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Background and Purpose: Improvements in treatment outcome for patients with locally advanced cervical cancer (LACC) require a better classification of patients according to their risk of recurrence. We investigated whether an imaging-based approach, combining pretreatment hypoxia and tumor response during therapy, could improve risk classification., Material and Methods: Ninety-three LACC patients with T2-weigthed (T2W)-, dynamic contrast enhanced (DCE)- and diffusion weighted (DW)-magnetic resonance (MR) images acquired before treatment, and T2W- and, for 64 patients, DW-MR images, acquired at brachytherapy, were collected. Pretreatment hypoxic fraction (HF pre ) was determined from DCE- and DW-MR images using the consumption and supply-based hypoxia (CSH)-imaging method. Volume regression at brachytherapy was assessed from T2W-MR images and combined with HF pre . In 17 patients with adequate DW-MR images at brachytherapy, the apparent diffusion coefficient (ADC), reflecting tumor cell density, was calculated. Change in ADC during therapy was combined with volume regression yielding functional regression as explorative response measure. Endpoint was disease free survival (DFS)., Results: HF pre was the strongest predictor of DFS, but a significant correlation with outcome was found also for volume regression. The combination of HF pre and volume regression showed a stronger association with DFS than HF pre alone. Patients with disease recurrence were selected to either the intermediate- or high-risk group with a 100 % accuracy. Functional regression showed a stronger correlation to HF pre than volume regression., Conclusion: The combination of pretreatment hypoxia and volume regression at brachytherapy improved patient risk classification. Integration of ADC with volume regression showed promise as a new tumor response parameter., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background and Purpose: MRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence., Materials and Methods: Eighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). K trans , reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and K trans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF)., Results: HF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV 50 ), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV 50 , respectively. This defined a multimodality biomarker, HF/SUV 50 . HF/SUV 50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints., Conclusion: Multimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Tumor hypoxia levels range from mild to severe and have different biological and therapeutical consequences but are not easily assessable in patients. Here we present a method based on diagnostic dynamic contrast enhanced (DCE) MRI that reflects a continuous range of hypoxia levels in patients with tumors of cervical cancer. Hypoxia images were generated using an established approach based on pixel-wise combination of DCE-MRI parameters ν e and K trans , representing oxygen consumption and supply, respectively. Using two tumor models, an algorithm to retrieve surrogate measures of hypoxia levels from the images was developed and validated by comparing the MRI-defined levels with hypoxia levels reflected in pimonidazole-stained histologic sections. An additional indicator of hypoxia levels in patient tumors was established on the basis of expression of nine hypoxia-responsive genes; a strong correlation was found between these indicator values and MRI-defined hypoxia levels in 63 patients. Chemoradiotherapy outcome of 74 patients was most strongly predicted by moderate hypoxia levels, whereas more severe or milder levels were less predictive. By combining gene expression profiles and MRI-defined hypoxia levels in cancer hallmark analysis, we identified a distribution of levels associated with each hallmark; oxidative phosphorylation and G 2 -M checkpoint were associated with moderate hypoxia, epithelial-to-mesenchymal transition, and inflammatory responses with significantly more severe levels. At the mildest levels, IFN response hallmarks together with HIF1A protein expression by IHC appeared significant. Thus, our method visualizes the distribution of hypoxia levels within patient tumors and has potential to distinguish levels of different prognostic and biological significance. SIGNIFICANCE: These findings present an approach to image a continuous range of hypoxia levels in tumors and demonstrate the combination of imaging with molecular data to better understand the biology behind these different levels., (©2020 American Association for Cancer Research.)

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

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide biomarkers of the outcome of locally-advanced cervical carcinoma (LACC). There is, however, no agreement on how DCE-MR recordings should be analyzed. Previously, we have analyzed DCE-MRI data of LACC using non-model-based strategies. In the current study, we analyzed DCE-MRI data of LACC using the Tofts pharmacokinetic model, and the biomarkers derived from this analysis were compared with those derived from the non-model-based analyses., Methods: Eighty LACC patients given cisplatin-based chemoradiotherapy with curative intent were included in the study. Treatment outcome was recorded as disease-free survival (DFS) and overall survival (OS). DCE-MRI series were analyzed voxelwise to produce K trans and v e frequency distributions, and ROC analysis was used to identify the parameters of the frequency distributions having the greatest potential as biomarkers. The prognostic power of these parameters was compared with that of the non-model-based parameters LETV (low-enhancing tumor volume) and TVIS (tumor volume with increasing signal)., Results: Poor DFS and OS were associated with low values of K trans , whereas there was no association between treatment outcome and v e . The K trans parameters having the greatest prognostic value were p35-K trans (the K trans value at the 35 percentile of a frequency distribution) and RV-K trans (the tumor subvolume with K trans values below 0.13 min - 1 ). Multivariate analysis including clinical parameters and p35-K trans or RV-K trans revealed that RV-K trans was the only independent prognostic factor of DFS and OS. There were significant correlations between RV-K trans and LETV and between RV-K trans and TVIS, and the prognostic power of RV-K trans was similar to that of LETV and TVIS., Conclusions: Biomarkers of the outcome of LACC can be provided by analyzing DCE-MRI series using the Tofts pharmacokinetic model. However, these biomarkers do not appear to have greater prognostic value than biomarkers determined by non-model-based analyses.

