Multi-parametric MRI in cervical cancer: early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection

OBJECTIVE

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients.

METHODS

Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis.

RESULTS

The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts.

CONCLUSION

ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy.

CLINICAL RELEVANCE STATEMENT

The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Relationships between intravoxel incoherent motion parameters and expressions of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) in patients with cervical cancer

AIM

To determine the associations of intravoxel incoherent motion (IVIM) parameters with expression of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), and evaluate the performance of the combined model established based on IVIM and clinicopathological parameters in predicting PD-L1and PD-1 status of cervical cancer (CC) patients.

MATERIALS AND METHODS

Seventy-eight consecutive CC patients were enrolled prospectively and underwent magnetic resonance imaging (MRI) including IVIM. IVIM quantitative parameters were measured, compared, and correlated with PD-L1 and PD-1 expression. Independent factors related to PD-L1 and PD-1 positivity were identified and were used to establish the combined model. The combined model's diagnostic performance was evaluated using the receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) algorithm was used to explain the contribution of each parameter in the combined model.

RESULTS

The real diffusion coefficient (D) value was significantly lower in the PD-L1-positive group than in the PD-L1-negative group (0.64 ± 0.12 versus 0.72 ± 0.11, p=0.021). The PD-1-positive and PD-1-negative groups showed similar trends (0.63 ± 0.13 versus 0.73 ± 0.09, p=0.003). Parametrial invasion, lymph node status, pathological grade, FIGO (International Federation of Gynecology and Obstetrics) staging, and D values were independently associated with PD-L1 and PD-1expression. A combined model incorporating these parameters showed good discrimination with the sensitivity, specificity of 90.9%, 82.6% for PD-L1, and 93.5%, 72% for PD-1. According to the SHAP value, FIGO staging and pathological grade were the most influential features of the prediction model.

CONCLUSION

IVIM parameters were found to correlate with PD-L1 and PD-1 expression. The combined model, incorporating parametrial invasion, lymph node status, pathological grade, FIGO staging, and D values, showed good discrimination in predicting PD-L1 and PD-1 status, providing the basis for CC immunotherapy.

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment

Cervical Cancer is the fourth most common cancer in women worldwide. Treatment with chemoradiotherapy followed by brachytherapy achieves high local control, but recurrence with metastatic disease impacts survival. This highlights the need for predictive and prognostic biomarkers identifying populations at risk of poorer treatment response and survival. Magnetic resonance imaging (MRI) is routinely used in cervical cancer and is a potential source for biomarkers. Functional MRI (fMRI) can characterise tumour beyond anatomical MRI, which is limited to the assessment of morphology. This review summarises fMRI techniques used in cervical cancer and examines the role of fMRI parameters as predictive or prognostic biomarkers. Different techniques characterise different tumour factors, which helps to explain the variation in patient outcomes. These can impact simultaneously on outcomes, making biomarker identification challenging. Most studies are small, focussing on single MRI techniques, which raises the need to investigate combined fMRI approaches for a more holistic characterisation of tumour.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review

Correct staging of cervical cancer is essential to establish the best therapeutic procedure and prognosis for the patient. MRI is the best imaging modality for local staging and follow-up. According to the latest ESUR guidelines, T2WI and DWI-MR sequences are fundamental in these settings, and CE-MRI remains optional. This systematic review, according to the PRISMA 2020 checklist, aims to give an overview of the literature regarding the use of contrast in MRI in cervical cancer and provide more specific indications of when it may be helpful. Systematic searches on PubMed and Web Of Science (WOS) were performed, and 97 papers were included; 1 paper was added considering the references of included articles. From our literature review, it emerged that many papers about the use of contrast in cervical cancer are dated, especially about staging and detection of tumor recurrence. We did not find strong evidence suggesting that CE-MRI is helpful in any clinical setting for cervical cancer staging and detection of tumor recurrence. There is growing evidence that perfusion parameters and perfusion-derived radiomics models might have a role as prognostic and predictive biomarkers, but the lack of standardization and validation limits their use in a research setting.

Beyond Sentinel Lymph Node: Outcomes of Indocyanine Green-Guided Pelvic Lymphadenectomy in Endometrial and Cervical Cancer

BACKGROUND

The aim of our study was to compare the number of lymph nodes removed during indocyanine green (ICG)-guided laparoscopic/robotic pelvic lymphadenectomy with standard systematic lymphadenectomy in endometrial cancer (EC) and cervical cancer (CC).

