Quantitative parameters in novel spectral computed tomography: Assessment of Ki-67 expression in patients with gastric adenocarcinoma.

BACKGROUND: The level of Ki-67 expression has served as a prognostic factor in gastric cancer. The quantitative parameters based on the novel dual-layer spectral detector computed tomography (DLSDCT) in discriminating the Ki-67 expression status are unclear. AIM: To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma (GC). METHODS: Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma. Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt (kev), the slope of the spectral curve (λHU), iodine concentration (IC), normalized IC (nIC), effective atomic number (Zeff) and normalized Zeff (nZeff) in the arterial phase (AP) and venous phase (VP) were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma. Spearman's correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status. Receiver operating characteristic (ROC) curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups. RESULTS: Thirty-seven and 71 patients were classified as having low and high Ki-67 expression, respectively. CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, and Zeff-related parameters were significantly higher, but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status, and other analyzed parameters showed no statistical difference between the two groups. Spearman's correlation analysis showed that CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, Zeff, and nZeff exhibited a negative correlation with Ki-67 status, whereas IC and nIC had positive correlation with Ki-67 status. The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status [area under the curve (AUC) = 0.967; sensitivity 95.77%; specificity 91.89%)]. Nevertheless, the differentiating capabilities of single-variable model were moderate (AUC value 0.630 - 0.835). In addition, the nZeff VP and nICVP (AUC 0.835 and 0.805) showed better performance than CT40 kev-VP, CT70 kev-VP and CT100 kev-VP (AUC 0.630, 0.631 and 0.662) in discriminating the Ki-67 status. CONCLUSION: Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma. Zeff and IC may be useful parameters for evaluating the Ki-67 expression.

Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma.

OBJECTIVES: Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively. METHODS: A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis. RESULTS: The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively. CONCLUSION: The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.

External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.

OBJECTIVES: The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS: This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS: The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS: • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.

Utility of dual-layer spectral-detector CT imaging for predicting pathological tumor stages and histologic grades of colorectal adenocarcinoma.

Objectives: To assess the utility of Dual-layer spectral-detector CT (DLCT) in predicting the pT stage and histologic grade for colorectal adenocarcinoma (CRAC). Methods: A total of 131 patients (mean 62.7 ± 12.9 years; 72 female, 59 male) with pathologically confirmed CRAC (35 pT1-2, 61 pT3, and 35 pT4; 32 high grade and 99 low grade), who received dual-phase DLCT were enrolled in this retrospective study. Normalized iodine concentration (NIC), slope of the spectral HU curve (λHU), and effective atomic number (Eff-Z) were measured for each lesion by two radiologists independently. Intraobserver reliability and interobserver agreement were assessed. The above values were compared between three pT-stage and two histologic-grade groups. The correlation between the pT stages and above values were assessed. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic efficacy. Results: Intra-class correlation coefficients were ranged from 0.856 to 0.983 for all measurements. Eff-Z [7.21(0.09) vs 7.31 (0.10) vs 7.35 (0.19)], NICAP [0.11 (0.05) vs 0.15 (0.08) vs 0.15 (0.08)], NICVP [0.27 (0.06) vs 0.34 (0.11) vs 0.35 (0.12)], λHUAP [1.20 (0.45) vs 1.93 (1.18) vs 2.37 (0.91)], and λHUVP [2.07 (0.68) vs 2.35 (0.62) vs 3.09 (1.07)] were significantly different among pT stage groups (all P<0.001) and exhibited a positive correlation with pT stages (r= 0.503, 0.455, 0.394, 0.512, 0.376, respectively, all P<0.001). Eff-Z [7.37 (0.10) vs 7.28 (0.08)], NICAP[0.20 (0.10) vs 0.13 (0.08)], NICVP[0.35 (0.07) vs 0.31 (0.11)], and λHUAP [2.59 (1.11) vs 1.63 (0.75)] in the high-grade group were markedly higher than those in the low-grade group (all P<0.05). For discriminating the advanced- from early-stage CARC, the AUCs of Eff-Z, NICAP, NICVP, λHUAP, and λHUVP were 0.83, 0.80, 0.79, 0.86, and 0.68, respectively (all P<0.001). For discriminating the high- from low-grade CARC, the AUCs of Eff-Z, NICAP, NICVP, and λHUAP were 0.81, 0.81, 0.64, and 0.81, respectively (all P<0.05). Conclusions: The quantitative parameters derived from DLCT may provide new markers for assessing pT stages and histologic differentiation in patients with CRAC.

Combining diffusion kurtosis imaging and clinical data for predicting the extramural venous invasion of rectal adenocarcinoma.