Competing Interests: Competing interests: None declared.

Background: There is significant evidence that DCE-MRI may have the potential to provide clinically useful biomarkers of the outcome of locally advanced cervical carcinoma. However, there is no consensus on how to analyze DCE-MRI data to arrive at the most powerful biomarkers. The purpose of this study was to analyze DCE-MRI data of cervical cancer patients by using the Brix pharmacokinetic model and to compare the biomarkers derived from the Brix analysis with biomarkers determined by non-model-based analysis [i.e., low-enhancing tumor volume (LETV) and tumor volume with increasing signal (TVIS)] of the same patient cohort. Material and methods: DCE-MRI recordings of 80 patients (FIGO stage IB-IVA) treated with concurrent cisplatin-based chemoradiotherapy were analyzed voxel-by-voxel, and frequency distributions of the three parameters of the Brix model ( A Brix , k ep , and k el ) were determined. Moreover, risk volumes were calculated from the Brix parameters and termed RV- A Brix , RV- k ep , and RV- k el , where the RVs represent the tumor volume with voxel values below a threshold value determined by ROC analysis. Disease-free survival (DFS) and overall survival (OS) were used as measures of treatment outcome. Results: Significant associations between the median value or any other percentile value of A Brix , k ep , or k el and treatment outcome were not found. However, RV- A Brix , RV- k ep , and RV- k el correlated with DFS and OS. Multivariate analysis revealed that the prognostic power of RV- A Brix , RV- k ep , and RV- k el was independent of well-established clinical prognostic factors. RV- A Brix , RV- k ep , and RV- k el correlated with each other as well as with LETV and TVIS. Conclusion: Strong biomarkers of the outcome of locally advanced cervical carcinoma can be provided by subjecting DCE-MRI series to pharmacokinetic analysis using the Brix model. The prognostic power of these biomarkers is not necessarily superior to that of biomarkers identified by non-model-based analyses.

Purpose: The poor outcome of locally advanced cervical cancer has been associated with extensive hypoxia and high interstitial fluid pressure (IFP) in the primary tumor. In the present study, measures of tumor hypoxia and IFP were provided using dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) and related to the treatment outcomes., Methods and Materials: The data from 54 cervical cancer patients treated with concurrent cisplatin-based chemoradiotherapy were studied. A low-enhancing tumor volume (LETV) and peritumoral fluid flow velocity (v 0 ) were used as measures of tumor hypoxia and IFP, respectively., Results: Poor disease-free survival and overall survival were associated with large LETV and high v 0 . The multivariate analysis results suggested that the prognostic power of v 0 and LETV is independent of established clinical prognostic factors and that the prognostic power of v 0 is strong compared with that of LETV. The outcomes was especially poor for patients with a high v 0 combined with a large LETV and especially good for those with a low v 0 combined with a small LETV, with 5-year disease-free survival and overall survival of 13% versus 100%, respectively., Conclusions: The outcome of locally advanced cervical carcinoma seems to be influenced strongly by the tumor IFP and to a lesser extent by tumor hypoxia. DCE-MRI might have the power to provide important biomarkers for the outcome of cervical cancer., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach., Materials and Methods: A method for tumour segmentation based on image voxel classification using Fisher?s Linear Discriminant Analysis (LDA) was developed. This was applied to magnetic resonance (MR) images of 78 patients with locally advanced cervical cancer. The segmentation was based on multiparametric MRI consisting of T2- weighted (T2w), T1-weighted (T1w) and dynamic contrast-enhanced (DCE) sequences, and included intensity and spatial information from the images. The model was trained and assessed using delineations made by two radiologists., Results: Segmentation based on T2w or T1w images resulted in mean sensitivity and specificity of 94% and 52%, respectively. Including DCE-MR images improved the segmentation model?s performance significantly, giving mean sensitivity and specificity of 85?93%. Comparisons with radiologists? tumour delineations gave Dice similarity coefficients of up to 0.44., Conclusion: Voxel classification using a machine learning approach is a flexible and fully automatic method for tumour delineation. Combining all relevant MR image series resulted in high sensitivity and specificity. Moreover, the presented method can be extended to include additional imaging modalities.

