Spectral computed tomography in advanced gastric cancer: Can iodine concentration non-invasively assess angiogenesis?

Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study

PURPOSE

This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.

METHODS

Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.

RESULTS

The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.

CONCLUSION

nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

Spectral CT in the evaluation of perineural invasion status in rectal cancer

PURPOSE

To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer.

METHODS

Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test.

RESULTS

The preoperative spectral CT parameters (40-70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%.

CONCLUSION

Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.

Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVES

To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).

MATERIALS AND METHODS

This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan-Meier survival analysis was adopted to analyze their relationship with patient survival.

RESULTS

nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043-6.029).

CONCLUSION

Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival.

CLINICAL RELEVANCE STATEMENT

Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy.

KEY POINTS

• The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early. • The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival. • Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

Diagnostic value of dual-source, dual-energy computed tomography combined with the neutrophil-lymphocyte ratio for discriminating gastric signet ring cell from mixed signet ring cell and non-signet ring cell carcinomas

PURPOSE

To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC).

METHODS

This retrospective study included patients with gastric adenocarcinoma who underwent DSCT between August 2019 and June 2021 at our Hospital. The iodine concentration in the venous phase (ICvp), standardized iodine concentration (NICVP), and the slope of the energy spectrum curve (kVP) were extracted from DSCT data. NLR was determined from laboratory results. DSCT (including ICVP, NICVP, and kVP) and combination (including DSCT model and NLR) models were established based on the multinomial logistic regression analysis. The receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic value.

RESULTS

A total of 155 patients (SRC [n = 45, aged 61.22 ± 11.4 years], mSRC [n = 60, aged 61.09 ± 12.7 years], and nSRC [n = 50, aged 67.66 ± 8.76 years]) were included. There were significant differences in NLR, ICVP, NICVP, and kVP among the SRC, mSRC, and nSRC groups (all P < 0.001). The AUC of the combination model for SRC vs. mSRC + nSRC was 0.964 (95% CI: 0.923-1.000), with a sensitivity of 98.3% and a specificity of 86.7%, higher than with DSCT (AUC: 0.959, 95% CI: 0.919-0.998, sensitivity: 90.0%, specificity: 89.9%) or NLR (AUC: 0.670, 95% CI: 0.577-0.768, sensitivity: 62.2%, specificity: 61.8%).

CONCLUSION

DSCT combined with NLR showed high diagnostic efficacy in differentiating SRC from mSRC and nSRC.

Preliminary differentiation of benign and malignant gastric wall thickening using dual-layer spectral-detector CT

BACKGROUND

Dual-layer spectral-detector computed tomography (DLCT) may have the potential to evaluate gastric wall thickening.

PURPOSE

To evaluate the efficacy of DLCT quantitative parameters in differentiating between benign and malignant thickening of the gastric wall.

MATERIAL AND METHODS

A total of 58 patients with "gastric wall thickening" who underwent multi-phase abdominal enhanced DLCT scans were included in this study. Of these patients, 33 were malignant and 25 were benign. Parameters such as iodine concentration (IC), effective atomic number (Zeff), and attenuation of the lesions were measured during the arterial phase (AP) and venous phase (VP). Binary logistic regression was employed to calculate the combined prediction probabilities. The accuracy of the DLCT parameters was assessed using receiver operating characteristic (ROC) curves.

RESULTS

The values of IC, nIC, Zeff, normalized Zeff, and attenuation in the AP and VP were significantly higher (all P < 0.05) in the malignant group compared to the benign group. The ROC curves revealed that the IC, Zeff, and attenuation in the VP exhibited high diagnostic performance, with area under the ROC curve (AUC) values of 0.864, 0.862, and 0.840, respectively. The new combination of these three factors and gastric wall thickness had an AUC of 0.884, and the sensitivity and specificity were determined to be 81.8% and 92.0%, respectively.

CONCLUSION

Spectral CT parameters, particularly the combination of gastric wall thickness, attenuation, IC, and Zeff in VP, have value in distinguishing between benign and malignant gastric wall thickening.

Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer

Purpose

To develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC).

Materials and methods

A total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively.

Results

The ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P < 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729-0.899) and 0.833 (0.675-0.935) in training and validation cohorts than single variables (P<0.05), with good calibration and clinical utility.

Conclusions

The nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer: The establishment of a spectral CT-based nomogram from prospective datasets

BACKGROUND

To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC).

METHODS

This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival.

RESULTS

This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02).

CONCLUSIONS

The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.

Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images

OBJECTIVE

This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients.

METHODS

A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated.

RESULTS

The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction.

CONCLUSIONS

The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Analysis of the Parotid Glands on an Energy Spectrum CT Iodine Map to Evaluate Irradiation-Induced Acute Xerostomia in Patients With Nasopharyngeal Carcinoma

Objective: This prospective study aims to evaluate acute irradiation-induced xerostomia during radiotherapy by utilizing the normalized iodine concentration (NIC) derived from energy spectrum computed tomography (CT) iodine maps. Methods: In this prospective study, we evaluated 28 patients diagnosed with nasopharyngeal carcinoma. At 4 distinct stages of radiotherapy (0, 10, 20, and 30 fractions), each patient underwent CT scans to generate iodine maps. The NIC of both the left and right parotid glands was obtained, with the NIC at the 0-fraction stage serving as the baseline measurement. After statistically comparing the NIC obtained in the arterial phase, early venous phase, late venous phase, and delayed phase, we chose the late venous iodine concentration as the NIC and proceeded to analyze the variations in NIC at each radiotherapy interval. Using the series of NIC values, we conducted hypothesis tests to evaluate the extent of change in NIC within the parotid gland across different stages. Furthermore, we identified the specific time point at which the NIC decay exhibited the most statistically significant results. In addition, we evaluated the xerostomia grades of the patients at these 4 stages, following the radiation therapy oncology group (RTOG) xerostomia evaluation standard, to draw comparisons with the changes observed in NIC. Results: The NIC in the late venous phase exhibited the highest level of statistical significance (P < .001). There was a noticeable attenuation in NIC as the RTOG dry mouth grade increased. Particularly, at the 20 fraction, the NIC experienced the most substantial attenuation (P < .001), a significant negative correlation was observed between the NIC of the left, right, and both parotid glands, and the RTOG evaluation grade of acute irradiation-induced xerostomia (P < .001, r = -0.46; P < .001, r = -0.45; P < .001, r = -0.47). The critical NIC values for the left, right, and both parotid glands when acute xerostomia occurred were 0.175, 0.185, and 0.345 mg/ml, respectively, with AUC = 0.73, AUC = 0.75, and AUC = 0.75. Conclusion: The NIC may be used to evaluate changes in parotid gland function during radiotherapy and acute irradiation-induced xerostomia.

Value of spectral CT parameters in predicting the efficacy of neoadjuvant chemotherapy for gastric cancer

AIM

To investigate the value of pre-chemotherapy spectral computed tomography (CT) parameters in predicting neoadjuvant chemotherapy (NAC) response in gastric cancer (GC).

MATERIALS AND METHODS

Sixty patients with GC who received NAC and underwent spectral CT examination before chemotherapy were enrolled retrospectively and divided into a responsive group and a non-responsive group according to the postoperative pathological tumour regression grade. Clinical characteristics were collected. The iodine concentration (IC), water concentration (WC), and effective atomic number (Eff-Z) of the portal venous phases were measured before chemotherapy, and IC was normalised to that of the aorta to provide the normalised IC (NIC). An independent samples t-test, Mann-Whitney U-test, or chi-square test was used to analyse the differences between the two groups, and the receiver operating curve (ROC) was used to evaluate the predictive performance of different variables.