METHODS

This is a multicenter retrospective comparative study (Clinical Trial ID: NCT04246580; updated on 31 January 2023). Women affected by EC and CC who underwent laparoscopic/robotic systematic pelvic lymphadenectomy, with (cases) or without (controls) the use of ICG tracer injection within the uterine cervix, were included in the study.

RESULTS

The two groups were homogeneous for age (p = 0.08), Body Mass Index, International Federation of Gynaecology and Obstetrics (FIGO) stages (p = 0.41 for EC; p = 0.17 for CC), median estimated blood loss (p = 0.76), median operative time (p = 0.59), and perioperative complications (p = 0.66). Nevertheless, the number of lymph nodes retrieved during surgery was significantly higher (p = 0.005) in the ICG group (n = 18) compared with controls (n = 16).

CONCLUSIONS

The accurate and precise dissection achieved with the use of the ICG-guided procedure was associated with a higher number of lymph nodes removed in the case of systematic pelvic lymphadenectomy for EC and CC.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

MRI-based radiomics for pretreatment prediction of response to concurrent chemoradiotherapy in locally advanced cervical squamous cell cancer

PURPOSE

To investigate the value of magnetic resonance imaging (MRI)-based radiomics in predicting the treatment response to concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical squamous cell cancer (LACSC).

METHODS

In total, 198 patients (training: n = 138; testing: n = 60) with LACSC treated with CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Responses were evaluated by MRI and clinical data performed at one month after completion of CCRT according to RECIST standards, and patients were divided into the residual group and nonresidual group. Overall, 200 radiomics features were extracted from T2-weighted imaging and apparent diffusion coefficient maps. The radiomics score (Rad-score) was constructed with a feature selection strategy. Logistic regression analysis was used for multivariate analysis of radiomics features and clinical variables. The performance of all models was assessed using receiver operating characteristic curves.

RESULTS

Among the clinical variables, tumor grade and FIGO stage were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.741 and 0.749 in the training and testing groups. The Rad-score, consisting of 4 radiomics features selected from 200 radiomics features, showed good predictive performance with an AUC of 0.819 in the training group and 0.776 in the testing group, which were higher than the clinical model, but the difference was not statistically significant. The combined model constructed with tumor grade, FIGO stage, and Rad-score achieved the best performance, with an AUC of 0.857 in the training group and 0.842 in the testing group, which were significantly higher than the clinical model.

CONCLUSION

MRI-based radiomics features could be used as a noninvasive biomarker to improve the ability to predict the treatment response to CCRT in patients with LACSC.

Greater increases in intratumoral apparent diffusion coefficients after chemoradiotherapy predict better overall survival of patients with cervical cancer

PURPOSE

To evaluate whether 1) the intratumoral apparent diffusion coefficients (ADCs) change during cervical cancer treatment and 2) the pretreatment ADC values or their change after treatment predict the treatment outcome or overall survival of patients with cervical cancer.

METHODS

We retrospectively enrolled 52 patients with inoperable cervical cancer treated with chemoradiotherapy, who had undergone diffusion weighted MRI before treatment and post external beam radiotherapy (EBRT) and concurrent chemotherapy. A subgroup of patients (n = 28) underwent altogether six consecutive diffusion weighted MRIs; 1) pretreatment, 2) post-EBRT and concurrent chemotherapy; 3-5) during image-guided brachytherapy (IGBT) and 6) after completing the whole treatment course. To assess interobserver and intertechnique reproducibility two observers independently measured the ADCs by drawing freehand a large region of interest (L-ROI) covering the whole tumor and three small ROIs (S-ROIs) in areas with most restricted diffusion.

RESULTS

Reproducibility was equally good for L-ROIs and S-ROIs. The pretreatment ADCs were higher in L-ROIs (883 mm2/s) than in S-ROIs (687 mm2/s, P < 0.001). The ADCs increased significantly between the pretreatment and post-EBRT scans (L-ROI: P < 0.001; S-ROI: P = 0.001). The ADCs remained significantly higher than pretreatment values during the whole IGBT. Using S-ROIs, greater increases in ADCs between pretreatment and post-EBRT MRI predicted better overall survival (P = 0.018).

CONCLUSION

ADC values significantly increase during cervical cancer treatment. Greater increases in ADC values between pretreatment and post-EBRT predicted better overall survival using S-ROIs. Standardized methods for timing and delineation of ADC measurements are advocated in future studies.

