scholarly journals The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2211
Author(s):  
Yung-Liang Wan ◽  
Patricia Wu ◽  
Pei-Ching Huang ◽  
Pei-Kwei Tsay ◽  
Kuang-Tse Pan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Low Dose ◽  
Statistical Significance ◽  
Surgical Excision ◽  
Time Frame ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Significant Difference ◽  
Benign Nodules ◽  
Low Dose Computed Tomography

The purpose of this work was to evaluate the performance of an existing commercially available artificial intelligence (AI) software system in differentiating malignant and benign lung nodules. The AI tool consisted of a vessel-suppression function and a deep-learning-based computer-aided-detection (VS-CAD) analyzer. Fifty patients (32 females, mean age 52 years) with 75 lung nodules (47 malignant and 28 benign) underwent low-dose computed tomography (LDCT) followed by surgical excision and the pathological analysis of their 75 nodules within a 3 month time frame. All 50 cases were then processed by the AI software to generate corresponding VS images and CAD outcomes. All 75 pathologically proven lung nodules were well delineated by vessel-suppressed images. Three (6.4%) of the 47 lung cancer cases, and 11 (39.3%) of the 28 benign nodules were ignored and not detected by the AI without showing a CAD analysis summary. The AI system/radiologists produced a sensitivity and specificity (shown in %) of 93.6/89.4 and 39.3/82.1 in distinguishing malignant from benign nodules, respectively. AI sensitivity was higher than that of radiologists, though not statistically significant (p = 0.712). Specificity obtained by the radiologists was significantly higher than that of the VS-CAD AI (p = 0.003). There was no significant difference between the malignant and benign lesions with respect to age, gender, pure ground-glass pattern, the diameter and location of the nodules, or nodules <6 vs. ≥6 mm. However, more part-solid nodules were proven to be malignant than benign (90.9% vs. 9.1%), and more solid nodules were proven to be benign than malignant (86.7% vs. 13.3%) with statistical significance (p = 0.001 and <0.001, respectively). A larger cohort and prospective study are required to validate the AI performance.

scholarly journals Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

Radiotherapy planning study comparing volumetric modulated arc therapy (VMAT) with intensity modulated radiotherapy (IMRT) in the treatment of bladder and pelvic lymph nodes.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 320-320
Author(s):  
Victoria Harris ◽  
Karole Warren-Oseni ◽  
Robert Anthony Huddart
Keyword(s):  
Low Dose ◽  
Treatment Time ◽  
Statistical Significance ◽  
Volumetric Modulated Arc Therapy ◽  
Radiotherapy Planning ◽  
Rank Test ◽  
Attractive Alternative ◽  
Planning Study ◽  
Significant Difference ◽  
3D Crt

320 Background: VMAT is increasingly used as an alternative to IMRT and has been shown to reduce treatment time and monitor units delivered. We report a radiotherapy (RT) planning study of bladder and pelvic lymph node (LN) RT comparing dosimetric outcomes of VMAT and IMRT techniques. Methods: 8 patients with/at high risk of LN+ bladder cancer were treated with bladder/pelvic LN IMRT. 4 clinical target volumes (CTVs) were defined: Whole bladder (CTV1), Pelvic LN (CTV2), Involved Bladder (CTV3) and Involved LNs (CTV4). Margins were applied to create 4 corresponding PTVs. IMRT plans were compared with VMAT plans in order to assess planning target volume (PTV) and organ at risk (OAR) coverage. The same PTV/OAR volumes and doses were used for each technique. Results: The mean dose statistics were compared for each dosimetric parameter for both techniques. The Wilcoxon signed-rank test was used to compare techniques with statistical significance assumed as p<0.05. Both techniques met prescription goals for PTV coverage. Comparison of conformity indices revealed no significant difference between techniques. VMAT achieved significantly better homogeneity in coverage of PTV2, although this finding was not replicated in the other PTVs (Table). Homogeneity index (HI) was defined as HI = 100x(D2-D98)/ Dp, where Dp = prescribed dose. VMAT resulted in significantly larger volumes of bowel (4.7%) and rectum (4.8%) receiving low dose radiation (15 Gy) than IMRT, although there was no significant difference seen at higher dose levels. Comparison with 3D conformal radiotherapy (3D-CRT) showed that both techniques resulted in a large reduction in bowel irradiation to 45Gy (IMRT = 123cc, VMAT = 145cc and 3D-CRT = 218cc). Conclusions: VMAT offers an attractive alternative to IMRT with similar conformality. Whilst increased low dose RT to OARs was seen with VMAT, it is of doubtful significance relative to the higher doses received by these structures. [Table: see text]