RESULTS

The neutrophil-to-lymphocyte ratio (NLR) was lower in the responsive group than in the non-responsive group (p<0.05). IC, NIC, and Eff-Z values were significantly higher in the responsive group than in the non-responsive group (p<0.01). The areas under the ROC curves for the NLR, IC, NIC, and Eff-Z were 0.694, 0.688, 0.799, and 0.690, respectively. The combination of NIC, Eff-Z, and NLR values showed good diagnostic performance in predicting response to NAC in GC, with an area under the ROC curve of 0.857, 76.92% sensitivity, 80% accuracy, and 85.71% specificity.

CONCLUSION

Spectral CT parameters may serve as non-invasive tools for predicting the response to NAC in patients with GC.

Correlations between iodine uptake, invasive CT features and pleural invasion in adenocarcinomas with pleural contact

Pleural contact in lung cancers does not always imply pleural invasion (PI). This study was designed to determine whether specific invasive CT characteristics or iodine uptake can aid in the prediction of PI. The sample population comprised patients with resected solid lung adenocarcinomas between April 2019 and May 2022. All participants underwent a contrast enhanced spectral CT scan. Two proficient radiologists independently evaluated the CT features and iodine uptake. Logistic regression analyses were employed to identify predictors for PI, via CT features and iodine uptake. To validate the improved diagnostic efficiency, accuracy analysis and ROC curves were subsequently used. A two-tailed P value of less than 0.05 was considered statistically significant. We enrolled 97 consecutive patients (mean age, 61.8 years ± 10; 48 females) in our study. The binomial logistic regression model revealed that a contact length > 10 mm (OR 4.80, 95% CI 1.92, 11.99, p = 0.001), and spiculation sign (OR 2.71, 95% CI 1.08, 6.79, p = 0.033) were independent predictors of PI, while iodine uptake was not. Enhanced sensitivity (90%) and a greater area under the curve (0.73) were achieved by integrating the two aforementioned CT features in predicting PI. We concluded that the combination of contact length > 10 mm and spiculation sign can enhance the diagnostic performance of PI.

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

OBJECTIVE

To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.

METHODS

A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.

RESULTS

Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).

CONCLUSION

The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.

CRITICAL RELEVANCE STATEMENT

The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.

Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning

Vessel invasion (VI) is an important factor affecting the prognosis of gastric cancer (GC), and the accurate determination of preoperative VI for locally advanced GC is of great clinical significance. Traditional methods for the evaluation of VI require postoperative pathological examination. Noninvasive preoperative evaluation of VI is therefore crucial to determine the best treatment strategy. To determine the value of preoperative prediction of gastric VI based on portal venous phase computed tomography (CT) radiomic features and machine-learning models, a retrospective analysis of 296 patients with locally advanced GC confirmed through pathological examination was performed. They were divided into two groups, VI+ (n=213) and VI- (n=83), based on pathological results. Using pyradiomics to extract two-dimensional radiomic features of the portal venous stage of locally advanced GC, data were divided into training (n=207) and validation sets (n=89), with a ratio of 7:3, and three feature selection methods were cascaded and merged. Finally, least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to obtain the optimal feature subset. Four current representative machine-learning algorithms were used to construct the prediction model, the receiver operating characteristic curve was constructed to evaluate the predictive performance of the model, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. The differentiation degree, and the Lauren's and CA199 classifications were independent risk factors for locally advanced GC VI. Pyradiomics extracted 864 quantitative features of portal vein images of locally advanced GC. After filtering out low variance features using R, 236 features remained. Next, 18 features were screened using the LASSO algorithm. Extreme gradient boosting (XGBoost), logistic regression, Gaussian naive Bayes, and support vector machine models were constructed based on the 18 best features screened out of the portal venous CT images of advanced GC and three independent risk factors of GC VI in clinical features predicted the training set AUC values of 0.914, 0.897, 0.880, and 0.814, respectively. The predicted validation set AUC values were 0.870, 0.877, 0.859, and 0.773, respectively. The DeLong test results indicated no statistically significant difference in AUC values between the XGBoost and logistic regression models in the training and validation sets. The four machine-learning models showed high predictive performance. The logistic regression model had the highest AUC value in the validation set (0.877), and the accuracy and F1 score were 77 and 87.6%, respectively. CT radiomic features and machine-learning models based on the portal venous phase can be used as a noninvasive imaging method for the preoperative prediction of VI in locally advanced GC. The logistic regression model exhibited the highest diagnostic performance.

Development of a dual energy CT based model to assess response to treatment in patients with high grade serous ovarian cancer: a pilot cohort study

BACKGROUND

In patients with cancer, the current gold standard for assessing response to treatment involves measuring cancer lesions on computed tomography (CT) imaging. The percentage change in size of specific lesions determines whether patients have had a complete/partial response or progressive disease, according to RECIST criteria. Dual Energy CT (DECT) permits additional measurements of iodine concentration, a surrogate marker of vascularity. Here we explore the role of changes in iodine concentration within cancer tissue on CT scans to assess its suitability for determining treatment response in patients with high grade serous ovarian cancer (HGSOC).

METHODS

Suitable RECIST measurable lesions were identified from the CT images of HGSOC patients, taken at 2 different time points (pre and post treatment). Changes in size and iodine concentration were measured for each lesion. PR/SD were classified as responders, PD was classified as non-responder. Radiological responses were correlated with clinical and CA125 outcomes.

RESULTS

62 patients had appropriate imaging for assessment. 22 were excluded as they only had one DECT scan. 32/40 patients assessed (113 lesions) had received treatment for relapsed HGSOC. RECIST and GCIG (Gynaecologic Cancer Inter Group) CA125 criteria / clinical assessment of response for patients was correlated with changes in iodine concentration, before and after treatment. The prediction of median progression free survival was significantly better associated with changes in iodine concentration (p = 0.0001) and GCIG Ca125 / clinical assessment (p = 0.0028) in comparison to RECIST criteria (p = 0.43).

CONCLUSION

Changes in iodine concentration from dual energy CT imaging may be more suitable than RECIST in assessing response to treatment in patients with HGSOC.

TRIAL REGISTRATION

CICATRIx IRAS number 198179, 14 Dec 2015, https://www.myresearchproject.org.uk/ .

Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study

OBJECTIVES

This work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC).

METHODS

This work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary (n = 130) and validation cohort (n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients' survival were explored by log-rank survival analysis.

RESULTS

Borrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744-0.928) and 0.782 (0.701-0.850) in primary and validation cohorts than any other parameters (p < 0.05). Both PNI and the nomogram were related to post-surgical treatment planning. Only PNI was associated with disease-free survival in the primary cohort (p < 0.05).

CONCLUSION

This work prospectively established a spectral CT-based nomogram, which can effectively predict PNI preoperatively and potentially guide post-surgical treatment strategy in LAGC.

KEY POINTS

• The present prospective study established a spectral CT-based nomogram for preoperative prediction of perineural invasion in LAGC. • The proposed nomogram, including morphological features and the quantitative iodine concentration values from spectral CT, had the potential to predict PNI for LAGC before surgery, along with guide post-surgical treatment planning. • Normalized iodine concentration at the delayed phase was the most valuable quantitative parameter, suggesting the importance of delayed enhancement in gastric CT.

Are radiomic spleen features useful for assessing the differentiation status of advanced gastric cancer?

Background

The differentiation status of gastric cancer is related to clinical stage, treatment and prognosis. It is expected to establish a radiomic model based on the combination of gastric cancer and spleen to predict the differentiation degree of gastric cancer. Thus, we aim to determine whether radiomic spleen features can be used to distinguish advanced gastric cancer with varying states of differentiation.

