Region of interest identification and diagnostic agreement in breast pathology.

A pathologist's accurate interpretation relies on identifying relevant histopathological features. Little is known about the precise relationship between feature identification and diagnostic decision making. We hypothesized that greater overlap between a pathologist's selected diagnostic region of interest (ROI) and a consensus derived ROI is associated with higher diagnostic accuracy. We developed breast biopsy test cases that included atypical ductal hyperplasia (n=80); ductal carcinoma in situ (n=78); and invasive breast cancer (n=22). Benign cases were excluded due to the absence of specific abnormalities. Three experienced breast pathologists conducted an independent review of the 180 digital whole slide images, established a reference consensus diagnosis and marked one or more diagnostic ROIs for each case. Forty-four participating pathologists independently diagnosed and marked ROIs on the images. Participant diagnoses and ROI were compared with consensus reference diagnoses and ROI. Regression models tested whether percent overlap between participant ROI and consensus reference ROI predicted diagnostic accuracy. Each of the 44 participants interpreted 39-50 cases for a total of 1972 individual diagnoses. Percent ROI overlap with the expert reference ROI was higher in pathologists who self-reported academic affiliation (69 vs 65%, P=0.002). Percent overlap between participants' ROI and consensus reference ROI was then classified into ordinal categories: 0, 1-33, 34-65, 66-99 and 100% overlap. For each incremental change in the ordinal percent ROI overlap, diagnostic agreement increased by 60% (OR 1.6, 95% CI (1.5-1.7), P<0.001) and the association remained significant even after adjustment for other covariates. The magnitude of the association between ROI overlap and diagnostic agreement increased with increasing diagnostic severity. The findings indicate that pathologists are more likely to converge with an expert reference diagnosis when they identify an overlapping diagnostic image region, suggesting that future computer-aided detection systems that highlight potential diagnostic regions could be a helpful tool to improve accuracy and education.

Flow cytometry-based enrichment for cell shape mutants identifies multiple genes that influence Helicobacter pylori morphology.

The helical cell shape of Helicobacter pylori is highly conserved and contributes to its ability to swim through and colonize the viscous gastric mucus layer. A multi-faceted peptidoglycan (PG) modification programme involving four recently characterized peptidases and two accessory proteins is essential for maintaining H. pylori's helicity. To expedite identification of additional shape-determining genes, we employed flow cytometry with fluorescence-activated cell sorting (FACS) to enrich a transposon library for bacterial cells with altered light scattering profiles that correlate with perturbed cell morphology. After a single round of sorting, 15% of our clones exhibited a stable cell shape defect, reflecting 37-fold enrichment. Sorted clones with straight rod morphology contained insertions in known PG peptidases, as well as an insertion in csd6, which we demonstrated has ld-carboxypeptidase activity and cleaves monomeric tetrapeptides in the PG sacculus, yielding tripeptides. Other mutants had only slight changes in helicity due to insertions in genes encoding MviN/MurJ, a protein possibly involved in initiating PG synthesis, and the hypothetical protein HPG27_782. Our findings demonstrate FACS robustly detects perturbations of bacterial cell shape and identify additional PG peptide modifications associated with helical cell shape in H. pylori.