Project description:BackgroundThe identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer.MethodsA total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study. The molecular subtypes of breast cancer were identified through immunohistochemical staining of biopsies or postoperative pathological specimens. DLCT quantitative parameters were compared between the luminal and nonluminal types of breast cancer. The diagnostic efficacy of these parameters was determined via receiver operating characteristic (ROC) curves. Univariate and multivariate regression analyses were conducted to identify independent predictors that could differentiate nonluminal from luminal breast cancer. A nomogram prediction model was established based on multivariate regression analysis. The performance of the nomogram model was assessed with ROC curve and calibration curve analyses.ResultsAmong the DLCT quantitative values, eight were significantly lower in the luminal type than in the nonluminal type of breast cancer (P<0.001-0.011). The area under the curve (AUC) values for these significant DLCT quantitative parameters ranged from 0.604 to 0.694. Multivariate logistic regression analysis identified CT-reported lymph node metastasis status [hazard ratio (HR) =4.214; P<0.001], the Hounsfield unit (HU) value of the virtual monoenergetic image at 40 keV (HUVMI40) (HR =2.628; P=0.012), and the normalized iodine concentration (nIC) (HR =2.182; P=0.041) as independent predictors of the nonluminal type, with an AUC of 0.754 [95% confidence interval (CI): 0.688-0.820]. The nomogram based on multivariate logistic regression analysis exhibited good discrimination and calibration (Hosmer-Lemeshow test; P=0.835). An average AUC value of 0.75 was obtained for the internal validation data.ConclusionsDLCT quantitative parameters are valuable noninvasive indexes for differentiating between the luminal and nonluminal types of invasive breast cancer. Furthermore, the nomogram constructed in this study could guide individualized predictions of molecular subtypes in patients with invasive breast cancer.

Project description:PurposeTo analyse the predictive effect of a nomogram combining dual-layer spectral computed tomography (DSCT) quantitative parameters with typical radiological features in distinguishing benign micro-nodule from thyroid microcarcinoma (TMC).MethodsData from 342 instances with thyroid micro-nodules (≤1 cm) who underwent DSCT (benign group: n = 170; malignant group: n = 172) were reviewed. Typical radiological features including micro-calcification and enhanced blurring, and DSCT quantitative parameters including attenuation on virtual monoenergetic images (40 keV, 70 keV and 100 keV), the slope of the spectral HU curve (λHU), normalized iodine concentration (NIC), and normalized effective atomic number (NZeff) in the arterial phase (AP) and venous phase (VP), were measured and compared between the benign and malignant groups. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of significant quantitative DSCT parameters or the models combining DSCT parameters respectively and typical radiological features based on multivariate logistic regression (LR) analysis. A nomogram was developed using predictors with the highest diagnostic performance in the above model, as determined by multivariate LR analysis.ResultsThe DSCT parameter APλHU showed the greatest diagnostic efficiency in identifying patients with TMC, with an area under the ROC curve (AUC) of 0.829, a sensitivity and specificity of 0.738 and 0.753, respectively. Then, APλHU was combined with the two radiological features to construct the DSCT-Radiological nomogram, which had an AUC of 0.858, a sensitivity of 0.791 and a specificity of 0.800. The calibration curve of the nomogram demonstrated that the prediction result was in good agreement with the actual observation. The decision curve revealed that the nomogram can result in a greater net benefit than the all/none-intervention strategy for all threshold probabilities.ConclusionAs a valid and visual noninvasive prediction tool, the DSCT-Radiological nomogram incorporating DSCT quantitative parameters and radiological features shows favourable predictive efficiency for identifying benign and malignant thyroid micro-nodules.

