Post-imaging colorectal cancer or interval cancer rates after computed tomographic colonography: A systematic review and meta-analysis

Background: CT colonography (CTC) is highly sensitive for colorectal cancer, but “interval” or postimaging colorectal cancer (PICRC) rates (diagnosis of cancer after initial negative CTC) are unknown, as are their underlying causes. Methods: We conducted a systematic review and meta-analysis of post-CTC PICRC rates and causes by searching MEDLINE, EMBASE and the Cochrane Register. We included randomised, cohort, cross-sectional or case-control studies published Jan 1994-Feb 2017, using CTC performed according to international consensus standards with aim of detecting cancer or polyps, and reporting PICRC rates or sufficient data to allow their calculation. Two independent reviewers extracted data from the study reports. We used random-effects meta-analysis to estimate pooled PICRC rates, expressed using (a) total number of cancers and (b) total number of CTC scans as denominators, and (c) per 1000 person-years. Primary study authors provided details of retrospective CTC image review and causes for each PICRC. The study is registered (PROSPERO:CRD42016046838). Findings: 2977 articles were screened and 12 analysed. These reported 19,867 patients (18-96 years; of 11,590 with sex data available, 6532 (56·4%) female) from March 2002-May 2015. At mean 34 months’ follow-up (range: 3 to 128·4 months), CTC detected 643 cancers and 29 PICRCs were diagnosed. The pooled PICRC rate was 4·42 PICRCs/100 cancers detected; 95%CI 3·03-6·42, corresponding to 1·61 PICRCs/1000 CTCs (95%CI 1·11-2·33) or 0·64 PICRCs/1000 person-years (95%CI 0·44-0·92). Heterogeneity was low (I2 =0%). Over half (17/28, 61%) of PICRCs were due to perceptual error and visible in retrospect. Interpretation: The 3-year PICRC rate post-CTC is 4·4%, or 0·64 per 1000 person-years, towards the lower end of range reported for colonoscopy. Most arise from perceptual errors. Radiologist training and quality assurance may help reduce PICRC rates. Funding: St Mark’s Hospital Foundation and the UCL/UCLH Biomedical Research Centre

Differential Analysis of Ovarian and Endometrial Cancers Identifies a Methylator Phenotype

Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer

Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometry-driven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Do prevalence expectations affect patterns of visual search and decision-making in interpreting CT colonography endoluminal videos?

Objective: To assess the effect of expected abnormality prevalence on visual search and decision-making in CT colonography (CTC).Methods: 13 radiologists interpreted endoluminal CTC flythroughs of the same group of 10 patient cases, 3 times each. Abnormality prevalence was fixed (50%), but readers were told, before viewing each group, that prevalence was either 20%, 50% or 80% in the population from which cases were drawn. Infrared visual search recording was used. Readers indicated seeing a polyp by clicking a mouse. Multilevel modelling quantified the effect of expected prevalence on outcomes.Results: Differences between expected prevalence were not statistically significant for time to first pursuit of the polyp (median 0.5 s, each prevalence), pursuit rate when no polyp was on screen (median 2.7 s21, each prevalence) or number of mouse clicks [mean 0.75/ video (20% prevalence), 0.93 (50%), 0.97 (80%)]. There was weak evidence of increased tendency to look outside the central screen area at 80% prevalence and reduction in positive polyp identifications at 20% prevalence.Conclusion: This study did not find a large effect of prevalence information on most visual search metrics or polyp identification in CTC. Further research is required to quantify effects at lower prevalence and in relation to secondary outcome measures.Advances in knowledge: Prevalence effects in evaluating CTC have not previously been assessed. In this study, providing expected prevalence information did not have a large effect on diagnostic decisions or patterns of visual search.</br

Post-imaging colorectal cancer or interval cancer rates after CT colonography: a systematic review and meta-analysis

Background CT colonography is highly sensitive for colorectal cancer, but interval or post-imaging colorectal cancer rates (diagnosis of cancer after initial negative CT colonography) are unknown, as are their underlying causes. We did a systematic review and meta-analysis of post-CT colonography and post-imaging colorectal cancer rates and causes to address this gap in understanding. Methods We systematically searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. We included randomised, cohort, cross-sectional, or case-control studies published between Jan 1, 1994, and Feb 28, 2017, using CT colonography done according to international consensus standards with the aim of detecting cancer or polyps, and reporting post-imaging colorectal cancer rates or sufficient data to allow their calculation. We excluded studies in which all CT colonographies were done because of incomplete colonoscopy or if CT colonography was done with knowledge of colonoscopy findings. We contacted authors of component studies for additional data where necessary for retrospective CT colonography image review and causes for each post-imaging colorectal cancer. Two independent reviewers extracted data from the study reports. Our primary outcome was prevalence of post-imaging colorectal cancer 36 months after CT colonography. We used random-effects meta-analysis to estimate pooled post-imaging colorectal cancer rates, expressed using the total number of cancers and total number of CT colonographies as denominators, and per 1000 person-years. This study is registered with PROSPERO, number CRD42016042437. Findings 2977 articles were screened and 12 studies were eligible for analysis. These studies reported data for 19 867 patients (aged 18–96 years; of 11 590 with sex data available, 6532 [56%] were female) between March, 2002, and May, 2015. At a mean of 34 months' follow-up (range 3–128·4 months), CT colonography detected 643 colorectal cancers. 29 post-imaging colorectal cancers were subsequently diagnosed. The pooled post-imaging colorectal cancer rate was 4·42 (95% CI 3·03–6·42) per 100 cancers detected, corresponding to 1·61 (1·11–2·33) post-imaging colorectal cancers per 1000 CT colonographies or 0·64 (0·44–0·92) post-imaging colorectal cancers per 1000 person-years. Heterogeneity was low (I2=0%). 17 (61%) of 28 post-imaging colorectal cancers were attributable to perceptual error and were visible in retrospect. Interpretation CT colonography does not lead to an excess of post-test cancers relative to colonoscopy within 3–5 years, and the low 5-year post-imaging colorectal cancer rate confirms that the recommended screening interval of 5 years is safe. Since most post-imaging colorectal cancers arise from perceptual errors, radiologist training and quality assurance could help to reduce post-imaging colorectal cancer rates