IVIM-DWI and MRI-based radiomics in cervical cancer: Prediction of concurrent chemoradiotherapy sensitivity in combination with clinical prognostic factors

PURPOSE

To identify the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and magnetic resonance imaging (MRI)-based radiomics combined with clinical prognostic factors (CPF) in predicting concurrent chemoradiotherapy (CCRT) sensitivity of locally advanced cervical cancer (LACC).

METHODS

A retrospective analysis of 163 patients (assigned to training or test groups) who underwent conventional MRI and IVIM-DWI before CCRT were divided into sensitive and resistant groups according to their efficacy at 6 months after CCRT. Per-treatment IVIM-DWI parameters (ADC, D, D^⁎ and f value), 3D texture features (from axial T₂WI) and CPF were measured, analyzed and screened. The prediction model and its nomogram were developed by combining screened parameters and then validated internally and externally.

RESULTS

Clinical stage, f value, D value, InverseVariance, SizeZoneNonUniformity, and Minimum were selected to construct prediction model. All parameters except D value showed independent diagnostic value in multivariate Logistic regression analysis and composed prediction model, with AUCs of 0.987 and 0.984 for training and test groups, respectively. The calibration curve (Brier score of 0.042, C-index of 0.987), decision curve and clinical impact curve further demonstrated the reliability and clinical value of prediction model.

CONCLUSION

IVIM-DWI, MRI-based radiomics and CPF showed high clinical value in predicting CCRT sensitivity for LACC with better predictive performance when combined.

Diffusion-weighted MRI to detect early response to chemoradiation in cervical cancer: A systematic review and meta-analysis

OBJECTIVE

Diffusion-weighted magnetic resonance imaging (DWI) has shown promise in predicting response to therapy in several malignancies. This systematic review and meta-analysis aimed to evaluate DWI in the prediction of response to treatment in patients with cervical cancer.

METHODS

A systematic search was conducted on PubMed, Web of Science, Cochrane and Google Scholar databases Studies that evaluated DWI and apparent diffusion coefficient (ADC) for response evaluation before, during and after treatment with a correlation to conventional response criteria were included. The primary endpoint was the mean ADC values of cervical cancer at these timepoints. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the quality of the studies.

RESULTS

Nine studies, comprising 270 patients, were included. Pre-treatment ADC values showed no correlation with eventual response. However, in our meta-analysis, there was a significant correlation with early treatment ADC values obtained within the first 3 weeks of therapy and response, as well as a significant correlation with the percentage change in ADC (ΔADC) and response. In addition, the pooled mean ΔADC percentage was also significantly higher in responders than in non-responders (49.7% vs 19.7%, respectively, p = 0.016).

CONCLUSION

DWI shows potential as a biomarker of early treatment response in patients with cervical carcinoma. Use of the change in ADC particularly within the first 3 weeks of therapy seems to be predictive of response and may serve as a suitable marker in the determination of early response.

Quantitative diffusion MRI of the abdomen and pelvis

Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Pre-treatment prediction of early response to chemoradiotherapy by quantitative analysis of baseline staging FDG-PET/CT and MRI in locally advanced cervical cancer

BACKGROUND

Early prediction of response to concurrent chemoradiotherapy (cCRT) could aid to further optimize treatment regimens for locally advanced cervical cancer (LACC) in the future.

PURPOSE

To explore whether quantitative parameters from baseline (pre-therapy) magnetic resonance imaging (MRI) and FDG-PET/computed tomography (CT) have potential as predictors of early response to cCRT.

MATERIAL AND METHODS

Forty-six patients with LACC undergoing cCRT after staging with FDG-PET/CT and MRI were retrospectively analyzed. Primary tumor volumes were delineated on FDG-PET/CT, T2-weighted (T2W)-MRI and diffusion-weighted MRI (DWI) to extract the following quantitative parameters: T2W volume; T2W signal_mean; DWI volume; ADC_mean; ADC_SD; MTV_42%; and SUV_max. Outcome was the early treatment response, defined as the residual tumor volume on MRI 3-4 weeks after start of external beam radiotherapy with chemotherapy (before the start of brachytherapy): patients with a residual tumor volume <10 cm³ were classified as early responders. Imaging parameters were analyzed together with FIGO stage to assess their performance to predict early response, using multivariable logistic regression analysis with bi-directional variable selection. Leave-one-out cross-validation with bootstrapping was used to simulate performance in a new, independent dataset.

