Epidermal growth factor receptor mutations in lung adenocarcinoma: associations between dual-energy spectral CT measurements and histologic results

Improved Diagnostic Performance Using Dual-Energy CT-Derived Slope Parameter Images in Crohn's Disease

The objective of the study is to explore the image quality and diagnosis performance of the dual-energy CT-derived slope parameter images (SPI) generated by the algorithm based on the slope function in the diagnosis of Crohn's disease (CD). Seventy-six CD patients and 53 disease-free control group subjects who underwent dual-energy CT enterography were retrospectively collected. Portal venous phase 120kVp-like and virtual monoenergetic images at 40-100 keV (VMI40-100) were reconstructed. SPIs corresponding to the spectral curve between 40 and 100 keV (SPI40-100) were generated using Python. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of normal and abnormal intestinal walls were calculated. Image quality, noise, and contrast were independently scored by two radiologists using a 5-point scale. Four radiologists conducted CD diagnosis with three reading models (120kVp-like, 120kVp-like with optimal VMI, and 120kVp-like with SPI40-100). The diagnostic performances of the three reading models for diagnosing CD were evaluated using receiver operating characteristic (ROC) curves. The CNR in SPI40-100 was higher than in the other images (P < 0.05). The subjective evaluation showed that there was no statistical difference between the contrast of SPI40-100 and VMI40 (P > 0.05), but that of the two images was higher than the other images (P < 0.05). The scoring on the overall image quality of VMI50 was superior to that of other images (P < 0.05). The combined model of 120kVp-like with SPI40-100 had the strongest confidence (cases with high confidence: 36, 58, 49, 47 for radiologists 1, 2, 3, 4) and the highest efficiency in diagnosing CD (areas under the ROC curve: 0.973, 0.977, 0.982, 0.991 for radiologists 1, 2, 3, 4). SPI40-100 generated by the algorithm based on the slope function exhibited good image quality. The combined model of 120kVp-like with SPI40-100 could improve radiologists' diagnostic efficiency and confidence in diagnosing CD.

Predictive value of spectral computed tomography parameters for EGFR gene mutation in non-small-cell lung cancer

AIM

To explore the predictive value of morphological signs and quantitative parameters from spectral CT for EGFR gene mutations in intermediate and advanced non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

This retrospective observational study included patients with intermediate or advanced NSCLC at Xinjiang Medical University Affiliated Tumor Hospital between January 2017 and December 2019. The patients were divided into the EGFR gene mutation-positive and -negative groups.

RESULTS

Seventy-nine patients aged 60.75 ± 9.66 years old were included: 32 were EGFR mutation-positive, and 47 were negative. There were significant differences in pathological stage (P<0.001), tumor diameter (P=0.019), lobulation sign, intrapulmonary metastasis, mediastinal lymph node metastasis, distant metastasis (P<0.001), bone metastasis (P<0.001), arterial phase normalized iodine concentration (NIC) (P=0.001), venous phase NIC (P=0.001), slope of the energy spectrum curve (λ) (P<0.001), and CT value at 70 keV in arterial phase (P=0.004) and venous phase (P=0.003) between the EGFR mutation-positive and -negative patients. The multivariable logistic regression analysis showed that intrapulmonary metastasis, distant metastasis, venous phase NIC, venous phase λ, and pathological stage were independent factors predicting EGFR gene mutations, with high diagnostic power (AUC = 0.975, 91.5% sensitivity, and 90.6% specificity).

CONCLUSION

The pathological stage and the spectral CT parameters of intrapulmonary metastasis, distant metastasis, venous phase NIC, and venous phase λ might pre-operatively predict EGFR gene mutations in intermediate and advanced NSCLC.

Added value of spectral computed tomography quantitative parameters for differentiating tuberculosis-associated fibrosing mediastinitis from endobronchial lung cancer: initial results

OBJECTIVE

The objective of this study was to explore the added value of spectral computed tomography (CT) parameters to conventional CT features for differentiating tuberculosis-associated fibrosing mediastinitis (TB-associated FM) from endobronchial lung cancer (EBLC).