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide prognostic biomarkers for cervix carcinoma. We have shown previously that the early phase of the signal intensity-versus-time curve (SITC) may have significant prognostic power. The purpose of the present investigation was to explore the prognostic value of the late phase of the SITC., Material and Methods: DCE-MRI data of 80 patients (FIGO stage IB-IVA) treated with concurrent chemoradiotherapy were examined. Four parameters were calculated from the late-phase SITC: tumor volume with decreasing signal, tumor fraction with decreasing signal, tumor volume with increasing signal (TVIS), and tumor fraction with increasing signal., Results: Multivariate analysis involving clinical parameters and late-phase SITC parameters suggested that TVIS is a strong independent prognostic factor for both disease-free and overall survival. When early-phase SITC parameters were included in the multivariate analysis, the early-phase SITC, but not the late-phase SITC, was found to have independent prognostic value., Conclusion: The late-phase SITC can provide prognostic factors for the outcome of cervix carcinoma, that is, a large tumor volume with increasing late-phase SITCs is associated with poor outcome. However, the prognostic power of the late-phase SITC is not as strong as that of the early-phase SITC.

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., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background: 18F fluoro-deoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) is a well-recognized diagnostic tool used for staging and monitoring of therapy response for lymphomas. During the past decade diffusion-weighted (DW) magnetic resonance imaging (MRI) is increasingly being included in the assessment of tumor response for various cancers., Purpose: To compare the change in maximum standardized uptake value (ΔSUVmax) from FDG PET/CT with the change in apparent diffusion coefficient (ΔADC) from DW MRI after initiation of the first cycle of chemotherapy in patients with Hodgkin's lymphoma (HL) and in patients with diffuse large B-cell lymphoma (DLBCL)., Material and Methods: Twenty-seven consecutive patients with histologically proven lymphoma and lymphomatous lymph nodes (LLN) of the neck (19 with HL, 8 with DLBCL) underwent FDG PET/CT and MRI of the neck before and after initiation of the first cycle of chemotherapy. The mean time interval from initiation of chemotherapy to imaging was 19 days and 2 days for FDG PET/CT and MRI, respectively. For each patient ΔSUVmax, ΔADC, and change in volume of the same LLN were compared., Results: There was a significant mean decrease of SUVmax by 70%, but no significant change in ADC. There was no significant reduction in LLN volume., Conclusion: There was no significant correlation between ΔSUVmax and ΔADC. Thus, our data do not support that FDG PET/CT can be replaced by early DW MRI for response evaluation in lymphoma patients., (© The Foundation Acta Radiologica 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Background and Purpose: High tumor interstitial fluid pressure (IFP) is associated with poor outcome in locally advanced carcinoma of the uterine cervix. We have recently developed a noninvasive assay of the IFP of tumors, and in this assay, the outward interstitial fluid flow velocity at the tumor surface (v0) is measured by Gd-DTPA-based DCE-MRI and used as a parameter for IFP. Here, we investigated the independent prognostic significance of v0 in cervical cancer patients given cisplatin-based concurrent chemoradiotherapy with curative intent., Patients: The study involved 62 evaluable patients from a cohort of 74 consecutive patients (Stage IB through IIIB) with a median follow-up of 5.5 years., Results: The actuarial disease-free survival (DFS) and overall survival (OS) at 5 years were 67% and 76%, respectively. Significant associations were found between v0 dichotomized about the median value and DFS and OS, both in the total patient cohort and a subcohort of 40 Stage IIB patients. Multivariate analysis involving stage, tumor volume, lymph node status, and v0 revealed that only v0 provided independent prognostic information about DFS and OS., Conclusion: This investigation demonstrates a strong, independent prognostic impact of the pretreatment peritumoral fluid flow velocity in cervical cancer., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Purpose: To assess the prognostic value of pharmacokinetic parameters derived from pre-chemoradiotherapy dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of cervical cancer patients., Materials and Methods: Seventy-eight patients with locally advanced cervical cancer underwent DCE-MRI with Gd-DTPA before chemoradiotherapy. The pharmacokinetic Brix and Tofts models were fitted to contrast enhancement curves in all tumor voxels, providing histograms of several pharmacokinetic parameters (Brix: A(Brix), k(ep), k(el), Tofts: K(trans), ν(e)). A percentile screening approach including log-rank survival tests was undertaken to identify the clinically most relevant part of the intratumoral parameter distribution. Clinical endpoints were progression-free survival (PFS) and locoregional control (LRC). Multivariate analysis including FIGO stage and tumor volume was used to assess the prognostic significance of the imaging parameters., Results: A(Brix), k(el), and K(trans) were significantly (P<0.05) positively associated with both clinical LRC and PFS, while ν(e) was significantly positively correlated with PFS only. k(ep) showed no association with any endpoint. A(Brix) was positively correlated with K(trans) and ν(e), and showed the strongest association with endpoint in the log-rank testing. k(el) and K(trans) were independent prognostic factors in multivariate analysis with LRC as endpoint., Conclusions: Parameters estimated by pharmacokinetic analysis of DCE-MR images obtained prior to chemoradiotherapy may be used for identifying patients at risk of treatment failure., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee., Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival., Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer., (Copyright © 2012 Elsevier Inc. All rights reserved.)