The Microenvironment of Cervical Carcinoma Xenografts: Associations with Lymph Node Metastasis and Its Assessment by DCE-MRI

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.

Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study

Introduction

Lymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer.

Methods

This retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort.

Results

The voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]).

Conclusions

The radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BACKGROUND

Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.

METHODS

A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.

RESULTS

Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.

CONCLUSION

The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer

PURPOSE This retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods. METHODS A total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann-Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA). RESULTS A total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE)=0.831 and sensitivity (SEN)=0.581) in the training cohort and 0.788 (SPE=0.727, SEN=0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model. CONCLUSION Our results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer.

MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer

This article aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted imaging (T2WI) MRI images from 167 early-stage cervical cancer patients (January 2014 - August 2018) were used to train and validate deep learning models. Predictive performances were evaluated using receiver operating characteristic (ROC) curve and confusion matrix analysis, with the DCE-T1 showing more discriminative results than T2WI MRI. By adopting an attention ensemble learning strategy that integrates both MRI sequences, the highest average area was obtained under the ROC curve (AUC) of 0.911 (Sensitivity = 0.881 and Specificity = 0.752). The superior performances in this article, when compared to existing radiomic methods, indicate that a wealth of deep learning-based radiomics could be developed to aid radiologists in preoperatively predicting vessel invasion in cervical cancer patients.

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.

Imaging of Preclinical Endometrial Cancer Models for Monitoring Tumor Progression and Response to Targeted Therapy

Endometrial cancer is the most common gynecologic malignancy in industrialized countries. Most patients are cured by surgery; however, about 15% of the patients develop recurrence with limited treatment options. Patient-derived tumor xenograft (PDX) mouse models represent useful tools for preclinical evaluation of new therapies and biomarker identification. Preclinical imaging by magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), single-photon emission computed tomography (SPECT) and optical imaging during disease progression enables visualization and quantification of functional tumor characteristics, which may serve as imaging biomarkers guiding targeted therapies. A critical question, however, is whether the in vivo model systems mimic the disease setting in patients to such an extent that the imaging biomarkers may be translatable to the clinic. The primary objective of this review is to give an overview of current and novel preclinical imaging methods relevant for endometrial cancer animal models. Furthermore, we highlight how these advanced imaging methods depict pathogenic mechanisms important for tumor progression that represent potential targets for treatment in endometrial cancer.

Intratumor Heterogeneity in Interstitial Fluid Pressure in Cervical and Pancreatic Carcinoma Xenografts

Preclinical studies have suggested that interstitial fluid pressure (IFP) is uniformly elevated in the central region of tumors, whereas clinical studies have revealed that IFP may vary among different measurement sites in the tumor center. IFP measurements are technically difficult, and it has been claimed that the intratumor heterogeneity in IFP reported for human tumors is due to technical problems. The main purpose of this study was to determine conclusively whether IFP may be heterogeneously elevated in the central tumor region, and if so, to reveal possible mechanisms and possible consequences. Tumors of two xenograft models were included in the study: HL-16 cervical carcinoma and Panc-1 pancreatic carcinoma. IFP was measured with Millar SPC 320 catheters in two positions in each tumor and related to tumor histology or the metastatic status of the host mouse. Some tumors of both models showed significant intratumor heterogeneity in IFP, and this heterogeneity was associated with a compartmentalized histological appearance (i.e., the tissue was divided into compartments separated by thick connective tissue bands) in HL-16 tumors and with a dense collagen-I-rich extracellular matrix in Panc-1 tumors, suggesting that these connective tissue structures prevented efficient interstitial convection. Furthermore, some tumors of both models developed lymph node metastases, and of the two IFP values measured in each tumor, only the higher value was significantly higher in metastatic than in non-metastatic tumors, suggesting that metastatic propensity was determined by the tumor region having the highest IFP.

Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast-enhanced MRI in a rat model of cerebral tumor

PURPOSE

This study demonstrates a DCE-MRI estimate of tumor interstitial fluid pressure (TIFP) and hydraulic conductivity in a rat model of glioblastoma, with validation against an invasive wick-in-needle (WIN) technique. An elevated TIFP is considered a mark of aggressiveness, and a decreased TIFP a predictor of response to therapy.

