Pharmacokinetic analysis of DCE-MRI data of locally advanced cervical carcinoma with the Brix model

Intravoxel incoherent motion diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting parametrial invasion in cervical cancer

PURPOSE

This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients.

METHODS

A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters.

RESULTS

Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78-27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49-29.10; P = 0.01), f (OR 5.12; 95% CI 1.23-21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19-17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25-19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%.

CONCLUSION

The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.

Added-value of dynamic contrast-enhanced MRI on prediction of tumor recurrence in locally advanced cervical cancer treated with chemoradiotherapy

OBJECTIVES

To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT).

METHODS

Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system.

RESULTS

Lymph node metastasis (LNM) and the mean value of v_e (v_{e_mean}) were independently associated with tumor recurrence (all p < 0.05). The prediction model based on T stage, LNM, and v_{e_mean} demonstrated a moderate predictive capability in identifying LACC patients with a high risk of tumor recurrence; the model was more accurate than the FIGO staging system alone (c-index: 0.735 vs. 0.661) and the combination of v_{e_mean} and the FIGO staging system (c-index: 0.735 vs. 0.688). Moreover, patients were grouped into low-, medial-, and high-risk levels based on the advanced T stage, positive LNM, and v_{e_mean} < 0.361, with which the 2-year DFS was significantly stratified (p < 0.001).

CONCLUSIONS

The v_{e_mean} from DCE-MRI could be used as a useful biomarker to predict DFS in LACC patients treated with CCRT as an assistant of LNM and T stage.

KEY POINTS

Lower v_{e_mean} is an independent predictor of poor prognosis for disease-free survival in locally advanced cervical cancer patients treated with concurrent chemoradiotherapy (hazard ratio [HR]: 0.016, p<0.023). A combined prediction model based on advanced T stage, LNM, and v_{e_mean} performed better than the FIGO staging system alone.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

A free time point model for dynamic contrast enhanced exploration

Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.

DCE-MRI of Tumor Hypoxia and Hypoxia-Associated Aggressiveness

Tumor hypoxia is associated with resistance to treatment, aggressive growth, metastatic dissemination, and poor clinical outcome in many cancer types. The potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess the extent of hypoxia in tumors has been investigated in several studies in our laboratory. Cervical carcinoma, melanoma, and pancreatic ductal adenocarcinoma (PDAC) xenografts have been used as models of human cancer, and the transfer rate constant (K^trans) and the extravascular extracellular volume fraction (v_e) have been derived from DCE-MRI data by using Tofts standard pharmacokinetic model and a population-based arterial input function. K^trans was found to reflect naturally occurring and treatment-induced hypoxia when hypoxia was caused by low blood perfusion, radiation responsiveness when radiation resistance was due to hypoxia, and metastatic potential when metastasis was hypoxia-induced. K^trans was also associated with outcome for patients with locally-advanced cervical carcinoma treated with cisplatin-based chemoradiotherapy. Together, the studies imply that DCE-MRI can provide valuable information on the hypoxic status of cervical carcinoma, melanoma, and PDAC. In this communication, we review and discuss the studies and provide some recommendations as to how DCE-MRI data can be analyzed and interpreted to assess tumor hypoxia.

DCE-MRI of locally-advanced carcinoma of the uterine cervix: Tofts analysis versus non-model-based analyses

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide biomarkers of the outcome of locally-advanced cervical carcinoma (LACC). There is, however, no agreement on how DCE-MR recordings should be analyzed. Previously, we have analyzed DCE-MRI data of LACC using non-model-based strategies. In the current study, we analyzed DCE-MRI data of LACC using the Tofts pharmacokinetic model, and the biomarkers derived from this analysis were compared with those derived from the non-model-based analyses.

METHODS

Eighty LACC patients given cisplatin-based chemoradiotherapy with curative intent were included in the study. Treatment outcome was recorded as disease-free survival (DFS) and overall survival (OS). DCE-MRI series were analyzed voxelwise to produce K^trans and v_e frequency distributions, and ROC analysis was used to identify the parameters of the frequency distributions having the greatest potential as biomarkers. The prognostic power of these parameters was compared with that of the non-model-based parameters LETV (low-enhancing tumor volume) and TVIS (tumor volume with increasing signal).

RESULTS

Poor DFS and OS were associated with low values of K^trans, whereas there was no association between treatment outcome and v_e. The K^trans parameters having the greatest prognostic value were p35-K^trans (the K^trans value at the 35 percentile of a frequency distribution) and RV-K^trans (the tumor subvolume with K^trans values below 0.13 min^- 1). Multivariate analysis including clinical parameters and p35-K^trans or RV-K^trans revealed that RV-K^trans was the only independent prognostic factor of DFS and OS. There were significant correlations between RV-K^trans and LETV and between RV-K^trans and TVIS, and the prognostic power of RV-K^trans was similar to that of LETV and TVIS.

CONCLUSIONS

Biomarkers of the outcome of LACC can be provided by analyzing DCE-MRI series using the Tofts pharmacokinetic model. However, these biomarkers do not appear to have greater prognostic value than biomarkers determined by non-model-based analyses.