The invasion status of lymphovascular space and lymph nodes in cervical cancer assessed by mono-exponential and bi-exponential DWI-related parameters

•ADC and IVIM offer valuable information for status of LVSI/LNM in cervical cancer. •ADC, f, D showed better diagnostic performance on tumour margin than tumour entirety. •f_margin and D∗_whole could help discriminate invasion status of lymphovascular space. •ADC_margin could help differentiate invasion status of lymph nodes. AIM To investigate whether mono-exponential and bi-exponential diffusion-weighted imaging (DWI)-related parameters of the primary tumour can evaluate the status of lymphovascular space invasion (LVSI) and lymph node metastasis (LNM) in patients with cervical carcinoma preoperatively. MATERIALS AND METHODS Eighty patients with cervical carcinoma were enrolled, who underwent preoperative multi b-value DWI and radical hysterectomy. They were classified into LVSI(+) versus LVSI(–) and LNM(+) versus LNM(–) according to postoperative pathology. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D∗), and perfusion fraction (f) were calculated from the whole tumour (_whole) and tumour margin (_margin). All parameters were compared between LVSI(+) and LVSI(–) and between LNM(+) and LNM(–). Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic performance of these parameters. RESULTS f_margin and D∗_whole showed significant differences in differentiating LVSI(+) from LVSI(–) tumours (p=0.002, 0.008, respectively), while LNM(+) tumours presented with significantly higher ADC_margin than that of LNM(–) tumours (p=0.009). The other parameters were not independent related factors with the status of LVSI or LNM according to logistic regression analysis (p>0.05). The area under the ROC curve of f_margin combined with D∗_whole in discriminating LVSI(+) from LVSI(–) was 0.826 (95% confidence interval [CI]: 0.691–0.961), while ADC_margin in differentiating LNM(+) from LNM(–) was 0.788 (95% CI: 0.648–0.928). CONCLUSIONS The parameters generated from mono-exponential and bi-exponential DWI of the primary cervical carcinoma could help discriminate its status regarding LVSI (f_margin and D∗_whole) and LNM (ADC_margin). To investigate whether mono-exponential and bi-exponential diffusion-weighted imaging (DWI)-related parameters of the primary tumour can evaluate the status of lymphovascular space invasion (LVSI) and lymph node metastasis (LNM) in patients with cervical carcinoma preoperatively. Eighty patients with cervical carcinoma were enrolled, who underwent preoperative multi b-value DWI and radical hysterectomy. They were classified into LVSI(+) versus LVSI(–) and LNM(+) versus LNM(–) according to postoperative pathology. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D∗), and perfusion fraction (f) were calculated from the whole tumour (_whole) and tumour margin (_margin). All parameters were compared between LVSI(+) and LVSI(–) and between LNM(+) and LNM(–). Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic performance of these parameters. f_margin and D∗_whole showed significant differences in differentiating LVSI(+) from LVSI(–) tumours (p=0.002, 0.008, respectively), while LNM(+) tumours presented with significantly higher ADC_margin than that of LNM(–) tumours (p=0.009). The other parameters were not independent related factors with the status of LVSI or LNM according to logistic regression analysis (p>0.05). The area under the ROC curve of f_margin combined with D∗_whole in discriminating LVSI(+) from LVSI(–) was 0.826 (95% confidence interval [CI]: 0.691–0.961), while ADC_margin in differentiating LNM(+) from LNM(–) was 0.788 (95% CI: 0.648–0.928). The parameters generated from mono-exponential and bi-exponential DWI of the primary cervical carcinoma could help discriminate its status regarding LVSI (f_margin and D∗_whole) and LNM (ADC_margin).