Materials and methods

January 2019 to January 2021, we retrospectively analyzed 147 patients with advanced gastric cancer confirmed by pathology. The clinical data were reviewed and analyzed. Three radiomics predictive models were built from radiomics features based on gastric cancer (GC), spleen (SP) and combination of two organ position (GC+SP) images. Then, three Radscores (GC, SP and GC+SP) were obtained. A nomogram was developed to predict differentiation statue by incorporating GC+SP Radscore and clinical risk factors. The area under the curve (AUC) of operating characteristics (ROC) and calibration curves were assessed to evaluate the differential performance of radiomic models based on gastric cancer and spleen for advanced gastric cancer with different states of differentiation (poorly differentiated group and non- poorly differentiated group).

Results

There were 147 patients evaluated (mean age, 60 years ± 11SD, 111 men). Univariate and multivariate logistic analysis identified three clinical features (age, cTNM stage and CT attenuation of spleen arterial phase) were independent risk factors for the degree of differentiation of GC (p =0.004,0.000,0.020, respectively). The clinical radiomics (namely, GC+SP+Clin) model showed powerful prognostic ability in the training and test cohorts with AUCs of 0.97 and 0.91, respectively. The established model has the best clinical benefit in diagnosing GC differentiation.

Conclusion

By combining radiomic features (GC and spleen) with clinical risk factors, we develop a radiomic nomogram to predict differentiation status in patients with AGC, which can be used to guide treatment decisions.

Clinico-radiological nomogram for preoperatively predicting post-resection hepatic metastasis in patients with gastric adenocarcinoma

OBJECTIVE

To establish and validate a model comprising clinical and radiological features to pre-operatively predict post-resection hepatic metastasis (HM) in patients with gastric adenocarcinoma (GAC).

METHODS

We retrospectively analyzed 461 patients (HM, 106 patients); and non-metastasis (NM, 355 patients) who were confirmed to have GAC post-surgery. The patients were randomly divided into the training (n = 307) and testing (n = 154) cohorts in a 2:1 ratio. The main clinical risk factors were filtered using the least absolute shrinkage and selection operator algorithm according to their diagnostic value. The selected factors were then used to establish a clinical-radiological model using stepwise logistic regression. The Akaike's information criterion and receiver operating characteristic (ROC) analyses were used to evaluate the prediction performance of the model.

RESULTS

Logistic regression analysis showed that the peak enhancement phase, tumor location, alpha-fetoprotein, cancer antigen (CA)-125, CA724 levels, CT-based Tstage and arterial phase CT values were important independent predictors. Based on these predictors, the areas under the ROC curve of the training and testing cohorts were 0.864 and 0.832, respectively, for predicting post-operative HM.

CONCLUSION

This study built a synthetical nomogram using the pre-operative clinical and radiological features of patients to predict the likelihood of HM occurring after GAC surgery. It may help guide pre-operative clinical decision-making and benefit patients with GAC in the future.

ADVANCES IN KNOWLEDGE

1. The combination of clinical risk factors and CT imaging features provided useful information for predicting HM in GAC.2. A clinicoradiological nomogram is a tool for the pre-operative prediction of HM in patients with GAC.

A radiomics nomogram analysis based on CT images and clinical features for preoperative Lauren classification in gastric cancer

PURPOSE

To develop a combined radiomics nomogram based on computed tomography (CT) images and clinical features to preoperatively distinguish Lauren's diffuse-type gastric cancer (GC) from intestinal-type GC.

METHODS

Ninety-five patients with Lauren's intestinal or diffuse-type GC confirmed by postoperative pathology had their preoperative clinical information and dynamic contrast CT images retrospectively analyzed and were subdivided into training and test groups in a 7:3 ratio. To select the optimal features and construct the radiomic signatures, we extracted, filtered, and minimized the radiomic features from arterial phase (AP) and venous phase (VP) CT images. We constructed four models (clinical model, AP radiomics model, VP radiomics model, and radiomics-clinical model) to assess and compare their predictive performance between the intestinal- and diffuse-type GC. Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), and the DeLong test were used for assessment and comparison. In this study, radiomic nomograms integrating combined radiomic signatures and clinical characteristics were developed.

RESULTS

Compared to the AP radiomics model, the VP radiomics model had better performance, with an AUC of 0.832 (95% confidence interval [CI], 0.735, 0.929) in the training cohort and 0.760 (95% CI 0.580, 0.940) in the test cohort. Among the combined models that assessed Lauren's type GC, the model including age and VP radiomics showed the best performance, with an AUC of 0.849 (95% CI 0.758, 0.940) in the training cohort and 0.793 (95% CI 0.629, 0.957) in the test cohort.

CONCLUSIONS

Nomogram incorporating radiomic signatures and clinical features effectively differentiated Lauren's diffuse-type from intestinal-type GC.

Pre-treatment spectral CT combined with CT perfusion can predict hemorrhagic transformation after thrombolysis in patients with acute ischemic stroke

OBJECTIVE

To investigate the value of pre-treatment spectral CT angiography (CTA) in predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment in acute ischemic stroke (AIS) patients.

MATERIALS AND METHODS

AIS patients who underwent IVT with recombinant tissue plasminogen activator and pre-treatment head and neck spectral CTA and head CT perfusion (CTP) from January 2018 to June 2020 were reviewed retrospectively. Finally, 20 patients were included in the HT group and 22 age-matched patients were included in the non-HT group. Spectral and CTP parameters of the region of interest on pre-treatment CTA axial raw images and CTP images, including the infarct core (IC) and ischemic penumbral (IP) regions, were recorded. The differences in clinical variables, CTP, collateral scores and spectral parameters between the two groups were analyzed. Three multivariate logistic regression models were then developed, where model 1 included clinical and spectral parameters, model 2 included clinical and CTP parameters and a combined model included clinical, CTP, and spectral parameters. Receiver operating characteristic analysis was used to evaluate the performance of the multivariate model.

RESULTS

Patients with HT had higher Safe Implementation of Treatments in Stroke (SITS) score (p = 0.023), the volume of perfusion lesions (p = 0.005), the volume of IP (p = 0.003), the mean transit time (MIT) in the IC area (p = 0.012), as well as the TTP in IP area (p = 0.015) compared with patients without HT. The HT group showed significantly lower CBF in the IC area (p = 0.019), iodine concentration (p = 0.017) and the effective atomic number (p = 0.024) in the IP area than non-HT group. And the slope of the spectral curve of the HT group in the IP region was larger than that of the non-HT group (p = 0.023). Gender, age, SITS score, the volume of entire perfusion lesion, CBF and MIT in the IC area, TTP in the IP area, as well as iodine concentration in the IP area were included in the final multivariate model for predicting HT. And CBF in the IC area (OR = 0.779, 95 % CI:0.609-0.996, p = 0.046) as well as the iodine concentration of IP area (OR = 0.343, 95 % CI: 0.131-0.901, p = 0.030) were proved to be independent predictors for HT. The combined model including clinical, spectral, and CTP parameters, showed improved accuracy compared to the other two models, while the Delong test did not suggest a statistically significant difference (both p values > 0.05).

CONCLUSIONS

The iodine concentration of IP area derived from pre-treatment spectral CTA was an independent predictor of HT after IVT treatment for AIS patients. Moreover, multivariate models combined with clinical, spectral, and CTP parameters may be able to predict HT.

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Objectives

To develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC.

Methods

The two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA).

