Tumor Doubling Time Using CT Volumetric Segmentation in Metastatic Adrenocortical Carcinoma.
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ABSTRACT:
SUBMITTER: Fuller SN
PROVIDER: S-EPMC8628706 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the "gold standard". The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81-0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.
| S-EPMC3866632 | biostudies-literature
Project description:The purpose of this work was to develop and validate a computer-aided method for the 3D segmentation of lymph nodes in CT images. The proposed method can be utilized to facilitate applications like biopsy planning, image guided radiation treatment, or assessment of response to therapy.An optimal surface finding based lymph node segmentation method was developed. Based on the approximate center point of a lymph node of interest, a graph is generated, which represents the local neighborhood around the lymph node at discrete locations (graph nodes). A cost function is calculated based on a weighted edge and region homogeneity term. By means of optimization, a surface-based segmentation of the lymph node is derived. In addition, an interactive segmentation refinement algorithm was developed, which allows the user to quickly correct segmentation errors, if needed. For assessment of segmentation accuracy, 111 lymph nodes of mediastinum, abdomen, head/neck, and axillary regions from 35 volumetric CT scans were utilized. For accuracy analysis, lymph nodes were divided into three test sets based on lymph node size and spatial resolution of the CT scan. The average lymph node size for test set I, II, and III was 1056, 1621, and 501 mm(3), respectively. Spatial resolution of test set II was lower than for test sets I and III. To generate an independent reference standard for comparison, all 111 lymph nodes were segmented by an expert with a live wire approach.All test sets were segmented with the proposed approach. Out of the 111 lymph nodes, 40 cases (36%) required computer-aided refinement of initial segmentation results. The refinement typically required 10 s per lymph node. The mean and standard deviation of the Dice coefficient for final segmentations was 0.847 ± 0.061, 0.836 ± 0.058, and 0.809 ± 0.070 for test sets I, II, and II, respectively. The average signed surface distance error was 0.023 ± 0.171, 0.394 ± 0.189, and 0.001 ± 0.146 mm for test sets I, II, and II, respectively. The time required for locating the approximate center point of a target lymph node in a scan, generating an initial OSF segmentation, and refining the segmentation, if needed, is typically less than one minute.Segmentation of lymph nodes in volumetric CT images is a challenging task due to partial volume effects, nearby strong edges, neighboring structures with similar intensity profiles and potentially inhomogeneous density of lymph nodes. The presented approach addresses many of these obstacles. In the majority of cases investigated, the initial segmentation method delivered results that did not require further processing. In addition, the computer-aided segmentation refinement framework was found to be effective in dealing with potentially occurring segmentation errors.
| S-EPMC3432102 | biostudies-other
Project description:BackgroundBecause shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth.MethodsWe evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected.ResultsAmong the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness.ConclusionsThis study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs.Key pointsSIGNIFICANT FINDINGS OF THE STUDY: We found a relationship between margin-related radiomic features and tumor doubling time.What this study addsMargin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.
| S-EPMC7471031 | biostudies-literature
Project description:Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37-57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs.
| S-EPMC6178360 | biostudies-literature
Project description:ObjectiveTo determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection.Materials and methodsThis retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters.ResultsThe time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation.ConclusionVolumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.
| S-EPMC8546140 | biostudies-literature
Project description:We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel "pyramid context" feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.
| S-EPMC4445881 | biostudies-literature
Project description:The major prognosis factor of adrenocortical carcinoma (ACC) is the completeness of surgery. The aim of our study was to identify preoperative imaging features associated with direct liver involvement (DLI) by right-sided ACC. Two radiologists, blinded to the outcome, independently reviewed preoperative CT and MRI examinations for eight signs of DLI, in patients operated for right-sided ACC and retrospectively included from November 2007 to January 2020. DLI was confirmed using surgical and histopathological findings. Kappa values were calculated. Univariable and multivariable analyses were performed by using a logistic regression model. Receiver operating characteristic (ROC) curves were built for CT and MRI. Twenty-nine patients were included. Seven patients had DLI requiring en bloc resection. At multivariable analysis, focal ACC bulge was the single independent sign associated with DLI on CT (OR: 60.00; 95% CI: 4.60-782.40; p < 0.001), and ACC contour disruption was the single independent sign associated with DLI on MRI (OR: 126.00; 95% CI: 6.82-2328.21; p < 0.001). Both signs were highly reproducible, with respective kappa values of 0.85 and 0.91. The areas under ROC curves of MRI and CT models were not different (p = 0.838). Focal ACC bulge on CT and ACC contour disruption on MRI are independent and highly reproducible signs, strongly associated with DLI by right-sided ACC on preoperative imaging. MRI does not improve the preoperative assessment of DLI by comparison with CT.
| S-EPMC8036813 | biostudies-literature
Project description:We quantify a recent five-category CT histogram based classification of ground glass opacities using a dynamic mathematical model for the spatial-temporal evolution of malignant nodules. Our mathematical model takes the form of a spatially structured partial differential equation with a logistic crowding term. We present the results of extensive simulations and validate our model using patient data obtained from clinical CT images from patients with benign and malignant lesions.
| S-EPMC8771482 | biostudies-literature
Project description:PurposeA semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images.MethodsFirst, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images.ResultsA comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average.ConclusionThe proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.
| S-EPMC10030333 | biostudies-literature
Project description:Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC?=?0.85±0.15, p?=?0.0009) compared to the features extracted from the manual segmentations (ICC?=?0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p?=?3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p?=?0.007, higher: p?=?5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.
| S-EPMC4098900 | biostudies-literature