METHODS

Chest spectral CT enhancement images from 109 patients with atelectasis were analyzed retrospectively. These patients were divided into two distinct categories: the TB-associated FM group (n = 77) and the EBLC group (n = 32), based on bronchoscopy and/or pathological findings. The selection of spectrum parameters was optimized with the least absolute shrinkage and selection operator regression analysis. The relationship between the spectrum parameters and conventional parameters was explored using Pearson's correlation. Multivariate logistic regression analysis was used to build spectrum model. The spectrum parameters in the spectrum model were replaced with their corresponding conventional parameters to build the conventional model. Diagnostic performances were evaluated using receiver operating characteristic curve analyses.

RESULTS

There was a moderate correlation between the parameters ㏒(L-AEFNIC) - ㏒(L-AEFC) (r= 0.419; p< 0.0001), ㏒(O-AEF40KeV) - ㏒(O-AEFC) (r= 0.475; p< 0.0001), [L-A-hydroxyapatite {HAP}(I)] - (L-U-CT) (r= 0.604; p< 0.0001), {arterial enhancement fraction (AEF) derived from normalized iodine concentration (NIC) of lymph node (L-AEFNIC), AEF derived from CT40KeV of bronchial obstruction (O-AEF40KeV), arterial-phase Hydroxyapatite (Iodine) concentration of lymph node [L-A-HAP(I)], AEF derived from conventional CT (AEFC), unenhanced CT value (U-CT)}. Spectrum model could improve diagnostic performances compared to conventional model (area under curve: 0.965 vs 0.916, p= 0.038).

CONCLUSION

There was a moderate correlation between spectrum parameters and conventional parameters. Integrating conventional CT features with spectrum parameters could further improve the ability in differentiating TB-associated FM from EBLC.

Quantitative parameter analysis of pretreatment dual-energy computed tomography in nasopharyngeal carcinoma cervical lymph node characteristics and prediction of radiotherapy sensitivity

BACKGROUND

Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor-node-metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck-lymph node radiotherapy in patients with NPC.

METHODS

Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression.

RESULTS

DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73-0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively.

CONCLUSION

Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.

Predicting epidermal growth factor receptor mutations in non-small cell lung cancer through dual-layer spectral CT: a prospective study

OBJECTIVE

To determine whether quantitative parameters of detector-derived dual-layer spectral computed tomography (DLCT) can reliably identify epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC).

METHODS

Patients with NSCLC who underwent arterial phase (AP) and venous phase (VP) DLCT between December 2021 and November 2022 were subdivided into the mutated and wild-type EGFR groups following EGFR mutation testing. Their baseline clinical data, conventional CT images, and spectral images were obtained. Iodine concentration (IC), iodine no water (INW), effective atomic number (Zeff), virtual monoenergetic images, the slope of the spectral attenuation curve (λHU), enhancement degree (ED), arterial enhancement fraction (AEF), and normalized AEF (NAEF) were measured for each lesion.

RESULTS

Ninety-two patients (median age, 61 years, interquartile range [51, 67]; 33 men) were evaluated. The univariate analysis indicated that IC, normalized IC (NIC), INW and ED for the AP and VP, as well as Zeff and λHU for the VP were significantly associated with EGFR mutation status (all p < 0.05). INW(VP) showed the best diagnostic performance (AUC, 0.892 [95% confidence interval {CI}: 0.823, 0.960]). However, neither AEF (p = 0.156) nor NAEF (p = 0.567) showed significant differences between the two groups. The multivariate analysis showed that INW(AP) and NIC(VP) were significant predictors of EGFR mutation status, with the latter showing better performance (p = 0.029; AUC, 0.897 [95% CI: 0.816, 0.951] vs. 0.774 [95% CI: 0.675, 0.855]).

CONCLUSION

Quantitative parameters of DLCT can help predict EGFR mutation status in patients with NSCLC.

