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Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry.

ABSTRACT: Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

SUBMITTER: Gierada DS

PROVIDER: S-EPMC7706890 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recently, a standardized framework system for interpreting somatostatin receptor (SSTR)-targeted PET/CT, termed the SSTR reporting and data system (RADS) 1.0, was introduced, providing reliable standards and criteria for SSTR-targeted imaging. We determined the interobserver reliability of SSTR-RADS for interpretation of 68Ga-DOTATOC PET/CT scans in a multicentric, randomized setting. Methods: A set of 51 randomized 68Ga-DOTATOC PET/CT scans was independently assessed by 4 masked readers with different levels of experience (2 experienced readers and 2 inexperienced readers) trained on the SSTR-RADS 1.0 criteria (based on a 5-point scale from 1 [definitively benign] to 5 [high certainty that neuroendocrine neoplasia is present]). For each scan, SSTR-RADS scores were assigned to a maximum of 5 target lesions (TLs). An overall scan impression based on SSTR-RADS was indicated, and interobserver agreement rates on a TL-based, on an organ-based, and on an overall SSTR-RADS score-based level were computed. The readers were also asked to decide whether peptide receptor radionuclide therapy (PRRT) should be considered on the basis of the assigned RADS scores. Results: Among the selected TLs, 153 were chosen by at least 2 readers (all 4 readers selected the same TLs in 58 of 153 [37.9%] instances). The interobserver agreement for SSTR-RADS scoring among identical TLs was good (intraclass correlation coefficient [ICC] ≥ 0.73 for 4, 3, and 2 identical TLs). For lymph node and liver lesions, excellent interobserver agreement rates were derived (ICC, 0.91 and 0.77, respectively). Moreover, the interobserver agreement for an overall scan impression based on SSTR-RADS was excellent (ICC, 0.88). The SSTR-RADS-based decision to use PRRT also demonstrated excellent agreement, with an ICC of 0.80. No significant differences between experienced and inexperienced readers for an overall scan impression and TL-based SSTR-RADS scoring were observed (P ≥ 0.18), thereby suggesting that SSTR-RADS seems to be readily applicable even for less experienced readers. Conclusion: SSTR-RADS-guided assessment demonstrated a high concordance rate, even among readers with different levels of experience, supporting the adoption of SSTR-RADS for trials, clinical routine, or outcome studies.

Project description:Recently, the standardized reporting and data system for prostate-specific membrane antigen (PSMA)-targeted PET imaging studies, termed PSMA-RADS version 1.0, was introduced. We aimed to determine the interobserver agreement for applying PSMA-RADS to imaging interpretation of 18F-DCFPyL (2-(3-{1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid) PET examinations in a prospective setting mimicking the typical clinical workflow at a prostate cancer referral center. Methods: Four readers (2 experienced readers (ERs, >3 y of PSMA-targeted PET interpretation experience) and 2 inexperienced readers (IRs, <1 y of experience)), who had all read the initial publication on PSMA-RADS 1.0, assessed 50 18F-DCFPyL PET/CT studies independently. Per scan, a maximum of 5 target lesions was selected by the observers, and a PSMA-RADS score for every target lesion was recorded. No specific preexisting conditions were placed on the selection of the target lesions, although PSMA-RADS 1.0 suggests that readers focus on the most avid or largest lesions. An overall scan impression based on PSMA-RADS was indicated, and interobserver agreement rates on a target lesion-based, on an organ-based, and on an overall PSMA-RADS score-based level were computed. Results: The number of target lesions identified by each observer was as follows: ER 1, 123; ER 2, 134; IR 1, 123; and IR 2, 120. Among those selected target lesions, 125 were chosen by at least 2 individual observers (all 4 readers selected the same target lesion in 58 of 125 [46.4%] instances, 3 readers in 40 of 125 [32%], and 2 observers in 27 of 125 [21.6%]). The interobserver agreement for PSMA-RADS scoring among identical target lesions was good (intraclass correlation coefficient [ICC] for 4, 3, and 2 identical target lesions, ≥0.60, respectively). For lymph nodes, an excellent interobserver agreement was derived (ICC, 0.79). The interobserver agreement for an overall scan impression based on PSMA-RADS was also excellent (ICC, 0.84), with a significant difference for ER (ICC, 0.97) vs. IR (ICC, 0.74) (P = 0.005). Conclusion: PSMA-RADS demonstrated a high concordance rate in this study, even among readers with different levels of experience. This finding suggests that PSMA-RADS can be effectively used for communication with clinicians and can be implemented in the collection of data for large prospective trials.

Project description:Breast cancers can be classified by hierarchical clustering using an "intrinsic" gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis. Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset. None of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81). Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required.

Project description:OBJECTIVE:To investigate the accuracy of model-based iterative reconstruction (MIR) for volume measurement of part-solid nodules (PSNs) and solid nodules (SNs) in comparison with filtered back projection (FBP) or hybrid iterative reconstruction (HIR) at various radiation dose settings. MATERIALS AND METHODS:CT scanning was performed for eight different diameters of PSNs and SNs placed in the phantom at five radiation dose levels (120 kVp/100 mAs, 120 kVp/50 mAs, 120 kVp/20 mAs, 120 kVp/10 mAs, and 80 kVp/10 mAs). Each CT scan was reconstructed using FBP, HIR, or MIR with three different image definitions (body routine level 1 [IMR-R1], body soft tissue level 1 [IMR-ST1], and sharp plus level 1 [IMR-SP1]; Philips Healthcare). The SN and PSN volumes including each solid/ground-glass opacity portion were measured semi-automatically, after which absolute percentage measurement errors (APEs) of the measured volumes were calculated. Image noise was calculated to assess the image quality. RESULTS:Across all nodules and dose settings, the APEs were significantly lower in MIR than in FBP and HIR (all p < 0.01). The APEs of the smallest inner solid portion of the PSNs (3 mm) and SNs (3 mm) were the lowest when MIR (IMR-R1 and IMR-ST1) was used for reconstruction for all radiation dose settings. (IMR-R1 and IMR-ST1 at 120 kVp/100 mAs, 1.06 ± 1.36 and 8.75 ± 3.96, p < 0.001; at 120 kVp/50 mAs, 1.95 ± 1.56 and 5.61 ± 0.85, p = 0.002; at 120 kVp/20 mAs, 2.88 ± 3.68 and 5.75 ± 1.95, p = 0.001; at 120 kVp/10 mAs, 5.57 ± 6.26 and 6.32 ± 2.91, p = 0.091; at 80 kVp/10 mAs, 5.84 ± 1.96 and 6.90 ± 3.31, p = 0.632). Image noise was significantly lower in MIR than in FBP and HIR for all radiation dose settings (120 kVp/100 mAs, 3.22 ± 0.66; 120 kVp/50 mAs, 4.19 ± 1.37; 120 kVp/20 mAs, 5.49 ± 1.16; 120 kVp/10 mAs, 6.88 ± 1.91; 80 kVp/10 mAs, 12.49 ± 6.14; all p < 0.001). CONCLUSION:MIR was the most accurate algorithm for volume measurements of both PSNs and SNs in comparison with FBP and HIR at low-dose as well as standard-dose settings. Specifically, MIR was effective in the volume measurement of the smallest PSNs and SNs.

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Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry.

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