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Background: The performance of MRI versus CT in the detection and evaluation of peritoneal surface malignancies (PSM) remains unclear in the current literature. Our study is the first prospective study in an Asian center comparing the two imaging modalities, validated against intra-operative findings. Methods: A total of 36 patients with PSM eligible for CRS-HIPEC underwent both MRI and CT scans up to 6 weeks before the operation. The scans were assessed for the presence and distribution of PSM and scored using the peritoneal cancer index (PCI), which were compared against PCI determined at surgery. Results: Both MRI and CT were 100% sensitive and specific in detecting the overall presence of PSM. Across all peritoneal regions, the sensitivity and specificity for PSM detection was 49.1% and 93.0% for MRI, compared to 47.8% and 95.1% for CT (p = 0.76). MRI was more sensitive than CT for small bowel disease, although the difference did not reach statistical significance. Comparing PCI on imaging with intra-operative PCI, the mean difference was found to be −3.4 ± 5.4 (p < 0.01) for MRI, and −3.9 ± 4.1 (p < 0.01) for CT. The correlation between imaging and intra-operative PCI was poor, with a concordance coefficient of 0.76 and 0.79 for MRI and CT, respectively. Within individual peritoneal regions, there was also poor agreement between imaging and intra-operative PCI for both modalities, other than in regions 1 and 3. Conclusion: MRI and CT are comparable in the detection and evaluation of PSM. While sensitive in the overall detection of PSM, they are likely to underestimate the true disease burden.

Mammography is extensively used for breast cancer screening but has high false-positive rates. Here, prospectively collected blood samples were used to identify circulating microRNA (miRNA) biomarkers to discriminate between malignant and benign breast lesions among women with abnormal mammograms. The Discovery cohort comprised 72 patients with breast cancer and 197 patients with benign breast lesions, while the Validation cohort had 73 and 196 cancer and benign cases, respectively. Absolute expression levels of 324 miRNAs were determined using RT-qPCR. miRNA biomarker panels were identified by: (1) determining differential expression between malignant and benign breast lesions, (2) focusing on top differentially expressed miRNAs, and (3) building panels from an unbiased search among all expressed miRNAs. Two-fold cross-validation incorporating a feature selection algorithm and logistic regression was performed. A six-miRNA biomarker panel identified by the third strategy, had an area under the curve (AUC) of 0.785 and 0.774 in the Discovery and Validation cohorts, respectively, and an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. Biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. Our work demonstrates that circulating miRNA signatures can potentially be used with mammography to differentiate between patients with malignant and benign breast lesions.

Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training ( n = 125) and test ( n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.