RESULTS

T2W volume (OR 0.94, P = 0.003) and SUV_max (OR 1.15, P = 0.18) were identified as independent predictors in multivariable analysis, rendering a model with an AUC of 0.82 in the original dataset, and AUC of 0.68 (95% CI 0.41-0.81) from cross-validation.

CONCLUSION

Although the predictive performance achieved in this small exploratory dataset was limited, these preliminary data suggest that parameters from baseline MRI and FDG-PET/CT (in particular pre-therapy tumor volume) may contribute to prediction of early response to cCRT in cervical cancer.

Combined dynamic contrast enhanced MRI parameter with clinical factors predict the survival of concurrent chemo-radiotherapy in patients with 2018 FIGO IIICr stage cervical cancer

PURPOSE

Combined clinical prognostic factors and magnetic resonance imaging (MRI) parameters on predicting the prognosis after concurrent chemo-radiotherapy (CCRT)in patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) IIICr stage patients.

METHODS

A total of 117 patients with cervical cancer (2018 FIGO stage IIICr) who underwent CCRT were enrolled from Dec.2014 to Jul.2017. 47 patients developed outcome events, including 32 recurrences and 15 deaths. Clinical and MR parameters of primary tumors were analyzed, including apparent diffusion coefficient (ADC) values (ADC_mean, ADC_min, and ADC_max) and dynamic contrast-enhanced MRI (DCE-MRI) parameters (K^trans, K_ep, V_e) were recorded. The short diameters of visible lymph nodes in the MRI and enhanced computed tomography (CT) images were measured. Progression-free survival (PFS) was compared by Kaplan-Meier analysis and independent predictors were identified using cox regression analysis.

RESULTS

The median PFS was 35 months (6-68 month). The 1-year and 3-year PFS rates were was 90.4 %, 74.4 %, respectively. Multivariate analysis showed that 2018 FIGOIIIC2r stage (HR 2.701,95 %CI1.259to. 5.797; p = 0.011), K^trans(HR 0.353;95 %CI 0.189 to 0.659; p = 0.001) and ADC_min (HR0.423,95 %CI0.229to0.783; p = 0.006) were highly correlated with poor PFS.

CONCLUSION

In conclusion, we have identified IIIC2r stage, K^trans value and ADC_min value as the most important factors in evaluating the survival rate and prognosis of patients with stage IIICr cervical cancer. For stage IIIC1r subgroup, K^trans, ADC_min value and site of positive lymph node >2 were independent prognostic factors.

Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

BACKGROUND

Apparent diffusion coefficient (ADC) value is an important quantitative parameter in the research of cervical cancer, affected by some factors.

PURPOSE

To investigate the effect of pathological type and menstrual status on the ADC value of cervical cancer.

MATERIAL AND METHODS

A total of 352 individuals with pathologically confirmed cervical cancer between January 2015 to December 2017 were retrospectively enrolled in this study, including 317 cases with squamous cell carcinomas (SCC) and 35 cases with adenocarcinomas (AC); 177 patients were non-menopausal and 175 were menopausal. All patients underwent a routine 3.0-T magnetic resonance imaging (MRI) scan and diffusion-weighted imaging (DWI) examination using b-values of 0, 800, and 1000 s/mm². Three parameters including mean ADC (ADC_mean), maximum ADC (ADC_max), and minimum ADC (ADC_min) of cervical cancer lesions were measured and retrospectively analyzed. Independent samples t-test was used to compare the difference of ADC values in different menstrual status and pathological types.

RESULTS

In all menopausal and non-menopausal patients, the ADC_mean and ADC_min values of SCC were lower than those of AC (P<0.05), the ADC_max of two pathological types showed no statistical difference (P > 0.05). In menopausal patients, the ADC_mean, ADC_max, and ADC_min values of SCC were not statistically different compared with those of AC (P > 0.05). The ADC_mean, ADC_max, and ADC_min values of different pathological types cervical cancers in non-menopausal patients were all higher than those in menopausal patients (P<0.05).

CONCLUSION

The ADC values of the cervical cancers were different in different pathological types and were also affected by menstrual status.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study

BACKGROUND

Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy.

PURPOSE

To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer.

MATERIAL AND METHODS

Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer-including arterial flow (AF), blood volume (BV), and permeability surface (PS)-were measured in two different ways: the region of interest incorporating the "local hot" with the highest enhancement and "cold spot" with the lowest enhancement; and "whole-tumor" measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups.

