Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients. METHODS: A total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist's assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models' discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC). RESULTS: The AUCs of the radiologist's assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05). CONCLUSIONS: A multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC. CRITICAL RELEVANCE STATEMENT: This study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients. KEY POINTS: The biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer. Radiomics features and clinical data before and after nCRT provide complementary tumor information. Preoperative prediction of LN status after nCRT contributes to clinical decision-making.

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.

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.