Microdose acquisition in adolescent leg length discrepancy using a low-dose biplane imaging system

2017 ◽  
Vol 58 (9) ◽  
pp. 1108-1114 ◽  
Author(s):  
Janni Jensen ◽  
Bo R Mussmann ◽  
John Hjarbæk ◽  
Zaid Al-Aubaidi ◽  
Niels W Pedersen ◽  
...  
Keyword(s):  
Low Dose ◽  
Imaging System ◽  
Statistical Significance ◽  
Leg Length Discrepancy ◽  
Leg Length ◽  
Planar Imaging ◽  
Length Discrepancy ◽  
Significant Difference ◽  
Length Measurements ◽  
Total Leg

Background Children with leg length discrepancy often undergo repeat imaging. Therefore, every effort to reduce radiation dose is important. Using low dose preview images and noise reduction software rather than diagnostic images for length measurements might contribute to reducing dose. Purpose To compare leg length measurements performed on diagnostic images and low dose preview images both acquired using a low-dose bi-planar imaging system. Material and Methods Preview and diagnostic images from 22 patients were retrospectively collected (14 girls, 8 boys; mean age, 12.8 years; age range, 10–15 years). All images were anonymized and measured independently by two musculoskeletal radiologists. Three sets of measurements were performed on all images; the mechanical axis lines of the femur and the tibia as well as the anatomical line of the entire extremity. Statistical significance was tested with a paired t-test. Results No statistically significant difference was found between measurements performed on the preview and on the diagnostic image. The mean tibial length difference between the observers was −0.06 cm (95% confidence interval [CI], −0.12 to 0.01) and −0.08 cm (95% CI, −0.21 to 0.05), respectively; 0.10 cm (95% CI, 0.02–0.17) and 0.06 cm (95% CI, −0.02 to 0.14) for the femoral measurements and 0.12 cm (95% CI, −0.05 to 0.26) and 0.08 cm (95% CI, −0.02 to 0.19) for total leg length discrepancy. ICCs were >0.99 indicating excellent inter- and intra-rater reliability. Conclusion The data strongly imply that leg length measurements performed on preview images from a low-dose bi-planar imaging system are comparable to measurements performed on diagnostic images.

scholarly journals Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jordan Chamberlin ◽  
Madison R. Kocher ◽  
Jeffrey Waltz ◽  
Madalyn Snoddy ◽  
Natalie F. C. Stringer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Low Dose ◽  
Chest Ct ◽  
Lung Nodules ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Cardiopulmonary Outcomes

Abstract Background Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. Methods A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen’s kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. Results Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen’s kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). Conclusion We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

scholarly journals Seasonal prevalence and characteristics of low-dose CT detected lung nodules in a general Dutch population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harriet L. Lancaster ◽  
Marjolein A. Heuvelmans ◽  
Gert Jan Pelgrim ◽  
Mieneke Rook ◽  
Marius G. J. Kok ◽  
...  
Keyword(s):  
Management Strategy ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Short Term ◽  
General Dutch Population ◽  
Study Participants ◽  
Low Dose Computed Tomography ◽  
Underlying Pathophysiology

AbstractWe investigated whether presence and characteristics of lung nodules in the general population using low-dose computed tomography (LDCT) varied by season. Imaging in Lifelines (ImaLife) study participants who underwent chest LDCT-scanning between October 2018 and October 2019 were included in this sub-study. Hay fever season (summer) was defined as 1st April to 30th September and Influenza season (winter) as 1st October to 31st March. All lung nodules with volume of ≥ 30 mm3 (approximately 3 mm in diameter) were registered. In total, 2496 lung nodules were found in 1312 (38%) of the 3456 included participants (nodules per participant ranging from 1 to 21, median 1). In summer, 711 (54%) participants had 1 or more lung nodule(s) compared to 601 (46%) participants in winter (p = 0.002). Of the spherical, perifissural and left-upper-lobe nodules, relatively more were detected in winter, whereas of the polygonal-, irregular-shaped and centrally-calcified nodules, relatively more were detected in summer. Various seasonal diseases with inflammation as underlying pathophysiology may influence presence and characteristics of lung nodules. Further investigation into underlying pathophysiology using short-term LDCT follow-up could help optimize the management strategy for CT-detected lung nodules in clinical practice.

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