Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists.

PURPOSE: Several reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia. METHODS: For this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar's test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar's test. RESULTS: A comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009). CONCLUSION: This multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.

Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect.

PURPOSE: Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting. MATERIALS AND METHODS: From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan-Meier method followed by Wilcoxon's signed-rank test. RESULTS: % GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008). CONCLUSION: ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.

Comparison of Interobserver Agreement and Diagnostic Accuracy for IASLC/ITMIG Thymic Epithelial Tumor Staging Among Co-registered FDG-PET/MRI, Whole-body MRI, Integrated FDG-PET/CT, and Conventional Imaging Examination with and without Contrast Media Administrations.

RATIONALE AND OBJECTIVES: The purpose of this study was to compare the interobserver agreements and diagnostic accuracies for IASLC/ITMIG (International Association for the Study of Lung Cancer/International Thymic Malignancies Interest Group) thymic epithelial tumor staging of co-registered fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (FDG-PET/MRI), MRI, integrated fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT), and conventional imaging examination. MATERIALS AND METHODS: Prospective whole-body MRI including diffusion-weighted imaging, integrated PET/CTs, conventional imaging examinations, pathological examinations, and surgical reports, as well as follow-up examinations, were performed for 64 consecutive patients with thymic epithelial tumor. All FDG-PET/MRIs were co-registered PET data with MRI. TNM staging was evaluated by two radiologists on the basis of the IASLC/ITMIG thymic epithelial tumor staging system. Kappa statistics were determined for evaluations of agreements of all factors between each of the methods and final diagnosis. Finally, the diagnostic accuracy of each factor and of determination of the clinical stage was statistically compared to each other using McNemar test. RESULTS: Agreements for all factors between each method and final diagnosis were assessed as fair, moderate, substantial, or almost perfect (0.28 ≤ kappa value ≤ 0.80; P < .0001). Diagnostic accuracy for N factor of PET/MRI (93.8% [60/64]) and MRI (93.8% [60/64]) was significantly higher than that of conventional imaging examination (81.3% [52/64] vs PET/MRI and MRI; P = .008). In addition, diagnostic accuracy for staging of PET/MRI (84.4% [54/64]) and MRI (84.4 [54/64]) was significantly higher than that of conventional imaging examination (71.9% [46/64] vs PET/MRI and MRI; P = .008). CONCLUSIONS: Whole-body PET/MRI, MRI, and PET/CT have better interobserver agreements and accuracies than conventional imaging examination for the new IASLC/ITMIG thymic epithelial tumor staging.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.

BACKGROUND: The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). PURPOSE: To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. MATERIAL AND METHODS: A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as "Stable" (n = 188), "Worse" (n = 98) and "Improved" (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. RESULTS: Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). CONCLUSION: ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

Small Cell Lung Cancer Staging: Prospective Comparison of Conventional Staging Tests, FDG PET/CT, Whole-Body MRI, and Coregistered FDG PET/MRI.

BACKGROUND. Whole-body MRI and FDG PET/MRI have shown encouraging results for staging of thoracic malignancy but are poorly studied for staging of small cell lung cancer (SCLC). OBJECTIVE. The purpose of our study was to compare the performance of conventional staging tests, FDG PET/CT, whole-body MRI, and FDG PET/MRI for staging of SCLC. METHODS. This prospective study included 98 patients (64 men, 34 women; median age, 74 years) with SCLC who underwent conventional staging tests (brain MRI; neck, chest, and abdominopelvic CT; and bone scintigraphy), FDG PET/CT, and whole-body MRI within 2 weeks before treatment; coregistered FDG PET/MRI was generated. Two nuclear medicine physicians independently reviewed conventional tests and FDG PET/CT examinations in separate sessions, and two chest radiologists independently reviewed whole-body MRI and FDG PET/MRI examinations in separate sessions. Readers assessed T, N, and M categories; TNM stage; and Veterans Administration Lung Cancer Study Group (VALSG) stage. Reader pairs subsequently reached consensus. Stages determined clinically during tumor board sessions served as the reference standard. RESULTS. Accuracy for T category was higher (p < .05) for whole-body MRI (94.9%) and FDG PET/MRI (94.9%) than for FDG PET/CT (85.7%). Accuracy for N category was higher (p < .05) for whole-body MRI (84.7%), FDG PET/MRI (83.7%), and FDG PET/CT (81.6%) than for conventional staging tests (75.5%). Accuracy for M category was higher (p < .05) for whole-body MRI (94.9%), FDG PET/MRI (94.9%), and FDG PET/CT (94.9%) than for conventional staging tests (84.7%). Accuracy for TNM stage was higher (p < .05) for whole-body MRI (88.8%) and FDG PET/MRI (86.7%) than for FDG PET/CT (77.6%) and conventional staging tests (72.4%). Accuracy for VALSG stage was higher (p < .05) for whole-body MRI (95.9%), FDG PET/MRI (95.9%), and FDG PET/CT (98.0%) than for conventional staging tests (82.7%). Interobserver agreement, expressed as kappa coefficients, ranged from 0.81 to 0.94 across imaging tests and staging endpoints. CONCLUSION. FDG PET/CT, whole-body MRI, and coregistered FDG PET/MRI outperformed conventional tests for various staging endpoints in patients with SCLC. Whole-body MRI and FDG PET/MRI outperformed FDG PET/CT for T category and thus TNM stage, indicating the utility of MRI for assessing extent of local invasion in SCLC. CLINICAL IMPACT. Incorporation of either MRI approach may improve initial staging evaluation in SCLC.

Adrenomedullin 2 and 5 activate the calcitonin receptor-like receptor (clr) - Receptor activity-modifying protein 3 (ramp3) receptor complex in Xenopus tropicalis.

The adrenomedullin (AM) family is involved in diverse biological functions, including cardiovascular regulation and body fluid homeostasis, in multiple vertebrate lineages. The AM family consists of AM1, AM2, and AM5 in tetrapods, and the receptor for mammalian AMs has been identified as the complex of calcitonin receptor-like receptor (CLR) and receptor activity-modifying protein 2 (RAMP2) or RAMP3. However, the receptors for AM in amphibians have not been identified. In this study, we identified the cDNAs encoding calcrl (clr), ramp2, and ramp3 receptor components from the western clawed frog (Xenopus tropicalis). Messenger RNAs of amphibian clr and ramp2 were highly expressed in the heart, whereas that of ramp3 was highly expressed in the whole blood. In HEK293T cells expressing clr-ramp2, cAMP response element luciferase (CRE-Luc) reporter activity was activated by am1. In HEK293T cells expressing clr-ramp3, CRE-Luc reporter activity was increased by the treatment with am2 at the lowest dose, but with am5 and am1 at higher dose. Our results provided new insights into the roles of AM family peptides through CLR-RAMP receptor complexes in the tetrapods.

Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases.

PURPOSE: To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases. MATERIALS AND METHODS: Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test. RESULTS: Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001). CONCLUSION: ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.

Comparison of Diagnostic Accuracy for TNM Stage Among Whole-Body MRI and Coregistered PET/MRI Using 1.5-T and 3-T MRI Systems and Integrated PET/CT for Non-Small Cell Lung Cancer.

OBJECTIVE. The purpose of this study was to compare diagnostic accuracy of TNM stage for whole-body MRI and coregistered PET/MRI using 1.5-T and 3-T MRI systems and PET/CT in patients with non-small cell lung cancer (NSCLC). SUBJECTS AND METHODS. A total of 104 patients with pathologically diagnosed NSCLC underwent whole-body MRI at 1.5 T and 3T and integrated PET/CT, as well as a combination of surgical, pathologic, or follow-up examinations. Whole-body MR images obtained by the five sequences were combined with the PET part of the PET/CT using proprietary software for the PET/MRI studies. The TNM stage obtained with all methods was visually assessed. Kappa statistics were used to determine agreement between TNM stage assessment and final diagnoses, and the McNemar test was used to compare diagnostic accuracy of all methods. RESULTS. Findings of TNM stage on whole-body MRI using 3-T (κ, 0.87; p < 0.0001) and 1.5-T (κ, 0.83; p < 0.0001) systems and for coregistered PET/MRI using a 3-T system (PET/MRI3T; κ, 0.85; p < 0.0001) were rated as significant and almost perfect, and findings for coregistered PET/MRI using a 1.5-T system (PET/MRI1.5T; κ, 0.80; p < 0.0001) and PET/CT (κ, 0.73; p < 0.0001) were rated significant and substantial. Diagnostic accuracy of whole-body MRI using the 3-T system was 88.5% (92/104; p = 0.0002, and using the 1.5-T system it was 84.6% (88/104; p = 0.004); results for PET/MRI3T and PET/MRI1.5T were 86.5% (90/104; p = 0.001) and 81.7% (85/104; p = 0.03), respectively, which were both significantly better than accuracy of results for PET/CT at 76.0% (79/104). Moreover, diagnostic accuracy of whole-body MRI using a 3-T system was significantly higher than that of PET/MRI using a 1.5-T system (p = 0.02). CONCLUSION. Whole-body MRI and coregistered PET/MRI using 3-T and 1.5-T systems are as accurate or more accurate than PET/CT, whereas differences between 3-T and 1.5-T MRI systems are not considered significant.

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size ± standard deviation, 11 mm ± 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 part-solid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P < .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P < .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P < .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P < .001) and DT of both methods (P < .001). Conclusion Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules. © RSNA, 2020 Online supplemental material is available for this article.

Whole-Body MRI: Comparison of Its Capability for TNM Staging of Malignant Pleural Mesothelioma With That of Coregistered PET/MRI, Integrated FDG PET/CT, and Conventional Imaging.

OBJECTIVE: The purpose of this study was to compare the diagnostic accuracy of whole-body MRI, coregistered FDG PET/MRI, integrated FDG PET/CT, and conventional imaging examination including bone scintigraphy, contrast-enhanced brain MRI, and CT for malignant pleural mesothelioma (MPM) staging according to the new International Association for the Study of Lung Cancer (IASLC) system. SUBJECTS AND METHODS: The study subjects were 23 consecutively registered patients with MPM (15 men, eight women; mean age, 68 years for both sexes) who had prospectively undergone whole-body FDG PET/CT, whole-body MRI, conventional radiologic examination, surgical or conventional treatments, pathologic examination, and follow-up conventional imaging examinations between January 2011 and December 2017. TNM staging was evaluated by two independent readers. Kappa statistics and chi-square tests were used for evaluation agreements on each factor and clinical stage between each method and final diagnosis. The diagnostic accuracy of each method was statistically compared by use of McNemar test. RESULTS: The kappa values for each factor between each method and final diagnosis were significant (p < 0.0001) and ranged between 0.33 and 0.91. Kappa values between final diagnosis and stage evaluation were also significant (p < 0.0001) and ranged between 0.57 and 0.91. The diagnostic accuracy of N and stage assessment of whole-body MRI and FDG PET/MRI was significantly higher than that of conventional imaging examination (N factor, p < 0.05; stage, p < 0.05). CONCLUSION: The diagnostic accuracy of whole-body MRI, FDG PET/MRI, and FDG PET/CT for TNM stage assessment based on the new IASLC MPM staging system is greater than that of conventional imaging examination.

Comparison of computer-aided detection (CADe) capability for pulmonary nodules among standard-, reduced- and ultra-low-dose CTs with and without hybrid type iterative reconstruction technique.

PURPOSE: To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D). MATERIALS AND METHODS: Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ±â€¯standard deviation: 63.1 ±â€¯11.0 years) and 19 females (mean age, 65.1 ±â€¯12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test. RESULTS: Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001). CONCLUSION: The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced.

Diagnostic performance of different imaging modalities in the assessment of distant metastasis and local recurrence of tumor in patients with non-small cell lung cancer.

PURPOSE: To compare the diagnostic performance of positron emission tomography with [18F] fluoro-2-deoxy-glucose (FDG-PET) coregistered with magnetic resonance imaging (FDG-PET/MRI), MRI with and without diffusion-weighted imaging (DWI), FDG-PET fused with computed tomography (FDG-PET/CT) with brain contrast-enhanced (CE-) MRI, and routine radiological examination for assessment of postoperative recurrence in nonsmall-cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: 96 consecutive postoperative NSCLC patients (52 men, 44 women; mean age 72 years) prospectively underwent whole-body 3T MRI with and without DWI; PET/CTs and routine radiological examinations consisted of CE-brain MRI, whole-body CE-CT, and bone scintigraphy. The patients were divided into a recurrence (n = 17) and a nonrecurrence (n = 79) group based on pathological and follow-up examinations. All coregistered PET/MRIs were generated by proprietary software. The probability of recurrence was visually assessed on a per-patient basis. Receiver operating characteristic analyses were used to compare the diagnostic performance of all methods. Finally, diagnostic capabilities were compared by means of McNemar's test. RESULTS: Areas under the curves (Azs) were significantly larger for PET/MRI and whole-body MRI with DWI (Az = 0.99) than for PET/CT (Az = 0.92, P < 0.05) and conventional radiological examination (Az = 0.91, P < 0.05). Specificity and accuracy of PET/MRI and MRI with and without DWI were significantly higher than those of PET/CT (P < 0.05) and routine radiological examination (P < 0.05). CONCLUSION: Whole-body FDG-PET/MRI and MRI with DWI were found to be more specific and accurate than FDG-PET/CT and routine radiological examinations for assessment of recurrence in NSCLC patients, although MRI with and without DWI demonstrated slightly lower sensitivity than PET/CT. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1707-1717.

Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study.

PURPOSE: To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study. MATERIALS AND METHODS: An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current). RESULTS: Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05). CONCLUSION: The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination.

Three-way Comparison of Whole-Body MR, Coregistered Whole-Body FDG PET/MR, and Integrated Whole-Body FDG PET/CT Imaging: TNM and Stage Assessment Capability for Non-Small Cell Lung Cancer Patients.

PURPOSE: To prospectively compare the capabilities for TNM classification and assessment of clinical stage and operability among whole-body magnetic resonance (MR) imaging, coregistered positron emission tomographic (PET)/MR imaging with and without MR signal intensity (SI) assessment, and integrated fluorine 18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) in non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: The institutional review board approved this study, and written informed consent was obtained from each patient. One hundred forty consecutive NSCLC patients (75 men, 65 women; mean age, 72 years) prospectively underwent whole-body MR imaging, FDG PET/CT, conventional radiologic examinations, and surgical, pathologic, and/or follow-up examinations. All factors and clinical stage and operability were then visually assessed. All PET/MR examinations were assessed with and without SI assessment. One examination used anatomic, metabolic, and relaxation-time information, and the other used only anatomic and metabolic information. κ statistics were used for assessment of all factors and clinical stages with final diagnoses. McNemar test was used to compare the capability of all methods to assess operability. RESULTS: Agreements of assessment of every factor (κ = 0.63-0.97) and clinical stage (κ = 0.65-0.90) were substantial or almost perfect. Regarding capability to assess operability, accuracy of whole-body MR imaging and PET/MR imaging with SI assessment (97.1% [136 of 140]) was significantly higher than that of MR/PET without SI assessment and integrated FDG PET/CT (85.0% [119 of 140]; P < .001). CONCLUSION: Accuracies of whole-body MR imaging and PET/MR imaging with SI assessment are superior to PET/MR without SI assessment and PET/CT for identification of TNM factor, clinical stage, and operability evaluation of NSCLC patients.