Project description:BackgroundBreast cancer (BC) is a common cancer among women worldwide, and although the use of neoadjuvant therapy (NAT) for BC has become more widespread, there is no standardized prediction of the efficacy of NAT for BC. This study aimed to evaluate the value of quantitative parameters of dual-layer detector spectral computed tomography (DLCT) in predicting whether BC patients can achieve pathological complete response (pCR) after NAT.MethodsPatients who were first diagnosed with BC in Shandong Cancer Hospital and Institute and received only NAT before surgery were selected for participation in this study. All breast computed tomography (CT) imaging examinations were performed using DLCT, within 1 week before initiating NAT. The gold standard for evaluating the effect of NAT is pathologic response established at surgery. The Miller-Payne grading system was applied to assess the response to NAT. Quantitative parameters were extracted from DLCT, including CT value, normalized CT value, iodine concentration (IC), normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit (HU) curve, effective atomic number, and the normalized effective atomic number. The Mann-Whitney U test was used to compare the distribution differences of DLCT quantitative parameters between the pCR group and the non-pCR group. The diagnostic performance of the quantitative parameters was analyzed by receiver operating characteristic curve.ResultsIn the neoadjuvant chemotherapy group (n=80), compared with the non-pCR group, the slope of the spectral HU curve, IC, effective atomic number, and NIC of arterial phase in the pCR group were higher, and the difference was statistically significant (P<0.05); area under the curve (AUC): 0.768, 0.791, 0.834, and 0.770, respectively. In the neoadjuvant targeted therapy group (n=40), compared with the pCR group, the CT value, IC, effective atomic number, and NIC of the arterial phase in the non-pCR group were higher, and the difference was statistically significant (P<0.05); AUC: 0.844, 0.813, 0.802, and 0.766, respectively. There was no significant difference (P>0.05) in DLCT venous phase quantitative parameters between pCR and non-pCR in 70 patients treated with NAT.ConclusionsThe study suggested a possibility that DLCT provided a potential tool to develop a model for predicting pCR to NAT in BC.

Project description:BackgroundHepatic steatosis is closely related to the occurrence and development of coronary plaques. Spectral detector computed tomography (SDCT) can provide more precise multiparameter quantitative parameters for hepatic steatosis. Hence, the purpose of this cross-sectional study was to explore the effect of quantitative liver metrics measured using SDCT on the extent and severity of coronary plaques.MethodsIn patients who underwent upper abdomen unenhanced SDCT and coronary computed tomography angiography, plaque extent and severity were assessed using segmental involvement score (SIS) and segmental stenosis score (SSS). Liver fat quantification was evaluated by polychromatic and virtual mono-energetic images at 40 and 70 kev, spectral attenuation curve slope, and effective atomic number (CT40 keV, CT70 kev, λHU, and Zeff, respectively). A logistic regression model evaluated the factors influencing high SIS and SSS.ResultsEnrolled patients (n=644) were divided into groups: low SIS (<5) (n=451), high SIS (≥5) (n=193), low SSS (<5) (n=461), and high SSS (≥5) (n=183). Zeff was more closely correlated with SIS (standard partial regression coefficient =-0.422, P<0.001) and SSS (standard partial regression coefficient =-0.346, P<0.001). Zeff was divided into four groups using interquartile intervals. Compared with the patients in the lowest quartile, those in the second [odds ratio (OR) =2.116, 95% confidence interval (CI): 1.134-3.949, P=0.018], third (OR =2.832, 95% CI: 1.461-5.491, P=0.002), and fourth (OR =3.584, 95% CI: 1.857-6.918, P<0.001) quartiles showed higher risk for high SIS. And correspondingly, the second (OR =1.933, 95% CI: 1.040-3.592, P=0.037), third (OR =2.900, 95% CI: 1.499-5.609, P=0.002), and fourth (OR =3.368, 95% CI: 1.743-6.510, P<0.001) quartiles showed higher risk for high SSS, especially in those who were <60 years old, male and had visceral adipose tissue/subcutaneous adipose tissue <1.18.ConclusionsThe SDCT-Zeff was an independent factor associated with high SIS and SSS. The quantification of liver fat may be useful for evaluating the risk and prognosis of coronary atherosclerosis.

