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

MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy

OBJECTIVE

This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.

METHODS

We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).

RESULTS

Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.

CONCLUSIONS

The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.

The value of multiparameter MRI of early cervical cancer combined with SCC-Ag in predicting its pelvic lymph node metastasis

Purpose

To investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM).

Material and methods

115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC.

Results

The PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s2 vs.1.06 ± 0.35 mm/s2, P<0.001), ADCmin [0.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively.

Conclusions

Quantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved.

Utility of interim apparent diffusion coefficient value in predicting treatment response among patients with locally advanced cervical cancer treated with radiotherapy

Background

For locally advanced cervical cancer (LACC), treatment response to radiotherapy (RT) can vary significantly even among those with the same stage classification of International Federation of Gynecology and Obstetrics (FIGO). This study investigated the value of ADC metric for forecasting end-of-treatment outcomes in LACC patients referred for RT.

Methods

Eighty patients with pathologically confirmed cervical squamous cell carcinoma with (SCC) were included in the research. Abdominal or pelvic MRI scans were conducted at least three times for all participants: before RT, three weeks after beginning of RT and approximately two months after RT was finalized. Calculated apparent diffusion coefficient (ADC) values of the LACC include: pre-ADC, interim-ADC, ΔADC and Δ%ADC. Based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, subjects were calculated and subsequently categorized into good responders group (complete response) and poor responders group (progressive disease, stable disease or partial response).

Results

Compared to good-responders, subjects of poor-responder group showed significantly lower values of interim-ADC, ΔADC, and Δ%ADC (all P < 0.05). To distinguish between good and poor responders, the optimal cutoff values of interim-ADC, ΔADC, and Δ%ADC were determined to be 1.067 × 10-3 mm2/sec, 0.209 × 10-3 mm2/sec, and 30.74 % using the ROC curve, with corresponding sensitivities of 83.78 %, 86.49 %, 75.68 %, and specificities of 88.37 %, 86.49 %, 75.68 %, respectively. Multivariate logistic regression revealed that the baseline tumor diameter and interim-ADC were significant prognostic factors for treatment response with an odds ratio (OR) of 0.105 (95 % confidence interval [95 % CI] 0.018-0.616) for baseline tumor diameter and 42.896 (95 % CI 8.205-224.262) for interim-ADC.

Conclusion

The interim-ADC value and baseline tumor diameter surfaced as possible indicative factors for predicting the response to RT in patients with LACC.

Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction

OBJECTIVE

To determine the performance of intravoxel incoherent motion (IVIM) parameters and the extracellular volume fraction (ECV) in distinguishing between different subtypes of lung cancer and predicting lymph node metastasis (LNM) status in patients with non-small-cell lung cancer (NSCLC).

METHODS

One hundred sixteen patients with lung cancer were prospectively recruited. IVIM, native, and postcontrast T1 mapping examinations were performed, and the T1 values were measured to calculate the ECV. The differences in IVIM parameters and ECV were compared between NSCLC and small-cell lung cancer (SCLC), adenocarcinoma (Adeno-Ca) and squamous cell carcinoma (SCC), and NSCLC without and with LNM. The assessment of each parameter's diagnostic performance was based on the area under the receiver operating characteristic curve (AUC).

RESULTS

The apparent diffusion coefficient (ADC), true diffusion coefficient (D), and ECV values in SCLC were considerably lower compared with NSCLC (all p < 0.001, AUC > 0.887). The D value in SCC was substantially lower compared with Adeno-Ca (p < 0.001, AUC = 0.735). The perfusion fraction (f) and ECV values in LNM patients were markedly higher compared with those without LNM patients (p < 0.01, < 0.001, AUC > 0.708).

CONCLUSION

IVIM parameters and ECV can serve as non-invasive biomarkers for assisting in the pathological classification and LNM status assessment of lung cancer patients.

