Utility of FDG PET/CT for Assessment of Lung Nodules Identified During Low Dose Computed Tomography Screening

Hadique, Sarah; Jain, Pranav; Hadi, Yousaf; Baig, Aneeqah; Parker, John E.

doi:10.21203/rs.2.21901/v2

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Prognostication based on PET may be inherently more difficult than that based on CT imaging alone, since patients sent for FDG-PET/CT often have indeterminate lesions that physicians have deemed difficult to evaluate with sequential CT. 37 Furthermore, the decision to pursue an FDG-PET/CT scan is often dependent on clinical judgement and institutional practices, which may lead to high variability in patient datasets and associated studies. The ComBAT data harmonization algorithm has been shown to remove site bias in imaging and radiomics features and improve prediction results in PET, CT, and MRI imaging for neuroimaging, NSCLC, cervical cancer, among others.…”

Section: Discussionmentioning

confidence: 99%

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

et al. 2022

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“…Coaxial needle biopsy can avoid repeated punctures and reduce complications. As a lung tumor identification method, CT emphasizes that scanning coaxial needle biopsy can significantly improve the positive detection rate of malignant lesions, which is of great value for the clinical decision of early lung cancer and appropriate treatment options [20]. rough the improvement and development of CT devices, and the improvement of puncture needles, CT-enhanced scanning coaxial needle biopsy can safely and effectively diagnose diseases with fewer complications, especially for lesions that cannot be detected by bronchoscopy.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography Image: Guided Needle Biopsy in the Diagnosis of Lung Malignant Tumors under Artificial Intelligence Algorithm

De-ping

Wang

et al. 2022

Scientific Programming

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The computed tomography (CT) image-guided needle biopsy was applied in the diagnosis of lung malignant tumors based on artificial intelligence (AI) algorithm under convolutional neural network (CNN) to explore the effect of artificial intelligence algorithms segmentation in needle biopsy surgery and to guide the diagnosis of lung malignant tumors. The subjects of the study were 100 patients with lung malignant tumors admitted to the hospital. The cases were diagnosed as lung cancers by CT, and they were divided into two groups with 50 people in each group. Among them, 50 people in the control group did not use algorithms for guidance and 50 people in the experimental group used algorithms for guidance. The 50 patients who received needle biopsy without guidance of any algorithm were included in the control group. The results showed that the average coincidence rate of automatic segmentation and manual segmentation by the artificial intelligence algorithm was 97.46%, and the average false positive rate (FPR) and false negative rate (FNR) were 0.07% and 0.08%, respectively. The segmentation time of the algorithm group was 12.5 s, which was significantly shorter than the 36.11 s of the control group, and the segmentation speed was significantly faster. The positive rate of pathology in the control group was 78%, and the pathological positive rate of the algorithm group was 80%. The difference in the pathological positive rate between the two groups was 2%, and the detection effect of the pathological positive rate in the algorithm group was close to that in the control group, which was of positive significance in pathological detection. The satisfaction rate of patients in the algorithm group with the detection effect was 88%, and that of the patients in the control group was 94%. The difference in the detection satisfaction rate between the two groups was 6%. The number of patients with pneumothorax in the algorithm group was 22, the number of patients with bleeding was 21, and the number of patients with infection was 2. The number of patients with pneumothorax in the control group was 17, the number of patients with bleeding was 19, and the number of patients with infection was 3. The patients had a higher probability of pneumothorax and bleeding and a lower probability of infection; there was no significant difference in the incidence of complications between the two groups of patients. In summary, the intelligent algorithm was effective and feasible in segmenting lesions, and the accuracy of segmentation could meet the clinical needs, which can be used as a reference for surgery. CT images based on artificial intelligence algorithms were a good way to guide the needle biopsy in the diagnosis of lung malignant tumors, improving the accuracy and sensitivity of the diagnosis of lung malignant tumors.

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“…Positive emission tomography may help characterize solid lesions for malignant potential and decrease the number of false positive lesions undergoing unnecessary invasive procedures. 146 Navigational bronchoscopy coupled with cone-beam CT imaging and augmented fluoroscopy may help increase the proportion of diagnosed lesions with less morbidity and mortality. 147…”

Section: Special Considerations Using Lung Cancer Screening In Copd P...mentioning

confidence: 99%

Chronic Obstructive Pulmonary Disease and Lung Cancer: A Review for Clinicians

Criner¹,

Agustí²,

Borghaei³

et al. 2022

J COPD F

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Utility of FDG PET/CT for Assessment of Lung Nodules Identified During Low Dose Computed Tomography Screening

Cited by 3 publications

References 20 publications

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Computed Tomography Image: Guided Needle Biopsy in the Diagnosis of Lung Malignant Tumors under Artificial Intelligence Algorithm

Chronic Obstructive Pulmonary Disease and Lung Cancer: A Review for Clinicians

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