Project description:ObjectiveTo develop and validate a model integrating dual-layer detector spectral computed tomography (DLCT) three-dimensional (3D) volume of interest (VOI)-based quantitative parameters and clinical features for predicting Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsA total of 162 patients with histopathologically confirmed PDAC who underwent DLCT examination were included and allocated to the training (114) and validation (48) sets. 3D VOI-iodine concentration (IC), 3D VOI-slope of the spectral attenuation curves, and 3D VOI-effective atomic number were obtained from the portal venous phase. The significant clinical features and DLCT quantitative parameters were identified through univariate analysis and multivariate logistic regression. The discrimination capability and clinical applicability of the clinical, DLCT, and DLCT-clinical models were quantified by the Receiver Operating Characteristic curve (ROC) and Decision Curve Analysis (DCA), respectively. The optimal model was then used to develop a nomogram, with the goodness-of-fit evaluated through the calibration curve.ResultsThe DLCT-clinical model demonstrated superior predictive capability and a satisfactory net benefit for Ki-67 PI in PDAC compared to the clinical and DLCT models. The DLCT-clinical model integrating 3D VOI-IC and CA125 showed area under the ROC curves of 0.939 (95% CI, 0.895-0.982) and 0.915 (95% CI, 0.834-0.996) in the training and validation sets, respectively. The nomogram derived from the DLCT-clinical model exhibited favorable calibration, as depicted by the calibration curve.ConclusionsThe proposed model based on DLCT 3D VOI-IC and CA125 is a non-invasive and effective preoperative prediction tool demonstrating favorable predictive performance for Ki-67 PI in PDAC.Critical relevance statementThe dual-layer detector spectral computed tomography-clinical model could help predict high Ki-67 PI in pancreatic ductal adenocarcinoma patients, which may help clinicians provide appropriate and individualized treatments.Key pointsDual-layer detector spectral CT (DLCT) could predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). The DLCT-clinical model improved the differential diagnosis of Ki-67. The nomogram showed satisfactory calibration and net benefit for discriminating Ki-67.

Project description:BackgroundEarly rectal neoplasms can be treated endoscopically with good prognosis, yet usually present with unspecific or an absence of signs and symptoms and are detected largely by invasive endoscopy with less compliance to screening. The purpose of this cross-sectional study was to explore the diagnostic value of dual-layer spectral detector computed tomography (DSCT) imaging for early rectal neoplasm.MethodsPatients who underwent DSCT for evaluation of rectal lesion or routine examination between September 2022 to September 2023 at West China Hospital were prospectively included and identified as group A (control, n=76), group B (rectal advanced adenomas and ≤T1 rectal cancer, n=59), and group C (≥T2 staging rectal cancer, n=74). Lesion visualization was graded to assess image quality. Spectral quantitative measurement, such as Hounsfield unit (HU)40 keV, HU70 keV, iodine concentration (IC), effective atomic number (Zeff), and the slope of spectral curve (λ), was analyzed and compared. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic efficacy of spectral parameters. A comparison of ROC curves was applied to test the significance of differences between the area under the curves (AUCs).ResultsCompared to poly-energetic images (PEIs), the multiple parameters from DSCT were of greater capability to recognize rectal lesions. There were significant differences in HU40 keV (208.01±43.60 vs. 255.53±45.16), HU70 keV (87.06±18.55 vs. 100.78±18.26), IC [1.91 (1.71, 2.28) vs. 2.58±0.49], Zeff [8.33 (8.25, 8.50) vs. 8.61±0.20], and λ [3.80 (3.41, 4.52) vs. 5.16±1.00] between the early neoplastic lesions in rectum and the advanced rectal cancer (P<0.001). Significant correlations were found between the DSCT parameters and tumor staging (P<0.001). Furthermore, the AUCs of IC, Zeff, λ, and HUPEI were all above 0.90 for early rectal neoplasm detection, with additional capability of discriminating early rectal neoplasm from advanced rectal cancer.ConclusionsDSCT improved tumor conspicuity and the detection of the early rectal neoplastic lesion, suggesting that it is a promising screening tool in clinical practice.

