Procedure-related pain during CT-guided percutaneous transthoracic needle biopsies of lung lesions: a prospective study.

BACKGROUND: The existing data on the degree of pain in patients during CT-guided percutaneous transthoracic needle biopsy (PTNB) of lung lesions are limited and the factors related to pain are unclear. In this study, we aimed to evaluate the prevalence and severity of pain reported during PTNB and to identify factors associated with increased reported pain. METHODS: Patients who underwent PTNB from April 2022 to November 2022 were prospectively evaluated using the numeric rating scale, which assesses subjective pain based on a 0-10 scoring system (0 = no pain; 10 = the worst pain imaginable). The scale divides the scores into three categories: mild pain (1-3 points), moderate pain (4-6 points), and severe pain (7-10 points). Pain scores from 4 to 10 were considered significant pain. Demographic data of patients, lesion characteristics, biopsy variables, complications, the patient's subjective feelings, and pathological result data were analyzed by multivariable logistic regression analysis to identify variables associated with significant pain. RESULTS: We enrolled 215 participants who underwent 215 biopsy procedures (mean age: 64.5 ± 9.3 years, 123 were men). The mean procedure-related pain score was 2 ± 2. Overall, 20% (43/215) of participants reported no pain (score of 0), 67.9% (146/215) reported pain scores of 1-3, 11.2% (24/215) reported scores of 4-6, and 0.9% (2/215) reported scores of 7 or higher. Furthermore, non-significant pain (scores of 0-3) was reported during 87.9% (189/215) of the procedures. In the adjusted model, significant pain was positively associated with lesions ≥ 34 mm (p = 0.001, odds ratio [OR] = 6.90; 95% confidence interval [CI]: 2.18, 21.85), a needle-pleural angle ≥ 77° (p = 0.047, OR = 2.44; 95% CI: 1.01, 5.89), and a procedure time ≥ 26.5 min (p = 0.031, OR = 3.11; 95% CI: 1.11, 8.73). CONCLUSIONS: Most participants reported no pain or mild pain from CT-guided percutaneous transthoracic needle biopsies of lung lesions. However, those with a larger lesion, a greater needle-pleural angle, and a longer procedure time reported greater pain.

Comparison of Radiation Dose and Image Quality Between Split-Filter Twin Beam Dual-Energy Images and Single-Energy Images in Single-Source Contrast-Enhanced Chest Computed Tomography.

OBJECTIVE: To compare image quality and radiation dose of split-filter TwinBeam dual-energy (SF-TBDE) with those of single-energy images (SECT) in the contrast-enhanced chest computed tomography (CT). METHODS: Two hundred patients who underwent SF-TBDE (n = 100) and SECT (n = 100) contrast-enhanced chest scanning were retrospectively analyzed. The contrast-to-noise ratio (CNR) and figure of merit (FOM)-CNR of 5 structures (lung, aorta, pulmonary artery, thyroid, and erector spinae) were calculated and subjectively evaluated by 2 independent radiologists. Radiation dose was compared using volume CT dose index and size-specific dose estimate. RESULTS: The CNR and FOM-CNR of lung and erector spinae in SF-TBDE were higher than those of SECT (P < 0.001). The differences in the subjective image quality between the 2 groups were not significant (P = 0.244). Volume CT dose index and size-specific dose estimate of SF-TBDE were lower than those of SECT (6.60 ± 1.56 vs 7.81 ± 3.02 mGy, P = 0.001; 9.25 ± 1.60 vs. 10.55 ± 3.54; P = 0.001). CONCLUSIONS: The SF-TBDE CT can provide similar image quality at a lower radiation dose compared with SECT.

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.

OBJECTIVE: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest. MATERIALS AND METHODS: A total of 150 pathologically confirmed pulmonary nodules (60% malignant) assessed and reported by radiologists were included. CT images were processed by the proposed DL-based method to generate the probability of malignancy (0-100%), and the nodules were divided into the groups of benign (0-39.9%), indeterminate (40.0-59.9%), and malignant (60.0-100%). Taking the pathological results as the gold standard, we compared the diagnostic performance of the proposed DL-based method with the radiologists' diagnostic approach using the McNemar-Bowker test. RESULTS: There was a statistically significant difference between the diagnosis results of the proposed DL-based method and the radiologists' diagnostic approach (p < 0.001). Moreover, there was no statistically significant difference in the composition of the diagnosis results between the proposed DL-based method and the radiologists' diagnostic approach (all p > 0.05). The difference in diagnostic accuracy between the proposed DL-based method (70%) and radiologists' diagnostic performance (64%) was not statistically significant (p = 0.243). CONCLUSIONS: The proposed DL-based method achieved an accuracy comparable with the radiologists' diagnostic approach in clinical practice. Furthermore, its advantage in improving diagnostic certainty may raise the radiologists' confidence in diagnosing pulmonary nodules and may help clinical management. Therefore, the proposed DL-based method showed great potential in a certain clinical application. KEY POINTS: • Deep learning-based method for diagnosing the pulmonary nodules in computed tomography provides a higher diagnostic certainty.