Background and Aims:
The aims of this study were to establish a maximum standardized uptake value (SUV
max
) cutoff to discriminate clinically significant prostate cancer (csPCa) from benign prostate disease (BPD) by
68
Ga-labeled prostate-specific membrane antigen (
68
Ga-PSMA-11) positron emission tomography/computed tomography (PET/CT) in patients with suspected prostate cancer (PCa), and to perform a prospective real-world validation of this cutoff value.
Methods:
The study included a training cohort to identify an SUV
max
cutoff value and a prospective real-world cohort to validate it. A retrospective analysis assessed 135 patients with suspected PCa in a large tertiary care hospital in China who underwent
68
Ga-PSMA-11 PET/CT. All patients were suspected of having PCa based on symptoms, digital rectal examination (DRE), total prostate-specific antigen (tPSA) level, and multiparameter magnetic resonance imaging (mpMRI). The
68
Ga-PSMA PET/CT results were evaluated using histopathological results from transrectal ultrasound-guided 12-core biopsy with necessary targeted biopsy as references. Patients with Gleason scores (GS) ≥7 from the biopsy results were diagnosed with csPCa, and patients with negative biopsy and follow-up results were diagnosed with BPD. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal SUV
max
cutoff value. The cutoff value was prospectively validated in 58 patients with suspected PCa. The diagnostic benefits of the cutoff value for clinical decision making were also evaluated.
Results:
According to ROC curve analysis, the most appropriate SUV
max
cutoff value for discriminating csPCa from BPD was 5.30 (sensitivity, 85.85%; specificity, 86.21%; area under the curve [AUC], 0.893). The cutoff achieved a sensitivity of 83.33%, a specificity of 81.25%, a positive predictive value (PPV) of 92.11%, a negative predictive value (NPV) of 65.00%, and an accuracy of 82.76% in the prospective validation cohort. Metastases were used as an indicator to reduce false negative results in patients with SUV
max
≤ 5.30. In patients without metastases, an SUV
max
value of 5.30 was also the best cutoff to diagnose localized csPCa (sensitivity, 80.43%; specificity, 86.21%; AUC, 0.852). The cutoff discriminated localized csPCa from BPD with a sensitivity of 76.19%, a specificity of 81.25%, a PPV of 84.21%, an NPV of 72.22%, and an accuracy of 78.38% in the prospective validation cohort. The cutoff, combined with metastases, achieved an accuracy of 89.12% in all patients, increasing accuracy by 8.29% and reducing equivocal results compared with manual reading. There was a strong correlation between SUV
max
and PSMA expression (
r
s
...
Background
Gut dysbacteriosis has been reported as one of the etiologies for irritable bowel syndrome (IBS). However, the association between gut microbiota and IBS is still inconclusive.
Method
A paired-sample study was designed by retrieving original multicenter 16 s-rRNA data of IBS patients and healthy controls from the GMrepo database. The propensity score matching (PSM) algorithm was applied to reduce confounding bias. The differential analysis of microbiota composition was performed at different taxonomic levels. The co-occurrence network was established. Subgroup analysis was performed to identify specific microbial compositions in different IBS subtypes.
Results
A total of 1522 amplicon samples were initially enrolled. After PSM, 708 individuals (354 IBS and 354 healthy controls) were eligible for further analysis. A total of 1,160 genera were identified. We identified significantly changed taxa in IBS groups (IBS-enriched: the families Enterobacteriaceae, Moraxellaceae and Sphingobacteriaceae; the genera Streptococcus, Bacillus, Enterocloster, Sphingobacterium, Holdemania and Acinetobacter. IBS-depleted: the phyla Firmicutes, Euryarchaeota, Cyanobacteria, Acidobacteria and Lentisphaerae; the families Bifidobacteriaceae, Ruminococcaceae, Methanobacteriaceae and the other 25 families; the genera Faecalibacterium, Bifidobacterium and other 68 genera). The co-occurrence network identified three hub genera and six hub species (including Faecalibacterium prausnitzii) that may be involved in IBS pathophysiology. Strong positive interactions were identified among the Bifidobacterium longum, Bifidobacterium breve and Bifidobacterium adolescentis in the Bifidobacterium community.
Conclusion
This study provides quantitative analysis and visualization of the interaction between the gut microbiota and IBS. The identification of key species should be further validated to evaluate their causal relationships with the pathogenesis of IBS.
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