RESULTS

There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups (P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group (P <0.05), but the AF in whole-tumor regions was not different between the two groups (P > 0.05). There were no differences in BV and PS between the two groups (P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion.

CONCLUSION

vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

DCE-MRI Quantitative Parameters as Predictors of Treatment Response in Patients With Locally Advanced Cervical Squamous Cell Carcinoma Underwent CCRT

Purpose

To evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters in treatment response to concurrent chemoradiotherapy (CCRT) for locally advanced cervical squamous cell carcinoma (LACSC).

Methods and materials

LACSC patients underwent CCRT had DCE-MRI before (e0) and after 3 days of treatment (e3). Extended Tofts Linear model with a user arterial input function was adopted to generate quantitative measurements. Endothelial transfer constant (K^trans), reflux rate (K_ep), fractional extravascular extracellular space volume (V_e), and fractional plasma volume (V_p) were calculated, and percentage changes ΔK^trans, ΔK_ep, ΔV_e, and ΔV_p were computed. The correlations of these measurements with the tumor regression rate were analyzed. The predictive value of these parameters on treatment outcome was generated by the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were conducted to find the independent variables.

Results

K^trans-e0, K_ep -e0, ΔK^trans, and ΔV_e were positively correlated with the tumor regression rate. Mean values of K^trans-e0, K^trans-e3, ΔK^trans, and ΔV_e were higher in the non-residual tumor group than residual tumor group and were independent prognostic factors for predicting residual tumor occurrence. K^trans-e3 showed the highest area under the curve (AUC) for treatment response prediction.

Conclusions

Quantitative parameters at e0 and e3 from DCE-MRI could be used as potential indicators for predicting treatment response of LACSC.

Prediction of early response to concurrent chemoradiotherapy in cervical cancer: Value of multi-parameter MRI combined with clinical prognostic factors

PURPOSE

This study aimed to investigate the prediction of early response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients.

METHODS

Eighty-five patients with pathologically confirmed cervical cancer underwent conventional MRI, intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumor groups according to post-treatment MRI. Univariable and multivariable analyses were performed to pretreatment MRI parameters and CPF between the two groups, and optimal thresholds and predictive performance for post-treatment residual tumor occurrence were estimated by drawing the receiver operating characteristic (ROC) curve.

RESULTS

There were 52 patients in non- and 33 in residual group. The residual group showed a lower perfusion fraction (f) value and volume transfer constant (Ktrans) value, a higher apparent diffusion coefficient (ADC) value, diffusion coefficient (D) value and volume fraction of extravascular extracellular space (Ve) value, and a higher stage than the non-residual tumor group (all P < .05). D, Ktrans, Ve and stage were independent prognostic factors. The combination of D, Ktrans and Ve improved the diagnostic performance compared with individual MRI parameters. A further combination of these three MRI parameters with stage exhibited the highest predictive performance.

CONCLUSIONS

Pretreatment D, Ktrans, Ve and stage were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance.

The Role of Multiparametric Magnetic Resonance Imaging in the Study of Primary Tumor and Pelvic Lymph Node Metastasis in Stage IB1-IIA1 Cervical Cancer

OBJECTIVE

The aim of this study was to investigate the value of multiparametric magnetic resonance imaging (MRI) in demonstrating the metastatic potential of primary tumor and differentiating metastatic lymph nodes (MLNs) from nonmetastatic lymph nodes (non-MLNs) in stage IB1-IIA1 cervical cancer.

METHODS

Fifty-seven stage IB1-IIA1 subjects were included. The apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced MRI (DCE-MRI) parameters of primary tumors and lymph nodes and the conventional imaging features of the lymph nodes were measured and analyzed. Mann-Whitney test and χ test were used to analyze statistically significant parameters, logistic regression was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance of the MLNs.

RESULTS

Nineteen subjects had lymph node metastasis. A total of 94 lymph nodes were evaluated, including 30 MLNs and 64 non-MLNs. There were no significant difference in ADC and DCE-MRI parameters between metastatic and nonmetastatic primary tumors. The heterogeneous signal was more commonly seen in MLNs than in non-MLNs (P = 0.001). The values of ADCmean, ADCmin, and ADCmax of MLNs were lower than those of non-MLNs (P < 0.001). The values of short-axis diameter, K, Kep, and Ve of MLNs were higher than those of non-MLNs (P < 0.05). Compared with individual MRI parameters, the combined evaluation of short-axis diameter, ADCmean, and K showed the highest area under the curve of 0.930.