CRITICAL RELEVANCE STATEMENT

Quantitative parameters of DLCT, especially NIC(VP), can help predict EGFR mutation status in patients with NSCLC, facilitating appropriate and individualized treatment for them.

KEY POINTS

Determining EGFR mutation status in patients with NSCLC before starting therapy is essential. Quantitative parameters of DLCT can predict EGFR mutation status in NSCLC patients. NIC in venous phase is an important parameter to guide individualized treatment selection for NSCLC patients.

Lung dual energy CT: Impact of different technological solutions on quantitative analysis

PURPOSE

To evaluated the accuracy of spectral parameters quantification of four different CT scanners in dual energy examinations of the lung using a dedicated phantom.

METHOD

Measurements were made with different technologies of the same vendor: one dual source CT scanner (DSCT), one TwinBeam (i.e. split filter) and two sequential acquisition single source scanners (SSCT). Angular separation of Calcium and Iodine signals were calculated from scatter plots of low-kVp versus high-kVp HUs. Electron density (ρe), effective atomic number (Zeff) and Iodine concentration (Iconc) were measured using Syngo.via software. Accuracy (A) of ρe, Zeff and Iconc was evaluated as the absolute percentage difference (D%) between reference values and measured ones, while precision (P) was evaluated as the variability σ obtained by repeating the measurement with different acquisition/reconstruction settings.

RESULTS

Angular separation was significantly larger for DSCT (α = 9.7°) and for sequential SSCT (α = 9.9°) systems. TwinBeam was less performing in material separation (α = 5.0°). The lowest average A was observed for TwinBeam (A_ρe = [4.7 ± 1.0], A_Z = [9.1 ± 3.1], A_Iconc = [19.4 ± 4.4]), while the best average A was obtained for Flash (A_ρe = [1.8 ± 0.4], A_Z = [3.5 ± 0.7], A_Iconc = [7.3 ± 1.8]). TwinBeam presented inferior average P (P_ρe = [0.6 ± 0.1], P_Z = [1.1 ± 0.2], P_Iconc = [10.9 ± 4.9]), while other technologies demonstrate a comparable average.

CONCLUSIONS

Different technologies performed material separation and spectral parameter quantification with different degrees of accuracy and precision. DSCT performed better while TwinBeam demonstrated not excellent performance. Iodine concentration measurements exhibited high variability due to low Iodine absolute content in lung nodules, thus limiting its clinical usefulness in pulmonary applications.

Role of iodine density value on dual-energy CT for detection of high tumor cell proportion region in lung cancer during CT-guided transthoracic biopsy

OBJECTIVE

This study aimed to identify regions with at least 20% tumor cell content in lung cancer tumors by using spectral parameters from dual-layer spectral detector computed tomography (SDCT) to design the puncture path for transthoracic lung biopsy (TTLB).

MATERIALS AND METHODS

This prospective study recruited patients with suspected lung cancer. Forty-one patients were enrolled to identify the high tumor cell proportion region (HTPR) and then another 15 patients to validate the accuracy of the HTPR. In each of the 41 patients, the suspected regions with high or low tumor cell proportions were punctured according to local iodine density (IoD) values for separate biopsies. The tumor cell proportions of 82 specimens were assessed and classified into high and low tumor cell proportions based on the threshold value of 20 %. The performance of spectral parameters was analyzed to distinguish the HTPR (tumor cell proportion ≥ 20 %) from the low tumor cell proportion region (LTPR). The cutoff value of optimal spectral parameter was used to prospectively guide the biopsy of the HTPR in 15 cases for further validation, and then the accuracy was calculated.

RESULTS

The AUC values of spectral parameters were all higher than those of CT_conventional in identifying the HTPR (all P < 0.05). The IoD with a cutoff value of 0.59 mg/mL in arterial phase (AP) yielded good performance (specificity: 97.10 %) in identifying the HTPR. It was applied to 15 cases for validation, and the accuracy rate was 100 %.