Project description:BackgroundProgrammed death ligand-1 (PD-L1) expression serves a predictive biomarker for the efficacy of immune checkpoint inhibitors (ICIs) in the treatment of patients with early-stage lung adenocarcinoma (LA). However, only a limited number of studies have explored the relationship between PD-L1 expression and spectral dual-layer detector-based computed tomography (SDCT) quantification, qualitative parameters, and clinical biomarkers. Therefore, this study was conducted to clarify this relationship in stage I LA and to develop a nomogram to assist in preoperative individualized identification of PD-L1-positive expression.MethodsWe analyzed SDCT parameters and PD-L1 expression in patients diagnosed with invasive nonmucinous LA through postoperative pathology. Patients were categorized into PD-L1-positive and PD-L1-negative expression groups based on a threshold of 1%. A retrospective set (N=356) was used to develop and internally validate the radiological and biomarker features collected from predictive models. Univariate analysis was employed to reduce dimensionality, and logistic regression was used to establish a nomogram for predicting PD-L1 expression. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, and external validation was performed in an independent set (N=80).ResultsThe proportions of solid components and pleural indentations were higher in the PD-L1-positive group, as indicated by the computed tomography (CT) value, CT at 40 keV (CT40keV; a/v), electron density (ED; a/v), and thymidine kinase 1 (TK1) exhibiting a positive correlation with PD-L1 expression. In contrast, the effective atomic number (Zeff; a/v) showed a negative correlation with PD-L1 expression [r=-0.4266 (Zeff.a), -0.1131 (Zeff.v); P<0.05]. After univariate analysis, 18 parameters were found to be associated with PD-L1 expression. Multiple regression analysis was performed on significant parameters with an area under the curve (AUC) >0.6, and CT value [AUC =0.627; odds ratio (OR) =0.993; P=0.033], CT40keV.a (AUC =0.642; OR =1.006; P=0.025), arterial Zeff (Zeff.a) (AUC =0.756; OR =0.102; P<0.001), arterial ED (ED.a) (AUC =0.641; OR =1.158, P<0.001), venous ED (ED.v) (AUC =0.607; OR =0.864; P<0.001), TK1 (AUC =0.601; OR =1.245; P=0.026), and diameter of solid components (Dsolid) (AUC =0.632; OR =1.058; P=0.04) were found to be independent risk factors for PD-L1 expression in stage I LA. These seven predictive factors were integrated into the development of an SDCT parameter-clinical nomogram, which demonstrated satisfactory discrimination ability in the training set [AUC =0.853; 95% confidence interval (CI): 0.76-0.947], internal validation set (AUC =0.824; 95% CI: 0.775-0.874), and external validation set (AUC =0.825; 95% CI: 0.733-0.918). Decision curve analyses also revealed the highest net benefit for the nomogram across a broad threshold probability range (20-80%), with a clinical impact curve (CIC) indicating its clinical validity. Comparisons with other models demonstrated the superior discriminatory accuracy of the nomogram over any individual variable (all P values <0.05).ConclusionsQuantitative parameters derived from SDCT demonstrated the ability to predict for PD-L1 expression in early-stage LA, with Zeff.a being notably effective. The nomogram established in combination with TK1 showed excellent predictive performance and good calibration. This approach may facilitate the improved noninvasive prediction of PD-L1 expression.

Project description:BackgroundIt is difficult to distinguish the pathological grade of lung adenocarcinoma (LUAD) with traditional computed tomography (CT). The aim of this study was to assess tumor differentiation by dual-layer spectral detector CT combined with morphological parameters.MethodsIn this prospective study, a total of 67 patients with pathologically diagnosed LUAD were enrolled: 39 patients in the well- and moderately-differentiated group (14 and 25 patients, respectively) and 28 patients in the poorly-differentiated group. Morphological parameters, non-enhanced CT number, double-enhanced CT number, effective atomic number, monoenergetic CT images (40 and 70 keV), iodine density, and thoracic aorta iodine density of tumors were measured. The slope of the spectral curve and normalized iodine density were calculated. The diagnostic efficiency of the spectral parameters alone, and the combined spectral and morphological parameters were obtained by statistical analysis.ResultsThe morphological signs of LUAD (the vessel convergence sign, bronchus encapsulated air sign, and liquefactive necrosis) were higher in the poorly-differentiated group than in the well-moderately-differentiated group (57.1% vs. 30.8%, 40.0%; 60.7% vs. 28.2%, 32.0%; 64.3% vs. 28.2%, 24.0%; all P<0.05). There were significant differences in normalized iodine density (arterial phase: 0.10±0.04 vs. 0.12±0.05, 0.13±0.04; venous phase: 0.31±0.07 vs. 0.39±0.17, 0.40±0.17) among the poorly-differentiated group and moderately-differentiated group as well as the well-differentiated group (all P<0.05). Receiver operating characteristic (ROC) curves of the poorly-differentiated group and well-moderately-differentiated group showed that the normalized iodine density had the best diagnostic efficiency in the arterial phase, with an area under the curve (AUC) of 0.817, sensitivity of 92.9%, and specificity of 82.1% (P<0.001). The AUC increased to 0.916 when the morphological parameters were included, and sensitivity and specificity were 96.4% and 82.1% (P<0.001), respectively.ConclusionsThe parameters of dual-layer spectral detector CT can help discriminate the pathological grade of LUAD. Among the spectral parameters, the normalized iodine density in the arterial phase has the best diagnostic efficiency, and the combination of spectral and morphological parameters improves the pathological grading of LUAD.

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A nomogram based on dual-layer detector spectral computed tomography quantitative parameters and morphological quantitative indicator for distinguishing metastatic and nonmetastatic regional lymph nodes in pancreatic ductal adenocarcinoma

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