CRITICAL RELEVANCE STATEMENT

IVIM parameters and ECV demonstrated remarkable potential in distinguishing pulmonary carcinoma subtypes and predicting LNM status in NSCLC.

KEY POINTS

Lung cancer is prevalent and differentiating subtype and invasiveness determine the treatment course. True diffusion coefficient and ECV showed promise for subtyping and determining lymph node status. These parameters could serve as non-invasive biomarkers to help determine personalized treatment strategies.

Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy

Background and Purpose

Intravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT).

Materials and Methods

Twenty cervical cancer patients (age: 63[41-85]) were retrospectively evaluated. Diffusion-weighted-images (DWIs) were acquired on 3.0 T MRIs using a free-breathing single-shot-spin echo-planar-imaging (EPI) sequence. Pre- and on-treatment (∼after four-weeks of CCRT) MRI and pre-treatment FDG-PET/CT were acquired. IVIM model-fitting on the DWIs was performed using a Bayesian-fitting simplified two-compartment model. Three-dimensional rigidly-registered maps of PET/CT standardized-uptake-value (SUV) and IVIM diffusion-coefficient (D) and perfusion-fraction (f) were generated. Population-means of PET-SUV, IVIM-D and IVIM-f from pre-treatment-scans were calculated and used to generate PRM via a voxel-wise joint-histogram-analysis to classify voxels as high/low metabolic-activity and with high/low (hi/lo) cellular-density. Similar PRM maps were generated for SUV and f.

Results

Tumor-volume (p < 0.001) significantly decreased, while IVIM-f (p = 0.002) and IVIM-D (p = 0.03) significantly increased on-treatment. Pre-treatment tumor-volume (r = -0.45,p = 0.04) and PRM-SUVhi D lo (r = -0.65,p = 0.002) negatively correlated with ΔGTV, while pre-treatment IVIM-D (r = 0.64,p = 0.002), PRM-SUVlo f hi (r = 0.52,p = 0.02), and PRM-SUVlo D hi (r = 0.74,p < 0.001) positively correlated with ΔGTV.

Conclusion

IVIM and PET was performed on cervical cancer patients undergoing CCRT and we observed that both IVIM-f and IVIM-D increased during treatment. Additionally, PRM was applied, and sub-volumes were identified that were related to ΔGTV.

ACR Appropriateness Criteria® Pretreatment Evaluation and Follow-Up of Invasive Cancer of the Cervix: 2023 Update

Cervical cancer is a common gynecological malignancy worldwide. Cervical cancer is staged based on the International Federation of Gynecology and Obstetrics (FIGO) classification system, which was revised in 2018 to incorporate radiologic and pathologic data. Imaging plays an important role in pretreatment assessment including initial staging and treatment response assessment of cervical cancer. Accurate determination of tumor size, local extension, and nodal and distant metastases is important for treatment selection and for prognostication. Although local recurrence can be diagnosed by physical examination, imaging plays a critical role in detection and follow-up of local and distant recurrence and subsequent treatment selection. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions

Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.

Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer

Objective

To investigate the performance of multiparametric magnetic resonance imaging (MRI)-based radiomics models in differentiating early stage of cervical cancer (Stage I-IIa vs. IIb-IV).

Methods

One hundred patients with cervical cancer who underwent preoperative MRI between June 2020 and March 2022 were retrospectively enrolled. Training (n = 70) and testing cohorts (n = 30) were assigned by stratified random sampling. The clinical and pathological features, including age, histological subtypes, tumor grades, and node status, were compared between the two cohorts by t-test or chi-square test. Radiomics features were extracted from each volume of interest (VOI) on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps. The data balance of the training cohort was resampled by synthesizing minority oversampling techniques. Subsequently, the adiomics signatures were constructed by the least absolute shrinkage and selection operator algorithm and minimum-redundancy maximum-relevance with 10-fold cross-validation. Logistic regression was applied to predict the cervical cancer stages (low [I-IIa]) and (high [IIb-IV] FIGO stages). The receiver operating characteristic curve (area under the curve [AUC]) and decision curve analysis were used to assess the performance of the radiomics model.

