Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in ...routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
Abstract We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). ...CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 threedimensional and 110 two-dimensional) was computed, quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT ). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups ( P ≤ .046).
A single click ensemble segmentation (SCES) approach based on an existing “Click & Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with ...multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.
► We proposed an automatic, stable and accurate segmentation algorithm for lung tumor CT scans. ► The approach requires just 1 seed point to obtain a good segmentation. ► High agreement between new algorithm and two reader’s results. ► It is consistent, the average SI is above 93% using 20 different start seeds.
Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal ...of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (
p
< 0.05) and was significantly higher on the phantom dataset compared to the other datasets (
p
< 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (
p
< 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.
Ceftriaxone-based antimicrobial therapies for gonorrhea are threatened by waning ceftriaxone susceptibility levels and the global dissemination of the high-level ceftriaxone-resistant gonococcal ...FC428 clone. Combination therapy can be an effective strategy to restrain the development of ceftriaxone resistance, and for that purpose, it is important to find an alternative antimicrobial to replace azithromycin, which has recently been removed in some countries from the recommended ceftriaxone plus azithromycin dual-antimicrobial therapy. Ideally, the second antimicrobial should display synergistic activity with ceftriaxone. We hypothesized that bacitracin might display synergistic activity with ceftriaxone because of their distinct mechanisms targeting bacterial cell wall synthesis. In this study, we showed that bacitracin indeed displays synergistic activity with ceftriaxone against
. Importantly, strains associated with the FC428 clone appeared to be particularly susceptible to the bacitracin plus ceftriaxone combination, which might therefore be an interesting dual therapy for further
testing.
Alternative antimicrobial therapies are urgently required for the multidrug-resistant bacterial pathogen Neisseria gonorrhoeae, for which currently ceftriaxone is the only remaining recommended ...first-line therapy. Repurposing of drugs that are approved for other clinical applications offers an efficient approach for development of alternative antimicrobial therapies. Auranofin, cannabidivarin, and tolfenamic acid were recently identified to display antimicrobial activity against N. gonorrhoeae. Here, we investigated their activity against a collection of 575 multidrug-resistant clinical isolates. All three compounds displayed consistent antimicrobial activity against all isolates, including against strains associated with the high-level ceftriaxone-resistant FC428 clone, with both the mode and MIC
for auranofin of 0.5 mg/L, while both the mode and MIC
for cannabidivarin and tolfenamic acid were 8 mg/L. Correlations between MICs of ceftriaxone and auranofin, cannabidivarin or tolfenamic acid were low, indicating that development of cross-resistance is unlikely. Furthermore, antimicrobial synergy analysis between ceftriaxone and auranofin, cannabidivarin, or tolfenamic acid by determination of the fractional inhibitory concentration index (FICI) resulted in an interpretation of indifference. Finally, time-kill analyses showed that all three compounds are bactericidal against both the N. gonorrhoeae ATCC 49226 reference strain and an FC428-associated clinical isolate, with particularly cannabidivarin displaying rapid bactericidal activity. Overall, auranofin, cannabidivarin, and tolfenamic acid displayed consistent antimicrobial activity against multidrug-resistant N. gonorrhoeae, warranting further exploration of their suitability as alternative antimicrobials for treatment of gonococcal infections.
Neisseria gonorrhoeae is a major public health concern because of the high incidence of gonorrhea and the increasingly limited options for antimicrobial therapy. Strains associated with the FC428 clone are a particular concern because they have shown global dissemination and they display high-level resistance against the currently recommended ceftriaxone therapy. Therefore, development of alternative antimicrobial therapies is urgently required to ensure treatment of gonorrhea remains available in the future. Repurposing of clinically approved drugs could be a rapid approach for the development of such alternative antimicrobials. In this study, we showed that repurposing of auranofin, cannabidivarin, and tolfenamic acid for antimicrobial therapy of gonorrhea deserves further clinical explorations because these compounds displayed consistent antimicrobial activity against a large collection of contemporary multidrug-resistant gonococcal isolates that included strains associated with the FC428 clone.
Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features from CT images ...of the lung toward providing prognostic information. Focusing on cases of the adenocarcinoma nonsmall cell lung cancer tumor subtype from a larger data set, we show that classifiers can be built to predict survival time. This is the first known result to make such predictions from CT scans of lung cancer. We compare classifiers and feature selection approaches. The best accuracy when predicting survival was 77.5% using a decision tree in a leave-one-out cross validation and was obtained after selecting five features per fold from 219.
Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple ...algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA).
The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type.
Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall.
The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.
Low-dose helical computed tomography (LDCT) has facilitated the early detection of lung cancer through pulmonary screening of patients. There have been a few attempts to develop a computer-aided ...diagnosis system for classifying pulmonary nodules using size and shape, with little attention to texture features. In this work, texture and shape features were extracted from pulmonary nodules selected from the LIDC data set. Several classifiers including Decision Trees, Nearest Neighbor, and Support Vector Machines (SVM) were used for classifying malignant and benign pulmonary nodules. An accuracy of 90.91% was achieved using a 5-nearest-neighbors algorithm and a data set containing texture features only. Laws and Wavelet features received the highest rank when using feature selection implying a larger contribution in the classification process. Considering the improvement in classification accuracy, the use of texture features appears to be a promising direction in computer-aided diagnosis of pulmonary nodules in LDCT.
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