Results

Tumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (P<0.001). It yielded an AUC of 0.825 (95% confidence interval [95% CI], 0.769-0.872) and 0.802 (95% CI, 0.716-0.871) in primary and validation datasets (all P<0.05), with promising clinical utility by DCA.

Conclusion

This study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

BACKGROUND

The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades.

METHODS

A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared.

RESULTS

Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis.

CONCLUSION

DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

Diagnostic value of spiral CT energy spectrum imaging in lymph node metastasis of colorectal cancer

OBJECTIVES

To evaluate the value of preoperative CT energy spectrum imaging in detecting lymph node metastasis of colorectal cancer.

METHODS

From September 2019 to November 2021, a retrospective study was performed for the eighty-two patients with colorectal cancer through preoperative colonoscopy or surgical pathology confirmed in our hospital. Based on the lymph node metastasis status, these cases were divided into the metastasis and non-metastasis groups. GE Revolution CT scanner was used to scan the patients with energy spectrum imaging, it measured and recorded the single-energy CT values from 40 to 140 keV and various energy spectrum parameters of lymph nodes around the lesions in the arterial and venous phases, and statistically analyze the above indices.

RESULTS

In the arterial and venous phases: the single-energy CT values of 40-140 keV in the non-metastatic group were higher than those in the metastatic group (all P < 0.05); the parameter values of IC (iodine concentration), NIC (normalized iodine concentration), λ (the slope of the energy spectrum curve), and Eff-Z (effective-Z) in the non-metastatic group were higher than those in the metastatic group (all P < 0.05). Further evaluation of ROC curve showed that the higher AUC (area under curve) of the single-energy CT value of 50 keV in the arterial phase was 0.889, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the NIC had the better diagnostic efficiency and the AUC of the NIC was 0.873, the highest AUC of the combination of NIC and λ was 0.885 when the energy spectrum parameters were combined. The higher AUC of the single-energy CT value of 60 keV in the venous phase was 0.853, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the λ had the better diagnostic efficiency and the AUC of the λ was 0.822, the higher AUC of the combination of NIC, λ, and Eff-Z was 0.840 when the energy spectra were combined.

CONCLUSIONS

Parameters of energy spectrum CT imaging can effectively evaluate whether lymph nodes have metastases, and provide favorable imaging diagnosis basis for the range and the number of lymph nodes to be cleaned during clinical operation and can evaluate the prognosis of patients. It is worthy of clinical recommendation.

Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization

Objective

This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images.

Materials and Methods

The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis.

Results

Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram.

Conclusion

We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Plan-Do-Check-Action Circulation Combined with Accelerated Rehabilitation Nursing under Computed Tomography in Prevention and Control of Hospital Infection in Elderly Patients Undergoing Elective Orthopedic Surgery

To explore the adoption of plan-do-check-action (PDCA) circulation combined with accelerated rehabilitation nursing based on gemstone spectral imaging computed tomography (GSICT) in the prevention and control of hospital infection in the elderly patients undergoing the elective orthopedic surgery, 80 elderly patients who underwent the elective orthopedic surgery in the hospital were selected. Then, according to the randomized controlled principle, these 80 patients were divided into control group (40 cases) with conventional nursing and observation group (40 cases) with accelerated rehabilitation surgical nursing combined with PDCA circulation. All the patients underwent the GSICT examination without any contraindicators. Compared with the conventional CT scan, metal artifacts in GSICT were considerably reduced. In the images processed by GSI and metal artifacts reduction system (MARS), metal artifacts were basically eliminated and the positions, forms, and edges of metal artifacts in the human body were clearly presented. Hospital infection occurred in 1 (2.5%) patient in the observation group and 5 (12.5%) patients in the control group, and the difference was statistically significant (P < 0.05). In terms of temperature increase, patients in control group (37.5%) had a remarkably higher value than that of observation group (7.5%). The increase rate of white blood cell (WBC) count in control group (12.5%) was obviously higher than that in observation group (2.5%). Besides, the differences were statistically significant (P < 0.05). After PDCA circulation combined with accelerated rehabilitation nursing mode was applied, the hospitalization time of observation group (5.3 ± 2.4 days) was markedly lower than that of control group (9.7 ± 3.8 days). Moreover, the total hospitalization cost of observation group (791.44 yuan) was notably lower than that of control group (4068.96 yuan), with significant differences (P < 0.05). Nursing satisfaction in observation group (92.5%) was higher than that in control group (77.5%), and the difference was statistically significant (P < 0.05). In short, GSICT could effectively reduce beam hardening artifacts and metal implant artifacts and improve image quality. Furthermore, accelerated rehabilitation nursing combined with PDCA circulation could effectively reduce the incidence of hospital infection and improve nursing satisfaction.

Dual-energy CT may predict post-operative recurrence in early-stage glottic laryngeal cancer: a novel nomogram and risk stratification system

OBJECTIVES

To establish and validate a predictive model integrating with clinical and dual-energy CT (DECT) variables for individual recurrence-free survival (RFS) prediction in early-stage glottic laryngeal cancer (EGLC) after larynx-preserving surgery.

METHODS

This retrospective study included 212 consecutive patients with EGLC who underwent DECT before larynx-preserving surgery between January 2015 and December 2018. Using Cox proportional hazard regression model to determine independent predictors for RFS and presented on a nomogram. The model's performance was assessed using Harrell's concordance index (C-index), time-dependent area under curve (TD-AUC) plot, and calibration curve. A risk stratification system was established using the nomogram with median scores of all cases to divide all patients into two prognostic groups.

RESULTS

Recurrence occurred in 39/212 (18.4%) cases. Normalized iodine concentration in arterial (NICAP) and venous phases (NICVP) were verified as significant predictors of RFS in multivariate Cox regression (hazard ratio [HR], 4.2; 95% confidence interval [CI]: 2.3, 7.7, p < .001 and HR, 3.0; 95% CI: 1.5, 5.9, p = .002, respectively). Nomogram based on clinical and DECT variables was better than did only clinical variables. The prediction model proved well-calibrated and had good discriminative ability in the training and validation samples. A risk stratification system was built that could effectively classify EGLC patients into two risk groups.

CONCLUSIONS

DECT could provide independent RFS indicators in patients with EGLC, and the nomogram based on DECT and clinical variables was useful in predicting RFS at several time points.

KEY POINTS

• Dual-energy CT(DECT) variables can predict recurrence-free survival (RFS) after larynx-preserving surgery in patients with early-stage glottic laryngeal cancer (EGLC). • The model that integrates clinical and DECT variables predicted RFS better than did only clinical variables. • A risk stratification system based on the nomogram could effectively classify EGLC patients into two risk groups.

Differentiation Between Solitary Pulmonary Inflammatory Lesions and Solitary Cancer Using Gemstone Spectral Imaging

BACKGROUND

The distinction between solitary inflammatory lesion and solitary lung cancer remains a challenge because of their considerable overlapping computed tomography (CT) imaging features.

PURPOSE

This study aimed to verify whether spectral CT parameters can differentiate solitary lung cancer from solitary inflammatory lesions and to find their correlations with lesion size.

