Erratum: Semiâ automated pulmonary nodule interval segmentation using the NLST data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/1/mp12905_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/2/mp12905.pd

Solvability of a Third-Order Multipoint Boundary Value Problem at Resonance

We discuss a third-order multipoint boundary value problem under some appropriate resonance conditions. By using the coincidence degree theory, we establish the existence result of solutions. The emphasis here is that the dimension of the linear operator is equal to two. Our results supplement other results

Imatinib revives the therapeutic potential of metformin on ewing sarcoma by attenuating tumor hypoxic response and inhibiting convergent signaling pathways.

Ewing sarcoma (EwS) is an aggressive pediatric tumor treated with intensive cytotoxic chemotherapies. Overall survival for metastatic or relapsed disease is only 20-30%. Metformin has long been an attractive therapeutic option for EwS, but hypoxia limits its efficacy. Through a systematic integration of drug combination screening, bioinformatics analyses, functional and in vivo studies, and correlation with clinical outcome, we identified another known drug, imatinib that could augment the in vivo anti-tumor capacity of metformin by attenuating tumor hypoxic response. This drug combination regimen widely suppressed multiple dominant mechanisms in EwS genesis, growth, and metastasis, including key EWS-FLI1 downstream targets that converge into the PI3K/AKT/mTOR signaling pathway. In addition, the combination significantly enhanced inhibition on tumor cell proliferation by standard EwS chemotherapy drugs, including cyclophosphamide and ifosfamide. This suggests a potential clinical benefit of the metformin/imatinib combination by allowing the reduction in dose intensity of standard chemotherapy without compromising survival outcome and represents a potential faster track application for EwS patients

Semi-automated pulmonary nodule interval segmentation using the NLST data.

PurposeTo study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.MethodsWe obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.ResultsWe find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).ConclusionsWe find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams)