Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort.

The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.

Collaborative multidisciplinary management and expertise of cT2-3 locally advanced operable esophageal squamous cell carcinoma: a report of two cases.

Background: The accurate clinical staging of esophageal squamous cell carcinoma (ESCC) is pivotal for guiding treatment strategies. However, the current precision in staging for clinical T (cT)2 and cT3 stages remains unsatisfactory. This article discusses the role of multidisciplinary teams (MDTs) in the clinical staging and formulation of neoadjuvant treatment strategies for locally advanced operable ESCC. These challenges underscore the importance of precise staging in the decision-making process for appropriate therapeutic interventions. Case Description: Through the lens of two patient case studies with locally advanced resectable ESCC, the article showcases the intricate process of treatment planning undertaken by MDTs. It captures a range of expert perspectives from Japan, China, Hong Kong (China), Korea, the USA, and Europe, focusing on the challenges of differentiating between cT2 and cT3 stages of the disease, which is a critical determinant in the management and therapeutic approach for patients. Conclusions: The article concludes that the accurate staging of ESCC is a cornerstone in determining the most suitable treatment strategies. It underscores the vital role that MDTs play in both clinical staging and the decision-making process for treatment. Highlighting the limitations in current diagnostic methods, the article emphasizes the urgent need for advanced research and the refinement of diagnostic tools to improve the precision of staging, particularly between the cT2 and cT3 stages. It suggests that future research should consider whether a reclassification of these stages could be warranted to enhance treatment planning and outcomes for patients with ESCC.

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography.

BACKGROUND: There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features. METHODS: A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve. RESULTS: The CTRV at attenuation threshold of -250 HU (CTRV- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05). CONCLUSIONS: The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening.

OBJECTIVES: To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. METHODS: A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. RESULTS: The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). CONCLUSIONS: The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. KEY POINTS: • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.

PURPOSE: Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS: A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS: No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS: The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.

Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules.

Objectives: This study aimed to investigate the ability of quantitative parameters of dual-energy computed tomography (DECT) and nodule size for differentiation between lung cancers and benign lesions in solid pulmonary nodules. Materials and Methods: A total of 151 pathologically confirmed solid pulmonary nodules including 78 lung cancers and 73 benign lesions from 147 patients were consecutively and retrospectively enrolled who underwent dual-phase contrast-enhanced DECT. The following features were analyzed: diameter, volume, Lung CT Screening Reporting and Data System (Lung-RADS) categorization, and DECT-derived quantitative parameters including effective atomic number (Zeff), iodine concentration (IC), and normalized iodine concentration (NIC) in arterial and venous phases. Multivariable logistic regression analysis was used to build a combined model. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results: The independent factors for differentiating lung cancers from benign solid pulmonary nodules included diameter, Lung-RADS categorization of diameter, volume, Zeff in arterial phase (Zeff_A), IC in arterial phase (IC_A), NIC in arterial phase (NIC_A), Zeff in venous phase (Zeff_V), IC in venous phase (IC_V), and NIC in venous phase (NIC_V) (all P < 0.05). The IC_V, NIC_V, and combined model consisting of diameter and NIC_V showed good diagnostic performance with AUCs of 0.891, 0.888, and 0.893, which were superior to the diameter, Lung-RADS categorization of diameter, volume, Zeff_A, and Zeff_V (all P < 0.001). The sensitivities of IC_V, NIC_V, and combined model were higher than those of IC_A and NIC_A (all P < 0.001). The combined model did not increase the AUCs compared with IC_V (P = 0.869) or NIC_V (P = 0.633). Conclusion: The DECT-derived IC_V and NIC_V may be useful in differentiating lung cancers from benign lesions in solid pulmonary nodules.

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening.

Background: Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS). Methods: A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. Results: The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively). Conclusions: The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.

Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening.

OBJECTIVE: To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). METHODS: A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. RESULTS: The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801-0.935), 0.929 (95% CI, 0.862-0.971), 0.941 (95% CI, 0.876-0.978), and 0.884 (95% CI, 0.805-0.938) in the primary cohort and 0.897 (95% CI, 0.768-0.968), 0.933 (95% CI, 0.815-0.986), 0.901 (95% CI, 0.773-0.970), and 0.828 (95% CI, 0.685-0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). CONCLUSION: The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be pre-operative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. ADVANCES IN KNOWLEDGE: The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be pre-operative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.

Improvement of image quality of laryngeal squamous cell carcinoma using noise-optimized virtual monoenergetic image and nonlinear blending image algorithms in dual-energy computed tomography.

BACKGROUND: Dual-energy computed tomography (DECT) has been used to improve image quality of head and neck squamous cell carcinoma (SCC). This study aimed to assess image quality of laryngeal SCC using linear blending image (LBI), nonlinear blending image (NBI), and noise-optimized virtual monoenergetic image (VMI+) algorithms. METHODS: Thirty-four patients with laryngeal SCC were retrospectively enrolled between June 2019 and December 2020. DECT images were reconstructed using LBI (80 kV and M_0.6), NBI, and VMI+ (40 and 55 keV) algorithms. Contrast-to-noise ratio (CNR), tumor delineation, and overall image quality were assessed and compared. RESULTS: VMI+ (40 keV) had the highest CNR and provided better tumor delineation than VMI+ (55 keV), LBI, and NBI, while NBI provided better overall image quality than VMI+ and LBI (all corrected p < 0.05). CONCLUSIONS: VMI+ (40 keV) and NBI improve image quality of laryngeal SCC and may be preferable in DECT examination.

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer.

OBJECTIVE: To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer. MATERIALS AND METHODS: A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models. RESULTS: The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05). CONCLUSION: Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

Chemotherapy-induced grey matter abnormalities in cancer survivors: a voxel-wise neuroimaging meta-analysis.

BACKGROUND: Findings regarding chemotherapy-induced grey matter abnormalities are heterogeneous, and no meta-analysis has quantitatively assessed brain structural alterations in cancer survivors treated with chemotherapy. PURPOSE: To investigate the grey matter abnormalities in non-CNS (central nervous system) cancer survivors treated with chemotherapy using Anisotropic Effect Size Signed Differential Mapping (AES-SDM) software. METHOD: We identified studies published up to Sep 2018 that compared grey matter in non-CNS cancer survivors treated with chemotherapy (CT+, 10 data sets including 433 individuals) and cancer survivors not treated with chemotherapy (CT-, 7 data sets including 210 individuals) or healthy controls (HC, 3 data sets including 407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. RESULTS: Compared with both CT- and HC, the CT + groups exhibited a reduced grey matter volume (GMV), mainly in the prefrontal and anterior cingulate cortex (ACC) and right fusiform gyrus (FG). A smaller GMV in the FG and prefrontal cortex were found in the CT + compared with the CT-groups and in the CT + groups with impaired cognition. GMV in two areas was positively associated with the time since chemotherapy. CONCLUSIONS: The present results suggest that non-CNS cancer survivors treated with chemotherapy exhibit grey matter abnormalities in the brain, especially in the prefrontal and ACC cortex. Grey matter volume changes after chemotherapy may contribute to cognitive impairments in cancer survivors that can be observed after chemotherapy.

Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study.

OBJECTIVE: The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT). METHODS: An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated. RESULTS: The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05). CONCLUSIONS: The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.

Merged image reconstruction for anomalous systemic arterial supply to a normal lung.

We present a rare case of anomalous systemic arterial supply to normal basal segments of the left lower lobe. Plain computed tomography (CT) showed an occupancy lesion in the left lower lobe. Contrast CT and merged three-dimensional (3D) image reconstruction showed that the anomalous systemic artery originated from the descending aorta and substituted the basilar segmental pulmonary artery and the arterial supply to the basilar segment of left lower lobe. We use the merged image reconstruction of 3D CT angiography and bronchography (3D-CTAB) to depict the precise location and stereoscopic shape of this vascular malformation. Therefore, we think that these data add a novel comprehensive perspective on the diagnosis of the feature of malformation and treatment planning for this rare disease.

Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules.

OBJECTIVES: This study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated. METHODS: A total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. RESULTS: Shape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models. CONCLUSIONS: The diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.

Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: The aim of the present study was to use pharmacokinetic quantitative parameters with histogram and texture features on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between the luminal A and luminal B molecular subtypes of breast cancer. METHODS: We retrospectively reviewed the data of 94 patients with histopathologically proven breast cancer. The pharmacokinetic quantitative parameters (Ktrans, Kep, and Ve) with their corresponding histogram and texture features based on preoperative DCE-MRI were obtained. The parameters were compared using the Mann-Whitney U-test between the luminal A and luminal B groups, the human epidermal growth factor receptor-2 (HER2)-positive luminal B and HER2-negative luminal B groups, and the lymph node metastasis (LNM)-positive and LNM-negative groups. Receiver operating characteristic curves were generated for parameters that presented significant between-group differences. RESULTS: The maximum values of Ktrans, Kep, and Ve, and the mean and 90th percentile values of Ve were significantly higher in the luminal B group than in the luminal A group. Among the texture features, only skewness of Ktrans significantly differed between the luminal A and B groups. All histogram features of Ktrans were higher in the HER2-positive luminal B group than in the HER2-negative luminal B group. However, no parameter differed between the LNM-positive and LNM-negative groups. CONCLUSION: Pharmacokinetic quantitative parameters with histogram and texture features obtained from DCE-MRI are associated with the molecular subtypes of breast cancer, and may serve as potential imaging biomarkers to differentiate between the luminal A and luminal B molecular subtypes.

Accuracy of Pulmonary Nodule Volumetry at Different Exposure Parameters in Low-Dose Computed Tomography: A Phantom Study.

