8 research outputs found
Erratum: SemiĂą 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
Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication
Lung cancer (LC) is leading in the number of deaths among the other types of cancer. According to the American Cancer Society, 135,720 deaths during 2020 in the USA will be associated with LC. The patient 5-year survival rate of 16\% was reported in 1986 and 19% in 2019. One of the reasons why survival rate remains low is that the majority of patients diagnosed with cancer had stages III and IV. In contrast, a 5-year survival rate of 70% was reported for patients of stage IA after surgical resection in the National Lung Screening Trial.
CT screening detects a number of pulmonary nodules that have to be classified as benign or malignant. Radiomics is based on the concept that quantitative features extracted from medical images can be effectively used for differentiation of abnormal tissue into benign or malignant categories by applying machine learning methods. Such computer-aided decision making (CAD) systems were shown to be effective tools for patient diagnosis, treatment response prediction, cancer aggressiveness estimation, and gene mutation type detection. Conventional radiomic features describe the size, shape, location, and structural patterns (texture) of tissue. Texture features are commonly computed over the entire nodules and thus they are averaged with respect to different texture patterns presented in a nodule. In comparison, a set of algorithms is focused on the detection of nodule subregions with similar properties (habitats), such as texture, as a part of the feature extraction step, and used information about habitats to describe a nodule.
This dissertation introduces new algorithms designed to increase the performance of patient diagnostic systems as well as lung cancer tumorâs aggressiveness categorization. Diagnosis experiments were performed on the National Lung Screening Trial (NLST) dataset. Cancer aggressiveness estimation experiments were performed on a set of patients diagnosed with Adenocarcinoma at the H. Lee. Moffitt Cancer Center & Research Institute. Due to the variance of reported nodule sizes, the dataset was split into size categories and each CAD system for a size-group was designed individually. As an extension for the size split project, delta features were computed and added into the feature set. Delta features characterize temporal changes in a nodule. A lung cancer diagnosis system that utilizes baseline and delta features is reported. A novel habitat revealing algorithm was presented and its utilization for lung cancer diagnosis and lung cancer aggressiveness classification is provided in detail. Considering the beneficial usage of the developed approaches as a set of independent methods, a delta habitat revealing algorithm was designed. The delta habitat revealing algorithms quantify information about habitats within a nodule and how these habitats changed in time. The performance evaluation was performed using the NLST dataset, thus a split of patients into size-groups was performed. Finally, we designed several experiments to show that size is an important feature not only in clinical practice and Radiomics but also for Convolution Neural Networks that process only image data. If warping (up-sampling) was applied as a pre-processing step, it is shown that the size of a nodule is encoded in texture and decoded by CNN for decision making.
Nodule classification Area Under Receiver Operating Curve (AUROC) in the NLST dataset was improved from 0.69 to 0.79 by developing CAD systems for nodule size-groups independently. The inclusion of delta features enhanced CAD classification AUROC to 0.86 in the NLST. Features that were produced by the habitat revealing algorithm statistically significantly improved lung cancer patient survival time classification AUROC from 0.71 to 0.91 in a set of adenocarcinoma patients. Finally, AUROCs of 0.91, 0.87 and 0.92 were achieved for âsmallâ, âmediumâ and âlargeâ size-groups in the NLST dataset by combining delta-habitat and conventional radiomic feature sets. A CNN model trained from scratch to differentiate small / large nodules and a CNN model, that originally was trained to classify cancer/non-cancer nodules, tuned to classify size categories showed accuracy more that 80% and AUROC more than 0.80 for a variety small / large labeling methods
Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication
Lung cancer (LC) is leading in the number of deaths among the other types of cancer. According to the American Cancer Society, 135,720 deaths during 2020 in the USA will be associated with LC. The patient 5-year survival rate of 16\% was reported in 1986 and 19% in 2019. One of the reasons why survival rate remains low is that the majority of patients diagnosed with cancer had stages III and IV. In contrast, a 5-year survival rate of 70% was reported for patients of stage IA after surgical resection in the National Lung Screening Trial.