METHODS

A total of 78 patients with solitary lung lesions were included in our study. All of them underwent enhanced CT scans with Gemstone Spectral Imaging (GSI) mode, which was one of the dual-energy imaging technologies. According to maximum diameter (Dmax) of the lesion, regions of interest were collected and divided into inflammatory (group I: <3 cm [IA], n = 17; ≥3 cm [IB], n = 14) and cancer groups (group II: <3 cm [IIA], n = 20; ≥3 cm [IIB], n = 27). Computed tomography values (HU40keV, HU70keV), effective atomic number (Zeff), iodine concentration (IC), normalized IC (NIC), and spectral curve slopes (λ30, λ40) of each region of interest were calculated. The NIC was defined as the IC ratio of the lesion to the descending aorta. Mann-Whitney U test was used for intergroup (I vs II, IA vs IIA, IB vs IIB) and intragroup (IA vs IB, IIA vs IIB) comparisons, and receiver operating characteristic curve analysis was performed. Correlation analysis was applied to find the relationship between Dmax and GSI parameters.

RESULTS

No significant correlation was found between GSI parameters and Dmax in the inflammatory group, whereas inverse correlations were found in the cancer group. Gemstone spectral imaging parameters (except HU70keV) of group IIA were significantly higher than those of group IIB. There were significant differences in HU40keV, IC, NIC, λ30, and λ40 between groups IB and IIB under both arterial and venous phase (P values < 0.05), whereas the area under the curve for λ30 under venous phase was largest, and sensitivity and specificity were 96.32% and 85.71%, respectively. However, only HU40keV and HU70keV values under the arterial phase of IIA were significantly higher than those of IA.

CONCLUSIONS

Quantitative parameters of GSI demonstrated an inverse correlation with the lesion size of solitary lung cancer, and GSI parameters can be new ways to differentiate solitary lung cancer from solitary inflammatory lesions.

Cross-Phase Adversarial Domain Adaptation for Deep Disease-free Survival Prediction with Gastric Cancer CT Images

Predicting gastric cancer disease-free survival (DFS) and identifying patients probably with high risk are imperative for more appropriate clinical treatment plans. Compared with CT-based radiomics researches adopting linear Cox proportional hazards models, deep neural networks can perform nonlinear transformations and investigate complex associations of image features with prognosis. Exploring shared information between post-contrast CT (with better visual enhancement) and pre-contrast CT (with few side effects and contraindications) is another challenge. In this work, a cross-phase adversarial domain adaptation (CPADA) framework is proposed to adapt a deep DFS prediction network (DDFS-Net) from arterial phase to pre-contrast phase. The DDFS-Net is designed for feature learning and trained by optimizing the average negative log function of Cox partial likelihood. The CPADA maps the feature space of pre-contrast phase (target) to arterial phase (source) in an adversarial manner by measuring Wasserstein distance. The proposed methods are evaluated on a dataset of 249 gastric cancer patients by concordance index, receiver operating characteristic curves, and Kaplan-Meier survival curves. The results demonstrate that our DDFS-Net outperforms linear survival analysis methods, and the CPADA works better than supervised learning and direct transfer schemes.Clinical Relevance-This work enables preoperative DFS prediction and risk stratification in gastric cancer. It is feasible and effective to infer a patient's risk of failure given a pre-contrast CT image by DDFS-Net adapted by CPADA.

Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

Objective

This study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC).

Material and Methods

We retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.

Results

The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility.

Conclusions

This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Objective

To develop and validate a dual-energy computed tomography (DECT) derived radiomics model to predict peritoneal metastasis (PM) in patients with gastric cancer (GC).

Methods

This retrospective study recruited 239 GC (non-PM = 174, PM = 65) patients with histopathological confirmation for peritoneal status from January 2015 to December 2019. All patients were randomly divided into a training cohort (n = 160) and a testing cohort (n = 79). Standardized iodine-uptake (IU) images and 120-kV-equivalent mixed images (simulating conventional CT images) from portal-venous and delayed phases were used for analysis. Two regions of interest (ROIs) including the peritoneal area and the primary tumor were independently delineated. Subsequently, 1691 and 1226 radiomics features were extracted from the peritoneal area and the primary tumor from IU and mixed images on each phase. Boruta and Spearman correlation analysis were used for feature selection. Three radiomics models were established, including the R_IU model for IU images, the R_MIX model for mixed images and the combined radiomics model (the R_comb model). Random forest was used to tune the optimal radiomics model. The performance of the clinical model and human experts to assess PM was also recorded.

Results

Fourteen and three radiomics features with low redundancy and high importance were extracted from the IU and mixed images, respectively. The R_IU model showed significantly better performance to predict PM than the R_MIX model in the training cohort (AUC, 0.981 vs. 0.917, p = 0.034). No improvement was observed in the R_comb model (AUC = 0.967). The R_IU model was the optimal radiomics model which showed no overfitting in the testing cohort (AUC = 0.967, p = 0.528). The R_IU model demonstrated significantly higher predictive value on peritoneal status than the clinical model and human experts in the testing cohort (AUC, 0.785, p = 0.005; AUC, 0.732, p <0.001, respectively).

Conclusion

DECT derived radiomics could serve as a non-invasive and easy-to-use biomarker to preoperatively predict PM for GC, providing opportunity for those patients to tailor appropriate treatment.

Dual-Energy CT-Based Nomogram for Decoding HER2 Status in Patients With Gastric Cancer

OBJECTIVE. The purpose of this study was to develop and evaluate a dual-energy CT (DECT)-based nomogram for noninvasive identification of the status of human epidermal growth factor receptor 2 (HER2; also known as ERBB2) expression in gastric cancer (GC). MATERIALS AND METHODS. A total of 206 patients with histologically proven GC who underwent pretreatment DECT were retrospectively recruited and randomly allocated to a training cohort (n = 144) or a test cohort (n = 62). Information on clinical characteristics, qualitative imaging features, and quantitative DECT parameters was collected. Univariate analysis and multivariate logistic regression were implemented to screen independent predictors of HER2 status. An individualized nomogram was built, and its discrimination, calibration, and clinical usefulness were assessed. RESULTS. Tumor location, the iodine concentration of the tumor in the venous phase, and the normalized iodine concentration of the tumor in the venous phase were significant factors predictive of HER2 status (all p < .05). After these three indicators were integrated, the proposed nomogram showed a favorable diagnostic performance, with AUCs of 0.807 (95% CI, 0.718-0.897) in the training cohort and 0.815 (95% CI, 0.661-0.968) in the test cohort. The nomogram showed a preferable fitting (all p > .05 by the Hosmer-Lemeshow test) and would offer more net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts. CONCLUSION. The DECT-based nomogram has great application potential in terms of detecting HER2 status in GC, and can serve as a novel substitute for invasive testing.

CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies

Objectives

To explore the application of computed tomography (CT) texture analysis in differentiating lymphomas from other malignancies of the small bowel.

Methods

Arterial and venous CT images of 87 patients with small bowel malignancies were retrospectively analyzed. The subjective radiological features were evaluated by the two radiologists with a consensus agreement. The region of interest (ROI) was manually delineated along the edge of the lesion on the largest slice, and a total of 402 quantified features were extracted automatically from AK software. The inter- and intrareader reproducibility was evaluated to select highly reproductive features. The univariate analysis and minimum redundancy maximum relevance (mRMR) algorithm were applied to select the feature subsets with high correlation and low redundancy. The multivariate logistic regression analysis based on texture features and radiological features was employed to construct predictive models for identification of small bowel lymphoma. The diagnostic performance of multivariate models was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

The clinical data (age, melena, and abdominal pain) and radiological features (location, shape, margin, dilated lumen, intussusception, enhancement level, adjacent peritoneum, and locoregional lymph node) differed significantly between the nonlymphoma group and lymphoma group (p < 0.05). The areas under the ROC curve of the clinical model, arterial texture model, and venous texture model were 0.93, 0.92, and 0.87, respectively.

Conclusion

The arterial texture model showed a great diagnostic value and fitted performance in preoperatively discriminating lymphoma from nonlymphoma of the small bowel.