OBJECTIVE: To explore the exposure parameters with minimized radiation dose for accurate pulmonary nodule volumetry using low-dose computed tomography (LDCT). METHODS: An anthropomorphic chest phantom with 11 pulmonary nodules (6 solid nodules and 5 ground-glass opacities) was scanned using 256-slice multidetector computed tomography scanner at various tube voltage and current (combinations of 80, 100 and 120 kV with 10 to 30 mAs). Raw data sets were reconstructed using the hybrid iterative reconstruction method and nodule volume was calculated by a semiautomatic software. The absolute percentage error (APE) of nodule volume relating to the reference acquisition and contrast-to-noise ratio was measured. RESULTS: Nodule characteristic and tube voltage (P < 0.0001) as well as the interaction between nodule characteristic and tube voltage (P = 0.0026) contributed significantly to the mean difference of APE, while tube current did not (P = 0.21). Post hoc analysis revealed no significant difference was found between the APE at 100 kV and 120 kV in both solid nodules (2.3 ± 0.4% vs 1.8 ± 0.6%, P = 0.14) and ground-glass opacities (6.0 ± 0.5% vs 4.9 ± 0.6%, P = 0.11). Exploratory analyses further showed that the APE at 100 kV with 10 mAs did not differ from that at 120 kV with 30 mAs in both solid nodules (2.5 ± 0.5% vs 1.7 ± 0.3%, P = 0.025, corrected P = 0.20) and ground-glass opacities (6.4 ± 0.4% vs 4.8 ± 1.0%, P = 0.0084, corrected P = 0.068). CONCLUSIONS: In our study, the exposure parameters with minimized radiation dose for accurate pulmonary nodule volumetry were found at 100 kV with 10 mAs, and the estimated effect radiation dose was as low as 0.2 mSv, suggesting the feasibility of further reducing radiation dose by decreasing tube voltage and current in LDCT lung screening.

Screening baseline characteristics of early lung cancer on low-dose computed tomography with computer-aided detection in a Chinese population.

OBJECTIVES: This study investigated appropriate baseline characteristics for screening a Chinese population at high risk of early lung cancer, assisted by low-dose computed tomography (LDCT) with computer-aided detection (CAD). Included is a discussion of the viability of using LDCT in the screening guideline and optimizing the guideline. METHODS: In 2014, 1016 individuals from Sichuan Province were enrolled who satisfied the criteria for high risk according to the 2013 National Comprehensive Cancer Network (NCCN) Guidelines for Non-Small Cell Lung Cancer. From 2014 to 2018, each subject was followed using LDCT with CAD, and pathologically confirmed lung cancers and baseline nodule characteristics (size and density) were recorded. Positive risk was considered a non-calcified solid or part-solid nodule on LDCT with diameter ≥5 mm and ground-glass nodule ≥8 mm, as newly recommended by the China National Lung Cancer Screening Guideline. RESULTS: From 2014-2018, 13 cases of lung cancer were detected; 5 of these were early stage (38.5%). According to the NCCN criteria, 54 women were included and one of these (1.8%) developed lung cancer. The prevalence of lung cancer was 0.7% at baseline. For the entire population (excluding subjects with a tumor mass at baseline, n = 4), the rate of positivity was 20.4% at baseline; applying the Chinese criteria, the false positive rate was 19.5% (197/1012). CONCLUSIONS: Further studies are warranted to establish appropriate eligible criteria and management strategies for Chinese populations.

Ex-vivo biodistribution and micro-PET/CT imaging of 18F-FDG, 18F-FLT, 18F-FMISO, and 18F-AlF-NOTA-PRGD2 in a prostate tumor-bearing nude mouse model.

OBJECTIVE: (18)F-Fluorodeoxyglucose ((18)F-FDG), (18)F-fluoro-3'-deoxy-3'-L-fluorothymidine ((18)F-FLT), (18)F-fluoromisonidazole ((18)F-FMISO), and (18)F-AlF-NOTA-PRGD2 ((18)F-RGD) are all commonly used PET tracers for tumor diagnosis based on different mechanisms of tissue uptake. This study compared the ex-vivo biodistribution and PET/computed tomography (CT) imaging studies of these four PET tracers in a xenograft prostate tumor-bearing mouse model. MATERIALS AND METHODS: Nude mice were inoculated with 5 × 10 PC-3 cells in the right armpit. The ex-vivo biodistribution of (18)F-FDG, (18)F-FLT, (18)F-FMISO, and (18)F-RGD at 30, 60, 90, and 120 min after injection was compared. Micro-PET/CT images of (18)F-FDG, (18)F-FLT, and (18)F-RGD were acquired at 60 min, whereas (18)F-FMISO images were acquired at 90 min after injection. RESULTS: The tumors were clearly visualized by micro-PET/CT using all four PET tracers. Ex-vivo biodistribution results showed highest tumor accumulation and tumor-to-muscle ratio of (18)F-FDG at each time point, accompanied by physiologically high uptakes in the brain, heart, and intestinal tract. Modest uptake of (18)F-FLT and (18)F-FMISO in tumors was observed at 60 and 90 min after injection, with less interference from other tissues compared with (18)F-FDG. Besides, (18)F-RGD also exhibited high tumor specificity; however, relatively low uptake was observed in the tumor. CONCLUSION: Our results demonstrated the potential of (18)F-FMISO and (18)F-FLT in the diagnosis of xenograft prostate cancer.