Thoracic Ultrasound for the Detection of Rib Metastases of Non-Small Cell Lung Cancer

Tomos, Ioannis; Tziolos, Nikolaos; Raptakis, Thomas; Kavatha, Dimitra

doi:10.5603/arm.2018.0014

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…While US might not surpass CT as the gold standard for detection of lung cancer, US can still be a useful supplement. US can reliably visualize the pleurae, lymph nodes, diaphragm, and anterosuperior mediastinum, making it superior to CT for detecting tumor invasion of the pleurae and thoracic wall, including metastasis to ribs and chest wall, and assessing adjacent lymphadenopathy [75,76]. US guidance is frequently used for percutaneous biopsy of peripheral lung lesions, with equal reliability and less risk of post-procedural pneumothorax than CT [77].…”

Section: Applications For Cancer Imagingmentioning

confidence: 99%

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Baniasadi,

Das,

Prendergast

et al. 2024

J Transl Med

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Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

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Section: Applications For Cancer Imagingmentioning

confidence: 99%

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Baniasadi,

Das,

Prendergast

et al. 2024

J Transl Med

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“… 11 Moreover, chest ultrasound can also be used to detect rib metastases from non-small cell lung cancer. 12 …”

Section: Introductionmentioning

confidence: 99%

“…11 Moreover, chest ultrasound can also be used to detect rib metastases from nonsmall cell lung cancer. 12 However, these radiologic examinations are prone to high false positive rates and high expenses. Additionally, many medical facilities lack proper equipment.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms

et al. 2023

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Objective We tested the performance of general machine learning and joint machine learning algorithms in the classification of bone metastasis, in patients with lung adenocarcinoma. Methods We used R version 3.5.3 for statistical analysis of the general information, and Python to construct machine learning models. Results We first used the average classifiers of the 4 machine learning algorithms to rank the features and the results showed that race, sex, whether they had surgery and marriage were the first 4 factors affecting bone metastasis. Machine learning results in the training group: for area under the curve (AUC), except for RF and LR, the AUC values of all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC for any single machine learning algorithm. Among the results related to accuracy and precision, the accuracy of other machine learning classifiers except the RF algorithm was higher than 70%, and only the precision of the LGBM algorithm was higher than 70%. Machine learning results in the test group: Similarly, for areas under the curve (AUC), except RF and LR, the AUC values for all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC value for any single machine learning algorithm. For accuracy, except for the RF algorithm, the accuracy of other machine learning classifiers was higher than 70%. The highest precision for the LGBM algorithm was .675. Conclusion The results of this concept verification study show that machine learning algorithm classifiers can distinguish the bone metastasis of patients with lung cancer. This will provide a new research idea for the future use of non-invasive technology to identify bone metastasis in lungcancer. However, more prospective multicenter cohort studies are needed.

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“…Virtual bronchoscopy (VB) is a non-invasive procedure and offers visualisation of the airway from the in-and outside along with its neighbouring blood vessels and lymph nodes, derived from computed tomography (CT) and/or positron emission tomography (PET) [3,[5][6][7]. Additionally, there are further imaging modalities capable of providing three-dimensional images of the bronchial tree and lung, including, among others, ultrasound [8,9].…”

Section: Introductionmentioning

confidence: 99%

Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy

et al. 2021

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Background Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. Results For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. Conclusion A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations.

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Thoracic Ultrasound for the Detection of Rib Metastases of Non-Small Cell Lung Cancer

Cited by 4 publications

References 4 publications

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms

Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy

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