CONCLUSION

Spectral CT parameters can be used to identify regions with at least 20% tumor cell content in lung cancer for biopsies.

Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign

OBJECTIVES

To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs).

MATERIALS AND METHODS

A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application.

RESULTS

The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC_65keV-AP = 0.92, AUC_65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability.

CONCLUSIONS

Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Z_eff-AP was significantly superior to conventional model in the discrimination of SPSNs.

The value of dual-energy spectral CT in differentiating solitary pulmonary tuberculosis and solitary lung adenocarcinoma

BACKGROUND

To explore the value of dual-energy spectral CT in distinguishing solitary pulmonary tuberculosis (SP-TB) from solitary lung adenocarcinoma (S-LUAD).

METHODS

A total of 246 patients confirmed SP-TB (n = 86) or S-LUAD (n = 160) were retrospectively included. Spectral CT parameters include CT40keV value, CT70keV value, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff), and spectral curve slope (λ70keV). Data were measured during the arterial phase (AP) and venous phase (VP). Chi-square test was used to compare categorical variables, Wilcoxon rank-sum test was used to compare continuous variables, and a two-sample t-test was used to compare spectral CT parameters. ROC curves were used to calculate diagnostic efficiency.

RESULTS

There were significant differences in spectral CT quantitative parameters (including CT40keV value [all P< 0.001] , CT70keV value [all P< 0.001], λ70keV [P< 0.001, and P = 0.027], Zeff [P =0.015, and P = 0.001], and IC [P =0.002, and P = 0.028]) between the two groups during the AP and VP. However, WC (P = 0.930, and P = 0.823) was not statistically different between the two groups. The ROC curve analysis showed that the AUC in the AP and VP was 90.9% (95% CI, 0.873-0.945) and 83.4% (95% CI, 0.780-0.887), respectively. The highest diagnostic performance (AUC, 97.6%; 95% CI, 0.961-0.991) was achieved when all spectral CT parameters were combined with clinical variables.

CONCLUSION

Dual-energy spectral CT has a significant value in distinguishing SP-TB from S-LUAD.

Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images

Background

Energy spectrum CT is an effective method to evaluate the biological behavior of lung cancer. Radiomics is a non-invasive technology to obtain histological information related to lung cancer.

Purpose

To investigate the value of the radiomics models on the bases of enhanced spectral CT images of peripheral lung cancer to predict the expression of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR).

Material and Methods

This study retrospectively analyzed 73 patients with peripheral lung cancer confirmed by postoperative pathology. All patients underwent dual-phase enhanced spectral CT scans before surgery. Regions of interest (ROI) were delineated in the arterial phase and venous phase. Key radiomics features were extracted and models were established to predict the expression of VEGF and EGFR, respectively. All models were established based on the expression levels of VEGF and EGFR in tissues detected by immunohistochemical staining as reference standards. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of each model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the models.

Results

In predicting the expression level of VEGF, the combined (COMB) model composed of one spectral feature and two radiomics features achieved the best performance with area under ROC (AUC) 0.867 (95% CI: 0.767–0.966), accuracy of 0.812, sensitivity of 0.879, and specificity of 0.667. According to the expression level of EGFR, three importance radiomics features were retained in the arterial and venous phases to establish the multiphase phase model which has the best performance with AUC of 0.950 (95% confidence interval: 0.89–1.00), accuracy of 0.896, sensitivity of 0.868, and specificity of 1.

Conclusion

The radiomics model of enhanced spectral CT images of peripheral lung cancer can predict the expression of EGFR and VEGF.

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

Background

This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.

Methods

In total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.

Results

There were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].

Conclusion

Our combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.

Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia

Objective

To investigate the spectral and perfusion computed tomography (CT) findings of peripheral lung cancer (PLC) and focal organizing pneumonia (FOP) and to compare the accuracy of spectral and perfusion CT imaging in distinguishing PLC from FOP.