OBJECTIVE: The aim of this study is to investigate the diagnostic ability of diffusion kurtosis imaging (DKI) -derived parameters combining with clinical data as risk factors for EMVI's involvement status in rectal adenocarcinoma. MATERIALS AND METHODS: Preoperative MR examination including DKI and conventional diffusion-weighted imaging (DWI) was performed on 154 rectal adenocarcinoma patients enrolled in this respective study. Kmean, Dmean, and apparent diffusion coefficient (ADC) values were calculated. Clinical information, serum tumor markers, MR and pathological assessment of EMVI were recorded. The Shapiro-Wilk test, two-sample t-test, Mann-Whitney U test, Spearman's rank-order correlation, univariate and multivariate logistic regression analyses were used for statistical analysis. Receiver operating characteristic (ROC) curve analyses were performed to identify risk factors in EMVI involvement. RESULTS: Of the 154 patients, pEMVI-positive rectal tumors had significantly higher Kmean values, lower ADCmean values compared to pEMVI-negative rectal tumors. Kmean values positively correlated with mrEMVI scores, whereas ADCmean values showed a negative correlation with mrEMVI scores. However, there was no significant correlation between the Dmean values and the mrEMVI scores. Univariate analysis demonstrated increased Kmean values, decreased ADCmean values, nodal involvement, an advanced tumor stage, and a G2 tumor grade were significantly related to the pEMVI of rectal adenocarcinoma. The multivariate analysis demonstrated that the Kmean values, lymph node involvement and an advanced tumor stage (T3) were independent risk factors for EMVI. CONCLUSION: The potential for diffusion kurtosis imaging as a biomarker for evaluating the EMVI of rectal cancer is feasible, especially given DKI's capability of detecting tumor heterogeneity noninvasively.

Predicting Treatment Response of Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Amide Proton Transfer MRI Combined With Diffusion-Weighted Imaging.

OBJECTIVE: To evaluate amide proton weighted (APTw) MRI combined with diffusion-weighted imaging (DWI) in predicting neoadjuvant chemoradiotherapy (NCRT) response in patients with locally advanced rectal cancer (LARC). METHODS: 53 patients with LARC were enrolled in this retrospective study. MR examination including APTw MRI and DWI was performed before and after NCRT. APTw SI, ADC value, tumor size, CEA level before and after NCRT were assessed. The difference of the above parameters between before and after NCRT was calculated. The tumor regression grading (TRG) was assessed by American Joint Committee on Cancer's Cancer Staging Manual AJCC 8th score. The Shapiro-Wilk test, paired t-test and Wilcoxon Signed Ranks test, two-sample t-test, Mann-Whitney U test and multivariate analysis were used for statistical analysis. RESULTS: Of the 53 patients, 19 had good responses (TRG 0-1), 34 had poor responses (TRG 2-3). After NCRT, all the rectal tumors demonstrated decreased APT values, increased ADC values, reduced tumor volumes and CEA levels (all p < 0.001). Good responders demonstrated higher pre-APT values, higher Δ APT values, lower pre- ADC values and higher Δ tumor volumes than poor responders. Pre-APT combined with pre-ADC achieved the best diagnostic performance, with AUC of 0.895 (sensitivity of 85.29%, specificity of 89.47%, p < 0.001) in predicting good response to NCRT. CONCLUSION: The combination of APTw and DWI may serve as a noninvasive biomarker for evaluating and identifying response to NCRT in LARC patients.

Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

BACKGROUND: It is a permanent challenge to differentiate small solid lung nodules. Massive data, extracted from medical image through radiomics analysis, may help early diagnosis of lung cancer. The aim of this study was to assess the usefulness of a quantitative radiomic model developed from baseline low-dose computed tomography (LDCT) screening for the purpose of predicting malignancy in small solid pulmonary nodules (SSPNs). METHODS: This retrospective study included malignant and benign SSPNs (6 to 15 mm) detected in baseline low-dose CT screening. The malignancy was confirmed pathologically, and benignity was confirmed by long term follow-up or pathological diagnosis. The non-contrast CT images were reconstructed with a lung kernel of a slice thickness of 1 mm and were processed to extract 385 quantitative radiomic features using Analysis-Kinetic software. A predictive model was established with the training set of 156 benign and 40 malignant nodules, and was tested with the validation set of 77 benign and 21 malignant nodules through the analysis of R square. The performance of the radiomic model in predicting malignancy was compared with that of the ACR Lung Imaging Reporting and Data System (ACR lung-RADS). RESULTS: In 294 cases of SSPNs, 61 lung cancers and 24 benign nodules were confirmed pathologically and the remaining 209 nodules were stable with long-term follow-up (4.1±0.9 years). Eleven non-redundant features, including 8 high-order texture features, were extracted from the training data set. The sensitivity and specificity of the prediction model in malignancy differentiation were 81.0% and 92.2% respectively. The accuracy was superior to ACR-lung RADS (89.8% vs. 76.5%). CONCLUSIONS: A radiomic model based on baseline low-dose CT screening for lung cancer can improve the accuracy in predicting malignancy of SSPNs.