Useful Parameters in Dynamic Contrast-enhanced Ultrasonography for Identifying Early Response to Chemotherapy in a Rat Liver Tumor Model

Objectives

The objective of the study is to determine a parameter on the time-intensity curve (TIC) of dynamic contrast-enhanced ultrasonography (DCE-US) that best correlates with tumor growth and to evaluate whether the parameter could correlate with the early response to irinotecan in a rat liver tumor model.

Material and Methods

Twenty rats with tumors were evaluated (control: Saline, n = 6; treatment: Irinotecan, n = 14) regarding four parameters from TIC: Peak intensity (PI), k value, slope (PI × k), and time to peak (TTP). Relative changes in maximum tumor diameter between day 0 and 10, and parameters in the first 3 days were evaluated. The Mann-Whitney U-test was used to compare differences in tumor size and other parameters. Pearson's correlation coefficients (r) between tumor size and parameters in the control group were calculated. In the treatment group, relative changes of parameters in the first 3 days were compared between responder and non-responder (<20% and ≥20% increase in size on day 10, respectively).

Results

PI, k value, PI × k, and TTP significantly correlated with tumor growth (r = 0.513, 0.911, 0.665, and 0.741, respectively). The mean RC in k value among responders (n = 6) was significantly lower than non-responders (n = 8) (mean k value, 4.96 vs. 72.5; P = 0.003).

Conclusion

Parameters of DCE-US could be a useful parameter for identifying early response to irinotecan.

Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas

PURPOSE

To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs).

METHODS

CT images of 63 patients with gastric NECs were collected retrospectively, and 63 patients with gastric ADCs were selected as the control group. Univariate analysis was used to identify the significant factors of clinical characteristics and CT findings for differentiating gastric NECs from ADCs. Radiomics analysis was applied to CT images of unenhanced, arterial phase and venous phase, respectively. A radiomics nomogram incorporating the radiomics signature and the subjective CT findings was developed and its diagnostic ability was evaluated. The diagnostic performances of CT findings model, radiomics signature and radiomics nomogram were compared using DeLong test.

RESULTS

The tumor margin and lymph node (LN) metastasis were independent predictors for differentiating gastric NECs from ADCs. The radiomics signature based on venous phase presented superior AUC of 0.798 [95 % confidence interval (CI), 0.657-0.938] in validation cohort. The nomogram incorporated the radiomics signature, tumor margin and LN metastasis showed AUCs of 0.821 (95 %CI: 0.725-0.895) in the primary cohort and 0.809 (95 %CI: 0.649-0.918) in the validation cohort. Moreover, the radiomics nomogram showed good discrimination and calibration. The diagnostic performance of CT findings model was significantly lower than that of radiomics nomogram (p =  0.001) and radiomics signature (p = 0.025).

CONCLUSIONS

Radiomics analysis exhibited good performance in differentiating gastric NECs from ADCs, and the radiomics nomogram may have significant clinical implications on preoperative detection of gastric malignant tumors.

Histological subtypes of solid-dominant invasive lung adenocarcinoma: differentiation using dual-energy spectral CT

AIM

To investigate the value of dual-energy spectral computed tomography (DESCT) for evaluating the histological subtypes of solid-dominant invasive lung adenocarcinoma (SILADC).

MATERIALS AND METHODS

Sixty-seven patients with SILADC were enrolled. All patients underwent DESCT and were divided into Group I (those with a lepidic/acinar/papillary predominant pattern) and Group II (those with a solid/micropapillary predominant pattern) based on their correlation with prognosis. Patient clinicopathological characteristics, DESCT morphological features, and quantitative parameters of the tumours were compared between both groups. Multiparametric analysis was performed using binary logistic regression with DESCT findings. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analysis.

RESULTS

Patient gender, lymph nodes status, pathological TNM stage, and histological differentiation significantly differed between the two groups (all p<0.05). Moreover, significant differences were observed between both groups in DESCT morphological features including tumour size, necrosis, calcification, air bronchogram, and vascular convergence sign, and quantitative parameters including K_{40-65 keV}, effective atomic number, and water concentration on unenhanced CT and iodine concentration in the arterial and venous phases (all p<0.05). Multiparametric analysis showed that tumour size, air bronchogram, K_{40-65 keV} and effective atomic number on unenhanced CT were the most effective variations for predicting the histological subtypes of SILADC and obtained an area under the ROC curve (AUC) of 0.906.

CONCLUSIONS

DESCT was useful for differentiating histological subtypes with different prognosis of SILADC.

Gastrointestinal Applications of Iodine Quantification Using Dual-Energy CT: A Systematic Review

Dual-energy computed tomography (DECT) can estimate tissue vascularity and perfusion via iodine quantification. The aim of this systematic review was to outline current and emerging clinical applications of iodine quantification within the gastrointestinal tract using DECT. The search was conducted with three databases: EMBASE, Pubmed and The Cochrane Library. This identified 449 studies after duplicate removal. From a total of 570 selected studies, 30 studies were enrolled for the systematic review. The studies were categorized into four main topics: gastric tumors (12 studies), colorectal tumors (8 studies), Crohn’s disease (4 studies) and miscellaneous applications (6 studies). Findings included a significant difference in iodine concentration (IC) measurements in perigastric fat between T1–3 vs. T4 stage gastric cancer, poorly and well differentiated gastric and colorectal cancer, responders vs. non-responders following chemo- or chemoradiotherapy treatment among cancer patients, and a positive correlation between IC and Crohn’s disease activity. In conclusion, iodine quantification with DECT may be used preoperatively in cancer imaging as well as for monitoring treatment response. Future studies are warranted to evaluate the capabilities and limitations of DECT in splanchnic flow.

Preoperative normalized iodine concentration derived from spectral CT is correlated with early recurrence of hepatocellular carcinoma after curative resection

OBJECTIVES

To investigate whether normalized iodine concentration (NIC) correlates with tumor microvessel density and early recurrence in patients with HCC.

MATERIALS AND METHODS

We included 71 patients with surgically resected single HCC in this prospective study who underwent preoperative spectral CT between November 2014 and June 2016. Two observers independently measured the NIC in the arterial phase (AP) and portal venous phase (PVP). The relationship between NIC and microvessel density was evaluated. Univariate and multivariate logistic regression was performed to evaluate independent predictors of early recurrence.

RESULTS

Early recurrence occurred in 28 of 71 patients (39.4%) during the 2-year follow-up. NIC-AP positively correlated with microvessel density for the two observers (r = 0.593 and 0.527). Based on multivariate analysis, independent risk factors for early HCC recurrence were tumor size (odds ratio, 1.200; p = 0.043) and NIC-AP (odds ratio, 2.522; p = 0.005). For the two observers, areas under the receiver operating characteristic curve for predicting early HCC recurrence were 0.719 and 0.677. Early recurrence rates were significantly higher among patients with NIC-AP values higher than the optimal cutoff than among those with values below the cutoff.

CONCLUSION

Normalized iodine concentration in the arterial phase from spectral CT reflects tumor-derived angiogenesis and is a potential predictive biomarker for early recurrence of hepatocellular carcinoma.

KEY POINTS

• Normalized iodine concentration in the arterial phase positively correlated with microvessel density of hepatocellular carcinoma. • In the patients with hepatocellular carcinoma, tumor size and normalized iodine concentration in the arterial phase were independent risk factors for early hepatocellular carcinoma recurrence. • Early hepatocellular carcinoma recurrence rates were significantly higher when normalized iodine concentration in the arterial phase values was above the optimal cutoff.

CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer

PURPOSE

This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer.

MATERIALS AND METHODS

A total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. Histological verification was based on the surgical specimens. CT findings were determined by a panel of three radiologists. Conventional hand-crafted features and deep learning features were extracted from three phases CT images and were utilized to build radiomics signatures via machine learning methods. Incorporating the radiomics signatures and CT findings, a radiomics nomogram was developed via multivariable logistic regression. Its diagnostic ability was measured using receiver operating characteristiccurve analysis.

RESULTS

The radiomics signatures, built with support vector machine or artificial neural network, showed good performance for discriminating T4a in the test I and II sets with area under curves (AUCs) of 0.76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values < 0.05).

CONCLUSIONS

The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.

Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network

Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.

Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis

OBJECTIVE. The purpose of this study was to investigate the correlation between iodine concentration (IC) derived from spectral CT and angiogenesis and the relationships between IC and clinical-pathologic features associated with lung cancer prognosis. SUBJECTS AND METHODS. Sixty patients with lung cancer were enrolled and underwent spectral CT. The IC, IC difference (ICD), and normalized IC (NIC) of tumors were measured in the arterial phase, venous phase (VP), and delayed phase. The microvessel densities (MVDs) of CD34-stained specimens were evaluated. Correlation analysis was performed for IC and MVD. The relationships between the IC index showing the best correlations with MVD and clinical-pathologic findings of pathologic types, histologic differentiation, tumor size, lymph node status, pathologic TNM stage, and intratumoral necrosis were investigated. RESULTS. The mean (± IQR) MVD of all tumors was 42.00 ± 27.50 vessels per field at ×400 magnification, with two MVD distribution types. The MVD of lung cancer correlated positively with the IC, ICD, and NIC on three-phase contrast-enhanced scanning (r range, 0.581-0.800; all p < 0.001), and the IC in the VP showed the strongest correlation with MVD (r = 0.800; p < 0.001). The correlations between IC and MVD, ICD and MVD, and NIC and MVD varied depending on whether the same scanning phase or same IC index was used. The IC in the VP showed statistically significant differences in the pathologic types of adenocarcinoma and squamous cell carcinoma, histologic differentiation, tumor size, and status of intratumoral necrosis of lung cancer (p < 0.05), but was not associated with nodal metastasis and pathologic TNM stages (p > 0.05). CONCLUSION. IC indexes derived from spectral CT, especially the IC in the VP, were useful indicators for evaluating tumor angiogenesis and prognosis.

Spatial heterogeneity of nanomedicine investigated by multiscale imaging of the drug, the nanoparticle and the tumour environment

Genetic and phenotypic tumour heterogeneity is an important cause of therapy resistance. Moreover, non-uniform spatial drug distribution in cancer treatment may cause pseudo-resistance, meaning that a treatment is ineffective because the drug does not reach its target at sufficient concentrations. Together with tumour heterogeneity, non-uniform drug distribution causes “therapy heterogeneity”: a spatially heterogeneous treatment effect. Spatial heterogeneity in drug distribution occurs on all scales ranging from interpatient differences to intratumour differences on tissue or cellular scale. Nanomedicine aims to improve the balance between efficacy and safety of drugs by targeting drug-loaded nanoparticles specifically to tumours. Spatial heterogeneity in nanoparticle and payload distribution could be an important factor that limits their efficacy in patients. Therefore, imaging spatial nanoparticle distribution and imaging the tumour environment giving rise to this distribution could help understand (lack of) clinical success of nanomedicine. Imaging the nanoparticle, drug and tumour environment can lead to improvements of new nanotherapies, increase understanding of underlying mechanisms of heterogeneous distribution, facilitate patient selection for nanotherapies and help assess the effect of treatments that aim to reduce heterogeneity in nanoparticle distribution.

In this review, we discuss three groups of imaging modalities applied in nanomedicine research: non-invasive clinical imaging methods (nuclear imaging, MRI, CT, ultrasound), optical imaging and mass spectrometry imaging. Because each imaging modality provides information at a different scale and has its own strengths and weaknesses, choosing wisely and combining modalities will lead to a wealth of information that will help bring nanomedicine forward.

Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer

Purpose: The aim of this study was to investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual-energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC).

Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported, and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for the next analysis. The feature dimension reduction was performed by using Student's t-test or Mann–Whitney U-test, Spearman's rank correlation test, min–max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multiparameter logistic regression model was established to predict MSI status. The model performances were evaluated: The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis; the calibration performance was tested by calibration curve accompanied by Hosmer–Lemeshow test; the clinical utility was assessed by decision curve analysis.

Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). A nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analyses, respectively.

Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

T and N Staging of Gastric Cancer Using Dual-Source Computed Tomography

Aim

This study is aimed at comparing gastric cancer T and N staging between virtual monochromatic energy images and fusion images generated by dual-source computed tomography (DSCT) dual-energy mode data acquisition prospectively while measuring the iodine concentration of gastric cancer and lymph nodes at different T and N stages from iodine map retrospectively.

Methods

A total of 71 patients (50 males and 21 females; mean age: 59 ± 11 years) confirmed with gastric cancer by endoscopic biopsy with no neoadjuvant chemotherapy were enrolled for the CT examination before surgeries. The preoperative T and N staging results were compared between groups with pathological results as the gold standard. The iodine concentrations of the gastric lesions and LNs were measured on the iodine-based material decomposition images. All iodine concentration values were normalized against those in the abdominal aorta and defined as normalized iodine concentration (nIC) values. The short axis length of LNs and nIC values were statistically analyzed.

Results

Group A was better than group B for T3 and T4 staging. No statistically significant difference in the overall accuracies for N staging was found between groups. For the late arterial and delayed phases, T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. The nIC values between N0 and Nm (N1-N3) showed statistically significant differences in the portal phase only.

Conclusions

T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. Hence, dual-source CT may be helpful in the differential diagnosis between T3 and T4.

Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference

Objective

The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT.

Methods

Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded. Normalized iodine concentration (NIC) and the differences in NIC between the proximal and the distal regions (dNIC) were calculated. The two-sample t-test and Mann-Whitney U-test were performed to compare the multiple parameters generated from spectral CT between malignant and benign nodules. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity.

Results

NIC in the proximal region (NIC_pro) and NIC in the distal region (NIC_dis) between malignant and benign nodules at AP (NIC_pro, P=0.012; NIC_dis, P=0.024), and VP (NIC_pro, P=0.005; NIC_dis, P =0.004) were significantly different. NIC_pro at PP (P = 0.037) was also found significantly different between malignant and benign nodules; however, no significant differences were found in NIC_dis at PP (P = 0.093). In addition, the dNIC of malignant nodules was significantly higher than that of benign ones at PP (median and interquartiles (0.31, 0.11, 0.57 versus -0.26, -0.5, -0.1); p≤0.001), AP (mean dNIC, 0.093 ±0.094 versus -0.075±0.060; p≤0.001), and VP (mean dNIC, 0.171±0.137 versus -0.183±0.127; p≤0.001). The sensitivity and specificity (93%, 95%, respectively) of dNIC during VP were higher than other parameters, with a threshold value of -0.07.

Conclusions

Spectral CT imaging with multiple parameters such as NIC_pro, NIC_dis, and dNIC may be a new method for differentiating malignant SPNs from benign ones.

Comparative study of dual energy CT iodine imaging and standardized concentrations before and after chemoradiotherapy for esophageal cancer

Background

To compare dual energy CT iodine imaging and standardized iodine concentration before and after chemoradiotherapy (CRT) for esophageal cancer and evaluate the efficacy of CRT for EC by examining DECT iodine maps and standard CT values.