METHODS

The DCE-MRI studies were conducted in 36 athymic rats (controls and posttreatment animals) with implanted U251 cerebral tumors, and with TIFP measured using a WIN method. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the MRI parameters required for estimating TIFP noninvasively were estimated. Two models, a fluid-mechanical model and a multivariate empirical model, were used for estimating TIFP, as verified against WIN-TIFP.

RESULTS

Using DCE-MRI, the mean estimated hydraulic conductivity (MRI-K) in U251 tumors was (2.3 ± 3.1) × 10^-5 (mm² /mmHg-s) in control studies. Significant positive correlations were found between WIN-TIFP and MRI-TIFP in both mechanical and empirical models. For instance, in the control group of the fluid-mechanical model, MRI-TIFP was a strong predictor of WIN-TIFP (R²  = 0.76, p < .0001). A similar result was found in the bevacizumab-treated group of the empirical model (R²  = 0.93, p = .014).

CONCLUSION

This research suggests that MRI dynamic studies contain enough information to noninvasively estimate TIFP in this, and possibly other, tumor models, and thus might be used to assess tumor aggressiveness and response to therapy.

DCE-MRI of patient-derived xenograft models of uterine cervix carcinoma: associations with parameters of the tumor microenvironment

BACKGROUND

Abnormalities in the tumor microenvironment are associated with resistance to treatment, aggressive growth, and poor clinical outcome in patients with advanced cervical cancer. The potential of dynamic contrast-enhanced (DCE) MRI to assess the microvascular density (MVD), interstitial fluid pressure (IFP), and hypoxic fraction of patient-derived cervical cancer xenografts was investigated in the present study.

METHODS

Four patient-derived xenograft (PDX) models of squamous cell carcinoma of the uterine cervix (BK-12, ED-15, HL-16, and LA-19) were subjected to Gd-DOTA-based DCE-MRI using a 7.05 T preclinical scanner. Parametric images of the volume transfer constant (K ^trans) and the fractional distribution volume (v _e) of the contrast agent were produced by pharmacokinetic analyses utilizing the standard Tofts model. Whole tumor median values of the DCE-MRI parameters were compared with MVD and the fraction of hypoxic tumor tissue, as determined histologically, and IFP, as measured with a Millar catheter.

RESULTS

Both on the PDX model level and the single tumor level, a significant inverse correlation was found between K ^trans and hypoxic fraction. The extent of hypoxia was also associated with the fraction of voxels with unphysiological v _e values (v _e > 1.0). None of the DCE-MRI parameters were related to MVD or IFP.

CONCLUSIONS

DCE-MRI may provide valuable information on the hypoxic fraction of squamous cell carcinoma of the uterine cervix, and thereby facilitate individualized patient management.

Methods: Using Three-Dimensional Culture (Spheroids) as an In Vitro Model of Tumour Hypoxia

Regions of hypoxia in tumours can be modelled in vitro in 2D cell cultures with a hypoxic chamber or incubator in which oxygen levels can be regulated. Although this system is useful in many respects, it disregards the additional physiological gradients of the hypoxic microenvironment, which result in reduced nutrients and more acidic pH. Another approach to hypoxia modelling is to use three-dimensional spheroid cultures. In spheroids, the physiological gradients of the hypoxic tumour microenvironment can be inexpensively modelled and explored. In addition, spheroids offer the advantage of more representative modelling of tumour therapy responses compared with 2D culture. Here, we review the use of spheroids in hypoxia tumour biology research and highlight the different methodologies for spheroid formation and how to obtain uniformity. We explore the challenge of spheroid analyses and how to determine the effect on the hypoxic versus normoxic components of spheroids. We discuss the use of high-throughput analyses in hypoxia screening of spheroids. Furthermore, we examine the use of mathematical modelling of spheroids to understand more fully the hypoxic tumour microenvironment.

E6/E7-P53-POU2F1-CTHRC1 axis promotes cervical cancer metastasis and activates Wnt/PCP pathway

Cervical cancer is an infectious cancer and the most common gynecologic cancer worldwide. E6/E7, the early genes of the high-risk mucosal human papillomavirus type, play key roles in the carcinogenic process of cervical cancer. However, little was known about its roles in modulating tumor microenvironment, particular extracellular matrix (ECM). In this study, we found that E6/E7 could regulate multiple ECM proteins, especially collagen triple helix repeat containing 1 (CTHRC1). CTHRC1 is highly expressed in cervical cancer tissue and serum and closely correlated with clinicopathological parameters. CTHRC1 promotes cervical cancer cell migration and invasion in vitro and metastasis in vivo. E6/E7 regulates the expression of CTHRC1 in cervical cancer by E6/E7-p53-POU2F1 (POU class 2 homeobox 1) axis. Futhermore, CTHRC1 activates Wnt/PCP signaling pathway. Take together, E6/E7-p53-POU2F1-CTHRC1 axis promotes cervical cancer cell invasion and metastasis and may act as a potential therapeutic target for interventions against cervical cancer invasion and metastasis.