Results

The characteristics of age, histological subtypes, tumor grades, and node status were not significantly different between the low [I-IIa] and high [IIb-IV] FIGO stages (p > 0.05 for both the training and test cohorts). Three models based on T2WI, ADC maps, and the combined were developed based on six radiomics features from T2WI and three radiomics features from ADC maps, with AUCs of 0.855 (95% confidence interval [CI], 0.777-0.934) and 0.823 (95% CI, 0.727-0.919), 0.861 (95% CI, 0.785-0.936) and 0.81 (95% CI, 0.701-0.918), 0.934 (95% CI, 0.884-0.984) and 0.902 (95% CI, 0.832-0.972) in the training and test cohorts.

Conclusion

The radiomics models combined T2W and ADC maps had good predictive performance in differentiating the early stage from locally advanced cervical cancer.

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.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features

Purpose

We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats.

Methods

A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment.

Results

Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level.

Conclusions

MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients

OBJECTIVE

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS).

METHODS

Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated.

RESULTS

Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms.

CONCLUSION

CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Predicting Preoperative Pathologic Grades of Bladder Cancer Using Intravoxel Incoherent Motion and Amide Proton Transfer-Weighted Imaging

RATIONALE AND OBJECTIVES

To investigate the predictive value of intravoxel incoherent motion (IVIM) combined with amide proton transfer-weighted (APTw) imaging for the preoperative grading of bladder cancer (BC).

MATERIALS AND METHODS

A total of 69 patients with histopathologically confirmed BC underwent diffusion-weighted imaging (DWI), IVIM, and APTw imaging at 3.0 T MRI. Two radiologists independently measured the mean apparent diffusion coefficient (ADC) in DWI, true diffusion coefficient (D), perfusion-related pseudo-diffusion coefficient (D*), and perfusion fraction (f) in IVIM, and APTw values, respectively. The areas under the receiver operating characteristic curves (AUCs) were utilized to compare the diagnostic efficacy of these single and combined quantitative parameters.

RESULTS

ADC and D values of low-grade BC were significantly higher than those of high-grade BC ([1.42 ± 0.20 ×10-3 mm2/s] vs. [1.09 ± 0.25 ×10-3 mm2/s] and [1.24 ± 0.24 ×10-3 mm2/s] vs. [0.89 ± 0.18 ×10-3 mm2/s], respectively; all P < 0.001). Opposite patterns were found for APTw ( [1.53 ± 0.42]% vs. [2.38 ± 0.71]%, P < 0.001). The ROC curves indicated that the combination of D and APTw values could distinguish low- from high-grades of BC with the highest predictive efficacy (AUC = 0.96), as well as a significant difference compared to those by ADC, D, and APTw values separately (AUC = 0.84, 0.88, 0.85, respectively; all P < 0.05).

CONCLUSION

IVIM combined with APTw imaging significantly improved the predictive efficacy of assessing low- and high-grade BC compared to the individual parameters on their own, providing an effective non-invasive method for clinical preoperative prediction of BC grading.

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.

Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer

PURPOSES

To investigate the value of nomograms based on clinical prognostic factors (CPF), intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and MRI-derived radiomics in predicting recurrence and disease-free survival (DFS) after concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC).

METHODS

Retrospective analysis of data from 115 patients with ⅠB-ⅣA cervical cancer who underwent CCRT and had been followed up consistently. All patients were randomized 2:1 into training and validation groups. Pre-treatment IVIM-DWI parameters (ADC-value, D-value, D*-value and f-value) and pre- and post-treatment three-dimensional radiomics parameters (from axial T₂WI) of primary lesions were measured. The LASSO algorithm and Logistic regression analysis were used to filter texture features and calculate radiomics score (Rad-score). Multivariate Logistic and Cox regression analysis was used to construct nomograms to predict recurrence and DFS for patients with LACC after CCRT respectively, with internal and external validation.

