Interstitial lung disease in the elderly

Background Despite the relationship between idiopathic pulmonary fibrosis (IPF) and advancing age, little is known about the epidemiology of interstitial lung disease (ILD) in the elderly. We describe the diagnoses, clinical characteristics, and outcomes of patients who were elderly at the time of ILD diagnosis. Methods Among subjects from a prospective cohort study of ILD, elderly was defined as age ≥ 70 years. Diagnoses were derived from a multidisciplinary review. Differences between elderly and nonelderly groups were determined using the χ2 test and analysis of variance. Results Of the 327 subjects enrolled, 80 (24%) were elderly. The majority of elderly subjects were white men. The most common diagnoses were unclassifiable ILD (45%), IPF (34%), connective tissue disease (CTD)-ILD (11%), and hypersensitivity pneumonitis (8%). Most elderly subjects (74%) with unclassifiable ILD had an imaging pattern inconsistent with usual interstitial pneumonia (UIP). There were no significant differences in pulmonary function or 3-year mortality between nonelderly and elderly subjects combined or in a subgroup analysis of those with IPF. Conclusions Although IPF was the single most common diagnosis, the majority of elderly subjects had non-IPF ILD. Our findings highlight the need for every patient with new-onset ILD, regardless of age, to be surveyed for exposures and findings of CTD. Unclassifiable ILD was common among the elderly, but for most, the radiographic pattern was inconsistent with UIP. Although the effect of ILD may be more pronounced in the elderly due to reduced global functionality, ILD was not more severe or aggressive in this group

Molecular imaging can identify the location to perform a frozen biopsy during intraoperative frozen section consultation.

BackgroundIntraoperative frozen section (FS) consultation is an important tool in surgical oncology that suffers from sampling error because the pathologist does not always know where to perform a biopsy of the surgical specimen. Intraoperative molecular imaging is a technology used in the OR to visualize lesions during surgery. We hypothesized that molecular imaging can address this pathology challenge in FS by visualizing the cancer cells in the specimen in the pathology suite. Here, we report the development and validation of a molecular-imaging capable cryostat called Smart-Cut.MethodsA molecular imaging capable cryostat prototype was developed and tested using a murine model. Tumors grown in mice were targeted with a NIR contrast agent, indocyanine green (ICG), via tail vein injection. Tumors and adjacent normal tissue samples were frozen sectioned with Smart-Cut. Fluorescent sections and non-fluorescent sections were prepared for H&E and fluorescent microscopy. Fluorescent signal was quantified by tumor-to-background ratio (TBR). NIR fluorescence was tested in one patient enrolled in a clinical trial.ResultsThe Smart-Cut prototype has a small footprint and fits well in the pathology suite. Fluorescence imaging with Smart-Cut identified cancerous tissue in the specimen in all 12 mice. No false positives or false negatives were seen, as confirmed by H&E. The mean TBR in Smart-Cut positive tissue sections was 6.8 (SD±3.8). In a clinical application in the pathology suite, NIR imaging identified two lesions in a pulmonary resection specimen, where traditional grossing only identified one.ConclusionMolecular imaging can be integrated into the pathology suite via the Smart-Cut device, and can detect cancer in frozen tissue sections using molecular imaging in a murine model

Polyploid cell volumes by stage and genotype.

Related to Fig 2B, histograms of cell volumes for all segmented cells regardless of ploidy (N = 21, n = 4082). Fits, upper left, Gaussian mixture model, other fits, lognormal. Small Gaussian fits, first 2n population from a mixture-model fit to normal AT2 cells. Goodness-of-fit statistics and parameters from regression fit to normal AT2 cells as well as sample sizes by stage and genotype available in S1 Data, tab G. (PDF)</p

Polyploid and euploid nuclear volumes binned over all stages.

Top, Normal AT2 cells (n = 802). All nuclei, N = 21, n = 4082. EGFR+, all ploidies, N = 10, n = 2194. KRAS+, all ploidies, N = 11, n = 1939. Euploids, nuclei with estimated ploidies of from 1.6–4.4n (n = 2671). EGFR euploids, n = 1411. KRAS euploids, n = 1260. Fits are a Gaussian mixture model, top, and lognormal, other histograms. Small Gaussians overlaid are the first Gaussian from the mixture model fit to normal AT2 nuclei, corresponding to the 2n population. Goodness-of-fit statistics available in S1 Data, tab G. (PDF)</p

Cell proliferation and genome dilution in lung adenocarcinomas.

A., Ki-67 proliferation marker is observed in cells across the range of volumes for LA. Green, Ki67, with merge to CK7, red and DNA, cyan at right. Quantitation of nuclear size data in proliferating cells is shown in Fig 7A, and related to S23 and S24 Figs. B, Estimated ploidy per unit volume within the nucleus, or genome concentration, plotted as a function of cell volume shows that nearly all cells with a volume greater than 2 pL display diluted genomes. n = 4082, related to S1 Data, tab B. (PDF)</p

Lamin A+C envelope wrinkling is only weakly associated with nuclear volume.

Related to S26 Fig, plots of the volume of the surface segmentation of Lamin A+C staining as a function of the sphericity reported by Imaris software, binned by genotype. Left, EGFR+, right, KRAS+. Lamin A+C Inner Volume = the volume of the envelope built from the Lamin A+C channel, which is significantly smaller than the volume of the nucleus as calculated from DNA thresholding (S5 Fig and Methods). Both large and small nuclei display a range of sphericities, which is not consistent with rounding of nuclei due to nuclear swelling. (PDF)</p

Cell volumes of each KRAS+ patient as a function of ploidy.

"-", " = ", "+", classifications based on cells with sub-, green, proportional, blue, or supraproportional, red, behavior, used to construct Fig 3B. See Methods and S1 Data tab E for classification regime. Each dot represents a single cell measurement (n = 4082, related to S1 Data, tab B). Rows, stages 1–3. Some outliers are excluded from the edges of the plot for clarity and their data can be found in S1 Data, tab B. (PDF)</p

Cell volumes of each EGFR+ patient as a function of ploidy.

"-", " = ", "+", classifications based on cells with sub-, green, proportional, blue, or supraproportional, red, behavior, used to construct Fig 3B. See Methods and S1 Data tab E for classification regime. Each dot represents a single cell measurement (n = 4082, related to S1 Data, tab B). Rows, stages 1–3. Some outliers are excluded from the edges of the plot for clarity and their data can be found in S1 Data, tab B. (PDF)</p

S11 Fig -

A., Polyploid and B., euploid N:C ratios by stage and genotype. Small Gaussian fits, first 2n population from a mixture-model fit to normal AT2 cell nuclei and cell bodies. Fits, top row, two Gaussian mixture, other fits, lognormal. Goodness-of-fit statistics and parameters from regression fit to normal AT2 cells as well as sample sizes by stage and genotype available in S1 Data, tab G. (PDF)</p

Polyploid and euploid N:C ratios binned over all stages.

Top, Normal AT2 cells (n = 802). All nuclei, N = 21, n = 4078. EGFR+, all ploidies, N = 10, n = 2194. KRAS+, all ploidies, N = 11, n = 1884. Euploids, N:C ratios from cells with estimated ploidies of from 1.6–4.4n (n = 2668). EGFR euploids, n = 1411. KRAS euploids, n = 1257. Fits are a Gaussian mixture model, top, and lognormal, other histograms. Small Gaussians overlaid are the first Gaussian from the mixture model fit to normal AT2 cells, corresponding to the 2n population. Goodness-of-fit statistics available in S1 Data, tab G. (PDF)</p