Identification of Metastasis-Associated Metabolic Profiles of Tumors by (1)H-HR-MAS-MRS

Tumors develop an abnormal microenvironment during growth, and similar to the metastatic phenotype, the metabolic phenotype of cancer cells is tightly linked to characteristics of the tumor microenvironment (TME). In this study, we explored relationships between metabolic profile, metastatic propensity, and hypoxia in experimental tumors in an attempt to identify metastasis-associated metabolic profiles. Two human melanoma xenograft lines (A-07, R-18) showing different TMEs were used as cancer models. Metabolic profile was assessed by proton high resolution magic angle spinning magnetic resonance spectroscopy (¹H-HR-MAS-MRS). Tumor hypoxia was detected in immunostained histological preparations by using pimonidazole as a hypoxia marker. Twenty-four samples from 10 A-07 tumors and 28 samples from 10 R-18 tumors were analyzed. Metastasis was associated with hypoxia in both A-07 and R-18 tumors, and ¹H-HR-MAS-MRS discriminated between tissue samples with and tissue samples without hypoxic regions in both models, primarily because hypoxia was associated with high lactate resonance peaks in A-07 tumors and with low lactate resonance peaks in R-18 tumors. Similarly, metastatic and non-metastatic R-18 tumors showed significantly different metabolic profiles, but not metastatic and non-metastatic A-07 tumors, probably because some samples from the metastatic A-07 tumors were derived from tumor regions without hypoxic tissue. This study suggests that ¹H-HR-MAS-MRS may be a valuable tool for evaluating the role of hypoxia and lactate in tumor metastasis as well as for identification of metastasis-associated metabolic profiles.

Feasibility of induced metabolic bioluminescence imaging in advanced ovarian cancer patients: first results of a pilot study

PURPOSE

The precise determination of energy metabolites is challenged by the heterogeneity of their distribution, their rapid changes after surgical resection and the architectural complexity of malignancies. Induced metabolic bioluminescence imaging (imBI) allows to determine energy metabolites in tissue sections and to co-localize these with histological structures based on consecutive sections stained with HE. In this prospective pilot study patients with suspected advanced ovarian cancer (OC) were enrolled to prove the feasibility of imBI.

METHODS

During surgery, suspicious peritoneal metastases were resected and transferred in liquid nitrogen within 30 s. ATP, glucose and lactate concentrations were measured. Furthermore, the expression of monocarboxylate transporters MCT1 and MCT4 was determined by immunofluorescence staining.

RESULTS

16 patients were screened, 12 entered the study. Final histological assessment revealed ten malignant and two benign peritoneal lesions. In all 12 cases high concentrations of ATP suggested that energy metabolism was not altered by the surgical and transport procedures (mean 0.56 μmol/g, range 0.24-1.21 μmol/g). The mean concentration of glucose was 1.95 μmol/g (range 0.58-4.71 μmol/g). The concentration of lactate was drastically higher in the ten OC cases (mean 24.79 μmol/g, range 17.51-37.16 μmol/g) compared to the benign samples (mean 5.98 μmol/g, range 5.43-6.54 μmol/g). Lactate concentrations seem to correlate with MCT1 (spearman rank correlation ρ = 0.624, 0.05 > p > 0.025), but not with MCT4 (spearman rank correlation ρ = 0.018, p > 0.1).

CONCLUSIONS

ImBI is feasible in peritoneal metastases of OC and encourages further effort to elucidate the role of glucose, lactate, MCT1 and MCT4 in OC.

