Objective: To determine whether gross tumor volume (GTV) and the maximum diameter of resectable cervical cancer at magnetic resonance imaging (MRI) could predict lymph node metastasis (LNM) and lymphovascular space invasion (LVSI).Materials and Methods: A total of 315 consecutive patients with cervical cancer were retrospectively identified. Gross tumor volume and the maximum diameter of tumor were evaluated on MRI. Univariate and multivariate logistic regression analyses were performed to determine whether tumor size could predict LNM and LVSI. Cutoffs of GTV, maximum diameter, and the International Federation of Gynecology and Obstetrics (FIGO) classification of tumor were first investigated in 255 patients (group A) and then validated in an independent cohort of 60 patients (group B) using area under the receiver operating characteristic curve (AUC) analysis for predicting the presence of LNM and LVSI.Results: Univariate analysis showed that GTV and the maximum diameter of tumor could predict LNM and LVSI (all P < 0.0001). Multivariate analyses indicated GTV as an independent risk factor of LNM and LVSI (all P < 0.0001). In group A, GTV, the maximum diameter, and the FIGO stage could identify LNM (AUC, 0.813, 0.741, and 0.69, respectively) and LVSI (AUC, 0.806, 0.751, and 0.684, respectively). In group B, GTV, the maximum diameter, and the FIGO stage could help identify LNM (AUC, 0.902, 0.825, and 0.759, respectively) and LVSI (AUC, 0.771, 0.748, and 0.700, respectively).Conclusions: Gross tumor volume and the maximum diameter of resectable cervical cancer at MRI demonstrated capability in predicting LNM and LVSI, which were more accurate than FIGO stage.

This study is to investigate optimum apparent diffusion coefficient (ADC) parameter for predicting lymphovascular invasion (LVI), lymph node metastasis (LNM) and histology type in resectable rectal cancer. 58 consecutive patients with resectable rectal cancer were retrospectively identified. The minimum, maximum, average ADC and ADC difference value were obtained on ADC maps. Maximum ADC and ADC difference value increased with the appearance of LVI (r = 0.501 and 0.495, P < 0.001, respectively) and development of N category (r = 0.615 and 0.695, P < 0.001, respectively). ADC difference value tended to rise with lower tumor differentiation (r = − 0.269, P = 0.041). ADC difference value was an independent risk factor for predicting LVI (odds ratio = 1.323; P = 0.005) and LNM (odds ratio = 1.526; P = 0.005). Maximum ADC and ADC difference value could distinguish N0 from N1 category, N0 from N1–N2, N0–N1 from N2 (all P < 0.001). Only ADC difference value could distinguish histology type (P = 0.041). ADC difference value had higher area under the receiver operating characteristic curve than maximum ADC in identifying LVI (0.828 vs 0.797), N0 from N1 category (0.947 vs 0.847), N0 from N1–N2 (0.935 vs 0.874), and N0–N1 from N2 (0.814 vs 0.770). ADC difference value may be superior to the other ADC value parameters to predict LVI, N category and histology type of resectable rectal cancer.

To determine whether the gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict the presence of regional lymph node metastasis and could determine N categories. A total of 202 consecutive patients with gastric adenocarcinoma who had undergone gastrectomy 1 week after contrast-enhanced multidetector computed tomography were retrospectively identified. The gross tumor volume was evaluated on multidetector computed tomography images. Univariate and multivariate analyses were performed to determine whether the gross tumor volume could predict regional lymph node metastasis, and the Mann-Whitney U test was performed to compare the gross tumor volume among N categories. Additionally, a receiver operating characteristic analysis was performed to identify the accuracy of the gross tumor volume in differentiating N categories. The gross tumor volume could predict regional lymph node metastasis (p<0.0001) in the univariate analysis, and the multivariate analyses indicated that the gross tumor volume was an independent risk factor for regional lymph node metastasis (p=0.005, odds ratio=1.364). The Mann-Whitney U test showed that the gross tumor volume could distinguish N0 from the N1-N3 categories, N0-N1 from N2-N3, and N0-N2 from N3 (all p<0.0001). In the T1-T4a categories, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 12.3 cm3), N0-N1 from N2-N3 (cutoff, 16.6 cm3), and N0-N2 from N3 (cutoff, 24.6 cm3). In the T4a category, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 15.8 cm3), N0-N1 from N2-N3 (cutoff, 17.8 cm3), and N0-N2 from N3 (cutoff, 24 cm3). The gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict regional lymph node metastasis and N categories.

Objectives: To explore intravoxel incoherent motion (IVIM) for evaluation of the perineural invasion (PNI) status and survival in patients with rectal cancer. Materials and methods: The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were recorded together with histogram metrics. Differences in IVIM histogram metrics between the PNI-positive group and the PNI-negative group were analyzed. Univariable and multivariable logistic regression analysis were used for model construction. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the models. Histopathology was used as the PNI endpoint. Kaplan–Meier curve analysis was employed to estimate the disease-free survival (DFS) and overall survival (OS) of patients. Results: A total of 175 patients were retrospectively enrolled in this study. Multivariable logistic regression analysis showed that higher D_median (odds ratio (OR) = 2.036, p = 0.003) and D_min (OR = 1.479, p = 0.002) and lower f_SD (OR = 0.697, p < 0.001) and f_kurtosis (OR = 0.485, p < 0.001) were independently associated with PNI-positive. The combined model showed the best performance in predicting the PNI status with AUCs, sensitivity, specificity, and accuracy of 0.885, 81.67%, 82.61%, and 82.29%, respectively. Kaplan–Meier curves analysis revealed that the patients with higher scores (> −1.12) of the combined model showed relatively lower 2-year DFS (81.6% vs 93.2%, p = 0.014) compared to the patients with lower scores (≤ −1.12). Conclusion: IVIM histogram metrics could predict the PNI status and serve as a preoperative risk stratification tool. Critical relevance statement: The combination of IVIM histogram metrics and clinical characteristics could discriminate the PNI status and serve as a surrogate for PNI. Key Points: The PNI is an important prognostic factor in rectal cancer. The IVIM histogram metrics were associated with the PNI status. The combination of IVIM and clinical factors could serve as a surrogate for PNI. Graphical