CONCLUSIONS

Diffusion-weighted imaging and DCE-MRI could not demonstrate the metastatic potential of primary tumor in stage IB1-IIA1 cervical cancer. Compared with individual MRI parameters, the combination of multiparametric MRI could improve the diagnostic performance of lymph node metastasis.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

BACKGROUND

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

PURPOSE

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

MATERIAL AND METHODS

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All histogram parameters of K^trans, k_ep, and v_e were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of K^trans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

CONCLUSION

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Diffusion MRI: Technical principles and application to uterine cervical cancer

Imaging is involved in the management of uterine cervical cancer with several objectives: 1/to assess local and lymph node extension of the initial disease; 2/evaluate treatment response to conservative therapy; 3/detect recurrences. Pelvic MRI is the first-line examination in all these indications. It is the key element for delineation after image fusion when the indication of chemoradiation therapy is made. It is also essential for guiding the placement of applicators and optimising the dosimetry of brachytherapy. The diffusion-weighted acquisition is a sequence sensitive to the motion of water molecules. It allows distinguishing water molecules with free diffusion from water molecules with diffusion restricted by obstacles such as cell membranes or the cytoskeleton. The diffusion is thus connected to the cellularity of the explored tissue, and the cancers, being hypercellular, will present a high signal. It thus provides additional information thanks to a high contrast between the tumour and the surrounding tissues, facilitating detection, evaluation of the volume and extent of the disease.

The value of DWI in predicting the response to synchronous radiochemotherapy for advanced cervical carcinoma: comparison among three mathematical models

Background

Diffusion weighted imaging(DWI) mode mainly includes intravoxel incoherent motion (IVIM), stretched exponential model (SEM) and Gaussian diffusion model, but it is still unclear which mode is the most valuable in predicting the response to radiochemotherapy for cervical cancer. This study aims to compare the values of three mathematical models in predicting the response to synchronous radiochemotherapy for cervical cancer.

Methods

Eighty-four patients with cervical cancer were enrolled into this study. They underwent DWI examination by using 12 b-values prior to treatment. The imaging parameters were calculated on the basis of IVIM, SEM and Gaussian diffusion models respectively. The imaging parameters derived from three mathematical modes were compared between responders and non-responders groups. The repeatability of each imaging parameter was assessed.

Results

The ADC, D or DDC value was lower in responders than in non-responders groups (P = 0.03, 0.02, 0.01). The α value was higher in responders group than in non-responders group (P = 0.03). DDC had the largest area under curves (AUC) (=0.948) in predicting the response to treatment. The imaging parameters derived from SEM had better repeatability (CCC for DDC and α were 0.969 and 0.924 respectively) than that derived from other exponential models.

Conclusion

Three exponential modes of DWI are useful for predicting the response to radiochemotherapy for cervical cancer, and SEM may be used as a potential optimal model for predicting treatment effect.

Value of blood oxygenation level-dependent MRI for predicting clinical outcomes in uterine cervical cancer treated with concurrent chemoradiotherapy

OBJECTIVES

To investigate the value of blood oxygenation level-dependent (BOLD) MRI as a predictor of clinical outcomes in cervical cancer patients treated with concurrent chemoradiotherapy (CCRT).

METHOD

Enrolled 92 patients with stage IB2-IVB cervical cancer who received CCRT underwent 3-T BOLD MRI before treatment. The R2* value (rate of spin dephasing, s^-1) was measured in the tumor. Cox regression analysis was used to evaluate the associations of imaging and clinical parameters with progression-free survival (PFS) and cancer-specific survival (CSS). Inter-reader reliability for the R2* measurements was evaluated using an intraclass correlation coefficient (ICC).

RESULTS

Tumor R2* values were significantly different between patients with and without disease progression (p < 0.001). Multivariate analysis demonstrated that tumor R2* value was significantly independent factor for PFS (hazard ratio [HR] = 5.746, p < 0.001) and CSS (HR = 12.878, p = 0.001). Additionally, squamous cell carcinoma antigen (HR = 1.027, p = 0.001) was significantly independent factor for PFS. Inter-reader reliability for the R2* measurements was good (ICC = 0.702).

CONCLUSION

Pretreatment 3-T BOLD MRI may be useful for predicting clinical outcomes in uterine cervical cancer patients treated with CCRT, with good inter-reader reliability.