CT screening detects a number of pulmonary nodules that have to be classified as benign or malignant. Radiomics is based on the concept that quantitative features extracted from medical images can be effectively used for differentiation of abnormal tissue into benign or malignant categories by applying machine learning methods. Such computer-aided decision making (CAD) systems were shown to be effective tools for patient diagnosis, treatment response prediction, cancer aggressiveness estimation, and gene mutation type detection. Conventional radiomic features describe the size, shape, location, and structural patterns (texture) of tissue. Texture features are commonly computed over the entire nodules and thus they are averaged with respect to different texture patterns presented in a nodule. In comparison, a set of algorithms is focused on the detection of nodule subregions with similar properties (habitats), such as texture, as a part of the feature extraction step, and used information about habitats to describe a nodule.
This dissertation introduces new algorithms designed to increase the performance of patient diagnostic systems as well as lung cancer tumorâs aggressiveness categorization. Diagnosis experiments were performed on the National Lung Screening Trial (NLST) dataset. Cancer aggressiveness estimation experiments were performed on a set of patients diagnosed with Adenocarcinoma at the H. Lee. Moffitt Cancer Center & Research Institute. Due to the variance of reported nodule sizes, the dataset was split into size categories and each CAD system for a size-group was designed individually. As an extension for the size split project, delta features were computed and added into the feature set. Delta features characterize temporal changes in a nodule. A lung cancer diagnosis system that utilizes baseline and delta features is reported. A novel habitat revealing algorithm was presented and its utilization for lung cancer diagnosis and lung cancer aggressiveness classification is provided in detail. Considering the beneficial usage of the developed approaches as a set of independent methods, a delta habitat revealing algorithm was designed. The delta habitat revealing algorithms quantify information about habitats within a nodule and how these habitats changed in time. The performance evaluation was performed using the NLST dataset, thus a split of patients into size-groups was performed. Finally, we designed several experiments to show that size is an important feature not only in clinical practice and Radiomics but also for Convolution Neural Networks that process only image data. If warping (up-sampling) was applied as a pre-processing step, it is shown that the size of a nodule is encoded in texture and decoded by CNN for decision making.
Nodule classification Area Under Receiver Operating Curve (AUROC) in the NLST dataset was improved from 0.69 to 0.79 by developing CAD systems for nodule size-groups independently. The inclusion of delta features enhanced CAD classification AUROC to 0.86 in the NLST. Features that were produced by the habitat revealing algorithm statistically significantly improved lung cancer patient survival time classification AUROC from 0.71 to 0.91 in a set of adenocarcinoma patients. Finally, AUROCs of 0.91, 0.87 and 0.92 were achieved for âsmallâ, âmediumâ and âlargeâ size-groups in the NLST dataset by combining delta-habitat and conventional radiomic feature sets. A CNN model trained from scratch to differentiate small / large nodules and a CNN model, that originally was trained to classify cancer/non-cancer nodules, tuned to classify size categories showed accuracy more that 80% and AUROC more than 0.80 for a variety small / large labeling methods
On the Origin of the Anomalous Behavior of Lipid Membrane Properties in the Vicinity of the Chain-Melting Phase Transition
Biomembranes are key objects of numerous studies in biology and biophysics of great importance to medicine. A few nanometers thin quasi two-dimensional liquid crystalline membranes with bending rigidity of a few kT exhibit unusual properties and they are the focus of theoretical and experimental physics. The first order chain-melting phase transition of lipid membranes is observed to be accompanied by a pseudocritical behavior of membrane physical-chemical properties. However, the investigation of the nature of the anomalous swelling of a stack of lipid membranes in the vicinity of the transition by different groups led to conflicting conclusions about the level of critical density fluctuations and their impact on the membrane softening. Correspondingly, conclusions about the contribution of Helfrichâs undulations to the effect of swelling were different. In our work we present a comprehensive complementary neutron small-angle and spin-echo study directly showing the presence of significant critical fluctuations in the vicinity of the transition which induce membrane softening. However, contrary to the existing paradigm, we demonstrate that the increased undulation forces cannot explain the anomalous swelling. We suggest that the observed effect is instead determined by the dominating increase of short-range entropic repulsion
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Erratum: Semi-automated pulmonary nodule interval segmentation using the NLST data.
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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 â„ 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 (â„ 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 (â„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)