Materials and Methods

Patients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. Patients who were suspected of having lung tumor and underwent “one-stop” chest spectral and perfusion CT, with their diagnosis confirmed pathologically, were prospectively enrolled from September 2020 to March 2021. A total of 57 and 35 patients with PLC and FOP were included, respectively. Spectral parameters (CT_40keV, CT_70keV, CT_100keV, iodine concentration [IC], water concentration [WC], and effective atomic number [Zeff]) of the lesions in the arterial and venous phases were measured in both groups. The slope of the spectral curve (K_70keV) was calculated. The perfusion parameters, including blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS), were measured simultaneously in both groups. The differences in the spectral and perfusion parameters between the groups were examined. Receiver operating characteristic (ROC) curves were generated to calculate and compare the area under the curve (AUC), sensitivity, specificity, and accuracy of both sets of parameters in both groups.

Results

The patients’ demographic and clinical characteristics were similar in both groups (P > 0.05). In the arterial and venous phases, the values of spectral parameters (CT_40keV, CT_70keV, spectral curve K_70keV, IC, and Zeff) were greater in the FOP group than in the PLC group (P < 0.05). In contrast, the values of the perfusion parameters (BV, BF, MTT, and PS) were smaller in the FOP group than in the PLC group (P < 0.05). The AUC of the combination of the spectral parameters was larger than that of the perfusion parameters. For the former imaging method, the AUC, sensitivity, and specificity were 0.89 (95% confidence interval [CI]: 0.82–0.96), 0.86, and 0.83, respectively. For the latter imaging method, the AUC, sensitivity, and specificity were 0.80 (95% CI: 0.70–0.90), 0.71, and 0.83, respectively. There was no significant difference in AUC between the two imaging methods (P > 0.05).

Conclusion

Spectral and perfusion CT both has the capability to differentiate PLC and FOP. However, compared to perfusion CT imaging, spectral CT imaging has higher diagnostic efficiency in distinguishing them.

Study on the correlation between energy spectrum computed tomography imaging and the pathological characteristics and prognosis of cervical cancer

BACKGROUND

The purpose of this study is to investigate the correlation between energy spectrum computed tomography (CT) imaging and the pathological characteristics and prognosis of cervical cancer.

METHODS

All participants underwent energy spectrum CT plain scan and enhanced scan of the cervix, uterine body, and common iliac vein. The correlation between the slope of energy spectrum attenuation curve and pathological characteristics and curative effect was analyzed, and the receiver operating characteristic (ROC) curve of the slope of energy spectrum attenuation curve to distinguish some pathological characteristics and curative effect was constructed.

RESULTS

The energy spectrum curves of cervix, uterine body, and common iliac vein all showed a downward trend. The slope of cervix energy spectrum curve showed a significant difference in different differentiation degree (P<0.05), and the slope of energy spectrum curve showed an upward trend. The slope of energy spectrum curve of common iliac vein was significantly different between high and low cell proliferation antigen marker (Ki67) (P<0.05), and the slope of Ki67 high expression was higher than that of Ki67 low expression. Treatment was effective in 17 participants and ineffective in 11. After treatment, the energy spectrum curve slope of cervix and energy spectrum curve slope of common iliac vein in the effective group were significantly increased compared with before treatment (P<0.05), and the energy spectrum curve slope of cervix in the ineffective group was increased compared with before treatment, but the difference was not significant (P>0.05). The area under the curve (AUC) of distinguishing Ki67 expression of energy spectrum curve slope of common iliac vein was 0.7008, sensitivity was 66.67%, and specificity was 62.34%. The AUC of distinguishing the curative effect of cervical energy spectrum curve slope was 0.6131, sensitivity was 56.25%, and specificity was 59.09%. The AUC of distinguishing the curative effect of energy spectrum curve slope of common iliac vein was 0.6563, sensitivity was 60.42%, and specificity was 58.33%.

CONCLUSIONS

The energy spectrum curve slope has potential value in the prediction of certain specific pathological types of cervical cancer and the evaluation of curative effect.