RESULTS

External beam radiotherapy dose, f-value, pre-treatment and post-treatment Rad-score were independent prognostic factors for recurrence and DFS in patients with cervical cancer, forming Model1 and Model2, with OR values of 0.480, 1.318, 3.071, 3.200 and HR values of 0.322, 3.372, 5.138, 7.204. The area under the curve (AUC) of Model1 for predicting recurrence of cervical cancer was 0.977, with internal and external validation C-indexes of 0.977 and 0.962. The AUC for Model2 predicting disease-free survival (DFS) at 1, 3, and 5 years was 0.895, 0.888 and 0.916 respectively, with internal and external C-indexes of 0.860 and 0.892. The decision curves analysis and clinical impact curves further indicate the high predictive efficiency and stability of nomograms.

CONCLUSION

The nomograms based on clinical, IVIM-DWI and radiomics parameters have high clinical value in predicting recurrence and DFS of patients with LACC after CCRT and can provide a reference for prognostic assessment and individualized treatment of cervical cancer patients.

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.

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.

Predicting treatment response to concurrent chemoradiotherapy in squamous cell carcinoma of the cervix using amide proton transfer imaging and intravoxel incoherent motion imaging

PURPOSE

The purpose of this study was to investigate whether amide proton transfer (APT) imaging and intravoxel incoherent motion (IVIM) imaging can predict tumor response to concurrent chemoradiotherapy (CCRT) in patients with squamous cell carcinoma of the cervix (SCCC).

MATERIAL AND METHODS

Fifty-nine women (mean age, 54 years ± 10 [standard deviation] years; age range: 32-81 years) with pathologically confirmed SCCC underwent magnetic resonance imaging examination of the pelvis including APT and IVIM before concurrent chemoradiotherapy. They were divided into complete remission (CR) and non-CR groups according to therapeutic effect. APT values and IVIM-derived parameters were measured. Intra- and interobserver agreement for IVIM and APT parameters was assessed using intraclass correlation coefficient (ICC) The independent samples t-test was performed to compare the evaluated parameters between the two groups. Predictive performance for treatment response was evaluated by receiver operator characteristic (ROC) curve analysis.

RESULTS

There were 38 and 21 patients in the non-CR and CR groups, respectively. Excellent interobserver and intraobserver agreement were obtained for all IVIM and APT parameters, with ICCs ranging from 0.844 to 0.962. Perfusion fraction (f) and APT values were lower in the CR group compared with the non-CR group (both P < 0.05). The combination of f and APT values showed good diagnostic performances in predicting response to concurrent chemoradiotherapy, with an area under the ROC curve of 0.852 (95% CI: 0.744-0.961), 79% sensitivity (95% CI: 63-90%), 90% specificity (95% CI: 70-99%) and 83% accuracy (95% CI: 71-92%).

CONCLUSION

APT and IVIM imaging may serve as noninvasive tools for predicting response to concurrent chemoradiotherapy in patients with SCCC.

Post treatment imaging in patients with local advanced cervical carcinoma

Cervical cancer (CC) is the fourth leading cause of death in women worldwide and despite the introduction of screening programs about 30% of patients presents advanced disease at diagnosis and 30-50% of them relapse in the first 5-years after treatment. According to FIGO staging system 2018, stage IB3-IVA are classified as locally advanced cervical cancer (LACC); its correct therapeutic choice remains still controversial and includes neoadjuvant chemo-radiotherapy, external beam radiotherapy, brachytherapy, hysterectomy or a combination of these modalities. In this review we focus on the most appropriated therapeutic options for LACC and imaging protocols used for its correct follow-up. We explore the imaging findings after radiotherapy and surgery and discuss the role of imaging in evaluating the response rate to treatment, selecting patients for salvage surgery and evaluating recurrence of disease. We also introduce and evaluate the advances of the emerging imaging techniques mainly represented by spectroscopy, PET-MRI, and radiomics which have improved diagnostic accuracy and are approaching to future direction.

Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer

RATIONALE AND OBJECTIVES

To investigate the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and texture parameters of primary lesions and lymph nodes for predicting pelvic lymph node metastasis in patients with cervical cancer.

MATERIALS AND METHODS

A total of 143 patients with cervical cancer confirmed by surgical pathology were analyzed retrospectively and 125 patients were enrolled in primary lesions study, 83 patients and 134 lymph nodes were enrolled in lymph nodes study. Patients and lymph nodes were randomly divided into training group and test group at a ratio of 2: 1. The IVIM-DWI parameters and 3D texture features of primary lesions and lymph nodes of all patients were measured. The least absolute shrinkage and selection operator algorithm, spearman's correlation analysis, independent two-sample t-test and Mann-Whitney U-test were used to select texture parameters. Multivariate Logistic regression analysis and receiver operating characteristic curves were used to model and evaluate diagnostic performances.

RESULTS

In primary lesions study, model 1 was constructed by combining f value, original_shape_Sphericity and original_firstorder_Mean of primary lesions. In lymph nodes study, model 2 was constructed by combining short diameter, circular enhancement and rough margin of lymph nodes. Model 3 was constructed by combining ADC, f value and original_glszm_Small Area Emphasis of lymph nodes. The areas under curve of model 1, 2 and 3 in training group and test group were 0.882, 0.798, 0.907 and 0.862, 0.771, 0.937 respectively.

CONCLUSION

Models based on IVIM-DWI and texture parameters of primary lesions and lymph nodes both performed well in diagnosing pelvic lymph node metastasis of cervical cancer and were superior to morphological features of lymph nodes. Especially, parameters of lymph nodes showed higher diagnostic efficiency and clinical significance.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Objective

To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM).

Methods

A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T₂WI, DWI, and contrast-enhanced T₁WI (CE-T₁WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model.

Results

The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs.

Conclusion

A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.

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.

Value of Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Prediction of Treatment Outcomes in Nasopharyngeal Carcinoma

OBJECTIVE

Magnetic resonance imaging (MRI) parameters that reflect the tumor microenvironment of nasopharyngeal carcinoma (NPC) may predict treatment response and facilitate treatment planning. This study aimed to evaluate the diffusion-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) values for predicting the treatment outcomes in NPC patients.

METHODS

Eighty-three patients with NPC underwent pretreatment MRI simulation with diffusion-weighted imaging and dynamic contrast-enhanced MRI. Average values of the apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, Vp, and tumor volume of the primary tumors were measured. Other potential clinical characteristics (age, sex, staging, pathology, pretreatment Epstein-Barr virus level, and treatment type) were analyzed. Patients underwent follow-up imaging 6 months after treatment initiation. Treatment responses were assigned according to the Response Evaluation Criteria in Solid Tumors guideline (version 1.1).

RESULTS

Fifty-one patients showed complete response (CR), whereas 32 patients did not (non-CR). Univariable logistic regression with variables dichotomized by optimal cutoff values showed that ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, tumor volume of ≥14.05 mL, high stage (stages III and IV), and Epstein-Barr virus level of ≥2300 copies/mL were predictors of non-CR (P = 0.008, 0.05, 0.01, 0.009, and 0.04, respectively). The final multivariable model, consisting of a combination of ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, and high stage, could predict non-CR with a good discrimination ability (area under the receiver operating characteristic curve, 0.76 [95% confidence interval, 0.66-0.87]; sensitivity, 62.50%; specificity, 80.39%; and accuracy 73.49%).

CONCLUSIONS

A multivariable prediction model using a combination of ADC ≥1.45 × 10-3 mm2/s, Vp ≥0.14, and high stage can be effective for treatment response prediction in NPC patients.

Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

Background

This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients’ prognosis, so as to provide a basis for clinical individualized precision treatment.

