Spectral Computed Tomography

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) is essential to improve patient prognosis and enable organ and function preservation treatments. The objective of this study is to assess the feasibility of using electrical bioimpedance (EBI) sensing technology to detect HNSCC tissue. A prospective study was carried out analyzing tissue from 46 patients undergoing surgery for HNSCC. The goal was the correct identification of pathologic tissue using a novel needle-based EBI sensing device and AI-based classifiers. Considering the data from the overall patient cohort, the system achieved accuracies between 0.67 and 0.93 when tested on tissues from the mucosa, skin, muscle, lymph node, and cartilage. Furthermore, when considering a patient-specific setting, the accuracy range increased to values between 0.82 and 0.95. This indicates that more reliable results may be achieved when considering a tissue-specific and patient-specific tissue assessment approach. Overall, this study shows that EBI sensing may be a reliable technology to distinguish pathologic from healthy tissue in the head and neck region. This observation supports the continuation of this research on the clinical use of EBI-based devices for early detection and margin assessment of HNSCC.

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.

Photon-Counting Detectors in Computed Tomography: A Review

Photon-counting computed tomography (CT) is a new technique that has the potential to revolutionize clinical CT and is predicted to be the next significant advancement. In recent years, tremendous research has been conducted to demonstrate the developments in hardware assembly and its working principles. The articles in this review were obtained by conducting a search of the MEDLINE database. Photon-counting detectors (PCDs) provide excellent quality diagnostic images with high spatial resolution, reduced noise, artifacts, increased contrast-to-noise ratio, and multienergy data acquisition as compared with conventionally used energy-integrating detector (EID). The search covered articles published between 2011 and 2021. The title and abstract of each article were reviewed as determined by the search strategy. From these, eligible studies and articles that provided the working and clinical application of PCDs were selected. This article aims to provide a systematic review of the basic working principles of PCDs, emphasize the uses and clinical applications of PCDs, and compare it to EIDs. It provides a nonmathematical explanation and understanding of photon-counting CT systems for radiologists as well as clinicians.

Laryngeal and hypopharyngeal squamous cell carcinoma: association between quantitative parameters derived from dual-energy CT and histopathological prognostic factors

BACKGROUND

Dual-energy computed tomography (DECT) can provide objective evaluation of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC).

PURPOSE

To investigate the relationship between quantitative parameters acquired from DECT and histopathological prognostic factors in LHSCC.

MATERIAL AND METHODS

A total of 65 patients with LHSCC who underwent arterial phase and venous phase DECT scans were retrospectively enrolled. Iodine concentration (IC) and normalized IC (NIC) of the tumor were calculated in both the arterial (IC_A and NIC_A) and venous (IC_V and NIC_V) phases, and compared among different pathological grades, T stages, and lymph node stages. Receiver operating characteristic (ROC) curves were generated to evaluate their diagnostic performance.

RESULTS

There were significantly differences on IC_A and NIC_A among three pathological grades (IC_A, P = 0.001; NIC_A, P = 0.002). For differentiating moderately and poorly differentiated from well-differentiated LHSCC using IC_A and NIC_A, the areas under curve (AUCs) were 0.753 and 0.726, respectively. High T stage (T3/4) LHSCC showed significantly higher IC_A (P = 0.012) and NIC_A (P = 0.005) than low T stage (T1/2) LHSCC. The AUCs of the IC_A and NIC_A were 0.674 and 0.703, respectively, in discriminating high from low T stage LHSCC. Lymph node metastasis (LNM)-positive (N1/2/3) LHSCC showed significantly higher IC_A (P = 0.008) and NIC_A (P = 0.003) than LNM-negative (N0) LHSCC. For discriminating the LNM-positive from the LNM-negative group using IC_A and NIC_A, the AUCs were 0.697 and 0.744, respectively.

CONCLUSION

IC_A and NIC_A might be helpful in assessing histopathological prognostic factors in patients with LHSCC.

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT

Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT.

In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Background

Stage T1 nasopharyngeal carcinoma (NPC_T1) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPC_T1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases.

Methods

A total of 91 patients (44 NPC_T1, 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPC_T1 and BH. The demarcation of NPC_T1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (λ_HU) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZ_eff). The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.

Results

The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPC_T1 yielded higher attenuation values on VMI (+) at 40 keV (A₄₀), NIC, λ_HU, and NZ_eff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A₄₀, NIC, λ_HU, and NZ_eff) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%).

Conclusions

The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPC_T1 and BH.

Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients

PURPOSE

The dual-energy computed tomography (DECT) technique is an emerging imaging tool that can better characterize material features and has the potential to be a noninvasive means of predicting lymph node metastasis. The purpose of this study was to establish a DECT-specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN).

