5]. Zamboglou et al. [
25] recently found that radiomics analysis of PSMA PET data was able to identify missing malignant lesions in the prostate gland. They enrolled patients with PCa and defined non-PCa tissue as the subtraction volume between the prostatic gland and PCa tumor, based on pathological tissue slices. However, further studies are needed to clarify the differences between radiomics features from the prostate tissue in the non-tumor area of prostate cancer patients and the prostate tissue in non-tumor patients. In the current study, we enrolled both PCa and non-PCa patients to comprehensively evaluate radiomics data from PSMA. Yi et al. [
26] constructed a random forest model developed by
68Ga-PSMA-11 PET-based radiomics features proven to be useful for the accurate prediction of invisible intraprostatic lesions on
68Ga-PSMA-11 PET in patients with primary PCa (AUC, 0.903). Their study differed from the current study in that we evaluated both negative and non-negative PSMA-PET image cases. The present study showed a poorer model performance (AUC = 0.85), possibly due to differences in the inclusion criteria and region of interest between the two studies.
This study also compared the diagnostic performances of the radiomics model and qualitative evaluation by radiologists. Visual assessment of primary PCa based on experience remains challenging [
27,
28]. Although PSMA is a transmembrane glycoprotein highly expressed on the cell surface of PCa cells, it is also expressed in benign pathologies such as BPD and prostatic intraepithelial neoplasia [
29]. Benign intraprostatic processes can be associated with relatively high PSMA expression levels, with significant overlap between low-volume malignancies and benign disease. Moreover, visual PET interpretation might miss a significant number of intraprostatic lesions because of their small size or configuration [
11], leading to potential false positives or false negatives [
30,
31]. In this study, the radiomics model detected PCa based solely on a numeric feature set from high-dimensional medical imaging data, regardless of the clinical situation, which could explain its higher sensitivity compared with the readers’ assessments. On the other hand however, there was no significant difference in specificity, which might reflect the fact that the radiologists’ reports tended to maximize the specificity for an organ-preserving strategy, unlike the radiomics model determined by the Youden index. Further studies are thus needed to consider the specificity.
Whole-prostate gland segmentation strategy were employed in this study for two reasons. First, the characteristic large and low-resolution voxels for PET images limit the radiomics analysis in small prostate lesions, and VOIs with few voxels cannot provide much complementary information [
32]. Second, it will be challenging to determine the value for negative or diffuse-pattern PET images. Recent studies [
25,
26] showed that radiomics features derived from
68Ga-PSMA-11 PET images based on half-glandular segmentation were helpful for predicting invisible PCa lesions. Solari et al. [
33] developed radiomics models based on delineating the whole prostate gland and showed good performances for predicting the postoperative Gleason score in PCa patients. In the present study, further comparison between the radiomics model and readers revealed that high-dimensional features offered better disease characterization for PCa.
This study had some limitations. First, its single-center design and relatively small sample size may compromise the model’s generalization ability and affect its sensitivity and specificity. It is therefore necessary to formulate a unified standard for multicenter studies and establish and test multicenter data using radiomics methods to improve the robustness of the model. Second, further studies using different PET/CT scanners are needed to validate the generalizability and robustness of the radiomics model. Third, we only analyzed PET-imaging data, and future research should aim to include multi-modal imaging data. Fourth, characterization of multifocality was not included in this study and is desirable in future research.
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