KEY POINTS

• Tumor R2* values are different between patients with and without disease progression. • The R2* value is an independent factor for treatment outcomes in cervical cancer. • Inter-reader reliability for R2* measurements using BOLD MRI is good.

Changes in magnetic resonance T2-weighted imaging signal intensity correlate with concurrent chemoradiotherapy response in cervical cancer

Purpose

This study is aimed to compare magnetic resonance imaging (MRI) parameters and clinical pathological factors (CPF) of residual tumor group with non-residual tumor group in cervical cancer (CC) patients during concurrent chemoradiotherapy (CCRT), and thus to establish a biomarker for individualized treatment strategy.

Material and methods

From May 2014 to November 2015, 164 CC patients were included in this retrospective study. T2-weighted MRI was performed at pre-treatment (week-0), the completion of external radiotherapy (RT) (week-4), and one month after the completion of CCRT, using 3.0T MR scanner with regular pelvic coil. Mean signal intensity and tumor size on T₂WI images were measured and calculated for each tumor, and lumbar 4-5 intervertebral disc at week-0 and week-4. All patients subsequently underwent routine follow-up, including periodic clinical and imaging examinations when necessary. Receiver operator characteristics (ROC) analysis were conducted to determine cut-off values.

Results

The residual tumor group showed a higher Δ tumor-to-disc signal intensity ratio (ΔTDR) than non-residual tumor group (0.78 ± 0.30 vs. 0.48 ± 0.19, t = 3.42, p < 0.05). The biomarker of combined MRI parameter and CPF showed the highest diagnostic performance than single MRI parameter or CPF alone.

Conclusions

MRI parameter ΔTDR may be an independent prognostic factor for predicting residual tumor occurrence in CC after CCRT treatment. The combination of MRI parameter and CPF can serve as a valuable biomarker to distinguish CC with higher possibility of residual tumor occurrence.

Combining Radiotherapy With Anti-angiogenic Therapy and Immunotherapy; A Therapeutic Triad for Cancer?

Radiotherapy has been used for the treatment of cancer for over a century. Throughout this period, the therapeutic benefit of radiotherapy has continuously progressed due to technical developments and increased insight in the biological mechanisms underlying the cellular responses to irradiation. In order to further improve radiotherapy efficacy, there is a mounting interest in combining radiotherapy with other forms of therapy such as anti-angiogenic therapy or immunotherapy. These strategies provide different opportunities and challenges, especially with regard to dose scheduling and timing. Addressing these issues requires insight in the interaction between the different treatment modalities. In the current review, we describe the basic principles of the effects of radiotherapy on tumor vascularization and tumor immunity and vice versa. We discuss the main strategies to combine these treatment modalities and the hurdles that have to be overcome in order to maximize therapeutic effectivity. Finally, we evaluate the outstanding questions and present future prospects of a therapeutic triad for cancer.

Associations between whole tumor histogram analysis parameters derived from ADC maps and expression of EGFR, VEGF, Hif 1-alpha, Her-2 and Histone 3 in uterine cervical cancer

OBJECTIVE

Diffusion weighted imaging (DWI) can be quantified by apparent diffusion coefficient (ADC) and can predict tissue microstructure. The aim of the present study was to analyze possible associations between ADC histogram based parameters with different histopathological parameters in cervical squamous cell carcinoma.

MATERIALS AND METHODS

18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled. In all cases, pelvic MRI was performed with a DWI (b-values 0 and 1000 s/mm²). Histogram analysis was performed as a whole lesion measurement. Histopathological parameters included expression of EGFR, VEGF, Hif1-alpha, Her2 and Histone 3. Spearman's correlation coefficient was used to analyze associations between investigated parameters.

RESULTS

Analyze of the investigated ADC histogram parameters showed a good interreader variability, ranging from 0.705 for entropy to 0.959 for ADCmedian. EGFR expression correlated statistically significant with several histogram parameters. The highest correlation was observed for p75 (p = -0.562, P = 0.015). There were several correlations with histone 3, the highest with p25 (p = -0.610, P = 0.007). None of the ADC related parameters correlated statistically significant with expression of VEGF, Hif1-alpha and Her2.

CONCLUSION

Histogram analysis showed a good interreader agreement. ADC histogram parameters might be able to reflect expression of EGFR and histone 3 in cervical squamous cell carcinomas, but not expression of VEGF, Hif1-alpha and Her2.