METHODS

With IRB approval, we retrospectively collected a total of 229 patients (100/229 metastasis) with biopsy proven breast cancer in this study. The chest and axillary spectral CT examinations were performed prior to the axillary lymph node (ALN) surgery. A decoupling convolution network with 11 ROIs from sequential keV (40 to 140 keV with 10 keV increment) was proposed to explicitly extract the spectral and spatial features in a DECT to predict the lymph node status. Focal loss was introduced as the loss function. The metric of the slope of the spectral Hounsfield unit curve measured at the venous phase was used as the baseline approach in comparison to our approach. In additional, a logistic model with radiomic features was also compared to our approach. The area under ROC curve (AUC) was used as the figure of merit to evaluate the classification performance.

RESULTS

By introducing spectral convolution and focal loss, AUC on test set could be improved by 0.15 and 0.01 separately. Compared to the slope of the spectral curve with the average AUC of 0.611 and radiomic model with AUC of 0.825, the proposed approach demonstrates a considerably better performance, with test set AUC value of 0.837, by using decoupling spectral and spatial convolution together with focal loss function.

CONCLUSIONS

We presented a new decoupling neural network based quantification method for DECT analysis, which might have potential as a noninvasive tool to predict metastasis lymph node status for breast cancer in clinical practice.

Multiparametric dual-energy CT for distinguishing nasopharyngeal carcinoma from nasopharyngeal lymphoma

OBJECTIVES

To determine the optimal kiloelectron volt of noise-optimized virtual monoenergetic images [VMI (+)] for visualization of nasopharyngeal carcinoma (NPC) and nasopharyngeal lymphoma (NPL), and to explore the clinical value of quantitative parameters derived from dual-energy computed tomography (DECT) for distinguishing the two entities.

MATERIALS AND METHODS

Eighty patients including 51 with NPC and 29 with NPL were enrolled. The VMIs (+) at 40-80 keV with an interval of 10 keV were reconstructed by contrast enhanced images. The overall image quality and demarcation of lesion margins, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed in VMIs (+) and polyenergetic images (PEI). Normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λ_HU) and effective atomic number (Z_eff) were calculated. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve.

RESULTS

The 40 keV VMI (+) yielded highest overall image quality scores, demarcation of lesion margins scores, SNR and CNR. The values of NIC, λ_HU and Z_eff in NPL were higher than those in NPC (P <  0.001). Multivariate logistic regression model combining NIC, λ_HU and Z_eff showed the best performance for distinguishing NPC from NPL (AUC: 0.947, sensitivity: 93.1 % and specificity: 92.2 %).

CONCLUSION

VMI (+) reconstruction at 40 keV was optimal for visualizing NPC and NPL. Quantitative parameters derived from DECT were helpful for differentiating NPC from NPL.

Impact of a dedicated radiologist as a member of the head and neck tumour board: a single-institution experience

The aim of this study was to quantify the impact of radiologic image review performed by experienced radiologists in a multidisciplinary team (MDT) for head and neck cancers (HNCs). We performed a retrospective review of cases discussed at MDT meetings from April 2014 to March 2017 for which radiologic review was required. All changes in the former radiologic report were collected and classified as follows: 1) modifications of radiological reports (patients for whom the treatment strategy had not been defined at the moment of MDT meeting) and 2) modifications in treatment strategy (patients for whom treatment strategy had previously been defined and subsequently modified according to the outcome of radiologic revision). The latter subgroup was further categorised as “major changes” and as “minor changes”. A total of 540 cases were retrieved. Imaging review was required at the time of tumour diagnosis in 310 (57.4%) cases. Most patients (69%) had advanced stage tumours (III and IV). In 262 (48%) cases, no change of the initial radiologic report was made. In a total of 144 (27%) cases, the available imaging was not considered sufficient for a final indication to treatment and further imaging was required. In the remaining 134 (25%) cases, radiologic review led to a modification of either tumour staging (55%) or treatment strategy (45%). Specifically, major and minor modifications were applied in 44 (13%) and 17 (11%) of the cases considered, respectively. Among 134 patients for whom the radiologic review led to stage/treatment modification, follow-up was available for 118. In all but one patient, we could confirm the original reports were correctly modified per MDT discussion results. Our data strongly support the importance of including an experienced radiologist as a core member of the MDT for HNCs.

Dual-energy CT for differentiating early glottic squamous cell carcinoma from chronic inflammation and leucoplakia of vocal cord: comparison with simulated conventional 120 kVp CT

AIM

To evaluate the value of dual-energy (DE) computed tomography (CT) in discriminating early glottic squamous cell carcinoma (eGSCC) from chronic inflammation and leucoplakia of the vocal cord, and to compare the diagnostic efficiency of DECT with that of simulated conventional 120 kVp CT.