Methods

The clinical data of 45 patients confirmed by pathology with newly diagnosed esophageal cancer who underwent concurrent CRT from February 2012 to January 2017 in our department of radiology were collected. All patients underwent dual-source dual-energy CT (DECT) before and after CRT. Normalized iodine concentration (NIC) and normalized CT (NCT) corresponding to the overall cancer lesion and its maximum cross-sectional area were observed and compared. Additionally, 30 healthy individuals were compared as control group. After treatment, the patients were divided into two groups according to RECIST1.1: treatment effective group and ineffective group.

Results

There were 33 patients (CR 9, PR 24) in the effective group and 12 patients (SD 12, PD 0) in the ineffective group. There was no significant difference in the NIC-A, NIC-V, NCT-A and NCT-A indexes between the effective group (B group) and the ineffective group (C group) before treatment (P > 0.05). After the treatment, the above-mentioned indexes in the effective group of patients were significantly lower than before treatment, and compared with the ineffective group, the NIC-A, NIC-V, NCT-A and NCT-V values of the effective group were significantly lower than those of ineffective group (P < 0.05). After treatment, the NIC-V and NCT-V in the ineffective group were lower than before treatment, and the difference was statistically significant (P < 0.05). However, their NIC-A and NCT-A were not statistically different from those before treatment (P > 0.05).

Conclusion

Using DECT iodine map, the changes of NIC and NIC before and after CRT in patients with esophageal cancer can evaluate the effect of CRT, and does not increase the radiation dose, so it is suitable for clinical use.

Detection of gastric cancer and its histological type based on iodine concentration in spectral CT

Background

Computed tomography (CT) imaging is the most common imaging modality for the diagnosis and staging of gastric cancer. The aim of this study is was to prospectively explore the ability of quantitative spectral CT parameters in the detection of gastric cancer and its histologic types.

Methods

A total of 87 gastric adenocarcinoma (43 poorly and 44 well-differentiated) patients and 36 patients with benign gastric wall lesions (25 inflammation and 11 normal), who underwent dual-phase enhanced spectral CT examination, were retrospectively enrolled in this study. Iodine concentration (IC) and normalized iodine concentration (nIC) during arterial phase (AP) and portal venous phase (PP) were measured thrice in each patient by two blinded radiologists. Moreover, intraclass correlation coefficient (ICC) was used to assess the interobserver reproducibility. Differences of IC and nIC values between gastric cancer and benign lesion groups were compared using Mann-Whitney U test. Furthermore, the gender, age, location, thickness and histological types of gastric adenocarcinoma were analyzed by Mann-Whitney U test or Kruskal-Wallis H test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of IC and nIC values, and the optimal cut-off value was calculated with Youden J.

Results

An excellent interobserver agreement (ICC >  0.6) was achieved for IC. Notably, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in gastric cancer group (Z = 5.870, 3.894, 2.009 and 10.137, respectively; P < 0.05) than those in benign lesion group. Additionally, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in poorly differentiated gastric adenocarcinoma group (Z = 4.118, 5.637, 6.729 and 2.950, respectively; P < 0.005) than those in well-differentiated gastric adenocarcinoma group. There were no statistically significant differences in the values of ICAP, ICPP, nICAP and nICPP between age, gender, tumor thickness and tumor location. Furthermore, the area under the curve (AUC) values of ICAP, nICAP, ICPP and nICPP were 0.745, 0.584, 0.662, and 0.932, respectively, for gastric cancer detection; while 0.756, 0.919, 0.851 and 0.684, respectively, in discriminating poorly differentiated gastric adenocarcinoma.

Conclusion

IC values exhibited great potential in the preoperative and non-invasive diagnosis of gastric cancer and its histological types. In particular, nICPP is more effective for the identification of gastric cancer, whereas nICAP is more effective in discriminating poorly differentiated gastric adenocarcinoma.

Monitoring of tumor vascular normalization: the key points from basic research to clinical application

Tumor vascular normalization alleviates hypoxia in the tumor microenvironment, reduces the degree of malignancy, and increases the efficacy of traditional therapy. However, the time window for vascular normalization is narrow; therefore, how to determine the initial and final points of the time window accurately is a key factor in combination therapy. At present, the gold standard for detecting the normalization of tumor blood vessels is histological staining, including tumor perfusion, microvessel density (MVD), vascular morphology, and permeability. However, this detection method is almost unrepeatable in the same individual and does not dynamically monitor the trend of the time window; therefore, finding a relatively simple and specific monitoring index has important clinical significance. Imaging has long been used to assess changes in tumor blood vessels and tumor changes caused by the oxygen environment in clinical practice; some preclinical and clinical research studies demonstrate the feasibility to assess vascular changes, and some new methods were in preclinical research. In this review, we update the most recent insights of evaluating tumor vascular normalization.

CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers

To investigate the ability of CT texture analysis to assess and predict the expression statuses of E-cadherin, Ki67, VEGFR2 and EGFR in gastric cancers, the enhanced CT images of 139 patients with gastric cancer were retrospectively reviewed. The region of interest was manually drawn along the margin of the lesion on the largest slice in the arterial and venous phases, which yielded a series of texture parameters. Our results showed that the standard deviation, width, entropy, entropy (H), correlation and contrast from the arterial and venous phases were significantly correlated with the E-cadherin expression level in gastric cancers (all P < 0.05). The skewness from the arterial phase and the mean and autocorrelation from the venous phase were negatively correlated with the Ki67 expression level in gastric cancers (all P < 0.05). The width, entropy and contrast from the venous phase were positively correlated with the VEGFR2 expression level in gastric cancers (all P < 0.05). No significant correlation was found between the texture features and EGFR expression level. CT texture analysis, which had areas under the receiver operating characteristic curve (AUCs) ranging from 0.612 to 0.715, holds promise in predicting E-cadherin, Ki67 and VEGFR2 expression levels in gastric cancers.

Gastric heterotopic pancreas and stromal tumors smaller than 3 cm in diameter: clinical and computed tomography findings

BACKGROUND

Identifying gastric heterotopic pancreas and stromal tumors is difficult. Few studies have reported computed tomography (CT) findings for differentiating lesions less than 3 cm in diameter. In this study, we aimed to identify clinical characteristics and CT findings that can differentiate gastric heterotopic pancreatic lesions from stromal tumors less than 3 cm in diameter.

METHODS

A total of 132 patients with pathologically confirmed gastric heterotopic pancreas (n = 66) and stromal tumors (n = 66) were included. Each group was divided into primary (n = 50) and validation cohort (n = 16). Clinical characteristics and CT findings were retrospectively reviewed. CT findings included location, border, contour, growth pattern, enhancement pattern and grade, the enhancement value of tumor, enhancement ratio of tumor, and enhancement ratio of tumor to pancreas in venous phase. The findings in the two groups were compared using the Pearson χ² test or Student t-test. Receiver operating characteristic curves were used to determine areas under the curve and optimal cut-offs.

RESULTS

Significant differences were observed between heterotopic pancreas and stromal tumors in the distribution of tumor location, border, contour (all P < 0.001), enhancement values (P < 0.001), enhancement ratios of tumors (P < 0.001), and enhancement ratios of tumors to pancreas (P < 0.001). No significant differences existed in growth pattern (P = 0.203). The area under the curve differed significantly between enhancement ratio of tumor to pancreas and enhancement ratio (P = 0.030). There were significant differences in above characteristics between two groups in validation cohort.

CONCLUSIONS

Heterotopic pancreas has characteristic CT features differentiating it from stromal tumors.