Hypoxia biomarkers in squamous cell carcinoma of the uterine cervix

Background

There is significant evidence that severe tumor hypoxia may cause resistance to chemoradiotherapy and promote metastatic spread in locally advanced carcinoma of the uterine cervix. Some clinical investigations have suggested that high expression of hypoxia-inducible factor-1α (HIF-1α) and/or its target gene carbonic anhydrase IX (CAIX) may be useful biomarkers of tumor hypoxia and poor outcome in cervical cancer. Here, we challenged this view by investigating possible associations between HIF-1α expression, CAIX expression, fraction of hypoxic tissue, and lymph node metastasis in experimental human tumors.

Methods

Tumors of two cervical carcinoma xenograft lines (CK-160 and TS-415) were included in the study. Pimonidazole was used as a hypoxia marker, and tumor hypoxia, HIF-1α expression, and CAIX expression were detected by immunohistochemistry. Metastatic status was assessed by examining external lymph nodes in the inguinal, axillary, interscapular, and submandibular regions and internal lymph nodes in the abdomen and mediastinum.

Results

Tissue regions staining positive for pimonidazole, HIF-1α, or CAIX were poorly colocalized, both in CK-160 and TS-415 tumors. The expression of HIF-1α or CAIX did not correlate with the fraction of hypoxic tissue in any of the two tumor lines. Furthermore, clinically relevant associations between HIF-1α or CAIX expression and lymph node metastasis were not found.

Conclusion

Because significant associations between HIF-1α expression, CAIX expression, fraction of hypoxic tissue, and incidence of lymph node metastases could not be detected in any of two preclinical models of human cervical cancer, it is not realistic to believe that high expression of HIF-1α or CAIX can be useful biomarkers of tumor hypoxia and poor outcome in a highly heterogeneous disease like cervical carcinoma.

Short-term pretreatment DCE-MRI in prediction of outcome in locally advanced cervical cancer

BACKGROUND AND PURPOSE

Several investigators have indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to provide biomarkers for personalized treatment of cervical carcinoma. However, some clinical studies have suggested that treatment failure is associated with low tumor signal enhancement, whereas others have reported associations between high signal enhancement and poor outcome. The purpose of this investigation was to clear up these conflicting reports and to provide a method for identifying biomarkers that easily can be implemented in routine DCE-MRI diagnostics.

METHODS

The study involved 85 patients (FIGO stage IB through IVA) treated with concurrent chemoradiotherapy. Low-enhancing tumor volume (LETV) and low-enhancing tumor fraction (LETF), defined as the volume and fractional volume of low-enhancing voxels, respectively, were calculated from signal intensities recorded within 1 min after contrast administration by using two methods reported to give conflicting conclusions.

RESULTS

Multivariate analysis involving tumor volume, lymph node status, FIGO stage, and LETV or LETF revealed that LETV and LETF provided independent prognostic information on treatment outcome, independent of the method of calculation.

CONCLUSION

Low signal enhancement is associated with poor prognosis in cervical carcinoma, and biomarkers predicting poor outcome can be provided by short-term DCE-MRI without advanced image analysis.

Biodistribution and in vivo activities of tumor-associated macrophage-targeting nanoparticles incorporated with doxorubicin

Tumor-associated macrophages (TAMs) are increasingly considered a viable target for tumor imaging and therapy. Previously, we reported that innovative surface-functionalization of nanoparticles may help target them to TAMs. In this report, using poly(lactic-co-glycolic) acid (PLGA) nanoparticles incorporated with doxorubicin (DOX) (DOX-NPs), we studied the effect of surface-modification of the nanoparticles with mannose and/or acid-sensitive sheddable polyethylene glycol (PEG) on the biodistribution of DOX and the uptake of DOX by TAMs in tumor-bearing mice. We demonstrated that surface-modification of the DOX-NPs with both mannose and acid-sensitive sheddable PEG significantly increased the accumulation of DOX in tumors, enhanced the uptake of the DOX by TAMs, but decreased the distribution of DOX in mononuclear phagocyte system (MPS), such as liver. We also confirmed that the acid-sensitive sheddable PEGylated, mannose-modified DOX-nanoparticles (DOX-AS-M-NPs) targeted TAMs because depletion of TAMs in tumor-bearing mice significantly decreased the accumulation of DOX in tumor tissues. Furthermore, in a B16-F10 tumor-bearing mouse model, we showed that the DOX-AS-M-NPs were significantly more effective than free DOX in controlling tumor growth but had only minimum effect on the macrophage population in mouse liver and spleen. The AS-M-NPs are promising in targeting cytotoxic or macrophage-modulating agents into tumors to improve tumor therapy.