MATERIALS AND METHODS

Seventy patients with glottic lesions confirmed by histopathology (38 cases with eGSCC, 11 cases with chronic inflammation, 21 cases with leucoplakia) were enrolled in this prospective study. The DECT-derived parameters were measured and compared using independent sample t-test. Receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance, and comparison of the area under the ROC curve (AUC) was made using the Z test to further select the best diagnostic parameters.

RESULTS

Significantly higher iodine concentration (IC), normalised IC (NIC), effective atomic number (Z_eff), 40-100 keV (20 keV-interval), slope(k), and Mix-0.3 values were found in eGSCC than those in chronic inflammation, leucoplakia, and inflammation + leucoplakia (all p<0.05). Compared with attenuation measurement of simulated conventional 120 kVp CT, the NIC, 60 keV values derived from DECT showed significantly higher AUC in discriminating these glottic lesions (p<0.05).

CONCLUSIONS

DECT is more accurate for differentiating eGSCC from chronic inflammation and leucoplakia when compared with simulated conventional 120 kVp CT.

Assessment of Solitary Pulmonary Nodules Based on Virtual Monochrome Images and Iodine-Dependent Images Using a Single-Source Dual-Energy CT with Fast kVp Switching

With lung cancer being the most common malignancy diagnosed worldwide, lung nodule assessment has proved to be one of big challenges of modern medicine. The aim of this study was to examine the usefulness of Dual Energy Computed Tomography (DECT) in solitary pulmonary nodule (SPN) assessment. Between January 2017 and June 2018; 65 patients (42 males and 23 females) underwent DECT scans in the late arterial phase (AP) and venous phase (VP). We concluded that imaging at an energy level of 65 keV was the most accurate in detecting malignancy in solitary pulmonary nodules (SPNs) measuring ≤30 mm in diameter on virtual monochromatic maps. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. As for iodine concentration maps, the analysis of venous phase images resulted in the highest clinical usefulness. To summarize, DECT may be a useful tool in the differentiation of benign and malignant SPNs. A single-phase DECT examination with scans acquired 90 s after contrast media injection is recommended.

Dual-energy CT in the differentiation of stage T1 nasopharyngeal carcinoma and lymphoid hyperplasia

PURPOSE

To explore the value of dual-energy CT for the differentiation between stage T1 nasopharyngeal carcinoma (NPC_T1) and lymphoid hyperplasia (LH).

METHOD

Patients with histopathological proven nasopharyngeal lesions (stage T1 NPCs, n = 30; LHs, n = 47) who underwent dual-energy CT were enrolled in this retrospective study. Quantitative parameters derived from dual-energy CT were measured. Statistical analyses were performed using the independent sample t-test, Wilcoxon rank sum test, and receiver operating characteristic curve (ROC) analysis.

RESULTS

There was significantly higher iodine concentration (IC), normalized iodine concentration (NIC, to internal jugular vein) in NPC_T1 compared with LH (p < 0.001). The effective atomic number (Z_eff) was significantly higher in NPC_T1 than that in LH (p <  0.001). The virtual monochromatic images (VMIs) at 50 keV-110 keV (20 keV-interval) of NPC_T1 were all significantly higher than those of LH (all p <0.001). The slope (k) value of spectral attenuation curve was also significantly higher in NPC_T1 than LH (p <  0.001). There was no significant difference in virtual noncontrast (VNC) and 130 keV-190 keV (20 keV-interval) between the NPC_T1 and LH. For discriminating NPC_T1 from LH, the area under curve (AUC) using 70 keV was the highest in all single parameter (AUC, 0.92; sensitivity, 80.00 %; specificity, 91.49 %). Combined multiple parameters (IC, NIC, Z_eff, 50 keV, 70 keV, 90 keV, slope (k)) by performing multivariate logistic regression model significantly improve the diagnostic capability in differentiating these two entities, with AUC, sensitivity, and specificity values of 0.99, 93.33 %, 97.87 %, respectively.

CONCLUSIONS

Dual-energy CT can be helpful for the differentiation between NPC_T1 and LH lesions.

Imaging biomarkers in upper gastrointestinal cancers

In parallel with the increasingly widespread availability of high performance imaging platforms and recent progresses in pathobiological characterisation and treatment of gastrointestinal malignancies, imaging biomarkers have become a major research topic due to their potential to provide additional quantitative information to conventional imaging modalities that can improve accuracy at staging and follow-up, predict outcome, and guide treatment planning in an individualised manner. The aim of this review is to briefly examine the status of current knowledge about imaging biomarkers in the field of upper gastrointestinal cancers, highlighting their potential applications and future perspectives in patient management from diagnosis onwards.