Methods

One hundred twenty-nine newly diagnosed LAGC patients who underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this study. Pathological tumor regression grade (TRG) served as the reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological responders (pR) and pathological non-responders (pNR) groups were analyzed. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS was compared using the Kaplan–Meier method and the log-rank test.

Results

Sixty-nine patients were classified as pR and 60 were pNR. K^trans, k_ep, and v_e values in the pR group were significantly higher, while ADC_standard and D values were significantly lower than those in the pNR group. Multivariate logistic regression analysis demonstrated that K^trans, k_ep, v_e, and D values were independent predictors for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an area under the curve (AUC) of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by the IVIM-DWI model were independent predictors of poor RFS. The IVIM-DWI model could significantly stratify median RFS (52 vs. 15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC.

Conclusion

Pretreatment DCE-MRI quantitative parameters K^trans, k_ep, v_e, and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI, and their combination could help RFS stratification of LAGC patients.

Intravoxel Incoherent Motion (IVIM) MR Quantification in Locally Advanced Cervical Cancer (LACC): Preliminary Study on Assessment of Tumor Aggressiveness and Response to Neoadjuvant Chemotherapy

The aim of this study was to determine whether quantitative parameters obtained from intravoxel incoherent motion (IVIM) model at baseline magnetic resonance imaging (MRI) correlate with histological parameters and response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer (LACC). Methods: Twenty patients with biopsy-proven cervical cancer, staged as LACC on baseline MRI and addressed for neoadjuvant chemotherapy were enrolled. At treatment completion, tumor response was assessed with a follow-up MRI evaluated using the revised response evaluation criteria in solid tumors (RECIST; version 1.1), and patients were considered good responders (GR) if they had complete response or partial remission, and poor responders/non-responders (PR/NR) if they had stable or progressive disease. MRI protocol included conventional diffusion-weighted imaging (DWI; b = 0 and 1000 s/mm²) and IVIM acquisition using eight b-values (range: 0–1500 s/mm²). MR-images were analyzed using a dedicated software to obtain quantitative parameters: diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp) from the IVIM model; apparent diffusion coefficient (ADC) from conventional DWI. Histologic subtype, grading, and tumor-infiltrating lymphocytes (TILs) were assessed in each LACC. Results: D showed significantly higher values in GR patients (p = 0.001) and in moderate/high TILs (p = 0.018). Fp showed significantly higher values in squamous cell tumors (p = 0.006). Conclusions: D extracted from the IVIM model could represent a promising tool to identify tumor aggressiveness and predict response to therapy.

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.

Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer

Objective

This study sought to determine the diagnostic value of combined intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) in predicting parametrial infiltration (PMI) in patients with cervical cancer.

Materials and Methods

We enrolled 65 patients with cervical cancer confirmed by radical hysterectomy (25 PMI-negative and 40 PMI-positive) who underwent IVIM and DTI pretreatment. The parameters of IVIM (ADC, D, D  ^∗ , and f) and DTI (average diffusion coefficient (DCavg) and fractional anisotropy (FA)) were recorded by two observers. All parameter differences were tested, and the receiver operating characteristic (ROC) curves were generated to estimate the diagnostic performance of significant metrics and their combinations.

Results

Compared to the PMI-negative group, the PMI-positive group had significantly lower D (0.632 ± 0.017 vs. 0.773 ± 0.024, p < 0.001) and lower FA (0.073 ± 0.002 vs. 0.085 ± 0.003, p=0.003). The area under the ROC curve (AUC) of D and FA was 0.801 and 0.726, respectively, and the combination of D and FA improved the AUC to 0.931, with a sensitivity and specificity of 80.0% and 97.5%, respectively.

Conclusion

D and FA values could be used to help diagnose PMI in patients with cervical cancer. The combination of IVIM and DTI was more valuable than either option alone.

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.

Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

Purpose

This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC.

Methods

A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm²). The maximum level of CSCC with a b value of 800 sec/mm² was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D^∗)) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination.

Results

The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group (P < 0.05). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834.

Conclusions

Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.

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.